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Staphylococcus protein A (SpA) is found in the cell wall of Staphylococcus aureus bacteria. Its ability to bind to the constant Fc regions of antibodies means it is useful for antibody extraction, and further integration with inorganic materials can lead to the development of diagnostics and therapeutics. We have investigated the adsorption of SpA on inorganic surface models such as experimentally relevant negatively charged silica, as well as positively charged and neutral surfaces, by use of fully atomistic molecular dynamics simulations. We have found that SpA, which is itself negatively charged at pH7, is able to adsorb on all our surface models. However, adsorption on charged surfaces is more specific in terms of protein orientation compared to a neutral Au (111) surface, while the protein structure is generally well maintained in all cases. The results indicate that SpA adsorption is optimal on the siloxide-rich silica surface, which is negative at pH7 since this keeps the Fc binding regions free to interact with other species in solution. Due to the dominant role of electrostatics, the results are transferable to other inorganic materials and pave the way for new diagnostic and therapeutic designs where SpA might be used to conjugate antibodies to nanoparticles. [ABSTRACT FROM AUTHOR]

The predictive power of simulation has become embedded in the infrastructure of modern economies. Computer‐aided design is ubiquitous throughout industry. In aeronautical engineering, built infrastructure and materials manufacturing, simulations are routinely used to compute the performance of potential designs before construction. The ability to predict the behaviour of products is a driver of innovation by reducing the cost barrier to new designs, but also because radically novel ideas can be piloted with relatively little risk. Accurate weather forecasting is essential to guide domestic and military flight paths, and therefore the underpinning simulations are critical enough to have implications for national security. However, in the pharmaceutical and biotechnological industries, the application of computer simulations remains limited by the capabilities of the technology with respect to the complexity of molecular biology and human physiology. Over the last 30 years, molecular‐modelling tools have gradually gained a degree of acceptance in the pharmaceutical industry. Drug discovery has begun to benefit from physics‐based simulations. While such simulations have great potential for improved molecular design, much scepticism remains about their value. The motivations for such reservations in industry and areas where simulations show promise for efficiency gains in preclinical research are discussed. In this, the first of two complementary papers, the scientific and technical progress that needs to be made to improve the predictive power of biomolecular simulations, and how this might be achieved, is firstly discussed (Part 1). In Part 2, the status of computer simulations in pharma is contrasted with aerodynamics modelling and weather forecasting, and comments are made on the cultural changes needed for equivalent computational technologies to become integrated into life‐science industries. [ABSTRACT FROM AUTHOR]

We reassess progress in the field of biomolecular modeling and simulation, following up on our perspective published in 2011. By reviewing metrics for the field's productivity and providing examples of success, we underscore the productive phase of the field, whose short-term expectations were overestimated and long-term effects underestimated. Such successes include prediction of structures and mechanisms; generation of new insights into biomolecular activity; and thriving collaborations between modeling and experimentation, including experiments driven by modeling. We also discuss the impact of field exercises and web games on the field's progress. Overall, we note tremendous success by the biomolecular modeling community in utilization of computer power; improvement in force fields; and development and application of new algorithms, notably machine learning and artificial intelligence. The combined advances are enhancing the accuracy andscope of modeling and simulation, establishing an exemplary discipline where experiment and theory or simulations are full partners. [ABSTRACT FROM AUTHOR]

The growing interest in the complexity of biological interactions is continuously driving the need to increase system size in biophysical simulations, requiring not only powerful and advanced hardware but adaptable software that can accommodate a large number of atoms interacting through complex forcefields. To address this, we developed and implemented strategies in the GENESIS molecular dynamics package designed for large numbers of processors. Long‐range electrostatic interactions were parallelized by minimizing the number of processes involved in communication. A novel algorithm was implemented for nonbonded interactions to increase single instruction multiple data (SIMD) performance, reducing memory usage for ultra large systems. Memory usage for neighbor searches in real‐space nonbonded interactions was reduced by approximately 80%, leading to significant speedup. Using experimental data describing physical 3D chromatin interactions, we constructed the first atomistic model of an entire gene locus (GATA4). Taken together, these developments enabled the first billion‐atom simulation of an intact biomolecular complex, achieving scaling to 65,000 processes (130,000 processor cores) with 1 ns/day performance. Published 2019. This article is a U.S. Government work and is in the public domain in the USA. [ABSTRACT FROM AUTHOR]

