Optimized Force Field Research Articles

Structure, vibrational characterization and SHG of the recrystallization product of L-phenylalanine from aqueous HCl solution

Direct recrystallization of L-phenylalanine from aqueous solution of HCl yields good quality single crystals of L-phenylalanine - L-phenylalaninium chloride (LPLP+Cl−). The compound crystallizes in the non-centrosymmetric space group P21 of the monoclinic system with two molecules in the asymmetric unit. This compound belongs to the ionic co-crystals. Both L-phenylalanine-L-phenylalaninium chloride molecules in the asymmetric unit are bound by a strong symmetric O–H‧‧‧O hydrogen bond with an O‧‧‧O distance of 2.43 Å. Such cationic dimers of (L-phenylalanine - L-phenylalanine) + interact with chloride anions forming two dimensional layers parallel to (001) plane. Hirshfeld surface analysis and the 2D fingerprint plot were used to visualize the many types of intermolecular interactions that govern the formation of supramolecular architecture. The compound was also characterized by vibrational spectroscopy. Molecular structure, vibration frequencies and vibration band intensities were interpreted by structure optimization and normal coordinate force field calculations based on density functional theory (DFT). Second harmonic generation (SHG) efficiency of the crystals LPLP+Cl− is about 50% of the standard KDP.

Modeling solvation dynamics of transition metal redox ion through on-the-fly multi-objective Bayesian-optimized force field.

Modeling solvation dynamics and properties is crucial for developing electrolytes for electrochemical energy storage and conversion devices. This work reports an on-the-fly multi-objective Bayesian optimization (OTF-MOBO) method to parameterize force fields for modeling ionic solvation structures, thermodynamics, and transport properties using molecular dynamics simulations. By leveraging solvation-free energy and solvation radii as training data, we employ the data-driven OTF-MOBO algorithm to actively optimize the force field parameters. The modeling accuracy was evaluated in molecular dynamics simulations until the Pareto front in the parameter space was reached through minimized prediction errors in both solvation-free energy and solvation radii. Using transition metal redox ions (Fe3+/Fe2+, Cr3+/Cr2+, and Cu2+/Cu+) in aqueous solution as examples, we demonstrate that simple force fields combining the Lenard-Jones potential and Coulombic potential can achieve relative error below 2% in both solvation free energy and solvation radii. The optimized force fields can be further extrapolated to predict solvation entropy and diffusivities with relative error below 10% compared with experiments.

The structure of water-ammonia mixtures from classical and abinitio molecular dynamics.

The structure of aqueous ammonia solutions is investigated through classical molecular dynamics (MD) and abinitio molecular dynamics (AIMD) simulations. We have preliminarily compared three well-known classical force fields for liquid water (SPC, SPC/E, and TIP4P) in order to identify the most accurate one in reproducing AIMD results obtained at the Generalized Gradient Approximation (GGA) and meta-GGA levels of theory. Liquid ammonia has been simulated by implementing an optimized force field recently developed by Chettiyankandy et al. [Fluid Phase Equilib. 511, 112507 (2020)]. Analysis of the radial distribution functions for different ammonia concentrations reveals that the three water force fields provide comparable estimates of the mixture structure, with the SPC/E performing slightly better. Although a fairly good agreement between MD and AIMD is observed for conditions close to the equimolarity, at lower ammonia concentrations, important discrepancies arise, with classical force fields underestimating the number and strength of H-bonds between water molecules and between water and ammonia moieties. Here, we prove that these drawbacks are rooted in a poor sampling of the configurational space spanned by the hydrogen atoms lying in the H-bonds of H2O⋯H2O and, more critically, H2O⋯NH3 neighbors due to the lack of polarization and charge transfer terms. This way, non-polarizable classical force fields underestimate the proton affinity of the nitrogen atom of ammonia in aqueous solutions, which plays a key role under realistic dilute ammonia conditions. Our results witness the need for developing more suited polarizable models that are able to take into account these effects properly.

