Polarizable Force Fields for Biomolecular Simulations: Recent Advances and Applications

Role of Electron Spin, Chirality, and Charge Dynamics in Promoting the Persistence of Nascent Nucleic Acid-Peptide Complexes

Primitive nucleic acids and peptides likely collaborated in early biochemistry. What forces drove their interactions and how did these forces shape the properties of primitive complexes? We investigated how two model primordial polypeptides associate with DNA. When peptides were coupled to a ferromagnetic substrate, DNA binding depended on the substrate's magnetic moment orientation. Reversing the magnetic field nearly abolished binding despite complementary charges. Inverting the peptide chirality or just the cysteine residue reversed this effect. These results are attributed to the chiral-induced spin selectivity (CISS) effect, where molecular chirality and electron spin alter a protein's electric polarizability. The presence of CISS in simple protein-DNA complexes suggests that it played a significant role in ancient biomolecular interactions. A major consequence of CISS is enhancement of the kinetic stability of protein-nucleic acid complexes. These findings reveal how chirality and spin influence bioassociation, offering insights into primitive biochemical evolution and shaping contemporary protein functions.

Discovering cryptic pocket opening and binding of a stimulant derivative in a vestibular site of the 5-HT3A receptor

A diverse set of modulators, including stimulants and anesthetics, regulates ion channel function in our nervous system. However, structures of ligand-bound complexes can be difficult to capture by experimental methods, particularly when binding is dynamic. Here, we used computational methods and electrophysiology to identify a possible bound state of a modulatory stimulant derivative in a cryptic vestibular pocket of a mammalian serotonin-3 receptor. We first applied a molecular dynamics simulation-based goal-oriented adaptive sampling method to identify possible open-pocket conformations, followed by Boltzmann docking that combines traditional docking with Markov state modeling. Clustering and analysis of stability and accessibility of docked poses supported a preferred binding site; we further validated this site by mutagenesis and electrophysiology, suggesting a mechanism of potentiation by stabilizing intersubunit contacts. Given the pharmaceutical relevance of serotonin-3 receptors in emesis, psychiatric, and gastrointestinal diseases, characterizing relatively unexplored modulatory sites such as these could open valuable avenues to understanding conformational cycling and designing state-dependent drugs.

Methods for Theoretical Treatment of Local Fields in Proteins and Enzymes

Electric fields generated by protein scaffolds are crucial in enzymatic catalysis. This review surveys theoretical approaches for detecting, analyzing, and comparing electric fields, electrostatic potentials, and their effects on the charge density within enzyme active sites. Pioneering methods like the empirical valence bond approach rely on evaluating ionic and covalent resonance forms influenced by the field. Strategies employing polarizable force fields also facilitate field detection. The vibrational Stark effect connects computational simulations to experimental Stark spectroscopy, enabling direct comparisons. We highlight how protein dynamics induce fluctuations in local fields, influencing enzyme activity. Recent techniques assess electric fields throughout the active site volume rather than only at specific bonds, and machine learning helps relate these global fields to reactivity. Quantum theory of atoms in molecules captures the entire electron density landscape, providing a chemically intuitive perspective on field-driven catalysis. Overall, these methodologies show protein-generated fields are highly dynamic and heterogeneous, and understanding both aspects is critical for elucidating enzyme mechanisms. This holistic view empowers rational enzyme engineering by tuning electric fields, promising new avenues in drug design, biocatalysis, and industrial applications. Future directions include incorporating electric fields as explicit design targets to enhance catalytic performance and biochemical functionalities.

Diabatic States of Charge Transfer with Constrained Charge Equilibration

Charge transfer (CT) processes that are electronically nonadiabatic are ubiquitous in chemistry, biology, and materials science, but their theoretical description requires diabatic states or adiabatic excited states. For complex systems, these latter states are more difficult to calculate than the adiabatic ground state. Here, we propose a simple method to obtain diabatic states, including energies and charges, by constraining the atomic charges within the charge equilibration framework. For two-state systems, the exact diabatic coupling can be determined, from which the adiabatic excited-state energy can also be calculated. The method can be viewed as an affordable alternative to constrained density functional theory (CDFT), and so we call it constrained charge equilibration (CQEq). We test the CQEq method on the anthracene-tetracyanoethylene CT complex and the reductive decomposition of ethylene carbonate on a lithium metal surface. We find that CQEq predicts diabatic energies, charges, and adiabatic excitation energies in good agreement with CDFT, and we propose that CQEq is promising for combination with machine learning force fields to study nonadiabatic CT in the condensed phase.

Shadow Molecular Dynamics with a Machine Learned Flexible Charge Potential

We present an extended Lagrangian shadow molecular dynamics scheme with an interatomic Born-Oppenheimer potential determined by the relaxed atomic charges of a second-order charge equilibration model. To parametrize the charge equilibration model, we use machine learning with neural networks to determine the environment-dependent electronegativities and chemical hardness parameters for each atom, in addition to the charge-independent energy and force terms. The approximate shadow molecular dynamics potential in combination with the extended Lagrangian formulation improves the numerical stability and reduces the number of Coulomb potential calculations required to evaluate accurate conservative forces. We demonstrate efficient and accurate simulations with excellent long-term stability of the molecular dynamics trajectories. The significance of choosing fixed or environment-dependent electronegativities and chemical hardness parameters is evaluated. Finally, we compute the infrared spectrum of molecules via the dipole autocorrelation function and compare to experiments to highlight the accuracy of the shadow molecular dynamics scheme with a machine learned flexible charge potential.

Isotropic Periodic Sum for Polarizable Gaussian Multipole Model

The isotropic periodic sum (IPS) method provides an efficient approach for computing long-range interactions by approximating distant molecular structures through isotropic periodic images of a local region. Here, we present a novel integration of IPS with the polarizable Gaussian multipole (pGM) model, extending its applicability to systems with Gaussian-distributed charges and dipoles. By developing and implementing the IPS multipole tensor theorem within the Gaussian multipole framework, we derive analytical expressions for IPS potentials that efficiently handle both permanent and induced multipole interactions. Our comprehensive validation includes energy conservation tests in the NVE ensemble, potential energy distributions in the NVT ensemble, structural analysis through radial distribution functions, diffusion coefficients, induced dipole calculations across various molecular systems, and ionic charging free energies. The results demonstrate that the pGM-IPS approach successfully reproduces energetic, structural, and dynamic properties of molecular systems with accuracy comparable to the traditional particle mesh Ewald method. Our work establishes pGM-IPS as a promising method for simulations of polarizable molecular systems, achieving a balance between computational efficiency and accuracy.

High-Resolution Molecular-Dynamics Simulations of the Pyruvate Kinase Muscle Isoform 1 and 2 (PKM1/2)

Glucose metabolism plays a pivotal role in physiological processes and cancer growth. The final stage of glycolysis, converting phosphoenolpyruvate (PEP) into pyruvate, is catalyzed by the pyruvate kinase (PK) enzyme. Whereas PKM1 is mainly expressed in cells with high energy requirements, PKM2 is preferentially expressed in proliferating cells, including tumor cells. Structural analysis of PKM1 and PKM2 is essential to design new molecules with antitumoral activity. To understand their structural dynamics, we performed extensive high-resolution molecular dynamics (MD) simulations using adaptive sampling techniques coupled to the polarizable AMOEBA force field. Performing more than 6 μs of simulation, we considered all oligomerization states of PKM2 and propose structural insights for PKM1 to further study the PKM2-specific allostery. We focused on key sites including the active site and the natural substrate Fructose Bi-Phosphate (FBP) fixation pocket. Additionally, we present the first MD simulation of biologically active PKM1 and uncover important similarities with its PKM2 counterpart bound to FBP. We also analysed TEPP-46's fixation, a pharmacological activator binding a different pocket, on PKM2 and highlighted the structural differences and similarities compared to PKM2 bound to FBP. Finally, we determined potential new cryptic pockets specific to PKM2 for drug targeting.

