Search

Your search keyword '"Algorithms for Modeling and Simulation of Nanosystems ( NANO-D )"' showing total 38 results
Start Over Author "Algorithms for Modeling and Simulation of Nanosystems ( NANO-D )" Topic 01 natural sciences
38 results on '"Algorithms for Modeling and Simulation of Nanosystems ( NANO-D )"'

1. Modelling time variations of root diameter and elongation rate as related to assimilate supply and demand

Author

Loïc Pagès, Marie Bernert, Guillaume Pagès, Unité de recherche Plantes et Systèmes de Culture Horticoles (PSH), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), BrainTech Laboratory [CHU Grenoble Alpes - Inserm U1205] (Brain Tech Lab ), CHU Grenoble-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes (UGA), Algorithms for Modeling and Simulating Nanosystems [2018-...] (NANO-D-POST [2018-2020]), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Lucas, Nelly, Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), and Université Grenoble Alpes (UGA)

Subjects
0106 biological sciences, 0301 basic medicine, phenotyping, root growth, Physiology, Meristem, Root (chord), Plant Science, Root system, Root tip, Plasticity, Plant Roots, 01 natural sciences, Growth model, 03 medical and health sciences, MESH: Growth model, photo-assimilates, root meristem, root traits, structure-function model, vascular resistance, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, [SDV.BV] Life Sciences [q-bio]/Vegetal Biology, Mathematics, AcademicSubjects/SCI01210, Maximal diameter, 15. Life on land, Research Papers, 030104 developmental biology, Homogeneous, structure–function model, Growth and Development, Time variations, Elongation, Biological system, human activities, 010606 plant biology & botany
Abstract

We present a simple and generic model, based on mechanistic hypotheses, to represent the longitudinal variations of the diameter of individual roots and the associated diversity of growth patterns., In a given root system, individual roots usually exhibit a rather homogeneous tip structure although highly different diameters and growth patterns, and this diversity is of prime importance in the definition of the whole root system architecture and foraging characteristics. In order to represent and predict this diversity, we built a simple and generic model at root tip level combining structural and functional knowledge on root elongation. The tip diameter, reflecting meristem size, is used as a driving variable of elongation. It varies, in response to the fluctuations of photo-assimilate availability, between two limits (minimal and maximal diameter). The elongation rate is assumed to be dependent on the transient value of the diameter. Elongation stops when the tip reaches the minimal diameter. The model could satisfactorily reproduce patterns of root elongation and tip diameter changes observed in various species at different scales. Although continuous, the model could generate divergent root classes as classically observed within populations of lateral roots. This model should help interpret the large plasticity of root elongation patterns which can be obtained in response to different combinations of endogenous and exogenous factors. The parameters could be used in phenotyping the root system.

Published
2020
Full Text
View/download PDF

2. Predicting Protein Functional Motions: an Old Recipe with a New Twist

Author

Sergei Grudinin, Alexandre Hoffmann, Elodie Laine, Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Biologie Computationnelle et Quantitative = Laboratory of Computational and Quantitative Biology (LCQB), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Algorithms for Modeling and Simulating Nanosystems [2018-...] (NANO-D-POST [2018-2020]), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and ANR-17-CE12-0009,MASSIV,Modèle(s) d'Evolution de l'Epissage Alternatif et de son Impact Structural(2017)

Subjects
Models, Molecular, Rigid block, Work (thermodynamics), Protein Conformation, Structure (category theory), Biophysics, Motion (geometry), Structural heterogeneity, Crystallography, X-Ray, 01 natural sciences, Structural transitions, Article, 03 medical and health sciences, 0302 clinical medicine, Protein structure, Collective motions, Normal mode, 0103 physical sciences, Cryo-EM fitting, Statistical physics, Twist, Nonlinear normal modes, Flexible docking, NMA, 030304 developmental biology, Physics, Quantitative Biology::Biomolecules, 0303 health sciences, 010304 chemical physics, Series (mathematics), Cryoelectron Microscopy, Proteins, Nonlinear system, Range (mathematics), [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], 030217 neurology & neurosurgery
Abstract

Large macromolecules, including proteins and their complexes, very often adopt multiple conformations. Some of them can be seen experimentally, for example with X-ray crystallography or cryo-electron microscopy. This structural heterogeneity is not occasional and is frequently linked with specific biological function. Thus, the accurate description of macromolecular conformational transitions is crucial for understanding fundamental mechanisms of life’s machinery. We report on a real-time method to predict such transitions by extrapolating from instantaneous eigen-motions, computed using the normal mode analysis, to a series of twists. We demonstrate the applicability of our approach to the prediction of a wide range of motions, including large collective opening-closing transitions and conformational changes induced by partner binding. We also highlight particularly difficult cases of very small transitions between crystal and solution structures. Our method guaranties preservation of the protein structure during the transition and allows to access conformations that are unreachable with classical normal mode analysis. We provide practical solutions to describe localized motions with a few low-frequency modes and to relax some geometrical constraints along the predicted transitions. This work opens the way to the systematic description of protein motions, whatever their degree of collectivity. Our method is available as a part of the NOn-Linear rigid Block (NOLB) package athttps://team.inria.fr/nano-d/software/nolb-normal-modes/.Significance StatementProteins perform their biological functions by changing their shapes and interacting with each other. Getting access to these motions is challenging. In this work, we present a method that generatesplausiblephysics-based protein motions and conformations. We model a protein as a network of atoms connected by springs and deform it along the least-energy directions. Our main contribution is to perform the deformations in a nonlinear way, through a series of twists. This allows us to produce a wide range of motions, some of them previously inaccessible, and to preserve the structure of the protein during the motion. We are able to simulate the opening or closing of a protein and the changes it undergoes to adapt to a partner.

Published
2020
Full Text
View/download PDF

3. HOPMA: Boosting protein functional dynamics with colored contact maps

Author

Sergei Grudinin, Elodie Laine, Biologie Computationnelle et Quantitative = Laboratory of Computational and Quantitative Biology (LCQB), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Données, Apprentissage et Optimisation (DAO), Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Grudinin, Sergei, Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), and Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )

Subjects
Models, Molecular, Boosting (machine learning), Macromolecular Substances, Protein Conformation, Computer science, Structure (category theory), Crystallography, X-Ray, 010402 general chemistry, Topology, 01 natural sciences, Set (abstract data type), 03 medical and health sciences, Protein structure, Normal mode, 0103 physical sciences, Materials Chemistry, Physical and Theoretical Chemistry, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], 030304 developmental biology, 0303 health sciences, 010304 chemical physics, Functional protein, Proteins, A protein, Function (mathematics), Protein structure prediction, 0104 chemical sciences, Surfaces, Coatings and Films, Nonlinear system, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Macromolecule
Abstract

In light of the recent very rapid progress in protein structure prediction, accessing the multitude of functional protein states is becoming more central than ever before. Indeed, proteins are flexible macromolecules, and they often perform their function by switching between different conformations. However, high-resolution experimental techniques such as X-ray crystallography and cryogenic electron microscopy can catch relatively few protein functional states. Many others are only accessible under physiological conditions in solution. Therefore, there is a pressing need to fill this gap with computational approaches.We present HOPMA, a novel method to predict protein functional states and transitions using a modified elastic network model. The method exploits patterns in a protein contact map, taking its 3D structure as input, and excludes some disconnected patches from the elastic network. Combined with nonlinear normal mode analysis, this strategy boosts the protein conformational space exploration, especially when the input structure is highly constrained, as we demonstrate on a set of more than 400 transitions. Our results let us envision the discovery of new functional conformations, which were unreachable previously, starting from the experimentally known protein structures.The method is computationally efficient and available at https://github.com/elolaine/HOPMA and https://team.inria.fr/nano-d/software/nolb-normal-modes.

Published
2021
Full Text
View/download PDF

4. VoroCNN: Deep convolutional neural network built on 3D Voronoi tessellation of protein structures

Author

Česlovas Venclovas, Kliment Olechnovič, Sergei Grudinin, Maria Kadukova, Ilia Igashov, Moscow Institute of Physics and Technology [Moscow] (MIPT), Données, Apprentissage et Optimisation (DAO), Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Vilnius University [Vilnius], Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), and Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )

Subjects
Statistics and Probability, 0303 health sciences, Computational model, 010304 chemical physics, Computer science, 030302 biochemistry & molecular biology, [INFO.INFO-CG]Computer Science [cs]/Computational Geometry [cs.CG], 01 natural sciences, Biochemistry, Convolutional neural network, Computer Science Applications, 03 medical and health sciences, Computational Mathematics, Protein structure, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Computational Theory and Mathematics, 0103 physical sciences, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Voronoi diagram, Molecular Biology, Algorithm, 030304 developmental biology
Abstract

Motivation Effective use of evolutionary information has recently led to tremendous progress in computational prediction of three-dimensional (3D) structures of proteins and their complexes. Despite the progress, the accuracy of predicted structures tends to vary considerably from case to case. Since the utility of computational models depends on their accuracy, reliable estimates of deviation between predicted and native structures are of utmost importance. Results For the first time, we present a deep convolutional neural network (CNN) constructed on a Voronoi tessellation of 3D molecular structures. Despite the irregular data domain, our data representation allows us to efficiently introduce both convolution and pooling operations and train the network in an end-to-end fashion without precomputed descriptors. The resultant model, VoroCNN, predicts local qualities of 3D protein folds. The prediction results are competitive to state of the art and superior to the previous 3D CNN architectures built for the same task. We also discuss practical applications of VoroCNN, for example, in recognition of protein binding interfaces. Availability and implementation The model, data and evaluation tests are available at https://team.inria.fr/nano-d/software/vorocnn/. Supplementary information Supplementary data are available at Bioinformatics online.

