Nonbonded Parameters Research Articles

Deciphering the Coarse-Grained Model of Ionic Liquid by Tunning the Interaction Level and Bead Types of Martini 3 Force Field

In recent years, ionic liquids (ILs) have served as potential solvents to dissolve organic, inorganic, and polymer materials. A copolymer (for example, Pluronic) can undergo self-organization by forming a micelle-like structure in pure IL medium, and its assembly depends upon the composition of IL. To evaluate the role of ILs, accurate coarse-grained (CG) modeling of IL is needed. Here, we modeled 1-ethyl-3-methylimidazolium dicyanamide ([EMIM][DCA]) ionic liquid (IL) using a CG framework. We optimized CG parameters for the [DCA]− anion by tuning the non-bonded parameters and selecting different kinds of beads. The molecular density (ρ) and radial distribution function (RDF) of our CG model reveal a good agreement with the all-atom (AA) simulation data. We further validated our model by choosing another imidazolium-based cation. Our modified CG model for the anion shows compatibility with the cation and the obtained density matches well with the experimental data. The strategies for developing the CG model will provide a guideline for accurate modeling of new types of ILs. Our CG model will be useful in studying the micellization of non-ionic Pluronic in the [EMIM][DCA] IL medium.

Constructing a temperature transferable coarse-grained model of cis-1,4-polyisoprene with the structural and thermodynamic consistency aided by machine learning

Polyisoprene (PI) is a widely used polymer and constructing a systematic coarse-grained (CG) PI model with the structural and thermodynamic consistency with the underlying atomic model over a wide range of thermodynamic conditions is very important for the predictive capability of CG model on overall properties of PI polymer materials and the establishment of their structure-property relationship. However, as the number of tunable CG potential parameters and target properties grows, traditional parameter tuning methods become impractical. In this work, we present a novel approach for determining the optimal CGPI non-bonded potential parameters by employing Particle Swarm Optimization as the calibrator with machine learning-based models trained using molecular dynamics data. The resulting CG model is further augmented with temperature factors through a multistate parameterization approach. This enhancement ensures the model's temperature transferability of structure and thermodynamics in a wide temperature of 150K∼750K.

The acquisition and calibration of coarse-grained force field parameters for graphene/nitrile rubber nanocomposites

Graphene (GNS) is an ideal reinforcing material for developing high-performance nitrile rubber (NBR) nanocomposites to withstand complex and harsh working conditions. The mesoscale molecular simulation is significant in exploring the microscopic mechanism of nanocomposites. Initially, a coarse-grained (CG) scheme for NBR was established, and the potential parameter files were obtained through the iterative Boltzmann inversion method. Then, the bond and angle parameters were fitted by harmonic potential, the non-bonded interactions were fitted by LJ 12–6 potential. The CG force field parameters were coefficients of the potential function. The parameters such as the stiffness values, potential well parameter, etc., were calibrated by stretching the NBR molecular chains, and comparing the density and stretching performances of the NBR all-atomic and CG models. A rapid method for obtaining the non-bonded parameters between NBR and GNS was proposed based on the radial distribution function curves. The non-bonded parameters were calibrated by comparing GNS pull-out simulations, and the density and tensile properties of the GNS/NBR models. Finally, the CG force field parameters applicable to the GNS/NBR nanocomposites were obtained. The study is expected to be significant for subsequent mesoscale studies on GNS/NBR nanocomposites.

A Temperature-Transferable Coarse-Grained Model for Poly(lactic Acid) Melts.

We present a temperature-transferable coarse-grained (CG) model for poly(lactic acid) (PLA), specifically designed to replicate its volumetric properties and solubility parameter in the molten state. The CG-bonded potentials were derived by using the iterative Boltzmann inversion (IBI) optimization method to match structural properties from detailed atomistic models. A parametrization workflow was employed to determine nonbonded interaction parameters with temperature-dependent corrections that provide agreement with the target properties across the melting temperature range. The CG model successfully replicates key features of the PLA melt. It satisfactory reproduces the density and solubility parameter, maintains the dependence of chain conformation on molecular weight, and captures the dynamic behavior through agreement in scaled mean squared displacement and diffusion coefficients with the atomistic model. Additionally, the CG model offers much faster equilibration compared with the atomistic model. The proposed model is expected to be particularly useful for investigating the miscibility characteristics of PLA in various blends and composites that remain challenging to explore using fully atomistic simulations or experiments.