Ion-Induced Dipole Interactions Matter in Metadynamics Simulation of Transition Metal Ion Transporters

Metal transporters play crucial roles in the homeostasis and detoxification of beneficial and toxic metals in the human body. Due to experimental limitations in studying some metal transporters, numerous simulation studies have been conducted to understand the mechanisms of metal transport. However, studying the transport of divalent metal ions across the plasma membrane by metal transporters has been challenging with traditional molecular dynamics (MD) simulations. The metal ions often become trapped inside the transporter due to encountering high energy barriers during the transport process. In this study, we combined a recently developed metadynamics setup, known as well-tempered (WT) volume-based MTD, with the 12-6-4 Lennard-Jones (LJ) model representing transition metal-His/Asp/Glu side chain interactions. We used this approach to investigate the mechanism of action of a Zrt-/Irt-like protein (ZIP) transporter and compared the results with simulations using standard 12-6 LJ parameters for the transition metal-His/Asp/Glu side chain interactions. Our results show that the 12-6-4 LJ model for transition metal-His/Asp/Glu side chain interactions samples conformational space more broadly than the standard 12-6 LJ model for the same interactions in MTD simulations, facilitating the sampling of states that are hard to reach with the standard 12-6 model within the same time scale. This is even more remarkable given the fact that the model is dominated by 12-6 LJ interactions for the majority of the system, while the transition metal-His/Asp/Glu side chain interactions are the only interactions using the 12-6-4 LJ model. Hence, a small subset of interactions significantly modifies the states sampled by the entire protein leading to a more frequent observation of the transport of the transition metal ion. Overall, using 12-6-4 LJ to model the transition metal-His/Asp/Glu side chain interactions increases the potential for discovering additional metastable states by enabling metal ions to traverse more freely along the defined transport pathways.

ABCG2: A Milestone Charge Model for Accurate Solvation Free Energy Calculation

In this report, we describe the development and validation of ABCG2, a new charge model with milestone free energy accuracy, while allowing instantaneous atomic charge assignment for arbitrary organic molecules. In combination with the second-generation general AMBER force field (GAFF2), ABCG2 led to a root-mean-square error (RMSE) of 0.99 kcal/mol on the hydration free energy calculation of all 642 solutes in the FreeSolv database, for the first time meeting the chemical accuracy threshold through physics-based molecular simulation against the golden-standard data set. Against the Minnesota Solvation Database, the solvation free energy calculation on 2068 pairs of a range of organic solutes in diverse solvents led to an RMSE of 0.89 kcal/mol. The 1913 data points of transfer free energies from the aqueous solution to organic solvents obtained an RMSE of 0.85 kcal/mol, corresponding to 0.63 log units for logP. The benchmark on densities of neat liquids for 1839 organic molecules and heat of vaporizations of 874 organic liquids achieved a comparable performance with the default restrained electrostatic potential (RESP) charge method of GAFF2. The fluctuations of assigned partial atomic charges over different input conformations from ABCG2 are demonstrated to be much smaller than those of RESP from statistics of 96 real drug molecules. The validation results demonstrated not only the accuracy but also the transferability and generality of the GAFF2/ABCG2 combination.

Velocity Jumps for Molecular Dynamics

We introduce the Velocity Jumps approach, denoted as JUMP, a new class of Molecular dynamics integrators, replacing the Langevin dynamics by a hybrid model combining a classical Langevin diffusion and a piecewise deterministic Markov process, where the expensive computation of long-range pairwise interactions is replaced by a resampling of the velocities at random times. This framework allows for an acceleration in the simulation speed while preserving sampling and dynamical properties such as the diffusion constant. It can also be integrated in classical multi-time-step methods, pushing further the computational speedup, while avoiding some of the resonance issues of the latter thanks to the random nature of jumps. The JUMP, JUMP-RESPA and JUMP-RESPA1 integrators have been implemented in the GPU-accelerated version of the Tinker-HP package and are shown to provide significantly enhanced performances compared to their BAOAB, BAOAB-RESPA and BAOAB-RESPA1 counterparts, respectively.

Computational Methods for Modeling Lipid-Mediated Active Pharmaceutical Ingredient Delivery

Lipid-mediated delivery of active pharmaceutical ingredients (API) opened new possibilities in advanced therapies. By encapsulating an API into a lipid nanocarrier (LNC), one can safely deliver APIs not soluble in water, those with otherwise strong adverse effects, or very fragile ones such as nucleic acids. However, for the rational design of LNCs, a detailed understanding of the composition-structure-function relationships is missing. This review presents currently available computational methods for LNC investigation, screening, and design. The state-of-the-art physics-based approaches are described, with the focus on molecular dynamics simulations in all-atom and coarse-grained resolution. Their strengths and weaknesses are discussed, highlighting the aspects necessary for obtaining reliable results in the simulations. Furthermore, a machine learning, i.e., data-based learning, approach to the design of lipid-mediated API delivery is introduced. The data produced by the experimental and theoretical approaches provide valuable insights. Processing these data can help optimize the design of LNCs for better performance. In the final section of this Review, state-of-the-art of computer simulations of LNCs are reviewed, specifically addressing the compatibility of experimental and computational insights.

Absolute and Relative Binding Free Energy Calculations of Nucleotides to Multiple Protein Classes

Polyphosphate nucleotides, such as ATP, ADP, GTP, and GDP, play a crucial role in modulating protein functions through binding and/or catalytically activating proteins (enzymes). However, accurately calculating the binding free energies for these charged and flexible ligands poses challenges due to slow conformational relaxation and the limitations of force fields. In this study, we examine the accuracy and reliability of alchemical free energy simulations with fixed-charge force fields for the binding of four nucleotides to nine proteins of various classes, including kinases, ATPases, and GTPases. Our results indicate that the alchemical simulations effectively reproduce experimental binding free energies for all proteins that do not undergo significant conformational changes between their triphosphate nucleotide-bound and diphosphate nucleotide-bound states, with 87.5% (7 out of 8) of the absolute binding free energy results for 4 proteins within ±2 kcal/mol of experimental values and 88.9% (8 out of 9) of the relative binding free energy results for 9 proteins within ±3 kcal/mol of experimental values. However, our calculations show significant inaccuracies when divalent ions are included, suggesting that nonpolarizable force fields may not accurately capture interactions involving these ions. Additionally, the presence of highly charged and flexible ligands necessitates extensive conformational sampling to account for the long relaxation times associated with long-range electrostatic interactions. The simulation strategy presented here, along with its demonstrated accuracy across multiple protein classes, will be valuable for predicting the binding of nucleotides or their analogs to protein targets.

