Methylation of KSHV vCyclin by PRMT5 contributes to cell cycle progression and cell proliferation.

Kaposi's sarcoma-associated herpesvirus (KSHV) is a double-stranded DNA virus that encodes numerous cellular homologs, including cyclin D, G protein-coupled protein, interleukin-6, and macrophage inflammatory proteins 1 and 2. KSHV vCyclin encoded by ORF72, is the homolog of cellular cyclinD2. KSHV vCyclin can regulate virus replication and cell proliferation by constitutively activating cellular cyclin-dependent kinase 6 (CDK6). However, the regulatory mechanism of KSHV vCyclin has not been fully elucidated. In the present study, we identified a host protein named protein arginine methyltransferase 5 (PRMT5) that interacts with KSHV vCyclin. We further demonstrated that PRMT5 is upregulated by latency-associated nuclear antigen (LANA) through transcriptional activation. Remarkably, knockdown or pharmaceutical inhibition (using EPZ015666) of PRMT5 inhibited the cell cycle progression and cell proliferation of KSHV latently infected tumor cells. Mechanistically, PRMT5 methylates vCyclin symmetrically at arginine 128 and stabilizes vCyclin in a methyltransferase activity-dependent manner. We also show that the methylation of vCyclin by PRMT5 positively regulates the phosphorylate retinoblastoma protein (pRB) pathway. Taken together, our findings reveal an important regulatory effect of PRMT5 on vCyclin that facilitates cell cycle progression and proliferation, which provides a potential therapeutic target for KSHV-associated malignancies.

RUNX3 inhibits KSHV lytic replication by binding to the viral genome and repressing transcription.

Kaposi's sarcoma-associated herpesvirus (KSHV) belongs to the gamma herpesvirus family, which can cause human malignancies including Kaposi sarcoma, primary effusion lymphoma, and multicentric Castleman's diseases. KSHV typically maintains a persistent latent infection within the host. However, after exposure to intracellular or extracellular stimuli, KSHV lytic replication can be reactivated. The reactivation process of KSHV triggers the innate immune response to limit viral replication. Here, we found that the transcriptional regulator RUNX3 is transcriptionally upregulated by the NF-κB signaling pathway in KSHV-infected SLK cells and B cells during KSHV reactivation. Notably, knockdown of RUNX3 significantly promotes viral lytic replication as well as the gene transcription of KSHV. Consistent with this finding, overexpression of RUNX3 impairs viral lytic replication. Mechanistically, RUNX3 binds to the KSHV genome and limits viral replication through transcriptional repression, which is related to its DNA- and ATP-binding ability. However, KSHV has also evolved corresponding strategies to antagonize this inhibition by using the viral protein RTA to target RUNX3 for ubiquitination and proteasomal degradation. Altogether, our study suggests that RUNX3, a novel host-restriction factor of KSHV that represses the transcription of viral genes, may serve as a potential target to restrict KSHV transmission and disease development.IMPORTANCEThe reactivation of Kaposi's sarcoma-associated herpesvirus (KSHV) from latent infection to lytic replication is important for persistent viral infection and tumorigenicity. However, reactivation is a complex event, and the regulatory mechanisms of this process are not fully elucidated. Our study revealed that the host RUNX3 is upregulated by the NF-κB signaling pathway during KSHV reactivation, which can repress the transcription of KSHV genes. At the late stage of lytic replication, KSHV utilizes a mechanism involving RTA to degrade RUNX3, thus evading host inhibition. This finding helps elucidate the regulatory mechanism of the KSHV life cycle and may provide new clues for the development of therapeutic strategies for KSHV-associated diseases.

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.

A Brain Network Analysis Model for Motion Sickness in Electric Vehicles Based on EEG and fNIRS Signal Fusion.

