Implicit Solvents for the Polarizable Atomic Multipole AMOEBA Force Field

TinkerModeller: An Efficient Tool for Building Biological Systems in Tinker Simulations

Polarizable force fields advance our understanding of electrostatic interactions in molecular systems; however, their widespread application is limited by the complexity of required molecular modeling. We here present TinkerModeller (TKM), a versatile software package designed to streamline the construction of biological systems in the Tinker molecular simulation software. The core functionality of TKM lies in its capacity to generate input files for complex molecular systems and facilitate the conversion from classical to polarizable force fields. With a user-friendly, standalone script, TKM provides an intuitive interface that supports users from molecular modeling through to postanalysis, creating a comprehensive platform for molecular dynamics simulations within Tinker. Furthermore, TKM includes an electric field (EF) postanalysis module, introducing a novel approach that employs charge methods and point charge approximations for efficient internal EF estimation. This module offers a computationally low-demand solution for high-throughput EF estimation. Our work paves the way for broader, more accessible use of polarizable force fields within Tinker and introduces a new method for EF estimation, advancing our capacity to explore electrostatic effects in biological and materials science applications.

Spatiotemporal characterization of water diffusion anomalies in saline solutions using machine learning force field

Understanding water behavior in salt solutions remains a notable challenge in computational chemistry. Conventional force fields have shown limitations in accurately representing water's properties across different salt types (chaotropes and kosmotropes) and concentrations, demonstrating the need for better methods. Machine learning force field applications in computational chemistry, especially through deep potential molecular dynamics (DPMD), offer a promising alternative that closely aligns with the accuracy of first-principles methods. Our research used DPMD to study how salts affect water by comparing its results with ab initio molecular dynamics, SPC/Fw, AMOEBA, and MB-Pol models. We studied water's behavior in salt solutions by examining its spatiotemporally correlated movement. Our findings showed that each model's accuracy in depicting water's behavior in salt solutions is strongly connected to spatiotemporal correlation. This study demonstrates both DPMD's advanced abilities in studying water-salt interactions and contributes to our understanding of the basic mechanisms that control these interactions.

Revised 4-Point Water Model for the Classical Drude Oscillator Polarizable Force Field: SWM4-HLJ

In this work the 4-point polarizable SWM4 Drude water model is reparametrized. Multiple models were developed using different strategies toward reproduction of specific target data. Results indicate that no individual model can reproduce all the selected target data in the context of the present form of the potential energy function. The changes considered in the new models include, 1) variations in the gas phase dipole moment, 2) variations in the molecular polarizability, 3) variations of the distance between the oxygen and the M site, 4) variation of the oxygen Lennard-Jones (LJ) parameters, 5) introduction of a LJ potential to the hydrogen atoms, and 6) variations of the H-O-H angle. Detailed analysis is presented for 3 new water models from which a final model, SWM4-HLJ, is selected as the future default model for the Drude polarizable force field. The model maintains the gas phase dipole moment as the experimental value while the remaining listed terms were adjusted including a larger H-O-H angle (108.12). Compared to its predecessor, SWM4-NDP, the self-diffusion coefficient, water dimer properties, and water cluster energies are greatly improved. The temperature dependence of the density of the new model also performs better. Overall, the new SWM4-HLJ water model is a general improvement and a good balance between microscopic and bulk properties is achieved.

Force Field X: A computational microscope to study genetic variation and organic crystals using theory and experiment

Force Field X (FFX) is an open-source software package for atomic resolution modeling of genetic variants and organic crystals that leverages advanced potential energy functions and experimental data. FFX currently consists of nine modular packages with novel algorithms that include global optimization via a many-body expansion, acid-base chemistry using polarizable constant-pH molecular dynamics, estimation of free energy differences, generalized Kirkwood implicit solvent models, and many more. Applications of FFX focus on the use and development of a crystal structure prediction pipeline, biomolecular structure refinement against experimental datasets, and estimation of the thermodynamic effects of genetic variants on both proteins and nucleic acids. The use of Parallel Java and OpenMM combines to offer shared memory, message passing, and graphics processing unit parallelization for high performance simulations. Overall, the FFX platform serves as a computational microscope to study systems ranging from organic crystals to solvated biomolecular systems.

