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Boron containing compounds (BCCs) aroused increasing interest in the scientific community due to their wide application as drugs in various fields. In order to design new compounds hopefully endowed with pharmacological activity and also investigate their conformational behavior, the support of computational studies is crucial. Nevertheless, the suitable molecular mechanics parameterization and the force fields needed to perform these simulations are not completely available for this class of molecules. In this paper, Amber force field parameters for phenyl-, benzyl-, benzylamino-, and methylamino-boronates, a group of boron-containing compounds involved in different branches of the medicinal chemistry, were created. The robustness of the obtained data was confirmed through molecular dynamics simulations on ligand/-lactamases covalent complexes. The ligand torsional angles, populated over the trajectory frames, were confirmed by values found in the ligand geometries, located through optimizations at the DFT/B3LYP/6-31g(d) level, using water as a solvent. In summary, this study successfully provided a library of parameters, opening the possibility to perform molecular dynamics simulations of this class of boron-containing compounds.

Exploratory chemistry is an important branch of computational chemistry where precursor molecules are simulated in reaction-prone conditions without pre-supposed hypotheses. These molecular dynamics simulations can yield many unexpected reaction pathways, confirming existing mechanisms or suggesting new ones. Decreasing cost of hardware and increasing savviness of software make it enticing to explore reaction spaces in this un-guided manner. However, reactions are still rare events buried in data where the molecules spend most of their time not clearing reaction barriers. Therefore, advanced data processing techniques that can parse the simulations for reaction events with high accuracy are valuable ina field with ever increasing data generation.In this work, a method is described in Chapter 2 which addresses the problem of extracting valuable reaction location data from exploratory simulations with high accuracy and low cost. The method uses first derivatives on bond order time series obtained from Ab Initio molecular dynamics data to predict temporal reaction locations with high accuracy and speed. The two tunable parameters (low pass filtering cutoff and threshold placement on the derivative) are parameterized against a “gold standard” approach that represents the ideal data processing scenario given infinite time and computing resources. This reaction event classifier is showcased on two single-molecule reactive test systems. The first, simpler test case being a heptanylium alkyl carbocation, C7H15+. The other more complicated system is Fe3(CO)9, which is a highly unsaturated iron carbonyl cluster. The effectiveness of the reaction event classifier is analyzed for both systems using heat maps and other custom plots and metrics.Deep Eutectic Solvents (DESs) are a relatively new type of mixture which exhibits similar properties to ionic liquids, but with lower cost and environmental impact. Until recently, most force fields describing DESs were not polarizable and

Computational modeling of lipids at the atomistic level provides insights into the chemical physics of biological membranes and opens the possibility to model membrane-protein interactions. This dissertation presents contributions to the CHARMM/Drude family of lipid force fields and applications of the CHARMM36 lipid force field to model membranes.Long-range Lennard-Jones interactions are critical for membrane simulations but were excluded from the CHARMM lipid force field for historical reasons. Re-parameterization of the CHARMM36 (C36) lipid force field for phosphatidylcholine, phosphatidylethanolamine, phosphatidylglycerol, and ether lipids is performed to incorporate these interactions through the Lennard-Jones particle-mesh Ewald (LJ-PME) method. The resulting force field is denoted C36/LJ-PME. C36/LJ-PME is in excellent agreement with experimental structure data for lipid bilayers and reproduces the experimental compression isotherm of monolayers. A semi-automated protocol is developed and used during this parameterization and significantly accelerates the whole process. The same protocol is used for the optimization of the Drude polarizable lipid force field. The optimization of this force field focuses on the structural and mechanical properties of bilayers and ab initio results of model compounds representing the lipid headgroup. Long-range dispersion interactions are incorporated into the force field as well. The resulting force field is validated against more structural and dynamic properties of bilayers and the compression isotherm of monolayers and demonstrates significant improvements over the past versions of the force field. In addition to these fully atomistic models, this dissertation also discusses the update to the CHARMM36 united atom chain model. Both the original model (C36UA) and the revised model (C36UAr) adopt the all-atom C36 lipid force field parameters for the headgroup and a united atom representation for the chain. The update focuses o

The longevity of asphalt pavements is a key focus of road engineering, which closely relates to the self-healing ability of bitumen. Our work aims to establish a CGMD model and matched force field for bitumen and break through the limitations of the research scale to further explore the microscopic mechanism of bitumen self-healing. In this study, a CGMD mapping scheme containing 16 kinds of beads is proposed, and the non-bond potential energy function and bond potential energy function are calculated based on all-atom simulation to construct and validate a coarse-grained model for bitumen. On this basis, a micro-crack model with a width of 36.6nm is simulated, and the variation laws of potential energy, density, diffusion coefficient, relative concentration and temperature in the process of bitumen self-healing are analyzed with the cracking rate parameter proposed to characterize the degree of bitumen crack healing. The results show that the computational size of the coarse-grained simulation is much larger than that of the all-atom, which can explain the self-healing mechanism at the molecular level. In the self-healing process, non-bonded interactions dominate the molecular movement, and differences in the decreased rate of diffusion among the components indicate that saturates and aromatics play a major role in self-healing. Meanwhile, the variations in crack rates reveal that healing time is inversely proportional to temperature. The impact of increasing temperature on reducing healing time is most obvious when the temperature approaches the glass transition temperature (300 K)., QC 20221116