HessFit: A Toolkit to Derive Automated Force Fields from Quantum Mechanical Information.

In this study, we introduce a novel approach to enhance the accuracy of molecular dynamics simulations by refining the force fields (FFs) through a combination of transferable parameters and molecule-specific characteristics derived from quantum mechanical (QM) calculations. Traditional FFs often prioritize generality over precision, leading to limitations in the accuracy of accurately capturing intra- and intermolecular interactions. To address this, we present an open-source toolkit, called HessFit, designed to integrate QM-derived bonded parameters and atomic charges into existing FFs. In combination with bond, angle, torsional, and nonbonded parameters derivation, HessFit can easily extract multiple barrier terms of dihedrals from QM Hessian and gradient or return all terms through a fitting procedure scheme of QM potential energy surface. We showcase the effectiveness of HessFit through comprehensive evaluations of vibrational properties across a diverse set of small molecules and demonstrate that experimental results support its ability in predicting thermodynamic properties of organic molecules compared to previous state-of-the-art approaches. We further explore its application to Zn2+ metalloprotein models, which are hard systems to treat with automatic approaches. Our results demonstrate that HessFit parameters compete with GAFF2 and OPLS parameters to describing small organic molecules, and its feasibility is also comparable to current FFs used to modeling nonstandard residues in Zn proteins for molecular dynamics simulations. The effectiveness of the HessFit protocol makes it a valuable tool for deriving or extending force field parameters for novel compounds in several molecular modeling applications.

Hydrodynamic slip characteristics of shear-driven water flow in nanoscale carbon slits.

This paper reports on the effects of shear rate and interface modeling parameters on the hydrodynamic slip length (LS) for water-graphite interfaces calculated using non-equilibrium molecular dynamics. Five distinct non-bonded solid-liquid interaction parameters were considered to assess their impact on LS. The interfacial force field derivations included sophisticated electronic structure calculation-informed and empirically determined parameters. All interface models exhibited a similar and bimodal LS response when varying the applied shear rate. LS in the low shear rate regime (LSR) is in good agreement with previous calculations obtained through equilibrium molecular dynamics. As the shear rate increases, LS sharply increases and asymptotes to a constant value in the high shear regime (HSR). It is noteworthy that LS in both the LSR and HSR can be characterized by the density depletion length, whereas solid-liquid adhesion metrics failed to do so. For all interface models, LHSR calculations were, on average, ∼28% greater than LLSR, and this slip jump was confirmed using the SPC/E and TIP4P/2005 water models. To address the LS transition from the LSR to the HSR, the viscosity of water and the interfacial friction coefficient were investigated. It was observed that in the LSR, the viscosity and friction coefficient decreased at a similar rate, while in the LSR-to-HSR transition, the friction coefficient decreased at a faster rate than the shear viscosity until they reached a new equilibrium, hence explaining the LS-bimodal behavior. This study provides valuable insights into the interplay between interface modeling parameters, shear rate, and rheological properties in understanding hydrodynamic slip behavior.

Transferable Anisotropic Mie Potential Force Field for Alkanediols.

The development of force fields for polyfunctional molecules, such as alkanediols, requires a careful account of different average intramolecular conformations for gas states compared to dense liquid states, where intra- and intermolecular hydrogen bonds compete. In the present work, the transferable anisotropic Mie (TAMie) potential is extended to 1,n-alkanediols. Using the convention that intramolecular nonbonded interactions up to and including the third neighbor are excluded, all force field parameters developed previously for 1-alcohols were transferred to 1,5-pentanediol and beyond, with good agreement with experimental phase equilibrium data. To obtain trans-gauche ratios of 1,2-ethanediol and 1,3-propanediol that are consistent with experimental results, the propensities for intra- and intermolecular hydrogen bonds had to be balanced. This was achieved by parameterizing the intramolecular dihedral energy functions governing the O-C-C-O and O-C-C-C angles while intramolecular charge-charge interactions were active. All partial charges belonging to a functional group are collected in a charge group and all interactions among two charge groups are evaluated even if they are separated by less than three bonds. With this approach, it is possible to apply the nonbonded parameters from 1-alcohols to alkanediols without further refinement. The agreement with experimental phase equilibrium and shear viscosity data is of similar quality as for the 1-alcohols and the trans-gauche ratio agrees with literature results from spectroscopic measurements and ab initio calculations.

A Structure Independent Molecular Fragment Interfuse Model for Mesoscale Dissipative Particle Dynamics Simulation of Peptides.