We present an approach to Langevin dynamics in the presence of holonomic constraints based on decomposition of the system into components representing geodesic flow, constrained impulse and constrained diffusion. We show that a particular ordering of the components results in an integrator that is an order of magnitude more accurate for configurational averages than existing alternatives. Moreover, by combining the geodesic integration method with a solvent-solute force splitting, we demonstrate that stepsizes of at least 8 fs can be used for solvated biomolecules with high sampling accuracy and without substantially altering diffusion rates, approximately increasing by a factor of two the efficiency of molecular dynamics sampling for such systems. The methods described in this article are easily implemented using the standard apparatus of modern simulation codes. [ABSTRACT FROM AUTHOR]

The capabilities of molecular simulations have been greatly extended by a number of widely used enhanced sampling methods that facilitate escaping from metastable states and crossing large barriers. Despite these developments there are still many problems which remain out of reach for these methods which has led to a vigorous effort in this area. One of the most important problems that remains unsolved is sampling high-dimensional free-energy landscapes and systems that are not easily described by a small number of collective variables. In this work we demonstrate a new way to compute freeenergy landscapes of high dimensionality based on the previously introduced variationally enhanced sampling, and we apply it to the miniprotein chignolin. [ABSTRACT FROM AUTHOR]

Despite huge advances in the computational techniques available for simulating biomolecules at the quantum-mechanical, atomistic and coarse-grained levels, there is still a widespread perception amongst the experimental community that these calculations are highly specialist and are not generally applicable by researchers outside the theoretical community. In this article, the successes and limitations of biomolecular simulation and the further developments that are likely in the near future are discussed. A brief overview is also provided of the experimental biophysical methods that are commonly used to probe biomolecular structure and dynamics, and the accuracy of the information that can be obtained from each is compared with that from modelling. It is concluded that progress towards an accurate spatial and temporal model of biomacromolecules requires a combination of all of these biophysical techniques, both experimental and computational. [ABSTRACT FROM AUTHOR]

Abstract: We describe a novel coarse-grained simulation method for modelling the dynamics of globular macromolecules, such as proteins. The macromolecule is treated as a continuum that is subject to thermal fluctuations. The model includes a non-linear treatment of elasticity and viscosity with thermal noise that is solved using finite element analysis. We have validated the method by demonstrating that the model provides average kinetic and potential energies that are in agreement with the classical equipartition theorem and that the nodal velocities have the correct Gaussian distribution. In addition, we have performed Fourier analysis on the simulation trajectories obtained for a series of linear beams to confirm that the correct average energies are present in the first two Fourier bending modes and that the probability distribution of the amplitudes of the first two Fourier modes match the theoretical results. We demonstrate spatial convergence of the model by showing that the anisotropy of the inertia tensor for a cubic mesh converges as a function of the mesh resolution. We have then used the new modelling method to simulate the thermal fluctuations of a representative protein over 500ns timescales. Using reasonable parameters for the material properties, we have demonstrated that the overall deformation of the biomolecule is consistent with the results obtained for proteins in general from atomistic molecular dynamics simulations. [Copyright &y& Elsevier]

Molecular simulation techniques are increasingly being used to study biomolecular systems at an atomic level. Such simulations rely on empirical force fields to represent the intermolecular interactions. There are many different force fields available-each based on a different set of assumptions and thus requiring different parametrization procedures. Recently, efforts have been made to fully automate the assignment of force-field parameters, including atomic partial charges, for novel molecules. In this work, we focus on a problem arising in the automated parametrization of molecules for use in combination with the gromos family of force fields: namely, the assignment of atoms to charge groups such that for every charge group the sum of the partial charges is ideally equal to its formal charge. In addition, charge groups are required to have size at most k. We show -hardness and give an exact algorithm that solves practical problem instances to provable optimality in a fraction of a second. [ABSTRACT FROM AUTHOR]