Published
2020
Full Text
View/download PDF

5. Docking rigid macrocycles using Convex-PL, AutoDock Vina, and RDKit in the D3R Grand Challenge 4

Author

Sergei Grudinin, Vladimir Chupin, Maria Kadukova, Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Moscow Institute of Physics and Technology [Moscow] (MIPT), Algorithms for Modeling and Simulating Nanosystems [2018-...] (NANO-D-POST [2018-2020]), and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
Macrocyclic Compounds, Computer science, Protein Conformation, Machine learning, computer.software_genre, Ligands, 01 natural sciences, Autodock vina, 0103 physical sciences, Drug Discovery, Humans, Physical and Theoretical Chemistry, Databases, Protein, Binding Sites, 010304 chemical physics, business.industry, Proteins, 0104 chemical sciences, Computer Science Applications, Molecular Docking Simulation, 010404 medicinal & biomolecular chemistry, Protein–ligand docking, 13. Climate action, Docking (molecular), Drug Design, Pose prediction, Artificial intelligence, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, computer, Software, Protein Binding
Abstract

International audience; The D3R Grand Challenge 4 provided a brilliant opportunity to test macrocyclic docking protocols on a diverse high-quality experimental data. We participated in both pose and affinity prediction exercises. Overall, we aimed to use an automated structure-based docking pipeline built around a set of tools developed in our team. This exercise again demonstrated a crucial importance of the correct local ligand geometry for the overall success of docking. Starting from the second part of the pose prediction stage, we developed a stable pipeline for sampling macrocycle conformers. This resulted in the subangstrom average precision of our pose predictions. In the affinity prediction exercise we obtained average results. However, we could improve these when using docking poses submitted by the best predictors. Our docking tools including the Convex-PL scoring function are available at https://team.inria.fr/nano-d/software/.

Published
2020
Full Text
View/download PDF

6. IM-UFF: Extending the universal force field for interactive molecular modeling

Author

Léonard Jaillet, Stephane Redon, Svetlana Artemova, Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])

Subjects
Models, Molecular, Theoretical computer science, Molecular perception, Molecular model, Computer science, Molecular Dynamics Simulation, Time step, Molecular systems, 010402 general chemistry, Network topology, 01 natural sciences, Force field (chemistry), Computational science, Software, 0103 physical sciences, Materials Chemistry, Physical and Theoretical Chemistry, Interactive simulation, Spectroscopy, Internet, Molecular Structure, 010304 chemical physics, business.industry, Physics based, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Computer Graphics and Computer-Aided Design, 0104 chemical sciences, Universality (dynamical systems), Empirical force fields, Universal Force Field, Interactive modeling, business
Abstract

International audience; The universal force field (UFF) is a broadly applicable classical force field that contains parameters for almost every atom type of the periodic table. This force field is non-reactive, i.e. the topology of the system under study is considered as fixed and no creation or breaking of covalent bonds is possible. This paper introduces interactive modeling-UFF (IM-UFF), an extension of UFF that combines the possibility to significantly modify molecular structures (as with reactive force fields) with a broad diversity of supported systems thanks to the universality of UFF. Such an extension lets the user easily build and edit molecular systems interactively while being guided by physics based inter-atomic forces. This approach introduces weighted atom types and weighted bonds, used to update topologies and atom parameterizations at every time step of a simulation. IM-UFF has been evaluated on a large set of benchmarks and is proposed as a self-contained implementation integrated in a new module for the SAMSON software platform for computational nanoscience available at http://www.samson-connect.net.

Published
2017
Full Text
View/download PDF

7. AnAnaS: Software for Analytical Analysis of Symmetries in Protein Structures

Author

Sergei Grudinin, Guillaume Pagès, Algorithms for Modeling and Simulating Nanosystems [2018-...] (NANO-D-POST [2018-2020]), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), and Université Grenoble Alpes (UGA)

Subjects
Quaternion arithmetic, Physics, Quantitative Biology::Biomolecules, 0303 health sciences, Pure mathematics, Icosahedral symmetry, Dihedral angle, Symmetry group, [INFO.INFO-CG]Computer Science [cs]/Computational Geometry [cs.CG], 010402 general chemistry, 01 natural sciences, Protein Assembly, Symmetry axes detection, Protein Symmetry, 0104 chemical sciences, 03 medical and health sciences, Protein structure, Octahedron, Continuous optimization, Homogeneous space, Tetrahedron, [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC], [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Symmetry (geometry), 030304 developmental biology
Abstract

International audience; Symmetry is very common among proteins found in structural databases such as the PDB. We present novel software, called AnAnaS, that finds positions and orientations of the symmetry axes in all types of symmetrical protein assemblies. It deals with five symmetry groups, cyclic, dihedral, tetrahedral, octahedral, and icosahedral. The software also assesses the quality of symmetry and can detect symmetries in incomplete cyclic assemblies. Internally, AnAnaS comprises discrete and continuous optimization steps and is applicable to assemblies with multiple chains in the asymmetric subunits or to those with pseudo-symmetry. The method is very fast, as most of the steps are performed analytically.

Published
2020
Full Text
View/download PDF

8. Combining molecular dynamics simulations with small-angle X-ray and neutron scattering data to study multi-domain proteins in solution

Author

Larsen, Andreas Haahr, Wang, Yong, Bottaro, Sandro, Grudinin, Sergei, Arleth, Lise, Lindorff-Larsen, Kresten, Niels Bohr Institute [Copenhagen] (NBI), Faculty of Science [Copenhagen], University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH), Structural Bioinformatics and Computational Biochemistry [Oxford] (SBCB), University of Oxford, Linderstrøm-Lang Centre for Protein Science [Copenhagen], IT University of Copenhagen (ITU), Università degli studi di Genova = University of Genoa (UniGe), Algorithms for Modeling and Simulating Nanosystems [2018-...] (NANO-D-POST [2018-2020]), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU), University of Oxford [Oxford], IT University of Copenhagen, Universita degli studi di Genova, Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), and Université Grenoble Alpes (UGA)

Subjects
0301 basic medicine, Protein Conformation, Entropy, Neutron scattering, Biochemistry, 01 natural sciences, Small-Angle Scattering, Scattering, Molecular dynamics, 0302 clinical medicine, X-Ray Diffraction, Biochemical Simulations, Macromolecular Structure Analysis, Statistical physics, Biology (General), Physics, Quantitative Biology::Biomolecules, 0303 health sciences, Ecology, 010304 chemical physics, Small-angle X-ray scattering, Simulation and Modeling, Principle of maximum entropy, [SDV.BBM.BP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biophysics, Computational Theory and Mathematics, Modeling and Simulation, Physical Sciences, Engineering and Technology, Thermodynamics, Small-angle scattering, Algorithms, Research Article, Protein Structure, Biophysical Simulations, QH301-705.5, Biophysics, Molecular Dynamics Simulation, Research and Analysis Methods, Cellular and Molecular Neuroscience, 03 medical and health sciences, Protein Domains, Robustness (computer science), Scattering, Small Angle, 0103 physical sciences, Genetics, Neutron, Protein Interactions, Signal to Noise Ratio, Molecular Biology, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology, Neutrons, Biology and Life Sciences, Computational Biology, Proteins, Function (mathematics), 030104 developmental biology, Structural biology, Signal Processing, 030217 neurology & neurosurgery
Abstract

Many proteins contain multiple folded domains separated by flexible linkers, and the ability to describe the structure and conformational heterogeneity of such flexible systems pushes the limits of structural biology. Using the three-domain protein TIA-1 as an example, we here combine coarse-grained molecular dynamics simulations with previously measured small-angle scattering data to study the conformation of TIA-1 in solution. We show that while the coarse-grained potential (Martini) in itself leads to too compact conformations, increasing the strength of protein-water interactions results in ensembles that are in very good agreement with experiments. We show how these ensembles can be refined further using a Bayesian/Maximum Entropy approach, and examine the robustness to errors in the energy function. In particular we find that as long as the initial simulation is relatively good, reweighting against experiments is very robust. We also study the relative information in X-ray and neutron scattering experiments and find that refining against the SAXS experiments leads to improvement in the SANS data. Our results suggest a general strategy for studying the conformation of multi-domain proteins in solution that combines coarse-grained simulations with small-angle X-ray scattering data that are generally most easy to obtain. These results may in turn be used to design further small-angle neutron scattering experiments that exploit contrast variation through 1H/2H isotope substitutions., Author summary Many proteins contain multiple folded domains separated by flexible linkers, and in order to understand how such multi-domain proteins function, we need to be able to describe how these domains are oriented in space. We have used the three-domain protein TIA-1 as an example to combine molecular simulations with biophysical experiments to describe the structural and dynamical properties of a multi-domain protein. We show that while standard simulations do not lead to good agreement with the experimental data, we can improve the agreement substantially by tuning a single parameter in the model that describes the interaction between protein and water. We can gain further information about the system by a more direct integration of the data, and we find that we can provide a detailed and robust description of the relative location of the different domains in TIA-1. The method is general and will be useful to study the relationship between structure, dynamics and function in multi-domain proteins in other systems.

Published
2019
Full Text
View/download PDF

9. Controlled‐advancement rigid‐body optimization of nanosystems

Author

Pavel Buslaev, Stephane Redon, Petr Popov, Sergei Grudinin, Andrii Kurdiuk, Skolkovo Institute of Science and Technology [Moscow] (Skoltech), Algorithms for Modeling and Simulating Nanosystems [2018-...] (NANO-D-POST [2018-2020]), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Moscow Institute of Physics and Technology [Moscow] (MIPT), Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])

Subjects
Energy gradient, Optimization problem, 010304 chemical physics, Optimization algorithm, Molecular model, Computer science, Energy landscape, General Chemistry, 010402 general chemistry, Rigid body, Topology, Energy minimization, 01 natural sciences, 0104 chemical sciences, [PHYS.PHYS.PHYS-COMP-PH]Physics [physics]/Physics [physics]/Computational Physics [physics.comp-ph], Computational Mathematics, Docking (molecular), 0103 physical sciences, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract

In this study, we propose a novel optimization algorithm, with application to the refinement of molecular complexes. Particularly, we consider optimization problem as the calculation of quasi-static trajectories of rigid bodies influenced by the inverse-inertia-weighted energy gradient and introduce the concept of advancement region that guarantees displacement of a molecule strictly within a relevant region of conformational space. The advancement region helps to avoid typical energy minimization pitfalls, thus, the algorithm is suitable to work with arbitrary energy functions and arbitrary types of molecular complexes without necessary tuning of its hyper-parameters. Our method, called controlled-advancement rigid-body optimization of nanosystems (Carbon), is particularly useful for the large-scale molecular refinement, as for example, the putative binding candidates obtained with protein-protein docking pipelines. Implementation of Carbon with user-friendly interface is available in the SAMSON platform for molecular modeling at https://www.samson-connect.net. © 2019 Wiley Periodicals, Inc.