Motion sickness is a common issue in electric vehicles, significantly impacting passenger comfort. This study aims to develop a functional brain network analysis model by integrating electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals to evaluate motion sickness symptoms. During real-world testing with the Feifan F7 series of new energy-electric vehicles from SAIC Motor Corp, data were collected from 32 participants. The EEG signals were divided into four frequency bands: delta-range, theta-range, alpha-range, and beta-range, and brain oxygenation variation was calculated from the fNIRS signals. Functional connectivity between brain regions was measured to construct functional brain network models for motion sickness analysis. A motion sickness detection model was developed using a graph convolutional network (GCN) to integrate EEG and fNIRS data. Our results show significant differences in brain functional connectivity between participants in motion and non-motion sickness states. The model that combined fNIRS data with high-frequency EEG signals achieved the best performance, improving the F1 score by 11.4% compared to using EEG data alone and by 8.2% compared to using fNIRS data alone. These results highlight the effectiveness of integrating EEG and fNIRS signals using GCN for motion sickness detection. They demonstrate the model's superiority over single-modality approaches, showcasing its potential for real-world applications in electric vehicles.

Material flow analysis of heavy metals in large-scale cattle farms and ecological risk assessment of cattle manure application to fields.

This study bridges the knowledge gap pertaining to the pathways of heavy metal accumulation and migration within the industrial chain of large-scale cattle farms. Two such farms in Shaanxi serve as a basis for our exploration into Zn, Cu, Cr, Pb, As, and Cd dynamics. Employing material flow analysis complemented by predictive models, we evaluate the potential ecological risks of arable soil from heavy metal influx via manure application. Our findings indicate that Zn and Cu predominate the heavy metal export from these operations, composing up to 60.00%-95.67% of their total content. Predictive models based on 2021 data reveal a potential increase in Cd soil concentration by 0.08 mg/kg by 2035, insinuating a reduced safe usage period for cattle manure at less than 50 years. Conversely, projections from 2022 data point towards a gradual Cu rise in soil, reaching risk threshold levels after 126 years. These outcomes inform limitations in cattle manure utilisation strategies, underscoring Cu and Cd content as key barriers. The study underscores the criticality of continuous heavy metal surveillance within farm by products to ensure environmental protection and sustainable agricultural practices.

Quantum Mechanics Characterization of Non-Covalent Interaction in Nucleotide Fragments.

Accurate calculation of non-covalent interaction energies in nucleotides is crucial for understanding the driving forces governing nucleic acid structure and function, as well as developing advanced molecular mechanics forcefields or machine learning potentials tailored to nucleic acids. Here, we dissect the nucleotides' structure into three main constituents: nucleobases (A, G, C, T, and U), sugar moieties (ribose and deoxyribose), and phosphate group. The interactions among these fragments and between fragments and water were analyzed. Different quantum mechanical methods were compared for their accuracy in capturing the interaction energy. The non-covalent interaction energy was decomposed into electrostatics, exchange-repulsion, dispersion, and induction using two ab initio methods: Symmetry-Adapted Perturbation Theory (SAPT) and Absolutely Localized Molecular Orbitals (ALMO). These calculations provide a benchmark for different QM methods, in addition to providing a valuable understanding of the roles of various intermolecular forces in hydrogen bonding and aromatic stacking. With SAPT, a higher theory level and/or larger basis set did not necessarily give more accuracy. It is hard to know which combination would be best for a given system. In contrast, ALMO EDA2 did not show dependence on theory level or basis set; additionally, it is faster.

Computational Investigation of a Series of Small Molecules as Potential Compounds for Lysyl Hydroxylase-2 (LH2) Inhibition.