Computer simulation of biomolecular systems has become a standard research instrument for the investigation of biomolecular processes at the atomic level of modelling and interpretation. A bird's eye view of the development of simulation methodology, of biomolecular interaction functions and simulation software is presented, together with the challenges in regard to these three aspects of biomolecular simulation. [ABSTRACT FROM AUTHOR]

We present a multipurpose computer code MesoBioNano Explorer (MBN Explorer). The package allows to model molecular systems of varied level of complexity. In particular, MBN Explorer is suited to compute system's energy, to optimize molecular structure as well as to consider the molecular and random walk dynamics. MBN Explorer allows to use a broad variety of interatomic potentials, to model different molecular systems, such as atomic clusters, fullerenes, nanotubes, polypeptides, proteins, DNA, composite systems, nanofractals, and so on. A distinct feature of the program, which makes it significantly different from the existing codes, is its universality and applicability to the description of a broad range of problems involving different molecular systems. Most of the existing codes are developed for particular classes of molecular systems and do not permit multiscale approach while MBN Explorer goes beyond these drawbacks. On demand, MBN Explorer allows to group particles in the system into rigid fragments, thereby significantly reducing the number of dynamical degrees of freedom. Despite the universality, the computational efficiency of MBN Explorer is comparable (and in some cases even higher) than the computational efficiency of other software packages, making MBN Explorer a possible alternative to the available codes. © 2012 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

CHARMM (Chemistry at HARvard Molecular Mechanics) is a highly versatile and widely used molecular simulation program. It has been developed over the last three decades with a primary focus on molecules of biological interest, including proteins, peptides, lipids, nucleic acids, carbohydrates, and small molecule ligands, as they occur in solution, crystals, and membrane environments. For the study of such systems, the program provides a large suite of computational tools that include numerous conformational and path sampling methods, free energy estimators, molecular minimization, dynamics, and analysis techniques, and model-building capabilities. The CHARMM program is applicable to problems involving a much broader class of many-particle systems. Calculations with CHARMM can be performed using a number of different energy functions and models, from mixed quantum mechanical-molecular mechanical force fields, to all-atom classical potential energy functions with explicit solvent and various boundary conditions, to implicit solvent and membrane models. The program has been ported to numerous platforms in both serial and parallel architectures. This article provides an overview of the program as it exists today with an emphasis on developments since the publication of the original CHARMM article in 1983. © 2009 Wiley Periodicals, Inc.J Comput Chem, 2009. [ABSTRACT FROM AUTHOR]

Abstract: Comparative molecular dynamics (MD) simulations enable us to explore the conformational dynamics of the active sites of distantly related enzymes. We have used the BioSimGrid (http://www.biosimgrid.org) database to facilitate such a comparison. Simulations of four enzymes were analyzed. These included three hydrolases and a transferase, namely acetylcholinesterase, outer-membrane phospholipase A, outer-membrane protease T, and PagP (an outer-membrane enzyme which transfers a palmitate chain from a phospholipid to lipid A). A set of 17 simulations were analyzed corresponding to a total of ∼0.1μs simulation time. A simple metric for active-site integrity was used to demonstrate the existence of clusters of dynamic conformational behaviour of the active sites. Small (i.e. within a cluster) fluctuations appear to be related to the function of an enzymatically active site. Larger fluctuations (i.e. between clusters) correlate with transitions between catalytically active and inactive states. Overall, these results demonstrate the potential of a comparative MD approach to analysis of enzyme function. This approach could be extended to a wider range of enzymes using current high throughput MD simulation and database methods. [Copyright &y& Elsevier]