Published
2019
Full Text
View/download PDF

10. Assessment of chemical-crosslink-assisted protein structure modeling in CASP13

Author

Bohdan Monastyrskyy, Juri Rappsilber, Krzysztof Fidelis, J. Eduardo Fajardo, Mikhail Karasikov, Alexander Leitner, Agnieszka S. Karczyńska, Andras Fiser, Adam Belsom, Silvia Crivelli, Emilia A. Lubecka, Celina Sikorska, Andriy Kryshtafovych, Nelson Gil, Cezary Czaplewski, Rojan Shrestha, Esben Trabjerg, Sergei Grudinin, Guillaume Pagès, Adam Liwo, Adam K. Sieradzan, Department of Systems & Computational Biology [New York], Albert Einstein College of Medicine [New York], Department of Biochemistry [New York], Institut für Biotechnologie [berlin], Technische Universität Berlin (TU), Department of Computer Science [Davis] (UC Davis), University of California [Davis] (UC Davis), University of California-University of California, Department of Environmental Analytics [Univ Gdańsk], Faculty of Chemistry [Univ Gdańsk], University of Gdańsk (UG)-University of Gdańsk (UG), Genome Center [UC Davis], Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Moscow Institute of Physics and Technology [Moscow] (MIPT), Skolkovo Institute of Science and Technology [Moscow] (Skoltech), Department of Computer Science [ETH Zürich] (D-INFK), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Institute of Molecular Systems Biology [Zurich], Korea Institute for Advanced Study (KIAS), Institute of Mathematics [Univ Gdańsk], University of Gdańsk (UG), Wellcome Trust Centre for Cell Biology, University of Edinburgh, Technical University of Berlin / Technische Universität Berlin (TU), Department of Computer Science [Univ California Davis] (CS - UC Davis), University of California (UC)-University of California (UC), Algorithms for Modeling and Simulating Nanosystems [2018-...] (NANO-D-POST [2018-2020]), and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
CASP13, Models, Molecular, Computer science, Protein Conformation, Context (language use), Computational biology, Routine practice, 010402 general chemistry, Mass spectrometry, Machine learning, computer.software_genre, 01 natural sciences, Biochemistry, Article, [PHYS.PHYS.PHYS-COMP-PH]Physics [physics]/Physics [physics]/Computational Physics [physics.comp-ph], 03 medical and health sciences, Protein structure, chemical-crosslink-assisted protein structure modeling, Structural Biology, Tandem Mass Spectrometry, Molecular Biology, 030304 developmental biology, 0303 health sciences, Chemistry, business.industry, 030302 biochemistry & molecular biology, Model protein, Computational Biology, Proteins, Reproducibility of Results, 0104 chemical sciences, [SDV.BBM.BP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biophysics, Workflow, Cross-Linking Reagents, Models, Chemical, chemical crosslinking/mass spectrometry, Critical assessment, Artificial intelligence, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, Protein structure modeling, computer, Function (biology), Algorithms, Chromatography, Liquid
Abstract

International audience; With the advance of experimental procedures obtaining chemical crosslinking information is becoming a fast and routine practice. Information on crosslinks can greatly enhance the accuracy of protein structure modeling. Here, we review the current state of the art in modeling protein structures with the assistance of experimentally determined chemical crosslinks within the framework of the 13th meeting of Critical Assessment of Structure Prediction approaches. This largest‐to‐date blind assessment reveals benefits of using data assistance in difficult to model protein structure prediction cases. However, in a broader context, it also suggests that with the unprecedented advance in accuracy to predict contacts in recent years, experimental crosslinks will be useful only if their specificity and accuracy further improved and they are better integrated into computational workflows.

Published
2019
Full Text
View/download PDF

11. Knodle: A Support Vector Machines-Based Automatic Perception of Organic Molecules from 3D Coordinates

Author

Sergei Grudinin, Maria Kadukova, Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and Moscow Institute of Physics and Technology [Moscow] (MIPT)

Subjects
0301 basic medicine, Informatics, Support Vector Machine, Computer science, protein ligand docking, SVM, General Chemical Engineering, Library and Information Sciences, Ligands, computer.software_genre, 01 natural sciences, Set (abstract data type), Automation, 03 medical and health sciences, Software, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], 3D structure perception, 0103 physical sciences, Organic Chemicals, 010304 chemical physics, business.industry, Atom (order theory), General Chemistry, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Computer Science Applications, Molecular Weight, Data set, Support vector machine, machine learning, 030104 developmental biology, Protein–ligand docking, Benchmark (computing), Data mining, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, Algorithm, computer, Algorithms
Abstract

International audience; Here we address the problem of the assignment of atom types and bond orders in low molecular weight compounds. For this purpose, we have developed a prediction model based on nonlinear Support Vector Machines (SVM), implemented in a KNOwledge-Driven Ligand Extractor called Knodle, a software library for the recognition of atomic types, hybridization states, and bond orders in the structures of small molecules. We trained the model using an excessive amount of structural data collected from the PDBbindCN database. Accuracy of the results and the running time of our method is comparable with other popular methods, such as NAOMI, fconv, and I-interpret. On the popular Labute’s benchmark set consisting of 179 protein–ligand complexes, Knodle makes five to six perception errors, NAOMI makes seven errors, I-interpret makes nine errors, and fconv makes 13 errors. On a larger set of 3,000 protein–ligand structures collected from the PDBBindCN general data set (v2014), Knodle and NAOMI have a comparable accuracy of approximately 3.9% and 4.7% of errors, I-interpret made 6.0% of errors, while fconv produced approximately 12.8% of errors. On a more general set of 332,974 entries collected from the Ligand Expo database, Knodle made 4.5% of errors. Overall, our study demonstrates the efficiency and robustness of nonlinear SVM in structure perception tasks. Knodle is available at https://team.inria.fr/nano-d/software/Knodle.

Published
2016
Full Text
View/download PDF

12. Error Analysis of Modified Langevin Dynamics

Author

Stephane Redon, Gabriel Stoltz, Zofia Trstanova, Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique (CERMICS), École des Ponts ParisTech (ENPC), MATHematics for MatERIALS (MATHERIALS), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique (CERMICS), École des Ponts ParisTech (ENPC)-École des Ponts ParisTech (ENPC), ANR-14-CE23-0012,COSMOS,Statistique numérique et simulation moléculaire(2014), École des Ponts ParisTech (ENPC)-École des Ponts ParisTech (ENPC)-Inria de Paris, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
Physics, Work (thermodynamics), Statistical Mechanics (cond-mat.stat-mech), Monte Carlo method, Ergodicity, FOS: Physical sciences, Statistical and Nonlinear Physics, Numerical Analysis (math.NA), 010103 numerical & computational mathematics, 01 natural sciences, Hypoelliptic operator, 0103 physical sciences, FOS: Mathematics, Ergodic theory, Variance reduction, Mathematics - Numerical Analysis, Statistical physics, [PHYS.COND.CM-SM]Physics [physics]/Condensed Matter [cond-mat]/Statistical Mechanics [cond-mat.stat-mech], 0101 mathematics, 010306 general physics, Galerkin method, Langevin dynamics, Condensed Matter - Statistical Mechanics, Mathematical Physics
Abstract

International audience; We consider Langevin dynamics associated with a modified kinetic energy vanishing for small momenta. This allows us to freeze slow particles, and hence avoid the re-computation of inter-particle forces, which leads to computational gains. On the other hand, the statistical error may increase since there are a priori more correlations in time. The aim of this work is first to prove the ergodicity of the modified Langevin dynamics (which fails to be hypoelliptic), and next to analyze how the asymptotic variance on ergodic averages depends on the parameters of the modified kinetic energy. Numerical results illustrate the approach, both for low-dimensional systems where we resort to a Galerkin approximation of the generator, and for more realistic systems using Monte Carlo simulations.

Published
2016
Full Text
View/download PDF

13. Principal Component Analysis of Lipid Molecule Conformational Changes in Molecular Dynamics Simulations

Author

Ivan Gushchin, Sergei Grudinin, Valentin Gordeliy, Pavel Buslaev, Moscow Institute of Physics and Technology [Moscow] (MIPT), Forschungszentrum Jülich GmbH | Centre de recherche de Juliers, Helmholtz-Gemeinschaft = Helmholtz Association, Institut de biologie structurale (IBS - UMR 5075 ), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
0301 basic medicine, Principal Component Analysis, Physics::Biological Physics, 010304 chemical physics, Chemistry, Bilayer, Molecular Conformation, Molecular Dynamics Simulation, Lipids, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, 01 natural sciences, Molecular conformation, Computer Science Applications, 03 medical and health sciences, Molecular dynamics, 030104 developmental biology, Chemical physics, Computational chemistry, 0103 physical sciences, Principal component analysis, Molecule, Lipid molecule, Physical and Theoretical Chemistry, Lipid bilayer
Abstract

International audience; Molecular dynamics simulations of lipid bilayers are ubiquitous nowadays. Usually, either global properties of the bilayer or some particular characteristics of each lipid molecule are evaluated in such simulations, but the structural properties of the molecules as a whole are rarely studied. Here, we show how a comprehensive quantitative description of conformational space and dynamics of a single lipid molecule can be achieved via the principal component analysis (PCA). We illustrate the approach by analyzing and comparing simulations of DOPC bilayers obtained using eight different force fields: all-atom generalized AMBER, CHARMM27, CHARMM36, Lipid14 and Slipids, and united-atom Berger, GROMOS43A1-S3 and GROMOS54A7. Similarly to proteins, most of the structural variance of a lipid molecule can be described by only a few principal components. These major components are similar in different simulations, although there are notable distinctions between the older and newer force fields, and between the all-atom and united-atom force fields. The DOPC molecules in the simulations generally equilibrate on the timescales of tens to hundreds of nanoseconds. The equilibration is the slowest in the GAFF simulation, and the fastest in the Slipids simulation. Somewhat unexpectedly, the equilibration in the united-atom force fields is generally slower than in the all-atom force fields. Overall, there is a clear separation between the more variable previous generation force fields, and significantly more similar new generation force fields (CHARMM36, Lipid14, Slipids). We expect that the presented approaches will be useful for quantitative analysis of conformations and dynamics of individual lipid molecules in other simulations of lipid bilayers.

Published
2016
Full Text
View/download PDF

14. Incremental solver for orbital-free density functional theory

Author

François Rousse, Stephane Redon, Algorithms for Modeling and Simulating Nanosystems [2018-...] (NANO-D-POST [2018-2020]), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])

Subjects
Particle system, 010304 chemical physics, Computer science, Orbital-free density functional theory, Adaptively Restrained Particle Simulations, Context (language use), General Chemistry, Solver, 010402 general chemistry, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, 01 natural sciences, Incremental algorithm, 0104 chemical sciences, Orbital-Free DFT, Computational Mathematics, 0103 physical sciences, Particle, Density functional theory, Ab-initio molecular dynamic, Statistical physics, Real-Space Finite-Differences
Abstract

First-principle calculations are still a challenge since they require a great amount of computational time. In this article, we introduce a new algorithm to perform orbital-free density functional theory (OF-DFT) calculations. Our new algorithm focuses computational efforts on important parts of the particle system, which, in the context of adaptively restrained particle simulations (ARPS) allows us to accelerate particle simulations. © 2019 Wiley Periodicals, Inc.