MBX V1.2: Accelerating Data-Driven Many-Body Molecular Dynamics Simulations

The MBX software provides an advanced platform for molecular dynamics simulations, leveraging state-of-the-art MB-pol and MB-nrg data-driven many-body potential energy functions. Developed over the past decade, these potential energy functions integrate physics-based and machine-learned many-body terms trained on electronic structure data calculated at the "gold standard" coupled-cluster level of theory. Recent advancements in MBX have focused on optimizing its performance, resulting in the release of MBX v1.2. While the inherently many-body nature of MB-pol and MB-nrg ensures high accuracy, it poses computational challenges. MBX v1.2 addresses these challenges with significant performance improvements, including enhanced parallelism that fully harnesses the power of modern multicore CPUs. These advancements enable simulations on nanosecond time scales for condensed-phase systems, significantly expanding the scope of high-accuracy, predictive simulations of complex molecular systems powered by data-driven many-body potential energy functions.

Identification and understanding of allostery hotspots in proteins: Integration of deep mutational scanning and multi-faceted computational analyses

Motivated by recent deep mutational scanning (DMS) experiments, we have carried out a diverse set of computations to better understand the distribution and contributions of allostery hotspot residues in a transcription factor, TetR. These include extensive atomistic simulations and free energy computations for different functional states of TetR, machine learning analysis of the DMS data and a statistical thermodynamic model for the experimental induction data for the WT protein and a handful of hotspot mutants. Collectively, these computations provided insights into the structural and energetic basis of allostery in TetR, and the distinct contributions of allostery hotspots. The results highlight that the allostery function (i.e., the induction activity) of TetR can be modulated by perturbing both inter-domain coupling and intra-domain properties, such as the population of the binding-competent conformation of each domain. This mechanistic degeneracy qualitatively explains the broad distribution of allostery hotspots across the protein structure observed in the DMS experiments, and also informs the design of strategies aimed at identifying allostery hotspots. The mechanistic framework and the multi-faceted computational approaches are expected to be applicable to the analysis of other allostery systems, especially those sharing the similar two-domain structural topology, and to the design of allostery modulators.

Performance Tuning of Polarizable Gaussian Multipole Model in Molecular Dynamics Simulations

Molecular dynamics (MD) simulations are essential for understanding molecular phenomena at the atomic level, with their accuracy largely dependent on both the employed force field and sampling. Polarizable force fields, which incorporate atomic polarization effects, represent a significant advancement in simulation technology. The polarizable Gaussian multipole (pGM) model has been noted for its accurate reproduction of ab initio electrostatic interactions. In this study, we document our effort to enhance the computational efficiency and scalability of the pGM simulations within the AMBER framework using MPI (message passing interface). Performance evaluations reveal that our MPI-based pGM model significantly reduces runtime and scales effectively while maintaining computational accuracy. Additionally, we investigated the stability and reliability of the MPI implementation under the NVE simulation ensemble. Optimal Ewald and induction parameters for the pGM model are also explored, and its statistical properties are assessed under various simulation ensembles. Our findings demonstrate that the MPI-implementation maintains enhanced stability and robustness during extended simulation times. We further evaluated the model performance under both NVT (constant number, volume, and temperature) and NPT (constant number, pressure, and temperature) ensembles and assessed the effects of varying timesteps and convergence tolerance on induced dipole calculations. The lessons learned from these exercises are expected to help the users to make informed decisions on simulation setup. The improved performance under these ensembles enables the study of larger molecular systems, thereby expanding the applicability of the pGM model in detailed MD simulations.

The evolution of the Amber additive protein force field: History, current status, and future

Molecular dynamics simulations are pivotal in elucidating the intricate properties of biological molecules. Nonetheless, the reliability of their outcomes hinges on the precision of the molecular force field utilized. In this perspective, we present a comprehensive review of the developmental trajectory of the Amber additive protein force field, delving into researchers' persistent quest for higher precision force fields and the prevailing challenges. We detail the parameterization process of the Amber protein force fields, emphasizing the specific improvements and retained features in each version compared to their predecessors. Furthermore, we discuss the challenges that current force fields encounter in balancing the interactions of protein-protein, protein-water, and water-water in molecular dynamics simulations, as well as potential solutions to overcome these issues.

Targeting the gut-skin axis by food-derived active peptides ameliorates skin photoaging: a comprehensive review

Food-derived active peptides (FDAPs) are a class of peptides that exert antioxidant, anti-inflammatory, anti-aging and other effects. In recent years, active peptides from natural foods have been reported to improve skin photoaging, but their mechanisms have not been summarized to date. In this review, we focused on the preparation of FDAPs, their mechanisms of photoaging, and their function against photoaging through the gastrointestinal barrier. Furthermore, the latest progress on FDAPs in the prevention and treatment of skin photoaging via the gut-skin axis is summarized and discussed. FDAPs can be directly absorbed into the gastrointestinal tract and enter skin tissues to exert anti-photoaging effects; they can also regulate the gut microbiota, leading to changes in metabolites to ameliorate light-induced skin aging. Future work needs to focus on the delivery system and clinical validation of anti-photoaging peptides to provide solutions or suggestions for improving photoaging.

Do selectivity filter carbonyls in K+ channels flip away from the pore? Two-dimensional infrared spectroscopy study

Molecular dynamics simulations revealed that the carbonyls of the Val residue in the conserved selectivity filter sequence TVGTG of potassium ion channels can flip away from the pore to form hydrogen bonds with the network of water molecules residing behind the selectivity filter. Such a configuration has been proposed to be relevant for C-type inactivation. Experimentally, X-ray crystallography of the KcsA channel admits the possibility that the Val carbonyls can flip, but it cannot decisively confirm the existence of such a configuration. In this study, we combined molecular dynamics simulations and line shape theory to design two-dimensional infrared spectroscopy experiments that can corroborate the existence of the selectivity filter configuration with flipped Val carbonyls. This ability to distinguish between flipped and unflipped carbonyls is based on the varying strength of the electric field inside and outside the pore, which is directly linked to carbonyl stretching frequencies that can be resolved using infrared spectroscopy.

Theoretically grounded approaches to account for polarization effects in fixed-charge force fields

Non-polarizable, or fixed-charge, force fields are the workhorses of most molecular simulation studies. They attempt to describe the potential energy surface (PES) of the system by including polarization effects in an implicit way. This has historically been done in a rather empirical and ad hoc manner. Recent theoretical treatments of polarization, however, offer promise for getting the most out of fixed-charge force fields by judicious choice of parameters (most significantly the net charge or dipole moment of the model) and application of post facto polarization corrections. This Perspective describes these polarization theories, namely the "halfway-charge" theory and the molecular dynamics in electronic continuum theory, and shows that they lead to qualitatively (and often, quantitatively) similar predictions. Moreover, they can be reconciled into a unified approach to construct a force field development workflow that can yield non-polarizable models with charge/dipole values that provide an optimal description of the PES. Several applications of this approach are reviewed, and avenues for future research are proposed.

Force Fields for Deep Eutectic Mixtures: Application to Structure, Thermodynamics and 2D-Infrared Spectroscopy

Parametrizing energy functions for ionic systems can be challenging. Here, the total energy function for an eutectic system consisting of water, SCN-, K+ and acetamide is improved vis-a-vis experimentally measured properties. Given the importance of electrostatic interactions, two different types of models are considered: the first (model M0) uses atom-centered multipole whereas the other two (models M1 and M2) are based on fluctuating minimal distributed charges (fMDCM) that respond to geometrical changes of SCN-. The Lennard-Jones parameters of the anion are adjusted to best reproduce experimentally known hydration free energies and densities, which are matched to within a few percent for the final models irrespective of the electrostatic model. Molecular dynamics simulations of the eutectic mixtures with varying water content (between 0 and 100%) yield radial distribution functions and frequency correlation functions for the CN-stretch vibration. Comparison with experiments indicates that models based on fMDCM are considerably more consistent than those using multipoles. Computed viscosities from models M1 and M2 are within 30% of measured values and their change with increasing water content is consistent with experiments. This is not the case for model M0.