Abstract: Computational tools and the underlying theoretical framework for direct simulation of complex molecular behavior have become extremely sophisticated. Nevertheless, further improvements in speed, accuracy, and usability can be expected. The future application of these tools to chemical, biochemical, and materials problems needs to be critically considered in any new developmental work, for it is important to understand the role of detailed information all the way down to the quantum mechanical level. To what extent information from the most fundamental treatments, particularly molecular electronic structure theory, needs to be incorporated should be assessed. This report offers several perspectives on the connections and developments that exist now and that may be ahead. [Copyright &y& Elsevier]

We present the latest version of the Groningen Molecular Simulation program package, GROMOS05. It has been developed for the dynamical modelling of (bio)molecules using the methods of molecular dynamics, stochastic dynamics, and energy minimization. An overview of GROMOS05 is given, highlighting features not present in the last major release, GROMOS96. The organization of the program package is outlined and the included analysis package GROMOS++ is described. Finally, some applications illustrating the various available functionalities are presented. © 2005 Wiley Periodicals, Inc. J Comput Chem 26: 1719–1751, 2005 [ABSTRACT FROM AUTHOR]

NAMD is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems. NAMD scales to hundreds of processors on high-end parallel platforms, as well as tens of processors on low-cost commodity clusters, and also runs on individual desktop and laptop computers. NAMD works with AMBER and CHARMM potential functions, parameters, and file formats. This article, directed to novices as well as experts, first introduces concepts and methods used in the NAMD program, describing the classical molecular dynamics force field, equations of motion, and integration methods along with the efficient electrostatics evaluation algorithms employed and temperature and pressure controls used. Features for steering the simulation across barriers and for calculating both alchemical and conformational free energy differences are presented. The motivations for and a roadmap to the internal design of NAMD, implemented in C++ and based on Charm++ parallel objects, are outlined. The factors affecting the serial and parallel performance of a simulation are discussed. Finally, typical NAMD use is illustrated with representative applications to a small, a medium, and a large biomolecular system, highlighting particular features of NAMD, for example, the Tcl scripting language. The article also provides a list of the key features of NAMD and discusses the benefits of combining NAMD with the molecular graphics/sequence analysis software VMD and the grid computing/collaboratory software BioCoRE. NAMD is distributed free of charge with source code at www.ks.uiuc.edu. © 2005 Wiley Periodicals, Inc. J Comput Chem 26: 1781–1802, 2005 [ABSTRACT FROM AUTHOR]

In this paper we describe the context, architecture, and challenges of Blue Matter, the application framework being developed in conjunction with the science effort within IBM''s Blue Gene project. The study of the mechanisms behind protein folding and related topics can require long time simulations on systems with a wide range of sizes and the application supporting these studies must map efficiently onto a large range of parallel partition sizes to optimize scientific throughput for a particular study. The design goals for the Blue Matter architecture include separating the complexities of the parallel implementation on a particular machine from those of the scientific simulation as well as minimizing system environmental dependencies so that running an application within a low overhead kernel with minimal services is possible. We describe some of the parallel decompositions currently being explored that target the first member of the Blue Gene family, BG/L, and present simple performance models for these decompositions that we are using to prioritize our development work. Preliminary results indicate that the high-performance networks on BG/L will allow us to use FFT-based techniques for periodic electrostatics with reasonable speedups on 512–1024 node count partitions even for systems with as few as 5000 atoms. [Copyright &y& Elsevier]

