Force Field Parameters Research Articles

Tuning Potential Functions to Host-Guest Binding Data.

Software to more rapidly and accurately predict protein-ligand binding affinities is of high interest for early-stage drug discovery, and physics-based methods are among the most widely used technologies for this purpose. The accuracy of these methods depends critically on the accuracy of the potential functions that they use. Potential functions are typically trained against a combination of quantum chemical and experimental data. However, although binding affinities are among the most important quantities to predict, experimental binding affinities have not to date been integrated into the experimental data set used to train potential functions. In recent years, the use of host-guest complexes as simple and tractable models of binding thermodynamics has gained popularity due to their small size and simplicity, relative to protein-ligand systems. Host-guest complexes can also avoid ambiguities that arise in protein-ligand systems such as uncertain protonation states. Thus, experimental host-guest binding data are an appealing additional data type to integrate into the experimental data set used to optimize potential functions. Here, we report the extension of the Open Force Field Evaluator framework to enable the systematic calculation of host-guest binding free energies and their gradients with respect to force field parameters, coupled with the curation of 126 host-guest complexes with available experimental binding free energies. As an initial application of this novel infrastructure, we optimized generalized Born (GB) cavity radii for the OBC2 GB implicit solvent model against experimental data for 36 host-guest systems. This refitting led to a dramatic improvement in accuracy for both the training set and a separate test set with 90 additional host-guest systems. The optimized radii also showed encouraging transferability from host-guest systems to 59 protein-ligand systems. However, the new radii are significantly smaller than the baseline radii and lead to excessively favorable hydration free energies (HFEs). Thus, users of the OBC2 GB model currently may choose between GB cavity radii that yield more accurate binding affinities and GB cavity radii that yield more accurate HFEs. We suspect that achieving good accuracy on both will require more far-reaching adjustments to the GB model. We note that binding free-energy calculations using the OBC2 model in OpenMM gain about a 10× speedup relative to corresponding explicit solvent calculations, suggesting a future role for implicit solvent absolute binding free-energy (ABFE) calculations in virtual compound screening. This study proves the principle of using host-guest systems to train potential functions that are transferrable to protein-ligand systems and provides an infrastructure that enables a range of applications.

Molecular Simulation of Nafion Swelling in the Cathode Catalyst Layer of Polymer Electrolyte Fuel Cells