The catalytic function of lysyl hydroxylase-2 (LH2), a member of the Fe(II)/αKG-dependent oxygenase superfamily, is to catalyze the hydroxylation of lysine to hydroxylysine in collagen, resulting in stable hydroxylysine aldehyde-derived collagen cross-links (HLCCs). Reports show that high amounts of LH2 lead to the accumulation of HLCCs, causing fibrosis and specific types of cancer metastasis. Some members of the Fe(II)/αKG-dependent family have also been reported to have intramolecular O2 tunnels, which aid in transporting one of the required cosubstrates into the active site. While LH2 can be a promising target to combat these diseases, efficacious inhibitors are still lacking. We have used computational simulations to investigate a series of 44 small molecules as lead compounds for LH2 inhibition. Tunneling analyses indicate the existence of several intramolecular tunnels. The lengths of the calculated O2-transporting tunnels in holoenzymes are relatively longer than those in the apoenzyme, suggesting that the ligands may affect the enzyme's structure and possibly block (at least partially) the tunnels. The sequence alignment analysis between LH enzymes from different organisms shows that all of the amino acid residues with the highest occurrence rate in the oxygen tunnels are conserved. Our results suggest that the enolate form of diketone compounds establishes stronger interactions with the Fe(II) in the active site. Branching the enolate compounds with functional groups such as phenyl and pyridinyl enhances the interaction with various residues around the active site. Our results provide information about possible leads for further LH2 inhibition design and development.

Molecular Cage Reports on Its Contents: Spectroscopic Signatures of Cryo-Cooled K+- and Ba2+-Benzocryptand Complexes.

UV photofragment spectroscopy and IR-UV double resonance methods are used to determine the structure and spectroscopic responses of a three-dimensional [2.2.2]-benzocryptand cage to the incorporation of a single K+ or Ba2+ imbedded inside it (labeled as K+-BzCrypt, Ba2+-BzCrypt). We studied the isolated ion-cryptand complex under cryo-cooled conditions, brought into the gas phase by nano-electrospray ionization. Incorporation of a phenyl ring in place of the central ethyl group in one of the three N-CH2-CH2-O-CH2-CH2-O-CH2-CH2-N chains provides a UV chromophore whose S0-S1 transition we probe. K+-BzCrypt and Ba2+-BzCrypt have their S0-S1 origin transitions at 35,925 and 36,446 cm-1, respectively, blue-shifted by 174 and 695 cm-1 from that of 1,2-dimethoxybenzene. These origins are used to excite a single conformation of each complex selectively and record their IR spectra using IR-UV dip spectroscopy. The alkyl CH stretch region (2800-3000 cm-1) is surprisingly sensitive to the presence and nature of the encapsulated ion. We carried out an exhaustive conformational search of cage conformations for K+-BzCrypt and Ba2+-BzCrypt, identifying two conformations (A and B) that lie below all others in energy. We extend our local mode anharmonic model of the CH stretch region to these strongly bound ion-cage complexes to predict conformation-specific alkyl CH stretch spectra, obtaining quantitative agreement with experiment for conformer A, the gas-phase global minimum. The large electrostatic effect of the charge on the O- and N-lone pairs affects the local mode frequencies of the CH2 groups adjacent to these atoms. The localized CH2 scissors modes are pushed up in frequency by the adjacent O/N-atoms so that their overtones have little effect on the alkyl CH stretch region. However, the localized CH2 wags are nearly degenerate and strongly coupled to one another, producing an array of delocalized wag normal modes, whose highest frequency members reach up above 1400 cm-1. As such, their overtones mix significantly with the CH stretch modes, most notably involving the CH2 symmetric stretch fundamentals of the central ethyl groups in the all-alkyl chains and the CH stretches adjacent to the N-atoms and antiperiplanar to the nitrogen lone pair.

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.

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.

Hydration of divalent lanthanides, Sm2+ and Eu2+ : A molecular dynamics study with polarizable AMOEBA force field.