Exploring Ion Polarizabilities and Their Correlation with van der Waals Radii: A Theoretical Investigation

Polarizability (α) is a fundamental property which measures the tendency of the electron cloud of an atom, ion, or molecule to be distorted by electric field. Polarizability contributes to important physical properties such as molecular interactions or dielectric constants; thus, it is essential to have accurate polarizabilities in molecular simulations. However, it remains a challenge to develop polarizable force fields (FFs) for ions in computational chemistry. In particular, a comprehensive set of polarizabilities for ions has not been derived. Herein, we derived a systematic set of polarizabilities for atoms and ions across the periodic table based on high-level quantum mechanics calculations. These values have excellent agreement with experimental data. Furthermore, we examined the relationship between the obtained polarizabilities and the van der Waals (VDW) radii (RVDW) that we previously determined (J. Chem. Theory Comput., 2023, 19, 2064). Two relationships, RVDW ∝ α1/7 and RVDW ∝ α1/3, proposed in previous studies were examined in the present work. Our results indicated the former relationship, which was derived based on the quantum harmonic oscillator model, prevails for atoms and cations, but neither relationship provides a satisfactory fit for anions. This is consistent with the tight-binding nature of the electrons in atoms and cations, while it is more challenging to quantify the polarizabilities of anions because of their more dispersed electron clouds. Moreover, we compared different approaches to determine the dispersion coefficients, including the London equation, Slater-Kirkwood equation, symmetry-adapted perturbation theory (SAPT) calculations, and time-dependent density functional theory method, along with the approach based on VDW constants. Our results indicated that although different approaches predict deviated magnitudes for the dispersion coefficients, their predictions are highly correlated, implying that each of these approaches can be used to evaluate dispersion interactions after proper scaling. Finally, we have developed a parametrization strategy for the 12-6-4 model based on the obtained insights. We specifically compared the performance of the 12-6-4 model with SAPT and SobEDA analyses to model interactions involving Na+/Mg2+ and various ligands containing He, Ne, Ar, H2O, NH3, [H2PO4]-, and [HPO4]2-. Our results demonstrate that the 12-6-4 parameters effectively reproduce both the total interaction energy and the individual energy components (electrostatics, exchange-repulsion, dispersion, and induction), highlighting the physical robustness of the 12-6-4 model and the effectiveness of our parametrization approach. This study has significant implications for advancing the development of next-generation ion models and polarizable FFs.

Effect of Protein-Polarized Ligand Charges on Relative Protein Ligand Binding Affinities

A major challenge in computer-aided drug design is predicting relative binding energies of different molecules to a target protein using fast and accurate free-energy calculation methods. Free-energy calculations are primarily computed by utilizing classical molecular dynamics simulations based on all-atom force fields (FF) to model the interactions in the system. The present standard classical all-atom FFs contain fixed partial charges on the atoms, and hence electrostatic interactions are modeled between them. The parametrization process to determine these partial charges usually relies on quantum mechanics or semiempirical calculations of the molecule in the gas phase or homogeneous water surrounding. These present standard parametrization schemes of the partial charges neglect, therefore, polarization effects from the protein surrounding. The absence of protein polarization effects can lead to significant errors in free-energy calculations in proteins. We present a parametrization scheme for the partial charges of ligands, named protein-induced polarization (PIP) charges, which account for the electrostatic polarization due to the protein surrounding. The scheme involves single-point quantum mechanics/molecular mechanics calculations of the ligand charges in the protein/water surrounding. Using PIP ligand partial charges, we have calculated the relative binding free energies (RBFEs) of well-studied protein-ligand systems. We show here that RBFEs computed with PIP charges are either significantly improved or at least comparable to those computed with nonpolarized standard GAFF charges. Overall, we present a simple-to-use parametrization scheme to include protein polarization in any type of binding free-energy calculations. The parametrization scheme increases the accuracy in RBFE calculations, while it does not add significant computation time to standard parametrization procedures.

Ion-Specific Effects on Ion and Polyelectrolyte Solvation

Ion-specific effects on aqueous solvation of monovalent counter ions, Na+ ${^+ }$ , K+ ${^+ }$ , Cl- ${^- }$ , and Br- ${^- }$ , and two model polyelectrolytes (PEs), poly(styrene sulfonate) (PSS) and poly(diallyldimethylammonium) (PDADMA) were here studied with ab initio molecular dynamics (AIMD) and classical molecular dynamics (MD) simulations based on the OPLS-aa force-field which is an empirical fixed point-charge force-field. Ion-specific binding to the PE charge groups was also characterized. Both computational methods predict similar response for the solvation of the PEs but differ notably in description of ion solvation. Notably, AIMD captures the experimentally observed differences in Cl- ${^- }$ and Br- ${^- }$ anion solvation and binding with the PEs, while the classical MD simulations fail to differentiate the ion species response. Furthermore, the findings show that combining AIMD with the computationally less costly classical MD simulations allows benefiting from both the increased accuracy and statistics reach.

Importance of Polarizable Embedding for Computing Optical Rotation: The Case of Camphor in Ethanol

Solvation effects on optical rotation are notoriously challenging to model for computational chemistry, as the specific rotatory power of a molecule can vary wildly going from apolar to polar or even protic solvents. To address such a problem, we present a polarizable embedding implementation of an electric and magnetic response property based on density functional theory and the AMOEBA polarizable force field, and apply such an implementation to the study of the optical rotation of camphor in ethanol. By comparing a continuum model, and electrostatic and polarizable embedding QM/MM models, we observe that accounting for the environment's polarization gives rise to not only a different quantitative prediction, in very good agreement with experiments for the QM/AMOEBA model, but also to a very different qualitative picture, with the values of the optical rotation computed along a classical molecular dynamics trajectory with electrostatic embedding being statistically uncorrelated to the ones obtained with the polarizable description.

FeNNol: An efficient and flexible library for building force-field-enhanced neural network potentials

Neural network interatomic potentials (NNPs) have recently proven to be powerful tools to accurately model complex molecular systems while bypassing the high numerical cost of ab initio molecular dynamics simulations. In recent years, numerous advances in model architectures as well as the development of hybrid models combining machine-learning (ML) with more traditional, physically motivated, force-field interactions have considerably increased the design space of ML potentials. In this paper, we present FeNNol, a new library for building, training, and running force-field-enhanced neural network potentials. It provides a flexible and modular system for building hybrid models, allowing us to easily combine state-of-the-art embeddings with ML-parameterized physical interaction terms without the need for explicit programming. Furthermore, FeNNol leverages the automatic differentiation and just-in-time compilation features of the Jax Python library to enable fast evaluation of NNPs, shrinking the performance gap between ML potentials and standard force-fields. This is demonstrated with the popular ANI-2x model reaching simulation speeds nearly on par with the AMOEBA polarizable force-field on commodity GPUs (graphics processing units). We hope that FeNNol will facilitate the development and application of new hybrid NNP architectures for a wide range of molecular simulation problems.