Development of a QM/MM(ABEEM) method for the deprotonation of neutral and cation radicals in the G-tetrad and GGX(8-oxo-G) tetrad

The rapid deprotonation of G˙+ in the DNA strand impedes positive charge (hole) transfer, whereas the slow deprotonation rate of G˙+ in the G-tetrad makes it a more suitable carrier for hole conduction. The QM/MM(ABEEM) combined method, which involves the integration of QM and the ABEEM polarizable force field (ABEEM PFF), was developed to investigate the deprotonation of neutral and cation free radicals in the G-tetrad and GGX(8-oxo-G) tetrad (xanthine and 8-oxoguanine dual substituted G-tetrad). By incorporating valence-state electronegativity piecewise functions χ*(r) and implementing charge local conservation conditions, QM/MM(ABEEM) possesses the advantage of accurately simulating charge transfer and polarization effect during deprotonation. The activation energy calculated by the QM method of X˙ is the lowest among other bases in the GGX(8-oxo-G) tetrad, which is supported by the computation of the average electronegativity calculated by ABEEM PFF. By utilizing QM/MM(ABEEM) with a two-way free energy perturbation method, the deprotonation activation energy of X˙ in the GGX(8-oxo-G) tetrad is determined to be 33.0 ± 2.1 kJ mol-1, while that of G˙+ in the G-tetrad is 20.7 ± 0.6 kJ mol-1, consistent with the experimental measurement of 20 ± 1.0 kJ mol-1. These results manifest that X˙ in the GGX(8-oxo-G) tetrad exhibits a slower deprotonation rate than G˙+ in the G-tetrad, suggesting that the GGX(8-oxo-G) tetrad may serve as a more favorable hole transport carrier. Furthermore, the unequal average electronegativities of bases in the GGX(8-oxo-G) tetrad impede the deprotonation rate. This study provides a potential foundation for investigating the microscopic mechanism of DNA electronic devices.

A generalized Kirkwood implicit solvent for the polarizable AMOEBA protein model

Computational simulation of biomolecules can provide important insights into protein design, protein-ligand binding interactions, and ab initio biomolecular folding, among other applications. Accurate treatment of the solvent environment is essential in such applications, but the use of explicit solvents can add considerable cost. Implicit treatment of solvent effects using a dielectric continuum model is an attractive alternative to explicit solvation since it is able to describe solvation effects without the inclusion of solvent degrees of freedom. Previously, we described the development and parameterization of implicit solvent models for small molecules. Here, we extend the parameterization of the generalized Kirkwood (GK) implicit solvent model for use with biomolecules described by the AMOEBA force field via the addition of corrections to the calculation of effective radii that account for interstitial spaces that arise within biomolecules. These include element-specific pairwise descreening scale factors, a short-range neck contribution to describe the solvent-excluded space between pairs of nearby atoms, and finally tanh-based rescaling of the overall descreening integral. We then apply the AMOEBA/GK implicit solvent to a set of ten proteins and achieve an average coordinate root mean square deviation for the experimental structures of 2.0 Å across 500 ns simulations. Overall, the continued development of implicit solvent models will help facilitate the simulation of biomolecules on mechanistically relevant timescales.

The Importance of Charge Transfer and Solvent Screening in the Interactions of Backbones and Functional Groups in Amino Acid Residues and Nucleotides

Quantum mechanical (QM) calculations at the level of density-functional tight-binding are applied to a protein–DNA complex (PDB: 2o8b) consisting of 3763 atoms, averaging 100 snapshots from molecular dynamics simulations. A detailed comparison of QM and force field (Amber) results is presented. It is shown that, when solvent screening is taken into account, the contributions of the backbones are small, and the binding of nucleotides in the double helix is governed by the base–base interactions. On the other hand, the backbones can make a substantial contribution to the binding of amino acid residues to nucleotides and other residues. The effect of charge transfer on the interactions is also analyzed, revealing that the actual charge of nucleotides and amino acid residues can differ by as much as 6 and 8% from the formal integer charge, respectively. The effect of interactions on topological models (protein -residue networks) is elucidated.