The current global health emergency in the form of the Coronavirus 2019 (COVID-19) pandemic has highlighted the need for fast, accurate, and efficient drug discovery pipelines. Traditional drug discovery projects relying on in vitro high-throughput screening (HTS) involve large investments and sophisticated experimental set-ups, affordable only to big biopharmaceutical companies. In this scenario, application of efficient state-of-the-art computational methods and modern artificial intelligence (AI)-based algorithms for rapid screening of repurposable chemical space [approved drugs and natural products (NPs) with proven pharmacokinetic profiles] to identify the initial leads is a powerful option to save resources and time. Structure-based drug repurposing is a popular in silico repurposing approach. In this review, we discuss traditional and modern AI-based computational methods and tools applied at various stages for structure-based drug discovery (SBDD) pipelines. Additionally, we highlight the role of generative models in generating molecules with scaffolds from repurposable chemical space., QC 20220718

The longevity of asphalt pavements is a key focus of road engineering, which closely relates to the self-healing ability of bitumen. Our work aims to establish a CGMD model and matched force field for bitumen and break through the limitations of the research scale to further explore the microscopic mechanism of bitumen self-healing. In this study, a CGMD mapping scheme containing 16 kinds of beads is proposed, and the non-bond potential energy function and bond potential energy function are calculated based on all-atom simulation to construct and validate a coarse-grained model for bitumen. On this basis, a micro-crack model with a width of 36.6nm is simulated, and the variation laws of potential energy, density, diffusion coefficient, relative concentration and temperature in the process of bitumen self-healing are analyzed with the cracking rate parameter proposed to characterize the degree of bitumen crack healing. The results show that the computational size of the coarse-grained simulation is much larger than that of the all-atom, which can explain the self-healing mechanism at the molecular level. In the self-healing process, non-bonded interactions dominate the molecular movement, and differences in the decreased rate of diffusion among the components indicate that saturates and aromatics play a major role in self-healing. Meanwhile, the variations in crack rates reveal that healing time is inversely proportional to temperature. The impact of increasing temperature on reducing healing time is most obvious when the temperature approaches the glass transition temperature (300 K).

The longevity of asphalt pavements is a key focus of road engineering, which closely relates to the self-healing ability of bitumen. Our work aims to establish a CGMD model and matched force field for bitumen and break through the limitations of the research scale to further explore the microscopic mechanism of bitumen self-healing. In this study, a CGMD mapping scheme containing 16 kinds of beads is proposed, and the non-bond potential energy function and bond potential energy function are calculated based on all-atom simulation to construct and validate a coarse-grained model for bitumen. On this basis, a micro-crack model with a width of 36.6nm is simulated, and the variation laws of potential energy, density, diffusion coefficient, relative concentration and temperature in the process of bitumen self-healing are analyzed with the cracking rate parameter proposed to characterize the degree of bitumen crack healing. The results show that the computational size of the coarse-grained simulation is much larger than that of the all-atom, which can explain the self-healing mechanism at the molecular level. In the self-healing process, non-bonded interactions dominate the molecular movement, and differences in the decreased rate of diffusion among the components indicate that saturates and aromatics play a major role in self-healing. Meanwhile, the variations in crack rates reveal that healing time is inversely proportional to temperature. The impact of increasing temperature on reducing healing time is most obvious when the temperature approaches the glass transition temperature (300 K).

Intrinsically disordered proteins (IDPs) are widely distributed across eukaryotic cells, playing important roles in molecular recognition, molecular assembly, post-translational modification, and other biological processes. IDPs are also associated with many diseases such as cancers, cardiovascular diseases, and neurodegenerative diseases. Due to their structural flexibility, conventional experimental methods cannot reliably capture their heterogeneous structures. Molecular dynamics simulation becomes an important complementary tool to quantify IDP structures. This review covers recent force field strategies proposed for more accurate molecular dynamics simulations of IDPs. The strategies include adjusting dihedral parameters, adding grid-based energy correction map (CMAP) parameters, refining protein-water interactions, and others. Different force fields were found to perform well on specific observables of specific IDPs but also are limited in reproducing all available experimental observables consistently for all tested IDPs. We conclude the review with perspective areas for improvements for future force fields for IDPs.

With the advent of the resolution revolution in cryoelectron microscopy (cryo-EM), low-resolution refinement is common, and likewise increases the need for a reliable force field. Here, we report on the incorporation of the OPLS3e force field with the VSGB2.1 solvation model in the structure determination package Phenix. Our results show significantly improved structure quality and reduced ligand strain at lower resolution for X-ray refinement. For refinement of cryo-EM-based structures, we find comparable quality structures, goodness-of-fit, and reduced ligand strain. We also show how structure quality and ligand strain are related to the map-model cross-correlation as a function of data weight, and how that can detect overfitting. Signs of overfitting are found in over half of our cryo-EM dataset, which can be remedied by a re-refinement at a lower data weight. Finally, a start-to-end script for refining structures with Phenix/OPLS3e is available in the Schrödinger 2020-3 distribution.

Nuclear magnetic resonance spectroscopy is used routinely for studying the three-dimensional structures and dynamics of proteins. Structure determination is usually done by adding restraints based upon NMR data to a classical energy function and performing restrained molecular simulations. Here we report on the implementation of a script to extract NMR restraints from a NMR-STAR file and export it to the GROMACS software. With this package, it is possible to model distance restraints, dihedral restraints, and orientation restraints. The output from the script is validated by performing simulations with and without restraints, including the ab initio refinement of one peptide.