Published
2019
Full Text
View/download PDF

15. Atomistic Modelling and Simulation of Transmission Electron Microscopy Images: Application to Intrinsic Defects of Graphene

Author

François Rousse, Cyril Guedj, Stephane Redon, Léonard Jaillet, Département Plate-Forme Technologique (DPFT), Commissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information (CEA-LETI), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])

Subjects
Materials science, business.industry, Graphene, 02 engineering and technology, [CHIM.MATE]Chemical Sciences/Material chemistry, 021001 nanoscience & nanotechnology, Microstructure, Radial distribution function, 01 natural sciences, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Characterization (materials science), law.invention, Software, law, 0103 physical sciences, Scanning transmission electron microscopy, Range (statistics), [PHYS.COND.CM-MS]Physics [physics]/Condensed Matter [cond-mat]/Materials Science [cond-mat.mtrl-sci], Optoelectronics, 010306 general physics, 0210 nano-technology, business, Energy (signal processing), ComputingMilieux_MISCELLANEOUS
Abstract

The characterization of advanced materials and devices in the nanometer range requires complex tools to understand the precise links between structure and properties. This paper demonstrates that the modelling of graphene-based defects can be obtained efficiently for various atomic arrangements using the Brenner module of the SAMSON software platform. The signatures of all kinds of defects are computed in terms of energy and simulated scanning transmission electron microscopy images. The results are in good agreement with the majority of the available theoretical and experimental data. This original methodology is an excellent compromise between the speed and the precision required by the semiconductor industry and opens the possibility of realistic in-silico research conjugated to the experimental nanocharacterization of these promising materials. We propose a novel approach to compare the agreement between experiment and simulation by using the projected radial distribution function. The maximum projected Euclidian distance between the model and the experiment is always better than 100 pm.

Published
2018
Full Text
View/download PDF

16. ART-RRT: As-Rigid-As-Possible exploration of ligand unbinding pathways

Author

Léonard Jaillet, Stephane Redon, Minh Khoa Nguyen, Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])

Subjects
0301 basic medicine, Computer science, dimension reduction, Dimensionality reduction, ART-RRT, General Chemistry, motion planning, morphing, 010402 general chemistry, Ligand (biochemistry), 01 natural sciences, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, 0104 chemical sciences, Reaction coordinate, Computer graphics, 03 medical and health sciences, Computational Mathematics, Tree (data structure), Morphing, 030104 developmental biology, Motion planning, Algorithm, ligand unbinding pathway
Abstract

This article proposes a method to efficiently generate approximate ligand unbinding pathways. It combines an efficient tree-based exploration method with a morphing technique from Computer Graphics for dimensionality reduction. This method is computationally cheap and, unlike many existing approaches, does not require a reaction coordinate to guide the search. It can be used for finding pathways with known or unknown directions beforehand. The approach is evaluated on several benchmarks and the obtained solutions are compared with the results from other state-of-the-art approaches. We show that the method is time-efficient and produces pathways in good agreement with other state-of-the-art solutions. These paths can serve as first approximations that can be used, analyzed, or improved with more specialized methods. © 2018 Wiley Periodicals, Inc.

Published
2018
Full Text
View/download PDF

17. Eurecon: Equidistant Uniform Rigid-body Ensemble Constructor

Author

Petr Popov, Sergei Grudinin, Moscow Institute of Physics and Technology [Moscow] (MIPT), Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])

Subjects
0301 basic medicine, Computation, Molecular Conformation, Molecular Dynamics Simulation, 010402 general chemistry, Translation (geometry), [INFO.INFO-CG]Computer Science [cs]/Computational Geometry [cs.CG], 01 natural sciences, 03 medical and health sciences, Materials Chemistry, Partition (number theory), Equidistant, Statistical physics, Physical and Theoretical Chemistry, Root-mean-square deviation, Conformational ensembles, Spectroscopy, Physics, Quantitative Biology::Biomolecules, Proteins, DNA, Rigid body, Computer Graphics and Computer-Aided Design, 0104 chemical sciences, 030104 developmental biology, Protein Multimerization, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Rotation (mathematics), Algorithms
Abstract

International audience; Conformational ensembles comprise one of the fundamental concepts in statistical bioinformatics and appear in a variety of applications, e.g. molecular docking, virtual screening, searching for pharmacophores, etc. High-throughput applications require billions of conformations to be considered, thus, one often uses the rigid-body representation of molecules or its fragments to cope with the computational cost. Of particular interest is generation of the near-native conformational ensembles, which consist of conformations structurally close to the biologically relevant ones. One possible way to compose such ensembles is to control the root mean square deviation (RMSD) between the original and the generated conformations. To the best of our knowledge there is no computational approach that guarantees that all the generated conformations have the desired RMSD with respect to the reference structure. In this study we presented a fast algorithm for the construction of rigid-body conformational ensembles, which possess two main properties: (i) each generated conformation has a fixed RMSD with respect to the original conformation, (ii) generated conformations are distributed uniformly over the sphere of axes corresponding to the rigid-body motions. The algorithm is very efficient, it does not require any standard RMSD computation between the conformations and has the O(N + M) complexity to generate the required rigid-body transforms, where N is the number of atoms in the system, and M is the size of the conformational ensemble. Eurecon is applicable to an arbitrary atomic system, thus, it could be used for molecular systems of various size and type. We demonstrated Eurecon application by generating near-native conformational ensembles for a ligand placed inside a binding site, a protein dimer embedded into a membrane, and a ribosomal complex. We implemented the developed algorithm in C++ and called it Eurecon, which stands for Equidistant Uniform Rigid-body Ensemble CONstructor. A user-friendly interface allows to define the desired RMSD value, the relative amplitudes for rotation and translation motions by means of the partition parameter, and the set of axes corresponding to the rigid-body motions. Eurecon is available as the SAMSON Element (https://samson-connect.net).

Published
2018
Full Text
View/download PDF

18. Incremental update of electrostatic interactions in adaptively restrained particle simulations

Author

Stephane Redon, Semeho Prince A. Edorh, Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])

Subjects
Molecular Simulations, 010304 chemical physics, Computational complexity theory, Computer science, Computation, General Chemistry, Multigrid, 01 natural sciences, P3M, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Computational science, Computational Mathematics, Molecular dynamics, Multigrid method, Electrostatics, Particle Mesh, 0103 physical sciences, Particle, Central processing unit, Adaptively Restrained Molecular Dynamics, 010306 general physics, LAMMPS
Abstract

International audience; The computation of long-range potentials is one of the demanding tasks in Molecular Dynamics. During the last decades, an inventive panoply of methods was developed in order to reduce the CPU time of this task. In this work, we propose a fast method dedicated to the computation of the elec-trostatic potential in adaptively restrained systems. We exploit the fact that, in such systems, only some particles are allowed to move at each timestep. We developed an incremental algorithm derived from a multigrid-based alternative to traditional Fourier-based methods. Our algorithm was implemented inside LAMMPS, a popular molecular dynamics simulation package. We evaluated the method on different systems. We showed that the new algorithm's computational complexity scales with the number of active particles in the simulated system, and is able to outperform the well-established Particle Particle Particle Mesh (P3M) for adaptively restrained simulations.

Published
2017
Full Text
View/download PDF

19. RapidRMSD: rapid determination of RMSDs corresponding to motions of flexible molecules

Author

Grégoire Danoy, Petr Popov, Angelo Migliosi, Emilie Neveu, Pascal Bouvry, Xavier Besseron, Sergei Grudinin, Alexandre Hoffmann, Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Université de Lausanne = University of Lausanne (UNIL), Moscow Institute of Physics and Technology [Moscow] (MIPT), Computer Science and Communications Research Unit [Luxembourg] (CSC), Laboratory of Advanced Software SYstems [Luxembourg] (LASSY), Université du Luxembourg (Uni.lu)-Université du Luxembourg (Uni.lu), and Université de Lausanne (UNIL)

Subjects
0301 basic medicine, Statistics and Probability, Computer science, Protein Conformation, Initialization, Molecular Dynamics Simulation, 01 natural sciences, Biochemistry, Molecular conformation, 03 medical and health sciences, Molecular dynamics, Motion, Protein structure, 0103 physical sciences, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, Cluster analysis, Pliability, Molecular Biology, Quantitative Biology::Biomolecules, Principal Component Analysis, 010304 chemical physics, Computational Biology, Proteins, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Computer Science Applications, Computational Mathematics, 030104 developmental biology, Computational Theory and Mathematics, Structural biology, Docking (molecular), Principal component analysis, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Algorithm, Algorithms, Software
Abstract

Motivation The root mean square deviation (RMSD) is one of the most used similarity criteria in structural biology and bioinformatics. Standard computation of the RMSD has a linear complexity with respect to the number of atoms in a molecule, making RMSD calculations time-consuming for the large-scale modeling applications, such as assessment of molecular docking predictions or clustering of spatially proximate molecular conformations. Previously, we introduced the RigidRMSD algorithm to compute the RMSD corresponding to the rigid-body motion of a molecule. In this study, we go beyond the limits of the rigid-body approximation by taking into account conformational flexibility of the molecule. We model the flexibility with a reduced set of collective motions computed with e.g. normal modes or principal component analysis. Results The initialization of our algorithm is linear in the number of atoms and all the subsequent evaluations of RMSD values between flexible molecular conformations depend only on the number of collective motions that are selected to model the flexibility. Therefore, our algorithm is much faster compared to the standard RMSD computation for large-scale modeling applications. We demonstrate the efficiency of our method on several clustering examples, including clustering of flexible docking results and molecular dynamics (MD) trajectories. We also demonstrate how to use the presented formalism to generate pseudo-random constant-RMSD structural molecular ensembles and how to use these in cross-docking. Availability and implementation We provide the algorithm written in C++ as the open-source RapidRMSD library governed by the BSD-compatible license, which is available at http://team.inria.fr/nano-d/software/RapidRMSD/. The constant-RMSD structural ensemble application and clustering of MD trajectories is available at http://team.inria.fr/nano-d/software/nolb-normal-modes/. Supplementary information Supplementary data are available at Bioinformatics online.