Effective Inclusion of Electronic Polarization Improves the Description of Electrostatic Interactions: The prosECCo75 Biomolecular Force Field.

prosECCo75 is an optimized force field effectively incorporating electronic polarization via charge scaling. It aims to enhance the accuracy of nominally nonpolarizable molecular dynamics simulations for interactions in biologically relevant systems involving water, ions, proteins, lipids, and saccharides. Recognizing the inherent limitations of nonpolarizable force fields in precisely modeling electrostatic interactions essential for various biological processes, we mitigate these shortcomings by accounting for electronic polarizability in a physically rigorous mean-field way that does not add to computational costs. With this scaling of (both integer and partial) charges within the CHARMM36 framework, prosECCo75 addresses overbinding artifacts. This improves agreement with experimental ion binding data across a broad spectrum of systems─lipid membranes, proteins (including peptides and amino acids), and saccharides─without compromising their biomolecular structures. prosECCo75 thus emerges as a computationally efficient tool providing enhanced accuracy and broader applicability in simulating the complex interplay of interactions between ions and biomolecules, pivotal for improving our understanding of many biological processes.

Development of All-Atom Empirical Potentials for Phloroglucinol (Phg) and Hexachlorocyclotriphosphazene (HCCP) Based on their Crystal Phase Structures.

Two existing generic force fields have been augmented with partial charges and tuned in order to give intercompatible all-atom empirical potentials that can satisfactorily represent the known crystal phase structures of the organic phloroglucinol (Phg) (C6H6O3) and inorganic hexachlorocyclotriphosphazene (HCCP) (N3P3Cl6) molecules at several temperatures. It has been proposed that HCCP-Phg network polymers could act as efficient H2 barrier layers in hydrogen storage tanks for cars. However, essential requirements for modeling such networks are adequate representations of both monomers in their pure dense phases using a common form of force field. Tests of their ability to maintain stable crystal structures have been made using classical molecular dynamics (MD) simulations on large 800-molecule supercells. The force fields have been optimized to match the densities calculated from the experimental unit cell dimensions at ambient conditions as well as the intermolecular potential energy, as estimated from experimental enthalpies of sublimation. For Phg, the crystal structure is stabilized by a network of hydrogen bonds and the Coulombic interactions contribute to over 55% of the total intermolecular potential energy. In contrast, the crystal structure of HCCP is intrinsically stabilized by the van der Waals terms. Both optimized force fields reproduce very well the orthorhombic symmetry of their respective crystals under constant-pressure NPT conditions. The model parameters tuned at ambient temperature also give reasonable agreement with crystallographic data at lower temperatures.

Optimizing Force Fields with Experimental Data Using Ensemble Reweighting and Potential Contrasting.

Despite force field improvements over the past decades, we still encounter situations where simulation results disagree with experiments due to force field inaccuracies. Such situations provide opportunities to improve force fields. In this study, we introduce a novel framework for optimizing force fields using experimental data. The unique feature of this framework is that it aims to optimize force fields to match experiments while minimizing the perturbation made to the original force field. To achieve this, we combine ensemble reweighting techniques with the potential contrasting method. Ensemble reweighting is used to reweight an ensemble of conformations generated using an existing force field to match experimental data while minimizing the perturbation to the original ensemble. Potential contrasting is then utilized to optimize force field parameters to reproduce the reweighted ensemble. We demonstrate the framework's effectiveness by optimizing a coarse-grained force field for intrinsically disordered proteins using experimental radius of gyration data.