We have developed an automated and efficient scheme for the fitting of data using Curvature Constrained Splines (CCS), to construct accurate two-body potentials. The approach enabled the construction of an oscillation-free, yet flexible, potential. We show that the optimization problem is convex and that it can be reduced to a standard Quadratic Programming (QP) problem. The improvements are demonstrated by the development of a two-body potential for Ne from ab initio data. We also outline possible extensions to the method. Program summary Program Title: CCS CPC Library link to program files: http://dx.doi.org/10.17632/7dt5nzxgbs.1 Developer’s repository link: http://github.com/aksam432/CCS Licensing provisions: GPLv3 Programming language: Python External routines/libraries: NumPy, matplotlib, ASE, CVXOPT Nature of problem: Ab initio quantum chemistry methods are often computationally very expensive. To alleviate this problem, the development of efficient empirical and semi-empirical methods is necessary. Two-body potentials are ubiquitous in empirical and semi-empirical methods. Solution method: The CCS package provides a new strategy to obtain accurate two body potentials. The potentials are described as cubic splines with curvature constraints.

The main goal of this compilation thesis has been to investigate the conformational ensemble of the intrinsically disordered protein (IDP) histatin 5 by using a mixture of experimental and computational techniques including, but not limited to, small-angle X-ray scattering (SAXS), circular dichroism (CD) spectroscopy, atomistic molecular dynamics (MD) simulations, and coarse-grained Monte Carlo (MC) simulations. Histatin 5 is a peptide of particular interest for two reasons. Firstly, histatin 5 is naturally found in the saliva, where it helps protect the teeth enamel from abrasion and degradation by being part of the dental pellicle. Additionally, it also possesses candidacidal properties and thus acts as a first line of defense against fungal infections. Therefore, by studying the conformational ensemble histatin 5, we hope to aid in the development of saliva substitutes and new antimicrobial agents. Secondly, histatin 5 acts as a typical IDP that is relatively easy to work with in the lab, which makes it a perfect model IDP for evaluating different MD force fields and other simulation techniques. In the first study of this thesis, simulations were used to investigate how the conformational ensemble of histatin 5 is affected by protonation of its histidine residues. The study also continues to investigate how this affects the interaction between histatin 5 and a negatively charged surface. This study is followed by an investigation of how histatin 5 is affected by temperature, and of how well modern simulation methods used for simulating IDPs can capture temperature-induced conformational changes. The results of this study led to another project, in which four different force fields were evaluated based on their ability to simulate histatin 5 and four other peptides that are known to possess polyproline II structure. In the final histatin 5 study, a shorter but more potent version of histatin 5 was conjugated to the polyamine spermidine, and was then investigated u

В роботі в якості предмета досліджень обрано спеціальні умови розробки рудних родовищ на гірських схилах, що обмежують висоту уступів та відповідно довжину свердловинних зарядів. Особливу увагу приділено початковій стадії формування механічного ефекту вибуху в крайових ділянках подовженого заряду. В дисертації досліджується специфіка розробки нагірних родовищ через значний перепад висот в межах одного блоку, який диктує необхідність зниження висоти уступу до 3...6м та довжини заряду до 2-х…4-х метрів. Внаслідок скорочення довжини свердловинного заряду стає більш істотною участь його торцевих частин, де до 35...40% складають дисипативні втрати енергії через зміну симетрії силового поля. В дослідженнях враховується відмінність завдань і особливості прояву та розвитку крайових ефектів в нижньому і верхньому торцях заряду. Нижня торцева частина заряду, яка відповідає за зону перебуру з метою запобігання порогів, перебуває в максимально затиснутих умовах, зустрічає найбільший опір скельного масиву руйнуванню і вимагає першочергової уваги в дослідженнях. Закономірності формування поля напружень з урахуванням крайових явищ вивчено шляхом аналітичних розрахунків, що основані на заміні лінійного заряду системою зосереджених зарядів. На основі складеного алгоритму обчислено параметри силового поля для різних варіантів миттєвого та сповільненого вибуху системи зосереджених зарядів. Розрахунки крайових явищ при вибуху свердловинного заряду обмежених розмірів свідчать, що за умови миттєвого ініціювання заряду одночасно по обох торцях заряду виродження осьової симетрії силового поля відбувається від торців заряду на глибину до 50 його радіусів, тобто в реальних умовах підривання коротких свердловинних зарядів їх механічний ефект в більшій мірі формується ослабленими кінцевими ділянками лінійного заряду. Для компенсації енергетичних втрат на початковій стадії розвитку силового поля запобіжником виродження фронту хвилі напружень можливе збільшення торцевої частини свердловинного за

Nuclear magnetic resonance spectroscopy is used routinely for studying the three-dimensional structures and dynamics of proteins. Structure determination is usually done by adding restraints based upon NMR data to a classical energy function and performing restrained molecular simulations. Here we report on the implementation of a script to extract NMR restraints from a NMR-STAR file and export it to the GROMACS software. With this package, it is possible to model distance restraints, dihedral restraints, and orientation restraints. The output from the script is validated by performing simulations with and without restraints, including the ab initio refinement of one peptide.

Nuclear magnetic resonance spectroscopy is used routinely for studying the three-dimensional structures and dynamics of proteins. Structure determination is usually done by adding restraints based upon NMR data to a classical energy function and performing restrained molecular simulations. Here we report on the implementation of a script to extract NMR restraints from a NMR-STAR file and export it to the GROMACS software. With this package, it is possible to model distance restraints, dihedral restraints, and orientation restraints. The output from the script is validated by performing simulations with and without restraints, including the ab initio refinement of one peptide.