Published
2017
Full Text
View/download PDF

20. Single-pass incremental force updates for adaptively restrained molecular dynamics

Author

Stephane Redon, Krishna Kant Singh, Krishna kant Singh, Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])

Subjects
0301 basic medicine, Single pass, Speedup, 010304 chemical physics, Computer science, Observable, Incremental Force Update, General Chemistry, 01 natural sciences, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, 03 medical and health sciences, Computational Mathematics, Molecular dynamics, 030104 developmental biology, Neighbor Lists, 0103 physical sciences, Convergence (routing), Adaptively Restrained Molecular Dynamics, Algorithm
Abstract

Adaptively restrained molecular dynamics (ARMD) allows users to perform more integration steps in wall-clock time by switching on and off positional degrees of freedoms. This article presents new, single-pass incremental force updates algorithms to efficiently simulate a system using ARMD. We assessed different algorithms for speedup measurements and implemented them in the LAMMPS MD package. We validated the single-pass incremental force update algorithm on four different benchmarks using diverse pair potentials. The proposed algorithm allows us to perform simulation of a system faster than traditional MD in both NVE and NVT ensembles. Moreover, ARMD using the new single-pass algorithm speeds up the convergence of observables in wall-clock time. © 2017 Wiley Periodicals, Inc.

Published
2017
Full Text
View/download PDF

21. Parallel Adaptively Restrained Molecular Dynamics

Author

Stephane Redon, Krishna Kant Singh, Dmitriy F. Marin, Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])

Subjects
ARMD, 010304 chemical physics, Computer science, Interface (computing), Computation, Parallel algorithm, Molecular Dynamics, 01 natural sciences, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Computational science, Molecular dynamics, 0103 physical sciences, Scalability, MPI, 010306 general physics, Single-pass Incremental Force Update, LAMMPS
Abstract

International audience; Force computations are one of the most time consuming part in performing Molecular Dynamics (MD) simulations. Adaptively Restrained Molecular Dynamics (ARMD) makes it possible to perform fewer force calculations by adap-tively restraining particles positions. This paper introduces parallel algorithms for single-pass incremental force computations to take advantage of adaptive restraints using the Message Passage Interface (MPI) standard. The proposed algorithms are implemented and validated in LAMMPS, however, these algorithms can be applied to other MD simulators. We compared our algorithms with LAMMPS for performance and scalability measurements.

Published
2017
Full Text
View/download PDF

22. Docking of small molecules to farnesoid X receptors using AutoDock Vina with the Convex-PL potential: lessons learned from D3R Grand Challenge 2

Author

Sergei Grudinin, Maria Kadukova, Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and Moscow Institute of Physics and Technology [Moscow] (MIPT)

Subjects
0301 basic medicine, Computer science, Protein Conformation, [PHYS.PHYS.PHYS-BIO-PH]Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph], Receptors, Cytoplasmic and Nuclear, Computational biology, Crystallography, X-Ray, Ligands, 01 natural sciences, ensemble docking, Autodock vina, Small Molecule Libraries, 03 medical and health sciences, Computational chemistry, Drug Discovery, scoring function, Humans, Physical and Theoretical Chemistry, Receptor, Databases, Protein, Lead Finder, Binding Sites, protein-ligand docking, Small molecule, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, 0104 chemical sciences, Computer Science Applications, Molecular Docking Simulation, 010404 medicinal & biomolecular chemistry, 030104 developmental biology, Protein–ligand docking, Searching the conformational space for docking, Docking (molecular), Drug Design, Computer-Aided Design, Farnesoid X receptor, flexible docking, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], D3R, Software, Protein Binding
Abstract

International audience; The 2016 D3R Grand Challenge 2 provided an opportunity to test multiple protein-ligand docking protocols on a set of ligands bound to farnesoid X receptor that has many available experimental structures. We participated in the Stage 1 of the Challenge devoted to the docking pose predictions, with the mean RMSD value of our submission poses of 2.9 Å. Here we present a thorough analysis of our docking predictions made with AutoDock Vina and the Convex-PL rescoring potential by reproducing our submission protocol and running a series of additional molecular docking experiments. We conclude that a correct receptor structure, or more precisely, the structure of the binding pocket, plays the crucial role in the success of our docking studies. We have also noticed the important role of a local ligand geometry, which seems to be not well discussed in literature. We succeed to improve our results up to the mean RMSD value of 2.15 – 2.33 Å dependent on the models of the ligands, if docking these to all available homologous receptors. Overall, for docking of ligands of diverse chemical series we suggest to perform docking of each of the ligands to a set of multiple receptors that are homologous to the target.

Published
2017
Full Text
View/download PDF

23. Langevin dynamics with general kinetic energies

Author

Gabriel Stoltz, Zofia Trstanova, Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique (CERMICS), École des Ponts ParisTech (ENPC), MATHematics for MatERIALS (MATHERIALS), École des Ponts ParisTech (ENPC)-École des Ponts ParisTech (ENPC)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), ANR-14-CE23-0012,COSMOS,Statistique numérique et simulation moléculaire(2014), European Project: 307629,EC:FP7:ERC,ERC-2012-StG_20111012,ADAPT(2012), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique (CERMICS), and École des Ponts ParisTech (ENPC)-École des Ponts ParisTech (ENPC)

Subjects
Physics, Statistical Mechanics (cond-mat.stat-mech), Ecological Modeling, General Physics and Astronomy, Sampling (statistics), FOS: Physical sciences, 010103 numerical & computational mathematics, General Chemistry, Kinetic energy, 01 natural sciences, Computer Science Applications, 010104 statistics & probability, Quadratic equation, Modeling and Simulation, Metastability, Statistical physics, 0101 mathematics, [PHYS.COND.CM-SM]Physics [physics]/Condensed Matter [cond-mat]/Statistical Mechanics [cond-mat.stat-mech], Langevin dynamics, Condensed Matter - Statistical Mechanics
Abstract

International audience We study Langevin dynamics with a kinetic energy different from the standard, quadratic one in order to accelerate the sampling of Boltzmann-Gibbs distributions. In particular, this kinetic energy can be nonglobally Lipschitz, which raises issues for the stability of discretizations of the associated Langevin dynamics. We first prove the exponential convergence of the law of the continuous process to the Boltzmann-Gibbs measure by a hypocoercive approach and characterize the asymptotic variance of empirical averages over trajectories. We next develop numerical schemes which are stable and of weak order two by considering splitting strategies where the discretizations of the fluctuation/dissipation are corrected by a Metropolis procedure. We use the newly developed schemes for two applications: optimizing the shape of the kinetic energy for the so-called adaptively restrained Langevin dynamics (which considers perturbations of standard quadratic kinetic energies vanishing around the origin) and reducing the metastability of some toy models using nonglobally Lipschitz kinetic energies.

Published
2016
Full Text
View/download PDF

24. Predicting binding poses and affinities for protein-ligand complexes in the 2015 D3R Grand Challenge using a physical model with a statistical parameter estimation

Author

Simon Marillet, Maria Kadukova, Frédéric Cazals, Sergei Grudinin, Andreas Eisenbarth, Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Moscow Institute of Physics and Technology [Moscow] (MIPT), Algorithms, Biology, Structure (ABS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Unité de recherche Virologie et Immunologie Moléculaires (VIM (UR 0892)), Institut National de la Recherche Agronomique (INRA), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut National Polytechnique de Grenoble (INPG), Molecular Immunology and Virology (VIM-UR892), and Université Côte d'Azur (UCA)

Subjects
0301 basic medicine, Protein Conformation, Computer science, Entropy, Ligands, computer.software_genre, [INFO.INFO-CG]Computer Science [cs]/Computational Geometry [cs.CG], 01 natural sciences, Molecular Docking Simulation, Structure-Activity Relationship, 03 medical and health sciences, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], 0103 physical sciences, Drug Discovery, Convex-PL, ridge regression, scoring function, Humans, Entropy (information theory), HSP90 Heat-Shock Proteins, Prospective Studies, Physical and Theoretical Chemistry, Databases, Protein, AutoDock Vina, Binding Sites, 010304 chemical physics, protein-ligand docking, Estimation theory, Statistical parameter, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Computer Science Applications, 030104 developmental biology, machine learning, Protein–ligand docking, Drug Design, Test set, Regression Analysis, Thermodynamics, Data mining, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], parameter estimation, computer, Protein Binding, Protein ligand, Free parameter
Abstract

International audience; The 2015 D3R Grand Challenge provided an opportunity to test our new model for the binding free energy of small molecules, as well as to assess our protocol to predict binding poses for protein-ligand complexes. Our pose predictions were ranked 3-9 for the HSP90 dataset, depending on the assessment metric. For the MAP4K dataset the ranks are very dispersed and equal to 2-35, depending on the assessment metric, which does not provide any insight into the accuracy of the method. The main success of our pose prediction protocol was the re-scoring stage using the recently developed Convex-PL potential. We make a thorough analysis of our docking predictions made with AutoDock Vina and discuss the effect of the choice of rigid receptor templates, the number of flexible residues in the binding pocket, the binding pocket size, and the benefits of re-scoring. However, the main challenge was to predict experimentally determined binding affinities for two blind test sets. Our affinity prediction model consisted of two terms, a pairwise-additive enthalpy, and a non pairwise-additive entropy. We trained the free parameters of the model with a regularized regression using affinity and structural data from the PDBBind database. Our model performed very well on the training set, however, failed on the two test sets. We explain the drawback and pitfalls of our model, in particular in terms of relative coverage of the test set by the training set and missed dynamical properties from crystal structures, and discuss different routes to improve it.