The chemistry of divalent lanthanides, Ln2+ , is a growing sub-field of heavy element chemistry owing to new synthetic approaches. However, some theoretical aspects of these unusual cations are currently underdeveloped, especially as they relate to their dynamic properties in solution. In this work, we address the hydration of two of the classical Ln2+ cations, Sm2+ and Eu2+ , using atomic multipole optimized energetic for biomolecular applications (AMOEBA) force fields. These cations have not been parameterized to date with AMOEBA, and few studies are available because of their instability with respect to oxidation in aqueous media. Coordination numbers (CN's) of 8.2 and 8.1 respectively for Sm2+ and Eu2+ , and 8.8 for both Sm3+ and Eu3+ have been obtained and are in good agreement with the few available AIMD and X-ray absorption fine structures studies. The decreased CN of Ln2+ compared with Ln3+ arises from progressive water exchange events that indicates the gradual stabilization of 8-coordinate structures with respect to 9-coordinate geometries. Moreover, the effects of the chloride counter anions on the coordination of Ln2+ cations have been studied at different chloride concentrations in this work. Lastly, water exchange times of Ln2+ cations have been calculated to provide a comprehensive understanding of the behavior of Eu2+ and Sm2+ in aqueous chloride media.

Atomic Polarizabilities for Interactive Dipole Induction Models.

Thole-style mutual induction models for molecular polarization have been adopted by several popular polarizable force fields (FFs) for their simplicity and transferability. The atomic polarizability parameters of these models are typically derived by fitting to ab initio or/and experimental molecular polarizabilities. In this work, we improve upon Thole polarizability parameters by employing both high-level quantum mechanics molecular polarizabilities and electrostatic potential (ESP) responses on three-dimensional grids. Our results indicate that the two approaches to derive atomic polarizability parameters are both effective, while the ESP approaches can also capture the polarization for the atoms with lone pair electrons. The resulting polarizability parameters have been validated on a set of over 7200 molecules covering the most common elements found in organic molecules (C, H, O, N, P, S, F, Cl, Br, and I). These parameters have also been tested on the experimentally measured molecular polarizabilities of 422 molecules. The final set of parameters derived in this work show notable improvement over the current AMOEBA set. The result is a highly transferable, expanded set of atomic polarizabilities defined by the local chemical environment in the form of SMARTS patterns. These parameters can be used directly in molecular mechanics polarizable potential energy functions such as AMOEBA, AMOEBA+, and other Thole-style models.

Trapping Ca+ inside a molecular cavity: computational study of the potential energy surfaces for Ca+-[n]cycloparaphenylene, n = 5-12.

Ion trap quantum computing utilizes electronic states of atomic ions such as Ca+ to encode information on to a qubit. To explore the fundamental properties of Ca+ inside molecular cavities, we describe here a computational study of Ca+ bound inside neutral [n]-cycloparaphenylenes (n = 5-12), often referred to as "nanohoops". This ab initio study characterizes optimized structures, harmonic vibrational frequencies, potential energy surfaces, and ion molecular orbital distortion as functions of increasing nanohoop size. The results of this work provide a first step in guiding experimental studies of the spectroscopy of these ion-molecular cavity complexes.

Inhibition of FAM83D displays antitumor effects in glioblastoma via down-regulation of the AKT/Wnt/ß-catenin pathway.

Up-regulation of family with sequence similarity 83 member D (FAM83D) has been acknowledged as a vital contributor for the carcinogenesis of numerous cancers. The relevance of FAM83D in glioblastoma (GBM), however, is not well understood. This current work aimed to determine the possible roles and mechanisms of FAM83D in GBM. By analyzing The Cancer Genome Atlas (TCGA) data, we found dramatic increases in FAM83D expression in GBM tissue. We also observed elevated levels of FAM83D in the clinical specimens of GBM. In vitro data showed that silencing FAM83D resulted in remarkable antitumor effects via inhibiting the proliferation, invasion and epithelial-mesenchymal transition of GBM cells. Moreover, the knockdown of FAM83D improved sensitivity to the chemotherapy drug temozolomide. In-depth mechanism research revealed that the silencing of FAM83D strikingly decreased the phosphorylation levels of AKT and glycogen synthase kinase-3ß, and prohibited activation of the Wnt/ß-catenin pathway. The suppression of AKT abolished FAM83D-mediated activation of the Wnt/ß-catenin pathway. The re-expression of ß-catenin reversed FAM83D-silencing-induced antitumor effects in GBM cells. In addition, GBM cells with FAM83D silencing exhibited reduced tumorigenic potential in vivo. Overall, the data from this work show that the inhibition of FAM83D displays antitumor effects in GBM via down-regulation of the AKT/Wnt/ß-catenin pathway and propose FAM83D as a new therapeutic target for GBM.