Condensed Matter Systems Exposed to Radiation: Multiscale Theory, Simulations, and Experiment

This roadmap reviews the new, highly interdisciplinary research field studying the behavior of condensed matter systems exposed to radiation. The Review highlights several recent advances in the field and provides a roadmap for the development of the field over the next decade. Condensed matter systems exposed to radiation can be inorganic, organic, or biological, finite or infinite, composed of different molecular species or materials, exist in different phases, and operate under different thermodynamic conditions. Many of the key phenomena related to the behavior of irradiated systems are very similar and can be understood based on the same fundamental theoretical principles and computational approaches. The multiscale nature of such phenomena requires the quantitative description of the radiation-induced effects occurring at different spatial and temporal scales, ranging from the atomic to the macroscopic, and the interlinks between such descriptions. The multiscale nature of the effects and the similarity of their manifestation in systems of different origins necessarily bring together different disciplines, such as physics, chemistry, biology, materials science, nanoscience, and biomedical research, demonstrating the numerous interlinks and commonalities between them. This research field is highly relevant to many novel and emerging technologies and medical applications.

Advancing Force Fields Parameterization: A Directed Graph Attention Networks Approach

Force fields (FFs) are an established tool for simulating large and complex molecular systems. However, parametrizing FFs is a challenging and time-consuming task that relies on empirical heuristics, experimental data, and computational data. Recent efforts aim to automate the assignment of FF parameters using pre-existing databases and on-the-fly ab initio data. In this study, we propose a graph-based force field (GB-FFs) model to directly derive parameters for the Generalized Amber Force Field (GAFF) from chemical environments and research into the influence of functional forms. Our end-to-end parametrization approach predicts parameters by aggregating the basic information in directed molecular graphs, eliminating the need for expert-defined procedures and enhances the accuracy and transferability of GAFF across a broader range of molecular complexes. Simulation results are compared to the original GAFF parametrization. In practice, our results demonstrate an improved transferability of the model, showcasing its improved accuracy in modeling intermolecular and torsional interactions, as well as improved solvation free energies. The optimization approach developed in this work is fully applicable to other nonpolarizable FFs as well as to polarizable ones.

In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back

Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving the Schrödinger equations and the increasing computational cost with the size of the molecular system. In response, there has been a surge of interest in leveraging artificial intelligence (AI) and machine learning (ML) techniques to in silico experiments. Integrating AI and ML into computational chemistry increases the scalability and speed of the exploration of chemical space. However, challenges remain, particularly regarding the reproducibility and transferability of ML models. This review highlights the evolution of ML in learning from, complementing, or replacing traditional computational chemistry for energy and property predictions. Starting from models trained entirely on numerical data, a journey set forth toward the ideal model incorporating or learning the physical laws of quantum mechanics. This paper also reviews existing computational methods and ML models and their intertwining, outlines a roadmap for future research, and identifies areas for improvement and innovation. Ultimately, the goal is to develop AI architectures capable of predicting accurate and transferable solutions to the Schrödinger equation, thereby revolutionizing in silico experiments within chemistry and materials science.

Convergence criteria for single-step free-energy calculations: the relation between the Π bias measure and the sample variance

Free energy calculations play a crucial role in simulating chemical processes, enzymatic reactions, and drug design. However, assessing the reliability and convergence of these calculations remains a challenge. This study focuses on single-step free-energy calculations using thermodynamic perturbation. It explores how the sample distributions influence the estimated results and evaluates the reliability of various convergence criteria, including Kofke's bias measure Π and the standard deviation of the energy difference ΔU, σ ΔU . The findings reveal that for Gaussian distributions, there is a straightforward relationship between Π and σ ΔU , free energies can be accurately approximated using a second-order cumulant expansion, and reliable results are attainable for σ ΔU up to 25 kcal mol-1. However, interpreting non-Gaussian distributions is more complex. If the distribution is skewed towards more positive values than a Gaussian, converging the free energy becomes easier, rendering standard convergence criteria overly stringent. Conversely, distributions that are skewed towards more negative values than a Gaussian present greater challenges in achieving convergence, making standard criteria unreliable. We propose a practical approach to assess the convergence of estimated free energies.

Aromatic and arginine content drives multiphasic condensation of protein-RNA mixtures

Multiphasic architectures are found ubiquitously in biomolecular condensates and are thought to have important implications for the organization of multiple chemical reactions within the same compartment. Many of these multiphasic condensates contain RNA in addition to proteins. Here, we investigate the importance of different interactions in multiphasic condensates comprising two different proteins and RNA using computer simulations with a residue-resolution coarse-grained model of proteins and RNA. We find that in multilayered condensates containing RNA in both phases, protein-RNA interactions dominate, with aromatic residues and arginine forming the key stabilizing interactions. The total aromatic and arginine content of the two proteins must be appreciably different for distinct phases to form, and we show that this difference increases as the system is driven toward greater multiphasicity. Using the trends observed in the different interaction energies of this system, we demonstrate that we can also construct multilayered condensates with RNA preferentially concentrated in one phase. The "rules" identified can thus enable the design of synthetic multiphasic condensates to facilitate further study of their organization and function.

The multi-scale polarizable pseudo-particle solvent coarse-grained approach: From NaCl salt solutions to polyelectrolyte hydration

We discuss key parameters that affect the reliability of hybrid simulations in the aqueous phase based on an efficient multi-scale coarse-grained polarizable pseudo-particle approach, denoted as pppl, to model the solvent water, whereas solutes are modeled using an all atom polarizable force field. Among those parameters, the extension of the solvent domain (SD) at the solute vicinity (domain in which each solvent particle corresponds to a single water molecule) and the magnitude of solute/solvent short range polarization damping effects are shown to be pivotal to model NaCl salty aqueous solutions and the hydration of charged systems, such as the hydrophobic polyelectrolyte polymer that we have recently investigated [Masella et al., J. Chem. Phys. 155, 114903 (2021)]. Strong short range damping is pivotal to simulate aqueous salt NaCl solutions at moderate concentration (up to 1.0M). The SD extension (as well as short range damping) has a weak effect on the polymer conformation; however, it plays a pivotal role in computing accurate polymer/solvent interaction energies. As the pppl approach is up to two orders of magnitude computationally more efficient than all atom polarizable force field methods, our results show it to be an efficient alternative route to investigate the equilibrium properties of complex charged molecular systems in extended chemical environments.

Water Solvent Reorganization upon Ultrafast Resonant Stimulated X-ray Raman Excitation of a Metalloporphyrin Dimer

We propose an X-ray Raman pump-X-ray diffraction probe scheme to follow solvation dynamics upon charge migration in a solute molecule. The X-ray Raman pump selectively prepares a valence electronic wavepacket in the solute, while the probe provides information about the entire molecular ensemble. A combination of molecular dynamics and ab initio quantum chemistry simulations is applied to a Zn-Ni porphyrin dimer in water. Using time-resolved X-ray diffraction and pair distribution functions, we extracted solvation shell dynamics.

Evaluating Polarizable Biomembrane Simulations against Experiments

Owing to the increase of available computational capabilities and the potential for providing a more accurate description, polarizable molecular dynamics force fields are gaining popularity in modeling biomolecular systems. It is, however, crucial to evaluate how much precision is truly gained with increasing cost and complexity of the simulation. Here, we leverage the NMRlipids open collaboration and Databank to assess the performance of available polarizable lipid models─the CHARMM-Drude and the AMOEBA-based parameters─against high-fidelity experimental data and compare them to the top-performing nonpolarizable models. While some improvement in the description of ion binding to membranes is observed in the most recent CHARMM-Drude parameters, and the conformational dynamics of AMOEBA-based parameters are excellent, the best nonpolarizable models tend to outperform their polarizable counterparts for each property we explored. The identified shortcomings range from inaccuracies in describing the conformational space of lipids to excessively slow conformational dynamics. Our results provide valuable insights for the further refinement of polarizable lipid force fields and for selecting the best simulation parameters for specific applications.