Published
2016
Full Text
View/download PDF

25. A comparison of neighbor search algorithms for large rigid molecules

Author

Stephane Redon, Svetlana Artemova, Sergei Grudinin, Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF)

Subjects
rigid body molecular docking, bounding volume hierarchy, Computation, Monte Carlo method, Bounding volume hierarchy, Molecular Dynamics Simulation, 01 natural sciences, Diffusion, 03 medical and health sciences, Ribonucleases, Bacterial Proteins, Bounding overwatch, 0103 physical sciences, Animals, Horses, Enzyme Inhibitors, 030304 developmental biology, Mathematics, 0303 health sciences, 010304 chemical physics, Hierarchy (mathematics), Proteins, General Chemistry, Grid, Rigid body, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, neighbor list, Streptomyces, protein docking, hierarchy-based algorithm, Computational Mathematics, Bounding volume, Apoferritins, Capsid Proteins, Monte Carlo Method, Algorithm, rigid body molecular simulations, Algorithms, Bluetongue virus, Protein Binding
Abstract

International audience; Fast determination of neighboring atoms is an essential step in molecular dynamics simulations or Monte Carlo computations, and there exists a variety of algorithms to efficiently compute neighbor lists. However, most of these algorithms are general, and not specifically designed for a given type of application. As a result, although their average performance is satisfactory, they might be inappropriate in some specific application domains. In this article, we study the case of detecting neighbors between large rigid molecules, which has applications in, e.g., rigid body molecular docking, Monte Carlo simulations of molecular self-assembly or diffusion, and rigid body molecular dynamics simulations. More precisely, we compare the traditional grid-based algorithm to a series of hierarchy-based algorithms that use bounding volumes to rapidly eliminate large groups of irrelevant pairs of atoms during the neighbor search. We compare the performance of these algorithms based on several parameters: the size of the molecules, the average distance between them, the cutoff distance, as well as the type of bounding volume used in the culling hierarchy (AABB, OBB, wrapped, or layered spheres). We demonstrate that for relatively large systems (> 100,000 atoms) the algorithm based on the hierarchy of wrapped spheres shows the best results and the traditional grid-based algorithm gives the worst timings. For small systems, however, the grid-based algorithm and the one based on the wrapped sphere hierarchy are beneficial.

Published
2011
Full Text
View/download PDF

26. Multigrid solvers and multigrid preconditioners for the solution of variational data assimilation problems

Author

Emilie Neveu, Arthur Vidard, Laurent Debreu, Ehouarn Simon, François-Xavier Le Dimet, Mathematics and computing applied to oceanic and atmospheric flows (AIRSEA), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS), Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Algorithmes Parallèles et Optimisation (IRIT-APO), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Institut National Polytechnique (Toulouse) (Toulouse INP), Modelling, Observations, Identification for Environmental Sciences (MOISE), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Mathématiques (LAMA), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Nansen Environmental and Remote Sensing Center [Bergen] (NERSC), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), and Université de Toulouse (UT)

Subjects
Atmospheric Science, Mathematical optimization, 010504 meteorology & atmospheric sciences, Discretization, Covariance matrix, Preconditioner, MathematicsofComputing_NUMERICALANALYSIS, 010103 numerical & computational mathematics, Optimal control, 01 natural sciences, Regularization (mathematics), Computer Science::Numerical Analysis, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Weighting, Mathematics::Numerical Analysis, Multigrid method, Computer Science::Mathematical Software, 0101 mathematics, Convection–diffusion equation, 0105 earth and related environmental sciences, Mathematics
Abstract

International audience; In order to lower the computational cost of the variational data assimilation process, we investigate the use of multigrid methods to solve the associated optimal control system. On a linear advection equation, we study the impact of the regularization term of the optimal control and the impact of discretization errors on the efficiency of the coarse grid correction step. We show that even if the optimal control problem leads to the solution of an elliptic system, numerical errors introduced by the discretization can alter the success of the multigrid methods. The view of the multigrid iteration as a preconditioner for a Krylov optimization method leads to a more robust algorithm. A scale dependent weighting of the multigrid preconditioner and the usual background error covariance matrix based preconditioner is proposed and brings significant improvements.

Published
2016
Full Text
View/download PDF

27. Adaptively restrained molecular dynamics in LAMMPS

Author

Stephane Redon, Krishna Kant Singh, Krishna kant Singh, Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])

Subjects
Materials science, Speedup, Theoretical computer science, 010304 chemical physics, Degrees of freedom (statistics), Molecular simulation, Molecular Dynamics, Condensed Matter Physics, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, 01 natural sciences, Computer Science Applications, Computational science, Incremental force computation, Molecular dynamics, Mechanics of Materials, Modeling and Simulation, 0103 physical sciences, A priori and a posteriori, General Materials Science, Adaptively Restrained Molecular Dynamics, 010306 general physics, Neighbor List, Algorithms, LAMMPS, Order of magnitude
Abstract

International audience; Adaptively Restrained Molecular Dynamics (ARMD) is a recently introduced particles simulation method that switches positional degrees of freedom on and off during simulation in order to speed up calculations. In the NVE ensemble, ARMD allows users to trade between precision and speed while, in the NVT ensemble, it makes it possible to compute statistical averages faster. Despite the conceptual simplicity of the approach, however, integrating it in existing molecular dynamics packages is non-trivial, in particular since implemented potentials should a priori be rewritten to take advantage of frozen particles and achieve a speed-up. In this paper, we present novel algorithms for integrating ARMD in LAMMPS, a popular multipurpose molecular simulation package. In particular, we demonstrate how to enable ARMD in LAMMPS without having to re-implement all available force fields. The proposed algorithms are assessed on four different benchmarks, and show how they allow us to speed up simulations up to one order of magnitude.

Published
2017
Full Text
View/download PDF

28. ExaViz: a flexible framework to analyse, steer and interact with molecular dynamics simulations

Author

Marc Baaden, Marc Piuzzi, Bruno Raffin, Sophie Robert, Nicolas Ferey, Jessica Prevoteau-Jonquet, Matthieu Dreher, Sébastien Limet, Mikael Trellet, PrograMming and scheduling design fOr Applications in Interactive Simulation (MOAIS), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), Laboratoire de biochimie théorique [Paris] (LBT (UPR_9080)), Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)-Institut de biologie physico-chimique (IBPC (FR_550)), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC), Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur (LIMSI), Université Paris Saclay (COmUE)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université - UFR d'Ingénierie (UFR 919), Sorbonne Université (SU)-Sorbonne Université (SU)-Université Paris-Saclay-Université Paris-Sud - Paris 11 (UP11), Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique Fondamentale d'Orléans (LIFO), Ecole Nationale Supérieure d'Ingénieurs de Bourges-Université d'Orléans (UO), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Université Paris Diderot - Paris 7 (UPD7)-Institut de biologie physico-chimique (IBPC), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Université Paris-Sud - Paris 11 (UP11)-Université Paris-Saclay-Sorbonne Université - UFR d'Ingénierie (UFR 919), Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Saclay (COmUE), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut National Polytechnique de Grenoble (INPG), Université d'Orléans (UO)-Ecole Nationale Supérieure d'Ingénieurs de Bourges, Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Institut de biologie physico-chimique (IBPC (FR_550)), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Université Paris-Sud - Paris 11 (UP11)-Sorbonne Université - UFR d'Ingénierie (UFR 919), and Sorbonne Université (SU)-Sorbonne Université (SU)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris Saclay (COmUE)

Subjects
0303 health sciences, 010304 chemical physics, Computer science, Distributed computing, Process (computing), Molecular Dynamics Simulation, Supercomputer, 01 natural sciences, Variety (cybernetics), Data set, 03 medical and health sciences, Molecular dynamics, 0103 physical sciences, Leverage (statistics), [INFO.INFO-DL]Computer Science [cs]/Digital Libraries [cs.DL], Humans, Physical and Theoretical Chemistry, Analysis tools, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], [MATH.MATH-NA]Mathematics [math]/Numerical Analysis [math.NA], 030304 developmental biology, Haptic technology
Abstract

International audience; The amount of data generated by molecular dynamics simulations of large molecular assemblies and the sheer size and complexity of the systems studied call for new ways to analyse, steer and interact with such calculations. Traditionally, the analysis is performed off-line once the huge amount of simulation results have been saved to disks, thereby stressing the supercomputer I/O systems, and making it increasingly difficult to handle post-processing and analysis from the scientist's office. The ExaViz framework is an alternative approach developed to couple the simulation with analysis tools to process the data as close as possible to their source of creation, saving a reduced, more manageable and pre-processed data set to disk. ExaViz supports a large variety of analysis and steering scenarios. Our framework can be used for live sessions (simulations short enough to be fully followed by the user) as well as batch sessions (long time batch executions). During interactive sessions, at run time, the user can display plots from analysis, visualise the molecular system and steer the simulation with a haptic device. We also emphasise how a Cave-like immersive environment could be used to leverage such simulations, offering a large display surface to view and intuitively navigate the molecular system.

Published
2014
Full Text
View/download PDF

29. Advances in GPCR Modeling Evaluated by the GPCR Dock 2013 Assessment: Meeting New Challenges

Author

Camillo Rosano, Jie Xia, David Rodriguez, Manuel Pastor, Stefano Moro, Ajit Jadhav, Balaji Selvam, Sebastien Fiorucci, Ingebrigt Sylte, Serge Antonczak, Vsevolod Katritch, Serdar Durdagi, Ki Chul Park, Gerard Van Westen, Henri Xhaard, Raymond Stevens, Jiye Shi, Philip Biggin, Davide Sabbadin, SHUGUANG YUAN, Slawomir Filipek, David Gloriam, Antonella Ciancetta, Marek Bajda, Supriyo Bhattacharya, Hahnbeom Park, Jens Carlsson, Aleksandrs Gutcaits, Bartosz Trzaskowski, Jianyi Yang, William Pitt, Dorota Latek, Liliana Halip, Elizabeth Nguyen, Irina Kufareva, Noel Southall, Joaquin Ambia, Woong-Hee Shin, Jana Selent, Uddhavesh Sonavane, Marco Ponassi, Julien Diharce, Jose Manuel Perez-Aguilar, Nicos Petasis, Sergei Grudinin, Skaggs School of Pharmacy and Pharmaceutical Sciences [San Diego], University of California [San Diego] (UC San Diego), University of California-University of California, Department of Molecular Biology [San Diego], The Scripps Research Institute [La Jolla], University of California-University of California-University of California [San Diego] (UC San Diego), Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF), Institut de Chimie de Nice (ICN), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), University of California (UC)-University of California (UC), The Scripps Research Institute [La Jolla, San Diego], Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])

Subjects
Models, Molecular, Protein Conformation, Bioinformatics, 01 natural sciences, Molecular Docking Simulation, Receptors, G-Protein-Coupled, Protein structure, Ligand docking, Models, Structural Biology, Receptors, 0303 health sciences, Crystallography, [SDV.BBM.BS]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Structural Biology [q-bio.BM], Homology modeling, Biological Sciences, [SDV.BBM.BS]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biomolecules [q-bio.BM], Smoothened Receptor, Structure-based drug discovery, Protein Binding, Serotonin, 1.1 Normal biological development and functioning, Allosteric regulation, Biophysics, Computational biology, Biology, 010402 general chemistry, Article, G-Protein-Coupled, 03 medical and health sciences, Underpinning research, Information and Computing Sciences, DOCK, Structure prediction, Humans, Molecular Biology, 030304 developmental biology, G protein-coupled receptor, Conformational sampling, Pharmacology, Participants of GPCR Dock 2013, Binding Sites, Molecular, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, 0104 chemical sciences, Docking (molecular), Receptors, Serotonin, Chemical Sciences, G-protein coupled receptor, [SDV.SP.PHARMA]Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology, Generic health relevance, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract

International audience; Despite tremendous successes of GPCR crystallography, the receptors with available structures represent only a small fraction of human GPCRs. An important role of the modeling community is to maximize structural insights for the remaining receptors and complexes. The community-wide GPCR Dock assessment was established to stimulate and monitor the progress in molecular modeling and ligand docking for GPCRs. The four targets in the present third assessment round presented new and diverse challenges for modelers, including prediction of allosteric ligand interaction and activation states in 5-hydroxytryptamine receptors 1B and 2B, and modeling by extremely distant homology for smoothened receptor. Forty-four modeling groups participated in the assessment. State-of-the-art modeling approaches achieved close-to-experimental accuracy for small rigid orthosteric ligands and models built by close homology, and they correctly predicted protein fold for distant homology targets. Predictions of long loops and GPCR activation states remain unsolved problems.