We have developed an automated and efficient scheme for the fitting of data using Curvature Constrained Splines (CCS), to construct accurate two-body potentials. The approach enabled the construction of an oscillation-free, yet flexible, potential. We show that the optimization problem is convex and that it can be reduced to a standard Quadratic Programming (QP) problem. The improvements are demonstrated by the development of a two-body potential for Ne from ab initio data. We also outline possible extensions to the method. Program summary Program Title: CCS CPC Library link to program files: http://dx.doi.org/10.17632/7dt5nzxgbs.1 Developer's repository link:gttp://github.com/aksam432/CCS Licensing provisions: GPLv3 Programming language: Python External routines/libraries: NumPy, matplotlib, ASE, CVXOPT Nature of problem: Ab initio quantum chemistry methods are often computationally very expensive. To alleviate this problem, the development of efficient empirical and semi-empirical methods is necessary. Two-body potentials are ubiquitous in empirical and semi-empirical methods. Solution method: The CCS package provides a new strategy to obtain accurate two body potentials. The potentials are described as cubic splines with curvature constraints.

We have developed an automated and efficient scheme for the fitting of data using Curvature Constrained Splines (CCS), to construct accurate two-body potentials. The approach enabled the construction of an oscillation-free, yet flexible, potential. We show that the optimization problem is convex and that it can be reduced to a standard Quadratic Programming (QP) problem. The improvements are demonstrated by the development of a two-body potential for Ne from ab initio data. We also outline possible extensions to the method. Program summary Program Title: CCS CPC Library link to program files: http://dx.doi.org/10.17632/7dt5nzxgbs.1 Developer’s repository link: http://github.com/aksam432/CCS Licensing provisions: GPLv3 Programming language: Python External routines/libraries: NumPy, matplotlib, ASE, CVXOPT Nature of problem: Ab initio quantum chemistry methods are often computationally very expensive. To alleviate this problem, the development of efficient empirical and semi-empirical methods is necessary. Two-body potentials are ubiquitous in empirical and semi-empirical methods. Solution method: The CCS package provides a new strategy to obtain accurate two body potentials. The potentials are described as cubic splines with curvature constraints.

Nuclear magnetic resonance spectroscopy is used routinely for studying the three-dimensional structures and dynamics of proteins. Structure determination is usually done by adding restraints based upon NMR data to a classical energy function and performing restrained molecular simulations. Here we report on the implementation of a script to extract NMR restraints from a NMR-STAR file and export it to the GROMACS software. With this package, it is possible to model distance restraints, dihedral restraints, and orientation restraints. The output from the script is validated by performing simulations with and without restraints, including the ab initio refinement of one peptide.

Nuclear magnetic resonance spectroscopy is used routinely for studying the three-dimensional structures and dynamics of proteins. Structure determination is usually done by adding restraints based upon NMR data to a classical energy function and performing restrained molecular simulations. Here we report on the implementation of a script to extract NMR restraints from a NMR-STAR file and export it to the GROMACS software. With this package, it is possible to model distance restraints, dihedral restraints, and orientation restraints. The output from the script is validated by performing simulations with and without restraints, including the ab initio refinement of one peptide.

Computer simulation methods have the power to greatly optimize drug design, reducing the time and cost that it takes to bring a new drug from lab to market. Two factors of concern in rational drug design are the binding affinity of some small molecule to a biomolecular target and the molecule's ability to permeate the cellular membrane to reach said target. In the first half of this work, I describe my efforts analyzing the protein-ligand binding affinity and membrane permeability of inhibitors of the Hv1 proton channel. Hv1 is a voltage-gated proton channel with clinical relevance, being highly expressed in highly metastatic cancer cell lines. The inhibition of Hv1 has been found to slow the growth of cancer cells. My research on Hv1 channel inhibitors lays the groundwork for future studies on adapting these molecules for more effective channel blocking.In the second half of this work, I examine the tools that are foundational to molecular simulations--force fields--as well as explore the conventional wisdom behind proton assignment in carboxylic acids. Biomolecular simulation represents a valuable computational microscope for drug discovery efforts; however, it is only through correct setup protocols and well-calibrated tools that we can obtain the most accurate and meaningful results.

Background: Force fields are used in a wide variety of contexts for classical molecular simulation, including studies on protein-ligand binding, membrane permeation, and thermophysical property prediction. The quality of these studies relies on the quality of the force fields used to represent the systems. Methods: Focusing on small molecules of fewer than 50 heavy atoms, our aim in this work is to compare nine force fields: GAFF, GAFF2, MMFF94, MMFF94S, OPLS3e, SMIRNOFF99Frosst, and the Open Force Field Parsley, versions 1.0, 1.1, and 1.2. On a dataset comprising 22,675 molecular structures of 3,271 molecules, we analyzed force field-optimized geometries and conformer energies compared to reference quantum mechanical (QM) data. Results: We show that while OPLS3e performs best, the latest Open Force Field Parsley release is approaching a comparable level of accuracy in reproducing QM geometries and energetics for this set of molecules. Meanwhile, the performance of established force fields such as MMFF94S and GAFF2 is generally somewhat worse. We also find that the series of recent Open Force Field versions provide significant increases in accuracy. Conclusions: This study provides an extensive test of the performance of different molecular mechanics force fields on a diverse molecule set, and highlights two (OPLS3e and OpenFF 1.2) that perform better than the others tested on the present comparison. Our molecule set and results are available for other researchers to use in testing.