Optimization of the OPLS force field for “water-in-salt” electrolytes

Water-in-salt (WIS) electrolytes have attracted considerable interest in rechargeable batteries and supercapacitors, owing to their wide electrochemical stability window, non-flammability, and safety. A comprehensive understanding of the microstructure in WIS electrolytes provides a crucial theoretical foundation for their further development. In this work, the optimized potentials for liquid simulations (OPLS) force field parameters have been revisited by scaling atom charges to consider their ionic polarization. The ionic conductivity of different WIS electrolytes, calculated using the optimized force field, aligns well with the experimental observations. The polarization effects of ions exhibit augmentation as their size enlarges, which is caused by the enlargement of ion size inducing the asymmetry in the charge distribution. Additionally, the microstructures indicate that nitrates and perchlorates offer superior ionic conductivity, benefiting from a less ionic coordination number and lower viscosity. These insights could be of assistance in selecting force fields and comprehending the microstructure of WIS electrolytes.

Structure Comparison of Beta Amyloid Peptide Aβ 1-42 Isoforms. Molecular Dynamics Modeling.

Beta amyloid peptide Aβ 1-42 (Aβ42) has a unique dual role in the human organism, as both the peptide with an important physiological function and one of the most toxic biological compounds provoking Alzheimer's disease (AD). There are several known Aβ42 isoforms that we discuss here that are highly neurotoxic and lead to the early onset of AD. Aβ42 is an intrinsically disordered protein with no experimentally solved structure under physiological conditions. The objective of this research was to establish the appropriate molecular dynamics (MD) methodology and model a uniform set of structures for the Aβ42 isoforms that form the core of this study. For that purpose, force field selection and verification including convergence testing for MD simulations was made. Replica exchange MD and conventional MD modeling of several Aβ42 and Aβ16 isoforms that have neurotoxic and amyloidogenic effects impacting the severity of Alzheimer's disease were carried out with the optimal force field and solvent parameters. A standardized ensemble of structures for the Aβ42 and Aβ16 isoforms covering 30-50% of the conformational ensembles extracted from the free energy minima was calculated from MD trajectories. The resulting data set of modeled structures includes Aβ42 wild type, isoD7, pS8, D7H, and H6R-Aβ42 and Aβ16 wild type, isoD7, pS8, D7H, and H6R-Aβ16. The representative structures are given in the Supporting Information; they are open for public access. In the study, we also evaluated the differences between the structures of Aβ42 isoforms and speculate on their possible relevance to the known functions. Utilizing several representative structures for a single disordered protein for docking, with their subsequent averaging by conformations, would markedly increase the reliability of docking results.

Mechanism Analysis and Property Prediction of Extended Surfactants Based on the Respectively Optimized Force Field.

Extended surfactants represent a novel class of anionic-nonionic surfactants with exceptional performance and unique application value in chemically enhanced oil recovery. Although molecular dynamics (MD) simulations can efficiently screen these surfactants, the current research is limited. Here, it is proven for the first time that existing generic force fields (GAFF and CHARMM) cannot accurately describe extended surfactants, and traditional approaches are insufficient for obtaining precise charge parameters. The concept of the respectively optimized force field (ROFF) with the purports of specialization and accuracy is proposed to construct high-accuracy models for MD simulations, and a new approach is developed to simulate the interface model. By combining the newly specialized alkane model, ROFF-based surfactant models, and the innovative simulation protocol, high accuracy and reliability can be obtained in predicting hydration free energies, minimum of area per molecule, and critical micelle concentration of extended surfactants. Key properties of the newly designed extended surfactants in conventional oil-water interfaces and oil reservoir environments are comprehensively predicted by using advanced analytical and characterization methods. Furthermore, the more rigorous mechanism underlying the special amphiphilicity of the extended surfactant is revealed, potentially offering significant improvements over previous empirical perspectives.

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.