An Efficient Approach to Large-Scale Ab Initio Conformational Energy Profiles of Small Molecules.

Accurate conformational energetics of molecules are of great significance to understand maby chemical properties. They are also fundamental for high-quality parameterization of force fields. Traditionally, accurate conformational profiles are obtained with density functional theory (DFT) methods. However, obtaining a reliable energy profile can be time-consuming when the molecular sizes are relatively large or when there are many molecules of interest. Furthermore, incorporation of data-driven deep learning methods into force field development has great requirements for high-quality geometry and energy data. To this end, we compared several possible alternatives to the traditional DFT methods for conformational scans, including the semi-empirical method GFN2-xTB and the neural network potential ANI-2x. It was found that a sequential protocol of geometry optimization with the semi-empirical method and single-point energy calculation with high-level DFT methods can provide satisfactory conformational energy profiles hundreds of times faster in terms of optimization.

Advanced Electrostatic Model for Monovalent Ions Based on Ab Initio Energy Decomposition.

Ions play important roles in the structures and functions of biomolecules. In biomolecular simulations, ions either directly interact with biomolecules or provide an ionic environment that influences electrostatic interactions of solutes. The AMOEBA+ water model has demonstrated significant advancement of the classical force field for describing molecular interactions due to its improvements on the functional forms to account for essential physics. This work expands the applicability of the AMOEBA+ model toward alkali metal (Li, Na, K, Rb, and Cs) and halogen (F, Cl, Br, and I) ions. Various quantum chemical data on ion-ion and ion-water interactions, experimental ion hydration free energies, and lattice energies of salt crystals are used in the parametrization. The final parameters are verified with other properties outside of the parametrization data, including lattice energies of additional salt crystals and ionic activity coefficients in solution. The new model captures a wide range of ion properties from the gas phase to solution phase and crystals. More importantly, AMOEBA+ provides energy components that are consistent with ab initio energy decomposition. Thus, we expect AMOEBA+ to be more general, transferable, and valuable for the interpretation of intermolecular forces in efficient classical simulations.

E2EDNA: Simulation Protocol for DNA Aptamers with Ligands.

We present E2EDNA, a simulation protocol and accompanying code for the molecular biophysics and materials science communities. This protocol is both easy to use and sufficiently efficient to simulate single-stranded (ss)DNA and small analyte systems that are important to cellular processes and nanotechnologies such as DNA aptamer-based sensors. Existing computational tools used for aptamer design focus on cost-effective secondary structure prediction and motif analysis in the large data sets produced by SELEX experiments. As a rule, they do not offer flexibility with respect to the choice of the theoretical engine or direct access to the simulation platform. Practical aptamer optimization often requires higher accuracy predictions for only a small subset of sequences suggested, e.g., by SELEX experiments, but in the absence of a streamlined procedure, this task is extremely time and expertise intensive. We address this gap by introducing E2EDNA, a computational framework that accepts a DNA sequence in the FASTA format and the structures of the desired ligands and performs approximate folding followed by a refining step, analyte complexation, and molecular dynamics sampling at the desired level of accuracy. As a case study, we simulate a DNA-UTP (uridine triphosphate) complex in water using the state-of-the-art AMOEBA polarizable force field. The code is available at https://github.com/InfluenceFunctional/E2EDNA.

Many-body effect determines the selectivity for Ca2+ and Mg2+ in proteins.