Knowledge on transport properties of fluids is of great interest for process and product design development in the chemical, food, pharmaceutical, and biotechnological industry. In the past few decades, molecular simulation has become a powerful tool to calculate these properties. In this context, Molecular Dynamics (MD) is important for the calculation of transport properties of complex systems, where currently available models are not valid, or at extreme conditions at which performing an experiment is dangerous or not feasible. MD simulations provide detailed information about the dynamics of the system at the molecular level. In this thesis, the aim is to investigate the computation of transport properties using force field-based MD simulations., Engineering Thermodynamics

Intrinsically Disordered Peptides and Proteins (IDPs) in solution can span a broad range of conformations that often are hard to characterize by both experimental and computational methods. However, obtaining a significant representation of the conformational space is important to understand mechanisms underlying protein functions such as partner recognition. In this work, we investigated the behavior of the Sic1 Kinase-Inhibitor Domain (KID) in solution by Molecular Dynamics (MD) simulations. Our results point out that application of common descriptors of molecular shape such as Solvent Accessible Surface (SAS) area can lead to misleading outcomes. Instead, more appropriate molecular descriptors can be used to define 3D structures. In particular, we exploited Weighted Holistic Invariant Molecular (WHIM) descriptors to get a coarse-grained but accurate definition of the variegated Sic1 KID conformational ensemble. We found that Sic1 is able to form a variable amount of folded structures even in absence of partners. Among them, there were some conformations very close to the structure that Sic1 is supposed to assume in the binding with its physiological complexes. Therefore, our results support the hypothesis that this protein relies on the conformational selection mechanism to recognize the correct molecular partners.

Knowledge on transport properties of fluids is of great interest for process and product design development in the chemical, food, pharmaceutical, and biotechnological industry. In the past few decades, molecular simulation has become a powerful tool to calculate these properties. In this context, Molecular Dynamics (MD) is important for the calculation of transport properties of complex systems, where currently available models are not valid, or at extreme conditions at which performing an experiment is dangerous or not feasible. MD simulations provide detailed information about the dynamics of the system at the molecular level. In this thesis, the aim is to investigate the computation of transport properties using force field-based MD simulations., Engineering Thermodynamics

The early stages of drug discovery is a long and costly process. A way to reduce the time, resources, and financial investment spent is to apply computational tools. With the tremendous progress and achievements in computational chemistry, the application of computational tools in the drug lead discovery and design has increased. Today, computational chemistry is considered a highly valuable and well established tool in drug discovery. A variety of computational chemistry methods are used for guiding molecular design and finding new potential drugs and targets. Some examples of these methods are molecular dynamics simulations, free energy calculations, virtual screening, structure-activity relationship analysis, and so on. Despite the fact that computational chemistry technics are widely used in industry and academic environment, there is still room for improvement. Here, I present several studies in which I developed and applied computational chemistry tools in drug discovery problems. The first study is the development of a tool as part of the Open Force Field consortium to learn chemical perception of force fields typing rules. Secondly, I describe my work on using a new hybrid method to calculate free energies of small molecules. Thirdly, I present a binding mode prediction study to help the understanding of structure-activity relationship in lissoclimides. Lastly, I present a study applying molecular dynamic simulation to guide the redesigning of a macrocyclic peptide.

In this thesis, I have used computational methods to the study of flexibility in metal-organic frameworks (MOFs) and the behavior of adsorbed gases and liquids in MOFs. Among nanoporous materials, MOFs are of particular interest for adsorption-based applications for reasons that include (1) their huge chemical space, which raises the possibility of tuning them for specific industrial processes and (2) the ability of the under-coordinated metals present in some MOFs to endow their respective materials with impressive adsorbate binding energies.Chapters 2 and 4 relate to analogs of a series of MOFs with under-coordinated metals (the M-MOF-74 series). In Chapter 2, I report the discovery of a novel argon adsorbate-induced deformation pattern for this framework series. This result was arrived at using a flexible framework model, and is presented as an explanation for an intriguing signal observed in experimental small-angle X-ray scattering profiles upon argon adsorption. This hypothesis is supported by a complementary investigation of an alternative explanation for the X-ray signal, in which argon atoms are proposed to adsorb at different densities in adjacent pores.In Chapter 3, I studied the dynamics of adsorbed xylene isomers in MOF-5. I compare the translational and rotational motion of the xylene isomers, and explain the differences based on molecular geometries. The most rod-like molecule, para-xylene, is more rotationally constricted in the pore due to its intermolecular interactions with the aromatic group of the MOF linker. This finding has implications for the rational design of MOFs, as a process that can take advantage of the MOF-induced variations in xylene isomer dynamics could be used for lucrative liquid-phase xylene separations.In Chapter 4, I present a method for parameterizing the type of flexible framework model used in Chapter 2 from ab initio quantum chemistry calculations. The goal of this work is to facilitate the rapid development of flexible

Undoubtedly, materials that can be tuned on a molecular level offer tremendous opportunities. However, to understand and customize such materials is challenging. In this context, molecular simulation can be helpful. The work presented in this thesis deals with two types of materials, Metal-Organic Frameworks and Ionic Liquids, and the study with molecular simulation to determine their potential for specific gas separations. For the prediction of their behavior and relevant materials properties with molecular simulation, force fields of sufficient quality are required.., Engineering Thermodynamics