Precision-dissipation trade-off for driven stochastic systems

Over the last few decades, stochastic thermodynamics has emerged as a framework to study the thermodynamics of small-scaled systems. The relation between entropy production and precision is one of the most prominent research topics in this field. In this paper, I answer the question how much dissipation is needed to follow a pre-determined trajectory. This will be done by deriving a trade-off relation between how precisely a mesoscopic system can follow a pre-defined trajectory and how much the system dissipates. In the high-precision limit, the minimal amount of dissipation is inversely proportional to the expected deviation from the pre-defined trajectory. Furthermore, I will derive the protocol that maximizes the precision for a given amount of dissipation. The optimal time-dependent force field is a conservative energy landscape which combines a shifted version of the initial energy landscape and a quadratic energy landscape. The associated time-dependent probability distribution conserves its shape throughout the optimal protocol. Potential applications are discussed in the context of bit erasure and electronic circuits.

RNA-protein complexes and force field polarizability.

Molecular dynamic (MD) simulations offer a way to study biomolecular interactions and their dynamics at the atomistic level. There are only a few studies of RNA-protein complexes in MD simulations, and here we wanted to study how force fields differ when simulating RNA-protein complexes: 1) argonaute 2 with bound guide RNA and a target RNA, 2) CasPhi-2 bound to CRISPR RNA and 3) Retinoic acid-inducible gene I C268F variant in complex with double-stranded RNA. We tested three non-polarizable force fields: Amber protein force fields ff14SB and ff19SB with RNA force field OL3, and the all-atom OPLS4 force field. Due to the highly charged and polar nature of RNA, we also tested the polarizable AMOEBA force field and the ff19SB and OL3 force fields with a polarizable water model O3P. Our results show that the non-polarizable force fields lead to compact and stable complexes. The polarizability in the force field or in the water model allows significantly more movement from the complex, but in some cases, this results in the disintegration of the complex structure, especially if the protein contains longer loop regions. Thus, one should be cautious when running long-scale simulations with polarizability. As a conclusion, all the tested force fields can be used to simulate RNA-protein complexes and the choice of the optimal force field depends on the studied system and research question.

De novo protein fold design through sequence-independent fragment assembly simulations

De novo protein design generally consists of two steps, including structure and sequence design. Many protein design studies have focused on sequence design with scaffolds adapted from native structures in the PDB, which renders novel areas of protein structure and function space unexplored. We developed FoldDesign to create novel protein folds from specific secondary structure (SS) assignments through sequence-independent replica-exchange Monte Carlo (REMC) simulations. The method was tested on 354 non-redundant topologies, where FoldDesign consistently created stable structural folds, while recapitulating on average 87.7% of the SS elements. Meanwhile, the FoldDesign scaffolds had well-formed structures with buried residues and solvent-exposed areas closely matching their native counterparts. Despite the high fidelity to the input SS restraints and local structural characteristics of native proteins, a large portion of the designed scaffolds possessed global folds completely different from natural proteins in the PDB, highlighting the ability of FoldDesign to explore novel areas of protein fold space. Detailed data analyses revealed that the major contributions to the successful structure design lay in the optimal energy force field, which contains a balanced set of SS packing terms, and REMC simulations, which were coupled with multiple auxiliary movements to efficiently search the conformational space. Additionally, the ability to recognize and assemble uncommon super-SS geometries, rather than the unique arrangement of common SS motifs, was the key to generating novel folds. These results demonstrate a strong potential to explore both structural and functional spaces through computational design simulations that natural proteins have not reached through evolution.

Optimal occlusal force field of masticatory system under clenching by simplex method and principal component analysis

In the present paper, a method to evaluate the occlusal state and occlusal morphology that can integrate an optimal occlusal force field under clenching is proposed using simplex optimization and principal component analysis (PCA). The objective function of the optimization is taken as a sum of the weighted occlusal forces and reaction on temporomandibular joint (TMJ) to maximize the former and to minimize the latter. The optimized solutions in the region defined by the combination among three kinds of muscle forces were obtained by two-step simplex method, and the subregion with no reaction of TMJ was summarized as a ratio to the whole region, newly defined here as a good balance ratio. As a result of PCA of total 528 samples of the good balance ratios for 13 different muscle force combinations, two principal components (PCs) are enough to represent the optimized masticatory system. The first PC suggests the ideal state of occlusion and the second one implies the characterization of combination among muscles. From the consideration of PCA, with the wider equalizer angle, the easier it is to support the force of the temporalis muscle in an ideal occlusal position, and the more the occlusal contact point is shifted backward, the harder it is to support the force of the temporalis muscle. Comprehensively, the proposed good balance ratio becomes the effective quantity to evaluate the ideal masticatory system with the optimized occlusal force field for the statistically reduced indexes showing the combination of muscles.