CHARMM-GUI Implicit Solvent Modeler for Various Generalized Born Models in Different Simulation Programs

Implicit solvent models are widely used because they are advantageous to speed up simulations by drastically decreasing the number of solvent degrees of freedom, which allows one to achieve long simulation time scales for large system sizes. CHARMM-GUI, a web-based platform, has been developed to support the setup of complex multicomponent molecular systems and prepare input files. This study describes an Implicit Solvent Modeler (ISM) in CHARMM-GUI for various generalized Born (GB) implicit solvent simulations in different molecular dynamics programs such as AMBER, CHARMM, GENESIS, NAMD, OpenMM, and Tinker. The GB models available in ISM include GB-HCT, GB-OBC, GB-neck, GBMV, and GBSW with the CHARMM and Amber force fields for protein, DNA, RNA, glycan, and ligand systems. Using the system and input files generated by ISM, implicit solvent simulations of protein, DNA, and RNA systems produce similar results for different simulation packages with the same input information. Protein-ligand systems are also considered to further validate the systems and input files generated by ISM. Simple ligand root-mean-square deviation (RMSD) and molecular mechanics generalized Born surface area (MM/GBSA) calculations show that the performance of implicit simulations is better than docking and can be used for early stage ligand screening. These reasonable results indicate that ISM is a useful and reliable tool to provide various implicit solvent simulation applications.

Automation of AMOEBA polarizable force field for small molecules: Poltype 2

A next-generation protocol (Poltype 2) has been developed which automatically generates AMOEBA polarizable force field parameters for small molecules. Both features and computational efficiency have been drastically improved. Notable advances include improved database transferability using SMILES, robust torsion fitting, non-aromatic ring torsion parameterization, coupled torsion-torsion parameterization, Van der Waals parameter refinement using ab initio dimer data and an intelligent fragmentation scheme that produces parameters with dramatically reduced ab initio computational cost. Additional improvements include better local frame assignment for atomic multipoles, automated formal charge assignment, Zwitterion detection, smart memory resource defaults, parallelized fragment job submission, incorporation of Psi4 quantum package, ab initio error handling, ionization state enumeration, hydration free energy prediction and binding free energy prediction. For validation, we have applied Poltype 2 to ~1000 FDA approved drug molecules from DrugBank. The ab initio molecular dipole moments and electrostatic potential values were compared with Poltype 2 derived AMOEBA counterparts. Parameters were further substantiated by calculating hydration free energy (HFE) on 40 small organic molecules and were compared with experimental data, resulting in an RMSE error of 0.59 kcal/mol. The torsion database has expanded to include 3543 fragments derived from FDA approved drugs. Poltype 2 provides a convenient utility for applications including binding free energy prediction for computational drug discovery. Further improvement will focus on automated parameter refinement by experimental liquid properties, expansion of the Van der Waals parameter database and automated parametrization of modified bio-fragments such as amino and nucleic acids.

Recent progress in general force fields of small molecules

Recent advances in computational hardware and free energy algorithms enable a broader application of molecular simulation of binding interactions between receptors and small-molecule ligands. The underlying molecular mechanics force fields (FFs) for small molecules have also achieved advancements in accuracy, user-friendliness, and speed during the past several years (2018-2020). Besides the expansion of chemical space coverage of ligand-like molecules among major popular classical additive FFs and polarizable FFs, new charge models have been proposed for better accuracy and transferability, new chemical perception of avoiding predefined atom types have been applied, and new automated parameterization toolkits, including machine learning approaches, have been developed for users' convenience.

Operando Characterization of Organic Mixed Ionic/Electronic Conducting Materials

Operando characterization plays an important role in revealing the structure-property relationships of organic mixed ionic/electronic conductors (OMIECs), enabling the direct observation of dynamic changes during device operation and thus guiding the development of new materials. This review focuses on the application of different operando characterization techniques in the study of OMIECs, highlighting the time-dependent and bias-dependent structure, composition, and morphology information extracted from these techniques. We first illustrate the needs, requirements, and challenges of operando characterization then provide an overview of relevant experimental techniques, including spectroscopy, scattering, microbalance, microprobe, and electron microscopy. We also compare different in silico methods and discuss the interplay of these computational methods with experimental techniques. Finally, we provide an outlook on the future development of operando for OMIEC-based devices and look toward multimodal operando techniques for more comprehensive and accurate description of OMIECs.

How much can physics do for protein design?

Physics and physical chemistry are an important thread in computational protein design, complementary to knowledge-based tools. They provide molecular mechanics scoring functions that need little or no ad hoc parameter readjustment, methods to thoroughly sample equilibrium ensembles, and different levels of approximation for conformational flexibility. They led recently to the successful redesign of a small protein using a physics-based folded state energy. Adaptive Monte Carlo or molecular dynamics schemes were discovered where protein variants are populated as per their ligand-binding free energy or catalytic efficiency. Molecular dynamics have been used for backbone flexibility. Implicit solvent models have been refined, polarizable force fields applied, and many physical insights obtained.