As the world moves towards carbon neutrality, polymer electrolyte fuel cells (PEFCs) have emerged as a vital technology in recent years. The cathode catalyst layer of PEFCs is characterized by its porous structure, which promotes the production of water molecules from oxygen and protons through chemical reactions. The ionomer thin films, predominantly composed of Nafion, are critical in regulating the transport and mobility of molecules and ions in the cathode catalyst layer. Sulfonic groups in the side chains of Nafion enable accumulation of water molecules, causing the ionomer thin films to swell.Previous studies have examined the swelling of Nafion under wet conditions[1][2]. However, precise swelling mechanisms remain unclear. While privious research has analyzed the relationship between relative humidity and the amount of water within bulk Nafion samples[3], the swelling behavior in films with thickness of several nanometers differs significantly, necessitating further investigation[4]. This study therefore investigates the swelling mechanisms and the properties of ionomer thin films used in the cathode catalyst layer of PEFCs. This study aims to establish guidelines for designing the molecular structures of ionomers by elucidating the mechanisms underlying ionomer swelling in the cathode catalyst layer. Grand canonical Monte Carlo (GCMC) simulations and molecular dynamics (MD) simulations were employed to reproduce swelling in Nafion thin films and examine the impact of Nafion's structure on water content and swelling. These molecular simulation techniques enable the elucidation of the influence exerted by polymer structure on water content and swelling at the molecular scale.In this study, we firstly created a simulation model of a Nafion system using LAMMPS[5], the DREIDING Force Field[6], and Mulliken charges[7] in which parameter settings were referred from literature values[8]. The Nafion system contains 10 Nafion molecules with a degree of polymerization of 10, water molecules, and 100 oxonium ions for electrical neutrality. The density of 1.9 g/cm3 achieved during simulations closely agrees with literature values[8].To reproduce Nafion swelling under humid conditions, GCMC simulations and MD simulations were employed. The rationale behind utilizing GCMC simulations lies in the fact that they enable the insertion of water molecules into the Nafion system under constant chemical potential conditions, thereby inducing Nafion swelling. Subsequent to the GCMC simulations, MD simulations were performed under the NPT ensemble, which facilitated the system to reach equilibrium. This alternating cycle of GCMC and MD simulations is repeated to realize the final swelling state. Preliminary results indicate lower water content values than those experimentally measured under similar conditions. Possible causes include inadequate force field parameters that lead to polymer morphology different from the actual Nafion structure.Future work will explore the causes of these discrepancies and examine the effects of Nafion's structure on water content and swelling under various conditions. This will involve testing alternative force field parameters and initial structures to better represent the molecular-level structure of ionomer thin films on carbon support particles in the cathode catalyst. Additionally, we will investigate how different relative humidity conditions, temperatures, and ionomer compositions influence swelling behavior, which could provide insights into optimizing PEFCs performance under various operating environments.[1] G. C. Abuin et al., J. Membr. Sci. 428, 507 (2013).[2] J. Catalano et al., Int. J. Hydrog. Energy 37, 6308 (2012).[3] T. A. Zawadzinski et al., J. Electrochem. Soc. 140, 1981 (1993).[4] M. A. Modestino et al., Macromolecules 46, 867 (2013).[5] A. P. Thompson et al., Comp. Phys. Comm. 271, 10817 (2022).[6] S. L. Mayo et al., J. Phys. Chem. 94, 8897 (1990).[7] S. S. Jang et al., J. Phys. Chem. B 108, 3149 (2004).[8] Y. Kurihara et al., J. Power Sources 414, 263 (2019).Fig. 1 An ionomer model composed of Nafion molecules, oxonium ions, and water molecules. The red and white spheres represent oxygen and hydrogen atoms making up water molecules. The size of the water molecules is emphasized for clarity. Figure 1

Automated preparation of nanoscopic structures: Graph-based sequence analysis, mismatch detection, and pH-consistent protonation with uncertainty estimates.

Structure and function in nanoscale atomistic assemblies are tightly coupled, and every atom with its specific position and even every electron will have a decisive effect on the electronic structure, and hence, on the molecular properties. Molecular simulations of nanoscopic atomistic structures therefore require accurately resolved three-dimensional input structures. If extracted from experiment, these structures often suffer from severe uncertainties, of which the lack of information on hydrogen atoms is a prominent example. Hence, experimental structures require careful review and curation, which is a time-consuming and error-prone process. Here, we present a fast and robust protocol for the automated structure analysis and pH-consistent protonation, in short, ASAP. For biomolecules as a target, the ASAP protocol integrates sequence analysis and error assessment of a given input structure. ASAP allows for p prediction from reference data through Gaussian process regression including uncertainty estimation and connects to system-focused atomistic modeling described in Brunken and Reiher (J. Chem. Theory Comput. 16, 2020, 1646). Although focused on biomolecules, ASAP can be extended to other nanoscopic objects, because most of its design elements rely on a general graph-based foundation guaranteeing transferability. The modular character of the underlying pipeline supports different degrees of automation, which allows for (i) efficient feedback loops for human-machine interaction with a low entrance barrier and for (ii) integration into autonomous procedures such as automated force field parametrizations. This facilitates fast switching of the pH-state through on-the-fly system-focused reparametrization during a molecular simulation at virtually no extra computational cost.

Generation of viable nanocrystalline structures using the melt-cool method: the influence of force field selection