A Vision for the Future of Multiscale Modeling

The rise of modern computer science enabled physical chemistry to make enormous progresses in understanding and harnessing natural and artificial phenomena. Nevertheless, despite the advances achieved over past decades, computational resources are still insufficient to thoroughly simulate extended systems from first principles. Indeed, countless biological, catalytic and photophysical processes require ab initio treatments to be properly described, but the breadth of length and time scales involved makes it practically unfeasible. A way to address these issues is to couple theories and algorithms working at different scales by dividing the system into domains treated at different levels of approximation, ranging from quantum mechanics to classical molecular dynamics, even including continuum electrodynamics. This approach is known as multiscale modeling and its use over the past 60 years has led to remarkable results. Considering the rapid advances in theory, algorithm design, and computing power, we believe multiscale modeling will massively grow into a dominant research methodology in the forthcoming years. Hereby we describe the main approaches developed within its realm, highlighting their achievements and current drawbacks, eventually proposing a plausible direction for future developments considering also the emergence of new computational techniques such as machine learning and quantum computing. We then discuss how advanced multiscale modeling methods could be exploited to address critical scientific challenges, focusing on the simulation of complex light-harvesting processes, such as natural photosynthesis. While doing so, we suggest a cutting-edge computational paradigm consisting in performing simultaneous multiscale calculations on a system allowing the various domains, treated with appropriate accuracy, to move and extend while they properly interact with each other. Although this vision is very ambitious, we believe the quick development of computer science will lead to both massive improvements and widespread use of these techniques, resulting in enormous progresses in physical chemistry and, eventually, in our society.

Anti-Aging Potential of the Two Major Flavonoids Occurring in Asian Water Lily Using In Vitro and In Silico Molecular Modeling Assessments

Our previous study investigated the major flavonoids and antioxidant potential of Asian water lily (Nymphaea lotus L., family Nymphaeaceae) stamens and perianth extracts. Quercetin-3-O-rhamnoside (Que-3-Rha) and kaempferol-3-O-galactoside (Kae-3-Gal) were reported as the two most prominent flavonoids found in these extracts. Many flavonoids have been reported on the skin anti-aging effect that are useful for cosmeceutical/phytopharmaceutical application. However, Que-3-Rha and Kae-3-Gal occurring in this medicinal plant have not yet been evaluated for their ability to inhibit skin-aging enzymes. Therefore, this study aimed (1) to assess the enzyme inhibitory activity of Que-3-Rha and Kae-3-Gal, and (2) to conduct molecular modeling of these compounds against critical enzymes involved in skin aging such as collagenase, elastase, and tyrosinase. In vitro enzymatic assays demonstrated that both of the two most prominent flavonoids exhibited moderate to good inhibitory activity toward these enzymes. These experimental findings were supported by molecular docking analysis, which indicated that Que-3-Rha and Kae-3-Gal showed superior binding affinity to the target enzymes compared to the positive controls. Additionally, computational predictions suggested favorable skin permeability and no severe toxicity for both compounds. The results from molecular dynamic (MD) simulation revealed that all the complexes remained stable during the 200 ns MD simulation. Structural analyses and binding free energy calculations also supported the inhibitory potential of these two flavonoids against skin-aging enzymes. In conclusion, this study provides valuable insights into the anti-aging potential of the two major flavonoids occurring in this medicinal plant, paving the way for further development of cosmeceutical/phytopharmaceutical products targeting skin aging.

QM/MM-Based Energy Decomposition Analysis Method for Large Systems

In this work, a QM/MM-based EDA method, called GKS-EDA(QM/MM), is proposed. As an extension of GKS-EDA, this scheme divides the total interaction energy into electrostatic, exchange-repulsion, polarization, and correlation/dispersion terms. GKS-EDA(QM/MM) can be applied to describe the interactions of large-scale systems combined with various QM/MM platforms. By using the examples of a hydrated hydronium ion complex in water solution, the barnase-barstar complex, and MMP-13-pyrimidinetrione in a metalloprotein, the capability of GKS-EDA(QM/MM) for various interactions in large systems is validated.

Origin of Enantioselectivity in Engineered Cytochrome c-Catalyzed Carbon-Radical FePP Hydrolysis Revealed Using QM/MM (ABEEM Polarizable Force Field) and MD Simulations

The origin of highly efficient asymmetric aminohydroxylation of styrene catalyzed by engineered cytochrome c is investigated by the developed Atom-Bond Electronegativity Equalization Method polarizable force field (ABEEM PFF), which is a combined outcome of electronic and steric effects. Model molecules were used to establish the charge parameters of the ABEEM PFF, for which the bond-stretching and angle-bending parameters were obtained by using a combination of modified Seminario and scan methods. The interactions between carbon-radical Fe-porphyrin (FePP) and waters are simulated by molecular dynamics, which shows a clear preference for the pre-R over the pre-S. This preference is attributed to the hydrogen-bond between the mutated 100S and 101P residues as well as van der Waals interactions, enforcing a specific conformation of the carbon-radical FePP complex within the binding pocket. Meanwhile, the hydrogen-bond between water and the nitrogen atom in the active intermediate dictates the stereochemical outcome. Quantum mechanics/molecular mechanics (QM/MM (ABEEM PFF)) and free-energy perturbation calculations elucidate that the 3RTS is characterized by sandwich-like structure among adjacent amino acid residues, which exhibits greater stability than crowed arrangement in 3STS and enables the R enantiomer to form more favorably. Thus, this study provides mechanistic insight into the catalytic reaction of hemoproteins.

Perspective on Integrative Simulations of Bioenergetic Domains

Bioenergetic processes in cells, such as photosynthesis or respiration, integrate many time and length scales, which makes the simulation of energy conversion with a mere single level of theory impossible. Just like the myriad of experimental techniques required to examine each level of organization, an array of overlapping computational techniques is necessary to model energy conversion. Here, a perspective is presented on recent efforts for modeling bioenergetic phenomena with a focus on molecular dynamics simulations and its variants as a primary method. An overview of the various classical, quantum mechanical, enhanced sampling, coarse-grained, Brownian dynamics, and Monte Carlo methods is presented. Example applications discussed include multiscale simulations of membrane-wide electron transport, rate kinetics of ATP turnover from electrochemical gradients, and finally, integrative modeling of the chromatophore, a photosynthetic pseudo-organelle.

Solvation of Model Biomolecules in Choline-Aminoate Ionic Liquids: A Computational Simulation Using Polarizable Force Fields

One can foresee a very near future where ionic liquids will be used in applications such as biomolecular chemistry or medicine. The molecular details of their interaction with biological matter, however, are difficult to investigate due to the vast number of combinations of both the biological systems and the variety of possible liquids. Here, we provide a computational study aimed at understanding the interaction of a special class of biocompatible ionic liquids (choline-aminoate) with two model biological systems: an oligopeptide and an oligonucleotide. We employed molecular dynamics with a polarizable force field. Our results are in line with previous experimental and computational evidence on analogous systems and show how these biocompatible ionic liquids, in their pure form, act as gentle solvents for protein structures while simultaneously destabilizing DNA structure.