Calcium ion is a versatile messenger in many cell-signaling processes. To achieve their functions, calcium-binding proteins selectively bind Ca2+ against a background of competing ions such as Mg2+ The high specificity of calcium-binding proteins has been intriguing since Mg2+ has a higher charge density than Ca2+ and is expected to bind more tightly to the carboxylate groups in calcium-binding pockets. Here, we showed that the specificity for Ca2+ is dictated by the many-body polarization effect, which is an energetic cost arising from the dense packing of multiple residues around the metal ion. Since polarization has stronger distance dependence compared with permanent electrostatics, the cost associated with the smaller Mg2+ is much higher than that with Ca2+ and outweighs the electrostatic attraction favorable for Mg2+ With the AMOEBA (atomic multipole optimized energetics for biomolecular simulation) polarizable force field, our simulations captured the relative binding free energy between Ca2+ and Mg2+ for proteins with various types of binding pockets and explained the nonmonotonic size dependence of the binding free energy in EF-hand proteins. Without electronic polarization, the smaller ions are always favored over larger ions and the relative binding free energy is roughly proportional to the net charge of the pocket. The many-body effect depends on both the number and the arrangement of charged residues. Fine-tuning of the ion selectivity could be achieved by combining the many-body effect and geometric constraint.

Three-site and five-site fixed-charge water models compatible with AMOEBA force field.

In a typical biomolecular simulation using Atomic Multipole Optimized Energetics for Biomolecular Applications (AMOEBA) force field, the vast majority molecules in the simulation box consist of water, and these water molecules consume the most CPU power due to the explicit mutual induction effect. To improve the computational efficiency, we here develop two new nonpolarizable water models (with flexible bonds and fixed charges) that are compatible with AMOEBA solute: the 3-site AW3C and 5-site AW5C. To derive the force-field parameters for AW3C and AW5C, we fit to six experimental liquid thermodynamic properties: liquid density, enthalpy of vaporization, dielectric constant, isobaric heat capacity, isothermal compressibility and thermal expansion coefficient, at a broad range of temperatures from 261.15 to 353.15 K under 1.0 atm pressure. We further validate our AW3C and AW5C water models by showing that they can well reproduce the radial distribution function g(r), self-diffusion constant D, and hydration free energy from the AMOEBA03 water model and the experimental observations. Furthermore, we show that our AW3C and AW5C water models can greatly accelerate (>5 times) the bulk water as well as biomolecular simulations when compared to AMOEBA water. Specifically, we demonstrate that the applications of AW3C and AW5C water models to simulate a DNA duplex lead to a threefold acceleration, and in the meanwhile well maintain the structural properties as the fully polarizable AMOEBA water. We expect that our AW3C and AW5C water models hold great promise to be widely applied to simulate complex bio-molecules using the AMOEBA force field.

Accurate description of molecular dipole surface with charge flux implemented for molecular mechanics.

The molecular dipole moment is strongly coupled to molecular geometry among different phases, conformational states, intermolecular interaction energy, and vibrational spectroscopy. Our previous inclusion of geometry dependent charge flux into the atomic multipole-based polarizable AMOEBA+ force field has shown significant improvement of water properties from gaseous to condensed phases [C. Liu et al., J. Phys. Chem. Lett. 11(2), 419-426 (2020)]. In this work, the parameterization of the CF model for a broad range of organic and biomolecular fragments is presented. Atom types are automatically assigned by matching the predefined SMARTS patterns. Comparing to the current AMOEBA+ model without the CF component, it is shown that the AMOEBA+ (CF) model improves the description of molecular dipole moments for the molecules we studied over both equilibrium and distorted geometries. For the equilibrium-geometry structures, AMOEBA+ (CF) reduces the mean square error (MSE) from 6.806 × 10-1 (without CF) to 4.249 × 10-4 D2. For non-equilibrium structures, the MSE is reduced from 5.766 × 10-1 (without CF) to 2.237 × 10-3 D2. Finally, the transferability of the CF model and parameters were validated on two sets of molecules: one includes molecules in the training set but with different geometries, and the other one involves new molecules outside of the training set. A similar improvement on dipole surfaces was obtained on the validation sets. The CF algorithms and parameters derived in this work are general and can be implemented into any existing molecular mechanical force fields.