ABSTRACT The influence of force field selection on the formation of nanocrystalline structures of pure copper using the Melt-Cool method within Molecular Dynamics (MD) was investigated. This is of utmost importance since the use of MD to investigate nanocrystalline materials is critical due to the ability to study on the appropriate length scale. The Melt-Cool simulation method involves annealing the starting single crystal structure to temperatures exceeding the melting point of the material, followed by rapid quenching and equilibration to room temperature. The heating and rapid quenching allows for the mixture of atoms to randomised orientations with realistic microstructures and randomised defects, such as interstitial or vacancy atoms. Due to the requirement of the mathematical model (force field) to predict the nanocrystalline structure, the influence of force field selection is of paramount importance – a major gap found in currently available literature. The current investigation was performed in two phases: initial investigation to understand influence of force field parameterisation on formation of nanocrystalline structures, followed with an investigation to enhance predictions of mechanical properties of nanocrystalline copper found in currently available literature. The initial phase demonstrated a clear dependence on force field selection for the formation of viable nanocrystalline structures. The second phase demonstrated that the most accurate force field for mechanical properties may not be the ideal selection for use with the Melt-Cool method. The most ideal force field selection when performing the Melt-Cool method with the goal of obtaining accurate mechanical properties for pure copper was determined.

Force field study of catechol dimers and catechol-(H2O)n clusters

Force field parameters were developed for catechol and used to produce optimized structures of catechol dimers and catechol-(H2O)n clusters. The force field optimized structures for the catechol dimer and catechol-(H2O)n (with n = 1 and 2) clusters are benchmarked against quantum chemistry calculations at the B3LYP and w97XD levels of theory; with the quantum chemistry calculations taking into account basis set superposition error. The force field predicts, in agreement with B3LYP and w97XD reasonable structures and relative binding energies for two dimer structures and six catechol-(H2O)n clusters (with n = 1, 2). The developed forcefield parameters are then used to conduct molecular dynamics (MD) simulations for catechol-(H2O)15 cluster to highlight the effect of increased coordination number on the H-bond network around catechol with potential impact on the reactivity of catechol towards ozone in wet conditions. The result of MD simulations suggests that water molecules form a cage of H-bonds network around the hydroxyl groups of catechol, thereby blocking the active sites on catechol.

Minimal Peptoid Dynamics Inform Self-Assembly Propensity.

Peptoids are structural isomers of natural peptides, with side chain attachment at the amide nitrogen, conferring this class of compounds with the ability to access both cis and trans ω torsions as well as an increased diversity of ψ/φ states with respect to peptides. Sampling within these dimensions is controlled through side chain selection, and an expansive set of viable peptoid residues exists. It has been shown recently that "minimal" di- and tripeptoids with aromatic side chains can self-assemble into highly ordered structures, with size and morphological definition varying as a function of sequence pattern (e.g., XFF and FXF, where X = a nonaromatic peptoid monomer). Aromatic groups, such as phenylalanine, are regularly used in the design of minimal peptide assemblers. In recognition of this, and to draw parallels between these compounds classes, we have developed a series of descriptors for intramolecular dynamics of aromatic side chains to discern whether these dynamics, in a preassembly condition, can be related to experimentally observed nanoscale assemblies. To do this, we have built on the atomistic peptoid force field reported by Weiser and Santiso (CGenFF-WS) through the rigorous fitting of partial charges and the collation of Charmm General Force Field (CGenFF) parameters relevant to these systems. Our study finds that the intramolecular dynamics of side chains, for a given sequence, is dependent on the specific combination of backbone ω torsions and that homogeneity of sampling across these states correlates well with the experimentally observed ability to assemble into nanomorphologies with long-range order. Sequence patterning is also shown to affect sampling, in a manner consistent for both tripeptoids and tripeptides. Additionally, sampling similarities between the nanofiber forming tripeptoid, Nf-Nke-Nf in the cc state, and the nanotube forming dipeptide FF, highlight a structural motif which may be relevant to the emergence of extended linear assemblies. To assess these properties, a variety of computational approaches have been employed.

Martini 3 Force Field Parameters for Protein Lipidation Post-Translational Modifications.