Influence of ion and hydration atmospheres on RNA structure and dynamics: insights from advanced theoretical and computational methods

RNA, a highly charged biopolymer composed of negatively charged phosphate groups, defies electrostatic repulsion to adopt well-defined, compact structures. Hence, the presence of positively charged metal ions is crucial not only for RNA's charge neutralization, but they also coherently decorate the ion atmosphere of RNA to stabilize its compact fold. This feature article elucidates various modes of close RNA-ion interactions, with a special emphasis on Mg2+ as an outer-sphere and inner-sphere ion. Through examples, we highlight how inner-sphere chelated Mg2+ stabilizes RNA pseudoknots, while outer-sphere ions can also exert long-range electrostatic interactions, inducing groove narrowing, coaxial helical stacking, and RNA ring formation. In addition to investigating the RNA's ion environment, we note that the RNA's hydration environment is relatively underexplored. Our study delves into its profound interplay with the structural dynamics of RNA, employing state-of-the-art atomistic simulation techniques. Through examples, we illustrate how specific ions and water molecules are associated with RNA functions, leveraging atomistic simulations to identify preferential ion binding and hydration sites. However, understanding their impact(s) on the RNA structure remains challenging due to the involvement of large length and long time scales associated with RNA's dynamic nature. Nevertheless, our contributions and recent advances in coarse-grained simulation techniques offer insights into large-scale structural changes dynamically linked to the RNA ion atmosphere. In this connection, we also review how different cutting-edge computational simulation methods provide a microscopic lens into the influence of ions and hydration on RNA structure and dynamics, elucidating distinct ion atmospheric components and specific hydration layers and their individual and collective impacts.

Incorporating Neural Networks into the AMOEBA Polarizable Force Field

Neural network potentials (NNPs) offer significant promise to bridge the gap between the accuracy of quantum mechanics and the efficiency of molecular mechanics in molecular simulation. Most NNPs rely on the locality assumption that ensures the model's transferability and scalability and thus lack the treatment of long-range interactions, which are essential for molecular systems in the condensed phase. Here we present an integrated hybrid model, AMOEBA+NN, which combines the AMOEBA potential for the short- and long-range noncovalent atomic interactions and an NNP to capture the remaining local covalent contributions. The AMOEBA+NN model was trained on the conformational energy of the ANI-1x data set and tested on several external data sets ranging from small molecules to tetrapeptides. The hybrid model demonstrated substantial improvements over the baseline models in term of accuracy as the molecule size increased, suggesting its potential as a next-generation approach for chemically accurate molecular simulations.

Efficient Sampling of Cavity Hydration in Proteins with Nonequilibrium Grand Canonical Monte Carlo and Polarizable Force Fields

Prediction of the hydration levels of protein cavities and active sites is important to both mechanistic analysis and ligand design. Due to the unique microscopic environment of these buried water molecules, a polarizable model is expected to be crucial for an accurate treatment of protein internal hydration in simulations. Here we adapt a nonequilibrium candidate Monte Carlo approach for conducting grand canonical Monte Carlo simulations with the Drude polarizable force field. The GPU implementation enables the efficient sampling of internal cavity hydration levels in biomolecular systems. We also develop an enhanced sampling approach referred to as B-walking, which satisfies detailed balance and readily combines with grand canonical integration to efficiently calculate quantitative binding free energies of water to protein cavities. Applications of these developments are illustrated in a solvent box and the polar ligand binding site in trypsin. Our simulation results show that including electronic polarization leads to a modest but clear improvement in the description of water position and occupancy compared to the crystal structure. The B-walking approach enhances the range of water sampling in different chemical potential windows and thus improves the accuracy of water binding free energy calculations.

Molecular driving forces for water adsorption in MOF-808: A comparative analysis with UiO-66

Metal-organic frameworks (MOFs), with their unique porous structures and versatile functionality, have emerged as promising materials for the adsorption, separation, and storage of diverse molecular species. In this study, we investigate water adsorption in MOF-808, a prototypical MOF that shares the same secondary building unit (SBU) as UiO-66, and elucidate how differences in topology and connectivity between the two MOFs influence the adsorption mechanism. To this end, molecular dynamics simulations were performed to calculate several thermodynamic and dynamical properties of water in MOF-808 as a function of relative humidity (RH), from the initial adsorption step to full pore filling. At low RH, the μ3-OH groups of the SBUs form hydrogen bonds with the initial water molecules entering the pores, which triggers the filling of these pores before the μ3-OH groups in other pores become engaged in hydrogen bonding with water molecules. Our analyses indicate that the pores of MOF-808 become filled by water sequentially as the RH increases. A similar mechanism has been reported for water adsorption in UiO-66. Despite this similarity, our study highlights distinct thermodynamic properties and framework characteristics that influence the adsorption process differently in MOF-808 and UiO-66.

Structural Insights and Influence of Terahertz Waves in Midinfrared Region on Kv1.2 Channel Selectivity Filter

Potassium ion channels are the structural basis for excitation transmission, heartbeat, and other biological processes. The selectivity filter is a critical structural component of potassium ion channels, whose structure is crucial to realizing their function. As biomolecules vibrate and rotate at frequencies in the terahertz band, potassium ion channels are sensitive to terahertz waves. Therefore, it is worthwhile to investigate how the terahertz wave influences the selectivity filter of the potassium channels. In this study, we investigate the structure of the selectivity filter of Kv1.2 potassium ion channels using molecular dynamics simulations. The effect of an electric field on the channel has been examined at four different resonant frequencies of the carbonyl group in SF: 36.75 37.06, 37.68, and 38.2 THz. As indicated by the results, 376GLY appears to be the critical residue in the selectivity filter of the Kv1.2 channel. Its dihedral angle torsion is detrimental to the channel structural stability and the transmembrane movement of potassium ions. 36.75 THz is the resonance frequency of the carbonyl group of 376GLY. Among all four frequencies explored, the applied terahertz electric field of this frequency has the most significant impact on the channel structure, negatively impacting the channel stability and reducing the ion permeability by 20.2% compared to the absence of fields. In this study, we simulate that terahertz waves in the mid-infrared frequency region can significantly alter the structure and function of potassium ion channels and that the effects of terahertz waves differ greatly based on frequency.

Multiscale computational simulation of pollutant behavior at water interfaces

The investigation of pollutant behavior at water interfaces is critical to understand pollution in aquatic systems. Computational methods allow us to overcome the limitations of experimental analysis, delivering valuable insights into the chemical mechanisms and structural characteristics of pollutant behavior at interfaces across a range of scales, from microscopic to mesoscopic. Quantum mechanics, all-atom molecular dynamics simulations, coarse-grained molecular dynamics simulations, and dissipative particle dynamics simulations represent diverse molecular interaction calculation methods that can effectively model pollutant behavior at environmental interfaces from atomic to mesoscopic scales. These methods provide a rich variety of information on pollutant interactions with water surfaces. This review synthesizes the advancements in applying typical computational methods to the formation, adsorption, binding, and catalytic conversion of pollutants at water interfaces. By drawing on recent advancements, we critically examine the current challenges and offer our perspective on future directions. This review seeks to advance our understanding of computational techniques for elucidating pollutant behavior at water interfaces, a critical aspect of water research.