Molecular-mechanical (MM) force fields are mathematical functions that map the geometry of a molecule to its associated energy. MM force fields have been extensively used for an atomistic view into the dynamic and thermodynamics of large molecular systems in their condensed phase. Nevertheless, the grand challenge in force field development—which remains to be addressed—is to predict­­­­ properties of materials with different chemistries and in all their physical phases. Force fields are, in principle, derived through supervised machine learning methods. Therefore, the first step toward more accurate force fields is to provide high-quality reference data from which the force fields can learn. Thus, we benchmarked quantum-mechanical methods—at different levels of theory—in predicting of molecular energetics and electrostatic properties. As the result, the Alexandria library was released as an open access database of molecular properties. The second step is to use potential functions describing interactions between molecules accurately. For this, we incorporated electronic polarization and charge penetration effects into the Alexandria force field. The Drude model was used for the explicit inclusion of electronic polarization. The distribution of the atomic charges was described by either a 1s-Gaussian or an ns-Slater density function to account for charge penetration effects. Moreover, the 12-6 Lennard-Jones (LJ) potential function, commonly used in force fields, was replaced by the Wang-Buckingham (WBK) function to describe the interaction of two particles at very short distances. In contrast to the 12-6 LJ function, the WBK function is well behaved at short distances because it has a finite limit as the distance between two particles approaches zero. The third step is free and open source software (FOSS) for systematic optimization of the built-in force field parameters. For this, we developed the Alexandria chemistry toolkit that is currently part of the GROM

Molecular-mechanical (MM) force fields are mathematical functions that map the geometry of a molecule to its associated energy. MM force fields have been extensively used for an atomistic view into the dynamic and thermodynamics of large molecular systems in their condensed phase. Nevertheless, the grand challenge in force field development—which remains to be addressed—is to predict­­­­ properties of materials with different chemistries and in all their physical phases. Force fields are, in principle, derived through supervised machine learning methods. Therefore, the first step toward more accurate force fields is to provide high-quality reference data from which the force fields can learn. Thus, we benchmarked quantum-mechanical methods—at different levels of theory—in predicting of molecular energetics and electrostatic properties. As the result, the Alexandria library was released as an open access database of molecular properties. The second step is to use potential functions describing interactions between molecules accurately. For this, we incorporated electronic polarization and charge penetration effects into the Alexandria force field. The Drude model was used for the explicit inclusion of electronic polarization. The distribution of the atomic charges was described by either a 1s-Gaussian or an ns-Slater density function to account for charge penetration effects. Moreover, the 12-6 Lennard-Jones (LJ) potential function, commonly used in force fields, was replaced by the Wang-Buckingham (WBK) function to describe the interaction of two particles at very short distances. In contrast to the 12-6 LJ function, the WBK function is well behaved at short distances because it has a finite limit as the distance between two particles approaches zero. The third step is free and open source software (FOSS) for systematic optimization of the built-in force field parameters. For this, we developed the Alexandria chemistry toolkit that is currently part of the GROM

Molecular-mechanical (MM) force fields are mathematical functions that map the geometry of a molecule to its associated energy. MM force fields have been extensively used for an atomistic view into the dynamic and thermodynamics of large molecular systems in their condensed phase. Nevertheless, the grand challenge in force field development—which remains to be addressed—is to predict­­­­ properties of materials with different chemistries and in all their physical phases. Force fields are, in principle, derived through supervised machine learning methods. Therefore, the first step toward more accurate force fields is to provide high-quality reference data from which the force fields can learn. Thus, we benchmarked quantum-mechanical methods—at different levels of theory—in predicting of molecular energetics and electrostatic properties. As the result, the Alexandria library was released as an open access database of molecular properties. The second step is to use potential functions describing interactions between molecules accurately. For this, we incorporated electronic polarization and charge penetration effects into the Alexandria force field. The Drude model was used for the explicit inclusion of electronic polarization. The distribution of the atomic charges was described by either a 1s-Gaussian or an ns-Slater density function to account for charge penetration effects. Moreover, the 12-6 Lennard-Jones (LJ) potential function, commonly used in force fields, was replaced by the Wang-Buckingham (WBK) function to describe the interaction of two particles at very short distances. In contrast to the 12-6 LJ function, the WBK function is well behaved at short distances because it has a finite limit as the distance between two particles approaches zero. The third step is free and open source software (FOSS) for systematic optimization of the built-in force field parameters. For this, we developed the Alexandria chemistry toolkit that is currently part of the GROM

Molecular-mechanical (MM) force fields are mathematical functions that map the geometry of a molecule to its associated energy. MM force fields have been extensively used for an atomistic view into the dynamic and thermodynamics of large molecular systems in their condensed phase. Nevertheless, the grand challenge in force field development—which remains to be addressed—is to predict­­­­ properties of materials with different chemistries and in all their physical phases. Force fields are, in principle, derived through supervised machine learning methods. Therefore, the first step toward more accurate force fields is to provide high-quality reference data from which the force fields can learn. Thus, we benchmarked quantum-mechanical methods—at different levels of theory—in predicting of molecular energetics and electrostatic properties. As the result, the Alexandria library was released as an open access database of molecular properties. The second step is to use potential functions describing interactions between molecules accurately. For this, we incorporated electronic polarization and charge penetration effects into the Alexandria force field. The Drude model was used for the explicit inclusion of electronic polarization. The distribution of the atomic charges was described by either a 1s-Gaussian or an ns-Slater density function to account for charge penetration effects. Moreover, the 12-6 Lennard-Jones (LJ) potential function, commonly used in force fields, was replaced by the Wang-Buckingham (WBK) function to describe the interaction of two particles at very short distances. In contrast to the 12-6 LJ function, the WBK function is well behaved at short distances because it has a finite limit as the distance between two particles approaches zero. The third step is free and open source software (FOSS) for systematic optimization of the built-in force field parameters. For this, we developed the Alexandria chemistry toolkit that is currently part of the GROM

