Force Field Parameters Research Articles

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.

Atomistic molecular simulations of Aβ-Zn conformational ensembles.

The amyloid-forming Aβ peptide is able to interact with metal cations to form very stable complexes that influence fibril formation and contribute to the onset of Alzheimer's disease. Multiple structures of peptides derived from Aβ in complex with different metals have been resolved experimentally to provide an atomic-level description of the metal-protein interactions. However, Aβ is intrinsically disordered, and hence more amenable to an ensemble description. Molecular dynamics simulations can now reach the timescales needed to generate ensembles for these type of complexes. However, this requires accurate force fields both for the protein and the protein-metal interactions. Here we use state-of-the-art methods to generate force field parameters for the Zn(II) cations in a set of complexes formed by different Aβ variants and combine them with the Amber99SB*-ILDN optimized force field. Upon comparison of NMR experiments with the simulation results, further optimized with a Bayesian/Maximum entropy approach, we provide an accurate description of the molecular ensembles for most Aβ-metal complexes. We find that the resulting conformational ensembles are more heterogeneous than the NMR models deposited in the Protein Data Bank.

Interfacial heat and mass transfer at silica/binary molten salt interface from deep potential molecular dynamics

Interfacial heat and mass transfer properties at molten salt/solid interfaces are crucial for the study of heat storage/transfer properties of molten salt nanocomposite materials as well as the microscopic mechanism of thermophysical property enhancement, but accurate force field parameters for describing molten salt-solid interactions are still lacking. This study has utilized a workflow that could meet the demand of high accuracy and efficiency in molecular dynamics (MD) simulations of molten salt/solid interface with the deep potentials (DPs) trained by deep learning (DL), using SiO2/LiCl-KCl interface as a model system. In terms of the short-ranged polarizability and long-ranged correlations of ions, DPs can give accurate description of potential energy surface over wide temperature range at the level of ab initio calculations. Further analysis on interfacial thermal resistance (ITR) and thermal conductivity shows that the slowing down of heat transfer is related to the disordered microstructure of molten salt ions near the SiO2 surfaces, which also leads to larger ITR. For the mass transfer of molten salt ions on the SiO2 surfaces, the more active ionic diffusion and permeation through the solid can be observed due to the weaker adsorption between them, which is ascribed primarily to the high temperature. The workflow, of which the feasibility has been verified for molten salt/solid interfaces in this work with rigorous modeling, will be used for building accurate interatomic interaction models and calculating thermophysical properties for various complex interfacial systems containing molten salts. The influence of interfacial microstructure on heat and mass transfer will provide theoretical support for the application of molten salts in energy industry.

Upgrading of the general AMBER force field 2 for fluorinated alcohol biosolvents: A validation for water solutions and melittin solvation.

The standard GAFF2 force field parameterization has been refined for the fluorinated alcohols 2,2,2-trifluoroethanol (TFE), 1,1,1,3,3,3-hexafluoro-2-propanol (HFIP), and 1,1,1,3,3,3-hexafluoropropan-2-one (HFA), which are commonly used to study proteins and peptides in biomimetic media. The structural and dynamic properties of both proteins and peptides are significantly influenced by the biomimetic environment created by the presence of these cosolvents in aqueous solutions. Quantum mechanical calculations on stable conformers were used to parameterize the atomic charges. Different systems, such as pure liquids, aqueous solutions, and systems formed by melittin protein and cosolvent/water solutions, have been used to validate the new models. The calculated macroscopic and structural properties are in agreement with experimental findings, supporting the validity of the newly proposed models.

Chitosan Polysaccharides from a Polarizable Multiscale Approach.

We report simulations of chitosan polysaccharides in the aqueous phase, at infinite dilute conditions and zero ionic strength. Those simulations are performed by means of a polarizable multiscale modeling scheme that relies on a polarizable all atom force field to model solutes and on a polarizable solvent coarse grained approach. Force field parameters are assigned only from quantum chemistry ab initio data. We simulate chitosan monomer units, dimers and 50-long chains. Regarding the 50-long chains we simulate three sets of ten randomly built chain replica at three different pH conditions (corresponding to different chain protonation states, the chain degree of deacetylation is 85%). Our simulations show the persistence length of 50-long chitosan chains at strong acidic conditions (pH <5) to be 24 ± 2 nm (at weak/negligible ionic strength conditions), and to be 1 order of magnitude shorter at usual pH conditions. Our simulation data support the most recent simulation and experimental studies devoted to chitosan polysaccharides in the aqueous phase.

Machine Learning-Guided Adaptive Parametrization for Coupling Terms in a Mixed United-Atom/Coarse-Grained Model for Diphenylalanine Self-Assembly in Aqueous Ionic Liquids.

Precise regulation of the peptide self-assembly into ordered nanostructures with intriguing properties has attracted intense attention. However, predicting peptide assembly at atomic resolution is a challenge due to both the structural flexibility of peptides and the associated huge computational costs. A machine learning-guided adaptive parametrization method was proposed for developing a mixed atomic and coarse-grained (CG) model through a multiobjective optimization strategy. Our model incorporates the united-atom (UA) model for diphenylalanine (P) and the polarizable electrostatic-variable coarse-grained (VaCG) model for aqueous ionic liquid [BMIM]+[BF4]- solution. In this mixed model, the coupling van der Waals (vdW) interaction is addressed by introducing virtual sites (VS) in the UA model to interact with solvent CG beads. The coupling parameters, including the electrostatic parameter and vdW parameters, are automatically optimized through ML-guided adaptive parametrization. The performance of this model was tested by some microstructural properties, e.g., the average number of P-P intermolecular hydrogen bonds (HBs) and radius distribution functions (RDFs) between P and different fragments of IL, in comparison with all-atom (AA) simulations. The computational cost is significantly reduced using such a parametrization scheme, which could search tens of thousands of force-field parameter sets, while needing only a small fraction of them to be assessed with molecular dynamics (MD) simulations. We used such a mixed resolution model to investigate the self-assembly in IL-water mixtures with variants of IL concentration (X). The long-range-ordered fibril structure is formed in a pure water system (X = 0). With an increase of IL concentrations, the formation of an ordered self-assembly nanostructure is prohibited, instead forming branched fibril at X = 2 mol % or amorphous aggregates when X > 10 mol %, resulting from the interplay between π-stacking and HB interactions between P and IL. The qualitative agreement between the simulated structures and the observed morphologies in experiments indicates the applicability of ML-guided parametrization strategy in the study of complex systems, such as polymers, lipid bilayers, and polysaccharides.