Published
2014
Full Text
View/download PDF

30. Unique DC-SIGN Clustering Activity of a Small Glycomimetic: A Lesson for Ligand Design

Author

Ieva Sutkeviciute, José J. Reina, Angela Berzi, Michel Thépaut, Sara Sattin, Aline Le Roy, Javier Rojo, John D. McGeagh, Mario Clerici, Sergei Grudinin, Anna Bernardi, Christine Ebel, Franck Fieschi, Jörg Weiser, Institut de biologie structurale (IBS - UMR 5075 ), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Department of Chemistry, Università degli Studi di Milano = University of Milan (UNIMI), Department of Biomedical and Clinical Sciences, Anterio Consult & Research GmbH, Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Glycosystems Laboratory, Instituto de Investigaciones Químicas (IIQ), Universidad de Sevilla / University of Sevilla-Consejo Superior de Investigaciones Científicas [Madrid] (CSIC)-Universidad de Sevilla / University of Sevilla-Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Department of Physiopatology and Transplantation, University of Milan (DEPT), Fondazione Don Carlo Gnocchi, Marie Curie ITN FP7 project CARMUSYS (PITN-GA-2008-213592), ANR-11-MONU-0006,PEPSI,Expansions Polynomiales de Structures de Protéines et Interactions(2011), Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS), University of Milan, Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC)-Universidad de Sevilla-Consejo Superior de Investigaciones Científicas [Madrid] (CSIC)-Universidad de Sevilla, European Commission (EC), and Agence Nationale de la Recherche. France

Subjects
ligand design, C-Type Lectin, Stereochemistry, protein-protein interactions, HIV Infections, Receptors, Cell Surface, 010402 general chemistry, Crystallography, X-Ray, 01 natural sciences, Biochemistry, Clustering, Protein–protein interaction, 03 medical and health sciences, Protein structure, Glycomimetic, C-type lectin, Biomimetics, Potency, Humans, Lectins, C-Type, Binding site, Isothermal microcalorimetry, 030304 developmental biology, 0303 health sciences, Binding Sites, biology, Molecular Structure, [SDV.BBM.BS]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Structural Biology [q-bio.BM], Ligand, Chemistry, HIV, General Medicine, 3. Good health, 0104 chemical sciences, Protein Structure, Tertiary, molecular modelling, DC-SIGN, Drug Design, Mannosides, Glycomimetics, biology.protein, Biophysics, HIV-1, Analytical ultracentrifugation, Molecular Medicine, Thermodynamics, pharmacology, Cell Adhesion Molecules, Ultracentrifugation
Abstract

DC-SIGN is a dendritic cell-specific C-type lectin receptor that recognizes highly glycosylated ligands expressed on the surface of various pathogens. This receptor plays an important role in the early stages of many viral infections, including HIV, which makes it an interesting therapeutic target. Glycomimetic compounds are good drug candidates for DC-SIGN inhibition due to their high solubility, resistance to glycosidases, and nontoxicity. We studied the structural properties of the interaction of the tetrameric DC-SIGN extracellular domain (ECD), with two glycomimetic antagonists, a pseudomannobioside (1) and a linear pseudomannotrioside (2). Though the inhibitory potency of 2, as measured by SPR competition experiments, was 1 order of magnitude higher than that of 1, crystal structures of the complexes within the DC-SIGN carbohydrate recognition domain showed the same binding mode for both compounds. Moreover, when conjugated to multivalent scaffolds, the inhibitory potencies of these compounds became uniform. Combining isothermal titration microcalorimetry, analytical ultracentrifugation, and dynamic light scattering techniques to study DC-SIGN ECD interaction with these glycomimetics revealed that 2 is able, without any multivalent presentation, to cluster DC-SIGN tetramers leading to an artificially overestimated inhibitory potency. The use of multivalent scaffolds presenting 1 or 2 in HIV trans-infection inhibition assay confirms the loss of potency of 2 upon conjugation and the equal efficacy of chemically simpler compound 1. This study documents a unique case where, among two active compounds chemically derived, the compound with the lower apparent activity is the optimal lead for further drug development

Published
2014
Full Text
View/download PDF

31. Block-Adaptive Quantum Mechanics: An Adaptive Divide-and-Conquer Approach to Interactive Quantum Chemistry

Author

Stephane Redon, Mael Bosson, Sergei Grudinin, Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF), and Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)

Subjects
Divide and conquer algorithms, ASED-MO, 010304 chemical physics, Computer science, Degrees of freedom (physics and chemistry), adaptive, Molecular orbital theory, General Chemistry, 01 natural sciences, Quantum chemistry, interactive quantum chemistry, reduced basis, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Maxima and minima, Computational Mathematics, Superposition principle, Models, Chemical, Quantum mechanics, 0103 physical sciences, Quantum Theory, divide-and-conquer, 010306 general physics, Algorithms, Block (data storage), Virtual prototyping
Abstract

We present a novel Block-Adaptive Quantum Mechanics (BAQM) approach to interactive quantum chemistry. Although quantum chemistry models are known to be computationally demanding, we achieve interactive rates by focusing computational resources on the most active parts of the system. BAQM is based on a divide-and-conquer technique and constrains some nucleus positions and some electronic degrees of freedom on the fly to simplify the simulation. As a result, each time step may be performed significantly faster, which in turn may accelerate attraction to the neighboring local minima. By applying our approach to the nonself-consistent Atom Superposition and Electron Delocalization Molecular Orbital theory, we demonstrate interactive rates and efficient virtual prototyping for systems containing more than a thousand of atoms on a standard desktop computer. © 2012 Wiley Periodicals, Inc.

Published
2013
Full Text
View/download PDF

32. Interactive Molecular Dynamics: Scaling up to Large Systems

Author

Marc Piuzzi, Matthieu Chavent, Sophie Robert, Bruno Raffin, Sébastien Limet, Marc Baaden, Matthieu Dreher, Ahmed Turki, Nicolas Ferey, PrograMming and scheduling design fOr Applications in Interactive Simulation (MOAIS), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF), Laboratoire d'Informatique Fondamentale d'Orléans (LIFO), Université d'Orléans (UO)-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA), Quality control and dynamic reliability (CQFD), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de biochimie théorique [Paris] (LBT (UPR_9080)), Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)-Institut de biologie physico-chimique (IBPC (FR_550)), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC), Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur (LIMSI), Université Paris-Sud - Paris 11 (UP11)-Sorbonne Université - UFR d'Ingénierie (UFR 919), Sorbonne Université (SU)-Sorbonne Université (SU)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris Saclay (COmUE), Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut de biologie physico-chimique (IBPC (FR_550)), Centre National de la Recherche Scientifique (CNRS), Ecole Nationale Supérieure d'Ingénieurs de Bourges-Université d'Orléans (UO), Université Paris Saclay (COmUE)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université - UFR d'Ingénierie (UFR 919), Sorbonne Université (SU)-Sorbonne Université (SU)-Université Paris-Saclay-Université Paris-Sud - Paris 11 (UP11), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Dreher, Matthieu, Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut National Polytechnique de Grenoble (INPG), Université Paris Diderot - Paris 7 (UPD7)-Institut de biologie physico-chimique (IBPC), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Université Paris-Sud - Paris 11 (UP11)-Université Paris-Saclay-Sorbonne Université - UFR d'Ingénierie (UFR 919), Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Saclay (COmUE), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Université d'Orléans (UO)-Ecole Nationale Supérieure d'Ingénieurs de Bourges, and Institut de biologie physico-chimique (IBPC)

Subjects
High Performance Interactive Computing, 010304 chemical physics, Computer science, Distributed computing, 020207 software engineering, 02 engineering and technology, Virtual reality, 01 natural sciences, Visualization, Computational Steering with Haptic Arm, Molecular dynamics, Interactivity, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Code (cryptography), [INFO.INFO-DC] Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], General Earth and Planetary Sciences, State (computer science), [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], Simulation, Interactive Molecular Dynamics, General Environmental Science, Haptic technology
Abstract

International audience; Combining molecular dynamics simulations with user interaction would have various applications in both education and research. By enabling interactivity the scientist will be able to visualize the experiment in real time and drive the simulation to a desired state more easily. However, interacting with systems of interesting size requires significant computing resources due to the complexity of the simulation. In this paper, we propose an approach to combine a classical parallel molecular dynamics simulator, Gromacs, to a 3D virtual reality environment allowing to steer the simulation through external user forces applied with an haptic device to a selection of atoms. We specifically focused on minimizing the intrusion in the simulator code, on efficient parallel data extraction and filtering to transfer only the necessary data to the visualization environment, and on a controlled asynchronism between various components to improve interactivity. We managed to steer molecular systems of 1.7 M atoms at about 25 Hz using 384 CPU cores. This framework allowed us to study a concrete scientific problem by testing one hypothesis of the transport of an iron complex from the exterior of the bacteria to the periplasmic space through the FepA membrane protein.