Protein lipidations are vital co/post-translational modifications that tether lipid tails to specific protein amino acids, allowing them to anchor to biological membranes, switch their subcellular localization, and modulate association with other proteins. Such lipidations are thus crucial for multiple biological processes including signal transduction, protein trafficking, and membrane localization and are implicated in various diseases as well. Examples of lipid-anchored proteins include the Ras family of proteins that undergo farnesylation; actin and gelsolin that are myristoylated; phospholipase D that is palmitoylated; glycosylphosphatidylinositol-anchored proteins; and others. Here, we develop parameters for cysteine-targeting farnesylation, geranylgeranylation, and palmitoylation, as well as glycine-targeting myristoylation for the latest version of the Martini 3 coarse-grained force field. The parameters are developed using the CHARMM36m all-atom force field parameters as reference. The behavior of the coarse-grained models is consistent with that of the all-atom force field for all lipidations and reproduces key dynamical and structural features of lipid-anchored peptides, such as the solvent-accessible surface area, bilayer penetration depth, and representative conformations of the anchors. The parameters are also validated in simulations of the lipid-anchored peripheral membrane proteins Rheb and Arf1, after comparison with independent all-atom simulations. The parameters, along with mapping schemes for the popular martinize2 tool, are available for download at 10.5281/zenodo.7849262 and also as supporting information.

Use of molecular simulation for evaluating adsorption equilibrium of inhalation anaesthetic agents on metal–organic frameworks

AbstractMetal–organic frameworks (MOFs) were studied as alternatives to zeolites and activated carbon for adsorptive removal of wasted inhalation anaesthetic agents (IAA). Monte Carlo simulation was used to predict equilibrium adsorption isotherms of IAA on selected MOFs. Rather than generic forcefields (FFs), the all‐atom FF parameters published by Arcario were used for IAA modelling. Continuous fractional component Monte Carlo (CFCMC) proved crucial for speedy simulation of large molecules. We found that allocating 70% probability to the CFlambdaSwap move gave optimum fits between simulation and experiment. The simulations provided us with an insight into the adsorption mechanisms of IAA in these structures. Heats of adsorption, Brauner‐Emmet‐Teller (BET) surface area, and total pore volume were deduced to be the crucial parameters for low, medium, and high range of relative pressures in the isotherm. Therefore, the chromium atoms in MIL‐101‐Cr are better adsorbers of IAA than MIL‐100‐Al at lower pressures despite the similarities in terms of the type of linkers and topology. Our simulation results corroborated the earlier published studies on the self‐association behaviour of sevoflurane molecules based on the experimental isotherms reported for MOF‐177‐Zn. Finally, the high polarity of IAA is thought to explain good low‐pressure simulation/experiment data agreement for the MOFs possessing coordinatively unsaturated sites (CUS) despite using generic DREIDING FF for the framework atoms. Our in‐house parsing code helped realize that the grand‐canonical Monte‐Carlo simulation speed is not the same for all pressure points but decreases for higher pressure points. This can be explained by increased density of the adsorbates making successful trial moves less probable.

Development of a Transferable Force Field between Metal-Organic Framework and Its Polymorph.

Conventionally, force fields for specific metal-organic frameworks (MOFs) are derived from quantum chemical simulations, but this method can be computationally intensive, especially in cases for large MOF structures. In this work, we devise a methodology to reduce the force field derivation costs by replacing the original MOF with a smaller polymorphic structure, with the hypothesis that the force field parameters will be transferrable among chemically identical, polymorphic MOF structures. Specifically, we demonstrate this transferability in MOF-177 structure for H2O and NH3 gas molecules and show that the force field parameters derived from a smaller polymorphic MOF-177 can be used accurately to the original MOF-177 structure. This methodology can accelerate the development of force field parameters for large porous materials, in which computational costs for conventional methods are expensive.

Novel Computational Chemistry Infrastructure for Simulating Astatide in Water: From Basis Sets to Force Fields Using Particle Swarm Optimization.

Using the example of astatine, the heaviest naturally occurring halogen whose isotope At-211 has promising medical applications, we propose a new infrastructure for large-scale computational models of heavy elements with strong relativistic effects. In particular, we focus on developing an accurate force field for At- in water based on reliable relativistic density functional theory (DFT) calculations. To ensure the reliability of such calculations, we design novel basis sets for relativistic DFT, via the particle swarm optimization algorithm to optimize the coefficients of the new basis sets and the polarization-consistent basis set idea's extension to heavy elements to eliminate the basis set error from DFT calculations. The resulting basis sets enable the well-grounded evaluation of relativistic DFT against "gold-standard" CCSD(T) results. Accounting for strong relativistic effects, including spin-orbit interaction, via our redesigned infrastructure, we elucidate a noticeable dissimilarity between At- and I- in halide-water force field parameters, radial distribution functions, diffusion coefficients, and hydration energies. This work establishes the framework for the systematic development of polarization-consistent basis sets for relativistic DFT and accurate force fields for molecular dynamics simulations to be used in large-scale models of complex molecular systems with elements from the bottom of the periodic table, including actinides and even superheavy elements.