From bonds to interactions: comprehensive molecular characterization via polarizable bond-dipole approach.

Accurately characterizing molecular interactions stands as a pivotal requirement for ensuring the reliability of molecular dynamics simulations. In line with our bond-dipole-based interaction model proposed by Gao et al. [X.-C. Gao, Q. Hao and C.-S. Wang, J. Chem. Theory Comput., 2017, 13, 2730-2741.], we have implemented an efficient and concise approach to compute electrostatic potential. This methodology capitalizes on the polarizable nature of chemical bond dipoles, resulting in a model of remarkable simplicity. In this study, we have revised the polarizable bond-dipole-based force field (PBFF) through the meticulous curation of quantum chemical data sets. These data sets encompass a comprehensive collection of 40 000 conformations, including those of water, methylamine, methanol, and N-methylacetamide. Additionally, we incorporate 520 hydrogen-bonded dimers into our data sets. In pursuit of enhanced accuracy in molecular dynamics simulations and a more faithful representation of potential energy landscapes, we undertook the re-optimization of the nonbonded parameters within the PBFF framework. Concurrently, we intricately fine-tuned the bonded parameters. The results of our comprehensive evaluation denote that this newly optimized force field method adeptly and efficiently computes structural characteristics, harmonic frequencies, and interaction energies. Overall, this study provides further validation for the applicability of PBFF in molecular dynamics simulations.

ReaxFF-based nonadiabatic dynamics method for azobenzene derivatives.

ReaxFF reactive force fields have been parameterized for the ground and first excited states of azobenzene and its derivatives. In addition, an extended set of ab initio reference data ensures wide applicability, including to azosystems in complex environments. Based on the optimized force fields, nonadiabatic surface hopping simulations produce photoisomerization quantum yields and decay times of azobenzene, both in the gas phase and in n-hexane solution, in reasonable agreement with higher level theory and experiment. The transferability to other azo-compounds is illustrated for different arylazopyrazoles as well as ethylene-bridged azobenzene. Moreover, it has been shown that the model can be easily extended to adsorbates on metal surfaces. The simulation of the ring-opening of cyclobutene triggered by the photoisomerization of azobenzene in a macrocycle highlights the advantages of a reactive force field model.

Expanding the Accessible Chemical Space of SIRT2 Inhibitors through Exploration of Binding Pocket Dynamics.

Considerations of binding pocket dynamics are one of the crucial aspects of the rational design of binders. Identification of alternative conformational states or cryptic subpockets could lead to the discovery of completely novel groups of the ligands. However, experimental characterization of pocket dynamics, besides being expensive, may not be able to elucidate all of the conformational states relevant for drug discovery projects. In this study, we propose the protocol for computational simulations of sirtuin 2 (SIRT2) binding pocket dynamics and its integration into the structure-based virtual screening (SBVS) pipeline. Initially, unbiased molecular dynamics simulations of SIRT2:inhibitor complexes were performed using optimized force field parameters of SIRT2 inhibitors. Time-lagged independent component analysis (tICA) was used to design pocket-related collective variables (CVs) for enhanced sampling of SIRT2 pocket dynamics. Metadynamics simulations in the tICA eigenvector space revealed alternative conformational states of the SIRT2 binding pocket and the existence of a cryptic subpocket. Newly identified SIRT2 conformational states outperformed experimentally resolved states in retrospective SBVS validation. After performing prospective SBVS, compounds from the under-represented portions of the SIRT2 inhibitor chemical space were selected for in vitro evaluation. Two compounds, NDJ18 and NDJ85, were identified as potent and selective SIRT2 inhibitors, which validated the in silico protocol and opened up the possibility for generalization and broadening of its application. The anticancer effects of the most potent compound NDJ18 were examined on the triple-negative breast cancer cell line. Results indicated that NDJ18 represents a promising structure suitable for further evaluation.