Molecular-mechanical (MM) force fields are mathematical functions that map the geometry of a molecule to its associated energy. MM force fields have been extensively used for an atomistic view into the dynamic and thermodynamics of large molecular systems in their condensed phase. Nevertheless, the grand challenge in force field development—which remains to be addressed—is to predict­­­­ properties of materials with different chemistries and in all their physical phases. Force fields are, in principle, derived through supervised machine learning methods. Therefore, the first step toward more accurate force fields is to provide high-quality reference data from which the force fields can learn. Thus, we benchmarked quantum-mechanical methods—at different levels of theory—in predicting of molecular energetics and electrostatic properties. As the result, the Alexandria library was released as an open access database of molecular properties. The second step is to use potential functions describing interactions between molecules accurately. For this, we incorporated electronic polarization and charge penetration effects into the Alexandria force field. The Drude model was used for the explicit inclusion of electronic polarization. The distribution of the atomic charges was described by either a 1s-Gaussian or an ns-Slater density function to account for charge penetration effects. Moreover, the 12-6 Lennard-Jones (LJ) potential function, commonly used in force fields, was replaced by the Wang-Buckingham (WBK) function to describe the interaction of two particles at very short distances. In contrast to the 12-6 LJ function, the WBK function is well behaved at short distances because it has a finite limit as the distance between two particles approaches zero. The third step is free and open source software (FOSS) for systematic optimization of the built-in force field parameters. For this, we developed the Alexandria chemistry toolkit that is currently part of the GROM

von Alexander Spinn, Universität Innsbruck, Masterarbeit, 2019, (VLID)4494774

Chemical specificity of lipid models used in molecular dynamics simulations is essential to accurately represent the complexity and diversity of biological membranes. This dissertation discusses contributions to the CHARMM36 (C36) family of lipid force fields, including a revised model for the glycerol-ether linkage found in plasmalogens and archaeal membranes; interaction parameters between ions and lipid oxygens; and evaluation of the effects of long-range Lennard-Jones parameters on alkanes. Long-range Lennard-Jones interactions have a significant impact on structural and thermodynamic properties of systems with nonpolar regions such as membranes. Effects of these interactions on properties of alkanes are investigated. Implementation of the Lennard-Jones particle-mesh Ewald (LJ-PME) method with the C36 additive and Drude polarizable force fields improves agreement with experiment for thermodynamic and kinetic properties of alkanes, with Drude outperforming the additive FF for nearly all quantities. Trends in the temperature dependence of the density and isothermal compressibility are also improved. Phospholipids containing an ether linkage between the glycerol backbone and hydrophobic tails are prevalent in human red blood cells and nerve tissue. Ab initio results are used to revise linear ether parameters and develop new parameters for the glycerol-ether linkage in lipids. The new force field, called C36e, more accurately represents the dihedral potential energy landscape and improves solution properties of linear ethers. C36e allows more water to penetrate an ether-linked lipid bilayer, increasing the surface area per lipid compared to simulations carried out with the original C36 parameters and improving structural properties. In addition to modulating membrane structure, lipid-ion interactions influence protein-ligand binding and conformations of membrane-bound proteins. Interaction parameters are introduced describing Be2+ affinity for binding sites on lipid

Undoubtedly, materials that can be tuned on a molecular level offer tremendous opportunities. However, to understand and customize such materials is challenging. In this context, molecular simulation can be helpful. The work presented in this thesis deals with two types of materials, Metal-Organic Frameworks and Ionic Liquids, and the study with molecular simulation to determine their potential for specific gas separations. For the prediction of their behavior and relevant materials properties with molecular simulation, force fields of sufficient quality are required.., Engineering Thermodynamics

Enzymes in the acetate and non-ribosomal peptide pathways generate chemically diverse and complex bioactive molecules, with the intermediates being chauffeured between catalytic partners via a carrier protein. Recent efforts have been made to engineer these systems to expand their product diversity. A major stumbling block is our poor understanding of the transient protein-protein and protein-substrate interactions between the carrier protein and its many catalytic partner domains. The innate reactivity of pathway intermediates has obfuscated our mechanistic understanding of these interactions during the biosynthesis of these natural products, ultimately impeding the engineering of these systems for the generation of “unnatural” natural products.Molecular dynamics can be used to provide models of these key interactions that are difficult to capture experimentally, providing the potential to expand the diversity in these systems. Current force fields support basic biochemical building blocks, and specialized force fields support post-translational modifications and non-canonical amino acids, yet none currently exist that are capable of modeling bound intermediates from these systems.The objective of this dissertation is to address this gap in knowledge and available technology and to present the description of the development of a force field that can be used with MD and other computational techniques to provide models of these experimentally intractable transient interactions. This objective will be divided into three aims.Aim 1: The development and validation of a force field for use in investigations and engineering efforts involving these pathways. A fragmentation approach was used, providing a degree of modularity to the force field while at the same time reducing the size, complexity and degrees of freedom during the charge fitting and parameterization steps. Tutorials and a web interface were also developed to provide end users access to this tool.Aim 2: The a