Review of deep learning algorithms in molecular simulations and perspective applications on petroleum engineering

In the last few decades, deep learning (DL) has afforded solutions to macroscopic problems in petroleum engineering, but mechanistic problems at the microscale have not benefited from it. Mechanism studies have been the strong demands for the emerging projects, such as the gas storage and hydrate production, and for some problems encountered in the storage process, which are common found as the chemical interaction between injected gas and mineral, and the formation of hydrate. Emerging advances in DL technology enable solving molecular dynamics (MD) with quantum accuracy. The conventional quantum chemical method is computational expensive, whereas the classical MD method cannot guarantee high accuracy because of its empirical force field parameters. With the help of the DL force field, precision at the quantum chemistry level can be achieved in MD. Moreover, the DL force field promotes the computational speed compared with first-principles calculations. In this review, the basic knowledge of the molecular force field and deep neural network (DNN) is first introduced. Then, three representative open-source packages relevant to the DL force field are introduced. As the most common components in the development of oil and gas reservoirs, water and methane are studied from the aspects of computational efficiency and chemical reaction respectively, providing the foundation of oil and gas researches. However, in the oil and gas problems, the complex molecular topo structures and various element types set a high challenge for the DL techniques in MD. Regarding the computational efficiency, it needs improvement via GPU and parallel accelerations to compete with classical MD. Even with such difficulties, the booming of this technique in the area of petroleum engineering can be predictable.

A study on the vapor–liquid equilibria of vinyl acrylate-acetic acid and vinyl acetate-acetic acid with GEMC method

Vinyl acetate (VAc) is one of the widely utilized industrial organic raw materials. In the acetoxylation of ethylene to vinyl acetate, there is a by-product vinyl acrylate. However, the vapor–liquid phase equilibrium data of this component with acetic acid and vinyl acetate have not been reported. Here, by Density Functional Theory calculations, we fitted the TraPPE-UA force field parameters of vinyl acrylate. The vapor–liquid phase equilibrium data of the vinyl acrylate-acetate and vinyl acrylate-vinyl acetate binary systems were calculated using the Gibbs Ensemble Monte Carlo (GEMC) method. The calculated phase equilibrium data were utilized to fit the binary interaction parameters of the NRTL equations for the binary systems which were used for the separation process simulation of the vinyl acrylate, acetic acid and vinyl acetate systems. It was verified that the TraPPE-UA force field parameters fitted in this paper have high accuracy. The results clarify the concentration distribution of vinyl acrylate gas–liquid phase components in the distillation column, which builds the foundation for the development of the separation process.

Quantum mechanics based non-bonded force field functions for use in molecular dynamics simulations of materials and systems: The nitrogen and oxygen columns.

Accurate Force Fields (FFs) are essential for Molecular Dynamics (MD) simulations of the dynamics of realistic materials in terms of atomic-level interactions. The FF parameters of short-range valence interactions can be derived through Quantum Mechanical (QM) calculations on model systems practical for QM (<300 atoms). Similarly, the dynamic electrostatic interactions can be described with methods such as QEq or PQEq that allow charges and polarization to adjust dynamically. However, accurately extracting long-range van der Waals (vdW) interactions from QM calculations poses challenges due to the absence of a definitive method to distinguish between the different energetic components of electrostatics, polarization, vdW, hydrogen bonding, and valence interactions. To do this we use the Perdew-Burke-Ernzerhof flavor of Density Functional Theory, including empirical D3 vdW corrections, to predict the Equation of State for each element (keeping any covalent bonds fixed), from which we obtain the two-body vdW nonbond potential. Here, we extend these calculations to include non-bonded parameters for the N and O columns of the periodic table so that we now describe columns 15 (N), 16 (O), 17 (F), and 18 (Ne) of the periodic table. For these 20 elements, we find that the two-body vdW potentials can all be mapped to a single universal two-body curve, with just three scaling parameters: Re, De, and L. We refer to this as the Universal NonBond (UNB) potential. We expect this to be useful for new MD simulations and a helpful starting point to obtain UNB parameters for the remainder of the periodic table.