A Fast, Convenient, Polarizable Electrostatic Model for Molecular Dynamics

We present an efficient polarizable electrostatic model, utilizing typed, atom-centered polarizabilities and the fast direct approximation, designed for efficient use in molecular dynamics (MD) simulations. The model provides two convenient approaches for assigning partial charges in the context of atomic polarizabilities. One is a generalization of RESP, called RESP-dPol, and the other, AM1-BCC-dPol, is an adaptation of the widely used AM1-BCC method. Both are designed to accurately replicate gas-phase quantum mechanical electrostatic potentials. Benchmarks of this polarizable electrostatic model against gas-phase dipole moments, molecular polarizabilities, bulk liquid densities, and static dielectric constants of organic liquids show good agreement with the reference values. Of note, the model yields markedly more accurate dielectric constants of organic liquids, relative to a matched nonpolarizable force field. MD simulations with this method, which is currently parametrized for molecules containing elements C, N, O, and H, run only about 3.6-fold slower than fixed charge force fields, while simulations with the self-consistent mutual polarization average 4.5-fold slower. Our results suggest that RESP-dPol and AM1-BCC-dPol afford improved accuracy relative to fixed charge force fields and are good starting points for developing general, affordable, and transferable polarizable force fields. The software implementing these approaches has been designed to utilize the force field fitting frameworks developed and maintained by the Open Force Field Initiative, setting the stage for further exploration of this approach to polarizable force field development.

Glass Transition Temperatures and Thermal Conductivities of Polybutadiene Crosslinked with Randomly Distributed Sulfur Chains Using Molecular Dynamic Simulation

The thermal conductivities and glass transition temperatures of polybutadiene crosslinked with randomly distributed sulfur chains having different lengths from mono-sulfur (S1) to octa-sulfur (S8) were investigated. The thermal conductivities of the related models as a function of the heat flux autocorrelation function, applying an equilibrium molecular dynamic (EMD) simulation and the Green-Kubo method, were studied for a wide range of temperatures. The influence of the length of sulfur chains, degree of crosslinking, and molar mass of the crosslinker on the glass transition temperature and final values of thermal conductivities were studied. First, the degree of crosslinking is considered constant for the eight simulation models, from mono-sulfur (S1) to octa-sulfur (S8), while the molar mass of the sulfur is increases. The results show that the thermal conductivities of the crosslinked structure decrease with increasing temperature for each model. Moreover, by increasing the lengths of the sulfur chains and the molar weight of the crosslinker, thermal conductivity increases at a constant temperature. The MD simulation demonstrates that the glass transition temperature and density of the crosslinked structure enhance as the length of the sulfur chains and molar mass of the sulfur increase. Second, the molar weight of sulfur is considered constant in these eight models; therefore, the degree of crosslinking decreases with the increase in the lengths of the sulfur chains. The results show that the thermal conductivities of the crosslinked structure decrease with the increase in the temperature for each model. Moreover, by increasing the lengths of sulfur chains and thus decreasing the degree of crosslinking, the trend in changes in thermal conductivities are almost the same for all of these models, so thermal conductivity is constant for a specific temperature. In addition, the glass transition temperature and density of the crosslinked structure decrease.

Thermodynamics of Metal-Acetate Interactions

Metal ions play crucial roles in protein- and ligand-mediated interactions. They not only act as catalysts to facilitate biological processes but are also important as protein structural elements. Accurately predicting metal ion interactions in computational studies has always been a challenge, and various methods have been suggested to improve these interactions. One such method is the 12-6-4 Lennard-Jones (LJ)-type nonbonded model. Using this model, it has been possible to successfully reproduce the experimental properties of metal ions in aqueous solution. The model includes induced dipole interactions typically ignored in the standard 12-6 LJ nonbonded model. In this we expand the applicability of this model to metal ion-carboxylate interactions. Using 12-6-4 parameters that reproduce the solvation free energies of the metal ions leads to an overestimation of metal ion-acetate interactions, thus, prompting us to fine-tune the model to specifically handle the latter. We also show that the standard 12-6 LJ model significantly falls short in reproducing the experimental binding free energy between acetate and 11 metal ions (Ni(II), Mg(II), Cu(II), Zn(II), Co(II), Cu(I), Fe(II), Mn(II), Cd(II), Ca(II), and Ag(I)). In this study, we describe optimized C4 parameters for the 12-6-4 LJ nonbonded model to be used with three widely employed water models (Transferable Intermolecular Potential with 3 Points (TIP3P), Simple Point Charge Extended (SPC/E), and Optimal Point Charge (OPC) water models). These parameters can accurately match the experimental binding free energy between 11 metal ions and acetate. These parameters can be applied to the study of metalloproteins and transition metal ion channels and transporters, as acetate serves as a representative of the negatively charged amino acid side chains from aspartate and glutamate.

A Critical Assessment on Calculating Vibrational Spectra in Nanostructured Materials

Vibrational spectroscopy is an omnipresent spectroscopic technique to characterize functional nanostructured materials such as zeolites, metal-organic frameworks (MOFs), and metal-halide perovskites (MHPs). The resulting experimental spectra are usually complex, with both low-frequency framework modes and high-frequency functional group vibrations. Therefore, theoretically calculated spectra are often an essential element to elucidate the vibrational fingerprint. In principle, there are two possible approaches to calculate vibrational spectra: (i) a static approach that approximates the potential energy surface (PES) as a set of independent harmonic oscillators and (ii) a dynamic approach that explicitly samples the PES around equilibrium by integrating Newton's equations of motions. The dynamic approach considers anharmonic and temperature effects and provides a more genuine representation of materials at true operating conditions; however, such simulations come at a substantially increased computational cost. This is certainly true when forces and energy evaluations are performed at the quantum mechanical level. Molecular dynamics (MD) techniques have become more established within the field of computational chemistry. Yet, for the prediction of infrared (IR) and Raman spectra of nanostructured materials, their usage has been less explored and remain restricted to some isolated successes. Therefore, it is currently not a priori clear which methodology should be used to accurately predict vibrational spectra for a given system. A comprehensive comparative study between various theoretical methods and experimental spectra for a broad set of nanostructured materials is so far lacking. To fill this gap, we herein present a concise overview on which methodology is suited to accurately predict vibrational spectra for a broad range of nanostructured materials and formulate a series of theoretical guidelines to this purpose. To this end, four different case studies are considered, each treating a particular material aspect, namely breathing in flexible MOFs, characterization of defects in the rigid MOF UiO-66, anharmonic vibrations in the metal-halide perovskite CsPbBr3, and guest adsorption on the pores of the zeolite H-SSZ-13. For all four materials, in their guest- and defect-free state and at sufficiently low temperatures, both the static and dynamic approach yield qualitatively similar spectra in agreement with experimental results. When the temperature is increased, the harmonic approximation starts to fail for CsPbBr3 due to the presence of anharmonic phonon modes. Also, the spectroscopic fingerprints of defects and guest species are insufficiently well predicted by a simple harmonic model. Both phenomena flatten the potential energy surface (PES), which facilitates the transitions between metastable states, necessitating dynamic sampling. On the basis of the four case studies treated in this Review, we can propose the following theoretical guidelines to simulate accurate vibrational spectra of functional solid-state materials: (i) For nanostructured crystalline framework materials at low temperature, insights into the lattice dynamics can be obtained using a static approach relying on a few points on the PES and an independent set of harmonic oscillators. (ii) When the material is evaluated at higher temperatures or when additional complexity enters the system, e.g., strong anharmonicity, defects, or guest species, the harmonic regime breaks down and dynamic sampling is required for a correct prediction of the phonon spectrum. These guidelines and their illustrations for prototype material classes can help experimental and theoretical researchers to enhance the knowledge obtained from a lattice dynamics study.