The statistical mechanics-based 3-dimensional reference interaction site model with the Kovalenko-Hirata closure (3D-RISM-KH) molecular solvation theory has proven to be an essential part of a multiscale modeling framework, covering a vast region of molecular simulation techniques. The successful application ranges from the small molecule solvation energy to the bulk phase behavior of polymers, macromolecules, etc. The 3D-RISM-KH successfully predicts and explains the molecular mechanisms of self-assembly and aggregation of proteins and peptides related to neurodegeneration, protein-ligand binding, and structure-function related solvation properties. Upon coupling the 3D-RISM-KH theory with a novel multiple time-step molecular dynamic (MD) of the solute biomolecule stabilized by the optimized isokinetic Nosé–Hoover chain thermostat driven by effective solvation forces obtained from 3D-RISM-KH and extrapolated forward by generalized solvation force extrapolation (GSFE), gigantic outer time-steps up to picoseconds to accurately calculate equilibrium properties were obtained in this new quasidynamics protocol. The multiscale OIN/GSFE/3D-RISM-KH algorithm was implemented in the Amber package and well documented for fully flexible model of alanine dipeptide, miniprotein 1L2Y, and protein G in aqueous solution, with a solvent sampling rate ~150 times faster than a standard MD simulation in explicit water. Further acceleration in computation can be achieved by modifying the extent of solvation layers considered in the calculation, as well as by modifying existing closure relations. This enhanced simulation technique has proven applications in protein-ligand binding energy calculations, ligand/solvent binding site prediction, molecular solvation energy calculations, etc. Applications of the RISM-KH theory in molecular simulation are discussed in this work. [ABSTRACT FROM AUTHOR]

The design and construction of de novo enzymes offer potentially facile routes to exploiting powerful chemistries in robust, expressible and customisable protein frameworks, while providing insight into natural enzyme function. To this end, we have recently demonstrated extensive catalytic promiscuity in a heme-containing de novo protein, C45. The diverse transformations that C45 catalyses include substrate oxidation, dehalogenation and carbon‑carbon bond formation. Here we explore the substrate promiscuity of C45's peroxidase activity, screening the de novo enzyme against a panel of peroxidase and dehaloperoxidase substrates. Consistent with the function of natural peroxidases, C45 exhibits a broad spectrum of substrate activities with selectivity dictated primarily by the redox potential of the substrate, and by extension, the active oxidising species in peroxidase chemistry, compounds I and II. Though the comparison of these redox potentials provides a threshold for determining activity for a given substrate, substrate:protein interactions are also likely to play a significant role in determining electron transfer rates from substrate to heme, affecting the kinetic parameters of the enzyme. We also used biomolecular simulation to screen substrates against a computational model of C45 to identify potential interactions and binding sites. Several sites of interest in close proximity to the heme cofactor were discovered, providing insight into the catalytic workings of C45. [Display omitted] • A de novo peroxidase exhibits excellent activity against many peroxidase substrates. • This promiscuity is likely limited by the redox potential of its compounds I and II. • A computational binding screen identifies substrate interaction sites close to the heme. [ABSTRACT FROM AUTHOR]

[Display omitted] • Efficient synthesis of PS by the surface adsorption-catalysis in aqueous solutions. • The enzymatic selectivity was improved and toxic solvents were completely avoided. • Adsorption regions and driving forces were confirmed by biomolecular simulations. • Diffusion processes of PC were reconstructed to illustrate biocatalytic processes. The adsorption of substrates/products on immobilized enzymes was a common bad phenomenon. A novel method was proposed to turn this "drawback" into treasure. Immobilized phospholipase D (PLD) acted as not only the catalyst, but also an "anchor molecule" facilitating the adsorption of hydrophobic substrate phosphatidylcholine (PC) in purely aqueous solutions. The PC loading even reached 96.7 %. The adsorption region and relevant driving forces were confirmed by the molecular docking and dynamics. The highest yield of phosphatidylserine (PS) reached 95.4 %. An "artificial interphase" was created, allowing a hydrophobic microenvironment for minimal hydrolysis. Simulation results indicated that adsorbed PC molecules would diffuse into the active center by rapid adsorption-desorption equilibrium or the parallel movement on the surface of PLD or combination diffusion. It explained why these "fixed" substrates could interact with the active center. Moreover, obtained PS was manufactured into microcapsules and toxic solvents were completely avoided in the whole process. [ABSTRACT FROM AUTHOR]