SwissParam 2023: A Modern Web-Based Tool for Efficient Small Molecule Parametrization.

Most steps of drug discovery are now routinely supported and accelerated by computer-aided drug design tools. Among them, structure-based approaches use the three-dimensional structure of the targeted biomacromolecule as a major source of information. When it comes to calculating the interactions of small molecules with proteins using the equations of molecular mechanics, topologies, atom typing, and force field parameters are required. However, generating parameters for small molecules remains challenging due to the large number of existing chemical groups. The SwissParam web tool was first released in 2011 with the aim of generating parameters and topologies for small molecules based on the Merck molecular force field (MMFF) while being compatible with the CHARMM22/27 force field. Here, we present an updated version of SwissParam, providing various new features, including the possibility to setup covalent ligands. Molecules can now be imported from different file formats or via a molecular sketcher. The MMFF-based approach has been updated to provide parameters and topologies compatible with the CHARMM36 force field. An option was added to generate small molecule parametrizations following the CHARMM General Force Field via the multipurpose atom-typer for CHARMM (MATCH) approach. Additionally, SwissParam now generates information on probable alternative tautomers and protonation states of the query molecule so that the user can consider all microspecies relevant to its compound. The new version of SwissParam is freely available at www.swissparam.ch and can also be accessed through a newly implemented command-line interface.

Exploring the Structural, Dynamic, and Functional Properties of Metal‐Organic Frameworks through Molecular Modeling

AbstractThis review spotlights the role of atomic‐level modeling in research on metal‐organic frameworks (MOFs), especially the key methodologies of density functional theory (DFT), Monte Carlo (MC) simulations, and molecular dynamics (MD) simulations. The discussion focuses on how periodic and cluster‐based DFT calculations can provide novel insights into MOF properties, with a focus on predicting structural transformations, understanding thermodynamic properties and catalysis, and providing information or properties that are fed into classical simulations such as force field parameters or partial charges. Classical simulation methods, highlighting force field selection, databases of MOFs for high‐throughput screening, and the synergistic nature of MC and MD simulations, are described. By predicting equilibrium thermodynamic and dynamic properties, these methods offer a wide perspective on MOF behavior and mechanisms. Additionally, the incorporation of machine learning (ML) techniques into quantum and classical simulations is discussed. These methods can enhance accuracy, expedite simulation setup, reduce computational costs, as well as predict key parameters, optimize geometries, and estimate MOF stability. By charting the growth and promise of computational research in the MOF field, the aim is to provide insights and recommendations to facilitate the incorporation of computational modeling more broadly into MOF research.

Unraveling the Molecular Dynamics of Glucose Oxidase Desorption Induced by Argon Cluster Collision.

The bombardment of a protein multilayer target by an energetic argon cluster ion beam enables protein transfer onto a collector in the vacuum while preserving their bioactivity (iBEAM method). In parallel to this new soft-landing variant, protein transfer in the gas phase is a prerequisite for their characterization by mass spectrometry. The successful transfer of bioactive lysozymes (14 kDa) by cluster-induced soft landing and its mechanistic explanation by molecular dynamics (MD) simulations have sparked an important inquiry: Can heavier biomolecules be desorbed while maintaining their tridimensional structure and hence their bioactivity? To address this question, we employed MD simulations using a reactive force field (ReaxFF). Specifically, the Ar cluster-induced desorption of glucose oxidase from either a gold substrate or a lysozyme underlayer was modeled using the LAMMPS code. First, the force field parameters were trained by computing the dissociation energetics of a series of organic molecules with ReaxFF and DFT, in order to realistically describe N-S and O-S interactions in the bombarded glucose oxidase molecule. Second, bombardment simulations investigated the effects of cluster size (ranging from 1000 to 10000 Ar atoms) and kinetic energy (1.5 and 3.0 eV/atom) on the structural features and energetics of the desorbing glucose oxidase. Our results show that large argon clusters (≥7000) are needed to desorb glucose oxidase from a gold surface, yet protein fragmentation and/or pronounced denaturation occur. However, the transfer of structurally preserved glucose oxidase in the gas phase is predicted by the simulations when an organic layer is used as a substrate.