Unified protein force field for simulations of liquid-liquid phase separation

Intrinsically disordered proteins undergo liquid-liquid phase separation to form biological condensates that play essential roles in a variety of cellular processes, including chromatin organization. Computational approaches can give unique insight into the molecular structures of such condensates; however, predicting the interplay between multiple proteins, which may have both ordered and disordered domains, remains challenging. To move toward a unified coarse-grained force field for ordered and disordered proteins, we developed the maximum entropy optimized force field, which can predict the radius of gyration of ordered and disordered proteins, while ensuring the folded structures of ordered proteins are lower in energy than other states.

Molecular dynamics study of Cr doping on the crystal structure and surficial/interfacial properties of 2H-MoS2.

Molecular doping has proved to be an efficient technique to improve the properties of pristine materials. A better understanding of it is quite necessary. For the first time, the force field parameters of the transition metal chromium (Cr) doped in 2H-MoS2 in molecular dynamics (MD) were developed. Compared with the DFT calculation results, the error in the stable-state lattice parameters is less than 1%. The optimized force field parameters were used for the MD simulation of different amounts of Cr substitution doping in 2H-MoS2. This study found that the Cr doping at different sites will have a significant impact on the stability of the bulk 2H-MoS2. With increasing doping amount, the water contact angle increases from 69.2° ± 2° to 78.5° ± 0.4°, and the hydrophobic performance is obviously improved. Finally, we also found that the adsorption energy of Cr-MoS2 decreased with increasing Cr doping content, indicating that bulk MoS2 is easier to separate to form single- or fewer-layer 2H-MoS2 in the case of higher doping content. Comparison between the simulated adsorption energies of typical solvents on the 2H-MoS2 surface shows that methanol (CH3OH) and water (H2O) can separate bulk 2H-MoS2, which matched with the experimental results. By using high-precision force field parameters, molecular dynamics were performed to study the surface/interface characteristics of Cr-doped 2H-MoS2, and provided an effective and detailed description for future experimental design.

Empirical optimization of molecular simulation force fields by Bayesian inference

The demands on the accuracy of force fields for classical molecular dynamics simulations are steadily growing as larger and more complex systems are studied over longer times. One way to meet these growing demands is to hand over the learning of force fields and their parameters to machines in a systematic (semi)automatic manner. Doing so, we can take full advantage of exascale computing, the increasing availability of experimental data, and advances in quantum mechanical computations and the calculation of experimental observables from molecular ensembles. Here, we discuss and illustrate the challenges one faces in this endeavor and explore a way forward by adapting the Bayesian inference of ensembles (BioEn) method [Hummer and Köfinger, J. Chem. Phys. (2015)] for force field parameterization. In the Bayesian inference of force fields (BioFF) method developed here, the optimization problem is regularized by a simplified prior on the force field parameters and an entropic prior acting on the ensemble. The latter compensates for the unavoidable over simplifications in the parameter prior. We determine optimal force field parameters using an iterative predictor–corrector approach, in which we run simulations, determine the reference ensemble using the weighted histogram analysis method (WHAM), and update the force field according to the BioFF posterior. We illustrate this approach for a simple polymer model, using the distance between two labeled sites as the experimental observable. By systematically resolving force field issues, instead of just reweighting a structural ensemble, the BioFF corrections extend to observables not included in ensemble reweighting. We envision future force field optimization as a formalized, systematic, and (semi)automatic machine-learning effort that incorporates a wide range of data from experiment and high-level quantum chemical calculations, and takes advantage of exascale computing resources.Graphic abstract