Published
2013
Full Text
View/download PDF

33. Adaptively Restrained Particle Simulations

Author

Stephane Redon, Svetlana Artemova, Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), and Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)

Subjects
Physics, Mechanical equilibrium, Speedup, 010304 chemical physics, Degrees of freedom, General Physics and Astronomy, Time step, 01 natural sciences, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, law.invention, Classical mechanics, law, 0103 physical sciences, Particle, Collision cascade, 010306 general physics, Simulation methods
Abstract

International audience; Interaction potentials used in particle simulations are typically written as a sum of terms which depend on just a few relative particle positions. Traditional simulation methods move all particles at each time step, and may thus spend a lot of time updating interparticle forces. In this Letter we introduce adaptively restrained particle simulations (ARPS) to speed up particle simulations by adaptively switching on and off positional degrees of freedom, while letting momenta evolve. We illustrate ARPS on several numerical experiments, including (a) a collision cascade example that demonstrates how ARPS make it possible to smoothly trade between precision and speed and (b) a polymer-in-solvent study that shows how one may efficiently determine static equilibrium properties with ARPS.

Published
2012
Full Text
View/download PDF

34. Interactive physically-based structural modeling of hydrocarbon systems

Author

Stephane Redon, Mael Bosson, Sergei Grudinin, Xavier Bouju, Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Centre d'élaboration de matériaux et d'études structurales (CEMES), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut de Chimie de Toulouse (ICT), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie de Toulouse (ICT-FR 2599), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut de Chimie du CNRS (INC)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut de Chimie du CNRS (INC)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)

Subjects
Physics and Astronomy (miscellaneous), Computer science, Computation, Monte Carlo method, 02 engineering and technology, computer.software_genre, 01 natural sciences, Brenner potential, Molecular dynamics, 0103 physical sciences, 010306 general physics, Topology (chemistry), Numerical Analysis, Hydrocarbon builder, Applied Mathematics, Numerical analysis, Construct (python library), 021001 nanoscience & nanotechnology, Potential energy, Adaptive algorithms, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Computer Science Applications, Computational Mathematics, Scripting language, Modeling and Simulation, 0210 nano-technology, Interactive modeling, Algorithm, computer
Abstract

International audience; Hydrocarbon systems have been intensively studied via numerical methods, including electronic structure computations, molecular dynamics and Monte Carlo simulations. Typically, these methods require an initial structural model (atomic positions and types, topology, etc.) that may be produced using scripts and/or modeling tools. For many systems, however, these building methods may be ineffective, as the user may have to specify the positions of numerous atoms while maintaining structural plausibility. In this paper, we present an interactive physically-based modeling tool to construct structural models of hydrocarbon systems. As the user edits the geometry of the system, atomic positions are also influenced by the Brenner potential, a well-known bond-order reactive potential. In order to be able to interactively edit systems containing numerous atoms, we introduce a new adaptive simulation algorithm, as well as a novel algorithm to incrementally update the forces and the total potential energy based on the list of updated relative atomic positions. The computational cost of the adaptive simulation algorithm depends on user-defined error thresholds, and our potential update algorithm depends linearly with the number of updated bonds. This allows us to enable efficient physically-based editing, since the computational cost is decoupled from the number of atoms in the system. We show that our approach may be used to effectively build realistic models of hydrocarbon structures that would be difficult or impossible to produce using other tools.

Published
2012
Full Text
View/download PDF

35. Interactive quantum chemistry: A divide-and-conquer ASED-MO method

Author

Mael Bosson, Sergei Grudinin, Stephane Redon, Antoine Plet, Caroline Richard, Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF)

Subjects
Divide and conquer algorithms, ASED-MO, Multi-core processor, Current (mathematics), 010304 chemical physics, Computer science, Linear scaling, 02 engineering and technology, General Chemistry, 021001 nanoscience & nanotechnology, analytical toy models, 01 natural sciences, Quantum chemistry, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Computational science, Computational Mathematics, CUDA, Superposition principle, interactive quantum chemistry modeling, 0103 physical sciences, Linear scale, Molecular orbital, divide-and-conquer, 0210 nano-technology
Abstract

International audience; We present interactive quantum chemistry simulation at the atom superposition and electron delocalization molecular orbital (ASED-MO) level of theory. Our method is based on the divide-and-conquer (D&C) approach, which we show is accurate and efficient for this non-self-consistent semiempirical theory. The method has a linear complexity in the number of atoms, scales well with the number of cores, and has a small prefactor. The time cost is completely controllable, as all steps are performed with direct algorithms, i.e., no iterative schemes are used. We discuss the errors induced by the D&C approach, first empirically on a few examples, and then via a theoretical study of two toy models that can be analytically solved for any number of atoms. Thanks to the precision and speed of the D&C approach, we are able to demonstrate interactive quantum chemistry simulations for systems up to a few hundred atoms on a current multicore desktop computer. When drawing and editing molecular systems, interactive simulations provide immediate, intuitive feedback on chemical structures. As the number of cores on personal computers increases, and larger and larger systems can be dealt with, we believe such interactive simulations--even at lower levels of theory--should thus prove most useful to effectively understand, design and prototype molecules, devices and materials.

Published
2012
Full Text
View/download PDF

36. Variable gain haptic coupling for molecular simulation

Author

Barthelemy Cagneau, Aude Bolopion, Stephane Regnier, Stephane Redon, Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Ingénierie des Systèmes de Versailles (LISV), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), This work was supported by the French National Agency of Research, through the NANOROL project., and Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF)

Subjects
Coupling, 0209 industrial biotechnology, 010304 chemical physics, Computer science, 02 engineering and technology, Variable displacement, 01 natural sciences, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Displacement (vector), 020901 industrial engineering & automation, Control theory, 0103 physical sciences, Teleoperation, Automatic gain control, Constant (mathematics), High dynamic range, Simulation, Haptic technology
Abstract

International audience; Molecular interactions typically have a high dynamic range (HDR), combining short-range stiff repulsive effects with long-range, soft attractive and repulsive terms. As a result, faithful haptic rendering of such molecular interactions is both important and difficult, in particular in applications where the precise perception of molecular forces is necessary (e.g. in molecular docking simulations). Traditionally, teleoperation coupling using constant gain control schemes have limited applications since they are unable to transmit to users low attractive forces without truncating repulsive ones. Furthermore, constant scaling displacement induces either instability or time-consuming experiments (displacements are slow), which deteriorates the ease of manipulation. In this paper, we describe a variable gain haptic coupling method specifically designed to render high dynamic range (molecular) forces. The proposed method is evaluated by user tests on an experiment involving two water molecules. We observe that variable force amplification is widely appreciated, whereas variable displacement scaling is appropriated only for users familiar with haptic manipulation. A complex experiment on a HIV molecule is carried out using this variable gain system. Advantages and limitations of this approach are discussed.

Published
2011
Full Text
View/download PDF

37. Fast construction of assembly trees for molecular graphs

Author

Svetlana Artemova, Sergei Grudinin, Stephane Redon, Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF)

Subjects
internal coordinates, 0102 computer and information sciences, Strength of a graph, 01 natural sciences, law.invention, 03 medical and health sciences, law, Line graph, adaptive algorithm, Complement graph, 030304 developmental biology, Mathematics, divide-and-conquer algorithm, Discrete mathematics, molecular graphs, 0303 health sciences, General Chemistry, Directed graph, Butterfly graph, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Hypercube graph, Computational Mathematics, 010201 computation theory & mathematics, Multiple edges, molecular simulations, Null graph, Algorithm, assembly tree
Abstract

International audience; A number of modeling and simulation algorithms using internal coordinates rely on hierarchical representations of molecular systems. Given the potentially complex topologies of molecular systems, though, automatically generating such hierarchical decompositions may be difficult. In this article, we present a fast general algorithm for the complete construction of a hierarchical representation of a molecular system. This two-step algorithm treats the input molecular system as a graph in which vertices represent atoms or pseudo-atoms, and edges represent covalent bonds. The first step contracts all cycles in the input graph. The second step builds an assembly tree from the reduced graph. We analyze the complexity of this algorithm and show that the first step is linear in the number of edges in the input graph, whereas the second one is linear in the number of edges in the graph without cycles, but dependent on the branching factor of the molecular graph. We demonstrate the performance of our algorithm on a set of specifically tailored difficult cases as well as on a large subset of molecular graphs extracted from the protein data bank. In particular, we experimentally show that both steps behave linearly in the number of edges in the input graph (the branching factor is fixed for the second step). Finally, we demonstrate an application of our hierarchy construction algorithm to adaptive torsion-angle molecular mechanics.

Published
2011
Full Text
View/download PDF

38. Comparing position and force control for interactive molecular simulators with haptic feedback

Author

Stephane Regnier, Barthelemy Cagneau, Aude Bolopion, Stephane Redon, Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Ingénierie des Systèmes de Versailles (LISV), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), This work was supported by the French National Agency of Research, through the PACMAN project., and Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF)

Subjects
Anti-HIV Agents, Computer science, 02 engineering and technology, Molecular Dynamics Simulation, Degrees of freedom (mechanics), computer.software_genre, Transparency, 01 natural sciences, User-Computer Interface, Software, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Materials Chemistry, Sensitivity (control systems), Physical and Theoretical Chemistry, Spectroscopy, Simulation, Haptic technology, Alanine, 010304 chemical physics, business.industry, Water, Control reconfiguration, 020207 software engineering, Usability, Computer Graphics and Computer-Aided Design, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Biomechanical Phenomena, Simulation software, Haptic coupling, Force feedback, Touch Perception, Molecular simulation, Nanoscale Phenomena, business, computer, Stability, Algorithms
Abstract

International audience; This paper presents a novel tool for the analysis of new molecular structures which enables a wide variety of manipulations. It is composed of a molecular simulator and a haptic device. The simulation software deals with systems of hundreds or thousands of degrees of freedom and computes the reconfiguration of the molecules in a few tenths of a second. For the ease of manipulation and to help the operator understand nanoscale phenomena, a haptic device is connected to the simulator. To handle a wide variety of applications, both position and force control are implemented. To our knowledge, this is the first time the applications of force control are detailed for molecular simulation. These two control modes are compared in terms of adequacy with molecular dynamics, transparency and stability sensitivity with respect to environmental conditions. Based on their specificity the operations they can realize are detailed. Experiments highlight the usability of our tool for the different steps of the analysis of molecular structures. It includes the global reconfiguration of a molecular system, the measurement of molecular properties and the comprehension of nanoscale interactions. Compared to most existing systems, the one developed in this paper offers a wide range of possible experiments. The detailed analysis of the properties of the control modes can be easily used to implement haptic feedback on other molecular simulators.

Published
2010
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results