Optimization of ReaxFF Reactive Force Field Parameters for Cu/Si/O Systems via Neural Network Inversion with Application to Copper Oxide Interaction with Silicon

Optimization of ReaxFF Reactive Force Field Parameters for Cu/Si/O Systems via Neural Network Inversion with Application to Copper Oxide Interaction with Silicon

Robust AMBER Force Field Parameters for Glutathionylated Cysteines.

S-glutathionylation is an oxidative post-translational modification, which is involved in the regulation of many cell signaling pathways. Increasing amounts of studies show that it is crucial in cell homeostasis and deregulated in several pathologies. However, the effect of S-glutathionylation on proteins' structure and activity is poorly understood, and a drastic lack of structural information at the atomic scale remains. Studies based on the use of molecular dynamics simulations, which can provide important information about modification-induced modulation of proteins' structure and function, are also sparse, and there is no benchmarked force field parameters for this modified cysteine. In this contribution, we provide robust AMBER parameters for S-glutathionylation, which we tested extensively against experimental data through a total of 33 μs molecular dynamics simulations. We show that our parameter set efficiently describes the global and local structural properties of S-glutathionylated proteins. These data provide the community with an important tool to foster new investigations into the effect of S-glutathionylation on protein dynamics and function, in a common effort to unravel the structural mechanisms underlying its critical role in cellular processes.

The role of structural heterogeneity in the homodimerization of transmembrane proteins.

The equilibrium association of transmembrane proteins plays a fundamental role in membrane protein function and cellular signaling. While the study of the equilibrium binding of single pass transmembrane proteins has received significant attention in experiment and simulation, the accurate assessment of equilibrium association constants remains a challenge to experiment and simulation. In experiment, there remain wide variations in association constants derived from experimental studies of the most widely studied transmembrane proteins. In simulation, state-of-the art methods have failed to adequately sample the thermodynamically relevant structures of the dimer state ensembles using coarse-grained models. In addition, all-atom force fields often fail to accurately assess the relative free energies of the dimer and monomer states. Given the importance of this fundamental biophysical process, it is essential to address these shortcomings. In this work, we establish an effective computational protocol for the calculation of equilibrium association constants for transmembrane homodimer formation. A set of transmembrane protein homodimers, used in the parameterization of the MARTINI v3 force field, are simulated using metadynamics, based on three collective variables. The method is found to be accurate and computationally efficient, providing a standard to be used in the future simulation studies using coarse-grained or all-atom models.

AutoParams: An Automated Web-Based Tool To Generate Force Field Parameters for Molecular Dynamics Simulations.

Many research questions benefit from molecular dynamics simulations to observe the motions and conformations of molecules over time, which rely on force fields that describe sets of common molecules by category. With the increase of importance for large data sets used in machine learning and growing computational efficiency, the ability to rapidly create large numbers of force field inputs is of high importance. Unusual molecules, such as nucleotide analogues, functionalized carbohydrates, and modified amino acids, are difficult to describe consistently using standard force fields, requiring the development of custom parameters for each unique molecule. While these parameters may be created by individual users, the process can become time-consuming or may introduce errors that may not be immediately apparent. We present an open-source automated parameter generation service, AutoParams, which requires minimal input from the user and creates useful Amber force field parameter sets for most molecules, particularly those that combine molecular types (e.g., a carbohydrate functionalized with a benzene). We include hierarchical atom-typing logic that makes it straightforward to expand with additional force fields and settings, and options for creating monomers in polymers, such as functionalized amino acids. It can be straightforwardly linked to any charge generation program and currently has interfaces to Psi4, PsiRESP, and TeraChem. It is open source and is available via GitHub. It includes error checking and testing protocols to ensure the parameters will be sufficient for subsequent molecular dynamics simulations and streamlines the creation of force field databases.