Many proteins display complex dynamical properties that are often intimately linked to their biological functions. As the native state of a protein is best described as an ensemble of conformations, it is important to be able to generate models of native state ensembles with high accuracy. Due to limitations in sampling efficiency and force field accuracy it is, however, challenging to obtain accurate ensembles of protein conformations by the use of molecular simulations alone. Here we show that dynamic ensemble refinement, which combines an accurate atomistic force field with commonly available nuclear magnetic resonance (NMR) chemical shifts and NOEs, can provide a detailed and accurate description of the conformational ensemble of the native state of a highly dynamic protein. As both NOEs and chemical shifts are averaged on timescales up to milliseconds, the resulting ensembles reflect the structural heterogeneity that goes beyond that probed, e.g., by NMR relaxation order parameters. We selected the small protein domain NCBD as object of our study since this protein, which has been characterized experimentally in substantial detail, displays a rich and complex dynamical behaviour. In particular, the protein has been described as having a molten-globule like structure, but with a relatively rigid core. Our approach allowed us to describe the conformational dynamics of NCBD in solution, and to probe the structural heterogeneity resulting from both short- and long-timescale dynamics by the calculation of order parameters on different time scales. These results illustrate the usefulness of our approach since they show thatNCBDis rather rigid on the nanosecond timescale, but interconverts within a broader ensemble on longer timescales, thus enabling the derivation of a coherent set of conclusions from variousNMRexperiments on this protein, which could otherwise appear in contradiction with each other.

Many proteins display complex dynamical properties that are often intimately linked to their biological functions. As the native state of a protein is best described as an ensemble of conformations, it is important to be able to generate models of native state ensembles with high accuracy. Due to limitations in sampling efficiency and force field accuracy it is, however, challenging to obtain accurate ensembles of protein conformations by the use of molecular simulations alone. Here we show that dynamic ensemble refinement, which combines an accurate atomistic force field with commonly available nuclear magnetic resonance (NMR) chemical shifts and NOEs, can provide a detailed and accurate description of the conformational ensemble of the native state of a highly dynamic protein. As both NOEs and chemical shifts are averaged on timescales up to milliseconds, the resulting ensembles reflect the structural heterogeneity that goes beyond that probed, e.g., by NMR relaxation order parameters. We selected the small protein domain NCBD as object of our study since this protein, which has been characterized experimentally in substantial detail, displays a rich and complex dynamical behaviour. In particular, the protein has been described as having a molten-globule like structure, but with a relatively rigid core. Our approach allowed us to describe the conformational dynamics of NCBD in solution, and to probe the structural heterogeneity resulting from both short- and long-timescale dynamics by the calculation of order parameters on different time scales. These results illustrate the usefulness of our approach since they show thatNCBDis rather rigid on the nanosecond timescale, but interconverts within a broader ensemble on longer timescales, thus enabling the derivation of a coherent set of conclusions from variousNMRexperiments on this protein, which could otherwise appear in contradiction with each other.

We re-investigate the image charge model of Iori and Corni (Iori and Corni, J. Comput. Chem. 2008, 29, 1656). We find that a simple symmetrization of their model allows to obtain quantitatively correct results for the electrostatic interaction of a water molecule with a metallic surface. This symmetrization reduces the magnitude of the electrostatic interaction to less than 10% of the total interaction energy. © 2017 Wiley Periodicals, Inc.

Free-energy calculations based on molecular simulations provide access to a wide range of thermodynamic properties such as the solubility and partitioning of a molecule in and between various phases. It is demonstrated how molecular dynamics free-energy simulations may be used to obtain the solubilities of primary alcohols and n-alkanes in water and binding affinities of primary alcohols to α-cyclodextrin. The equivalence of two distinct routes to calculate binding free energies is shown leading to the conclusion that host-guest binding affinities may be used to probe the underlying molecular force field.

Описано електричну дугу в поперечному потоці рідини. Наведено повна система рівнянь, що описує модель електродугової плазми, яка рухається, з умови припущення рівності температур усіх її компонентів. Основною задачею дослідження стало вивчення і опис особливостей і характеру взаємодії дуги з поперечним потоком рідини. Наведено кадри швидкісної кінозйомки дуги, що горить в поперечному потоці рідини в умовах розмірної обробки електричною дугою, яка переміщується по поверхні електродів, причому переміщення дуги носить дискретний характер, тобто дуга після деякого вистоювання на місці переміщується на іншу, частіше ближню ділянку, або ж виникає в іншому місці поміж електродами. The paper purpose - Development of theoretical bases of an electric arc in a cross flow of fluid. Studying and the description of features and character of interaction of an arch with a cross flow of fluid. In a paper describes an arc in a cross flow of fluid. Shows the complete system of equations that describes the model of electric arc plasma, moving from the condition that of equality of the temperatures of all her components. Shows frames from high-speed filming burning arc in a cross flow of fluid in conditions of dimensional processing by an electric arc, which moves along the surface of the electrodes, moving in an arc is discrete, that is, the arc after some standing in place moves to another, usually the nearest land, or arises in a different place between the electrodes. Describes an electric arc in a cross flow of fluid as a source of magnetic energy. Described triangle interconversion energy magnetic, power, energy fields.

In the second volume of his Systematic Theology Wolfhart Pannenberg connects Christian doctrine about the Holy Spirit with Faraday’s understanding of the physical force field and suggests an ontological link between both realities. In this article the main ideas of the Systematic Theology about this issue are presented, an interpretation of their scope according to other texts from the author is given and a balance of this proposal’s contributions and limits is offered., En el segundo tomo de su Teología sistemática Wolfhart Pannenberg relaciona la doctrina cristiana sobre el Espíritu Santo con la comprensión de Faraday del campo físico de fuerzas, apuntando hacia una vinculación ontológica entre ambas realidades. En este artículo se exponen las principales ideas del texto de la Teología sistemática en que se aborda esta cuestión, se hace una interpretación de su alcance utilizando otros textos de Pannenberg y se ofrece un balance de las aportaciones de la propuesta y sus principales límites.