There is a need to develop robust computational models for mesoscale simulation of the structure of peptides over large length scales toward the discovery of novel peptides for medical applications to address the issues of peptide aggregation, enzymatic degradation, and short half-life. The primary objective was to predict the structure and conformation of peptides whose native structures are not known. This work presents a new model for computation of interaction parameters between the beads in coarse-grained dissipative particle dynamics (DPD) simulation that is properly calibrated for amino acids, supports compressibility requirement of water molecules, and accounts for subtle differences in the structure of amino acids and the charge in the side chain of charged amino acids. This new model is referred to as Structure Independent Molecular Fragment Interfuse Model, abbreviated as SIMFIM, because it accounts for specific interactions between different beads, which represent molecular fragments of the amino acids, in calculating nonbonded interaction parameters in the absence of knowing the actual peptide structure. The electrostatic interactions are incorporated in this model by using a normal distribution of charges around the center of the beads to prevent the collapse of oppositely charged soft beads. The uniquely parameterized DPD force field in the SIMFIM model is optimized for a given peptide with respect to the degree of coarse-grained graining for simulating the peptide over long times and length scales. The SIMFIM model was tested in this work using four peptides, namely, TrpZip2, Rubrivinodin, Lihuanodin, and IC3-CB1/Gai peptides, whose structures were sourced from the Protein Data Bank. The SIMFIM model predicted radius of gyration (Rg) values for the peptides closer to the actual structures as compared to the conventional model, and there was less deviation between the predicted and actual structures of the peptides.

A formally exact theory to construct nonreactive forcefields using linear regression to optimize bonded parameters.

This article derives theoretical foundations of force field functional theory (FFFT). FFFT studies topics related to the functional representation of nonreactive forcefields to achieve various desirable properties such as: (a) formal exactness of the forcefield's energy functional under certain conditions, (b) a formally exact ansatz separating the bonded potential energy from the nonbonded potential energy within a bonded cluster in a way that enables bonded parameters to be optimized using linear regression instead of requiring nonlinear regression, (c) the potential energy's continuous differentiability to various orders with respect to energetically accessible internal coordinate displacements within a subdomain defined by one electronic ground state, (d) forcefield design that guarantees the reference ground-state geometry is exactly reproduced as an equilibrium structure on the forcefield's potential energy landscape, (e) reasonably accurate and broadly applicable frugal model potentials, (f) computationally efficient embedded feature selection that identifies and removes unimportant forcefield terms, (g) well-designed methods to parameterize the forcefield from quantum-mechanically-computed and (optionally) experimental reference data, and (h) forcefields that approximately reproduce experimentally-measured properties. This article also introduces: (1) an angle-bending model potential that more accurately describes physical dynamics and is continuously differentiable to all orders with respect to internal coordinate displacements even when the bond angle is linear (i.e., θ = π (180°)) and (2) a first-principles-derived stretch potential that accurately describes short-range Pauli repulsion and the long-range bond dissociation energy. This new angle-bending potential gave good agreement to CCSD quantum-chemistry calculations for CaH2, CO2, H2O, HNO, Li2O, NO2, NS2, SF2, SiH2, and SO2 molecules. This new bond-stretch potential reproduced the first 12+ and 30+ vibrational energy levels of H2 and O2 molecules, respectively, within a few percent of experimental values. Studying the C-F bond stretch in C6F6 as an example, the new ansatz (item (b) above) reduced sensitivity of the optimized force constant's value to choice of nonbonded interaction parameters by an order of magnitude compared to the old ansatz. Normal mode analysis of optimized flexibility models for CO2, H2O, HNO, and SO2 molecules yielded vibrational transition frequencies within a few percent of experimental values. These results demonstrate advantages of this new approach.

Improved Protein Model in SPICA Force Field.

The previous version of the SPICA coarse-grained (CG) force field (FF) protein model focused primarily on membrane proteins and successfully reproduced the dimerization free energies of several transmembrane helices and the stable structures of various membrane protein assemblies. However, that model had limited accuracy when applied to other proteins, such as intrinsically disordered proteins (IDPs) and peripheral proteins, because the dimensions of the IDPs in an aqueous solution were too compact, and protein binding on the lipid membrane surface was overstabilized. To improve the accuracy of the SPICA FF model for the simulation of such systems, in this study, we introduce protein secondary structure-dependent nonbonded interaction parameters to the backbone segments and reoptimize almost all nonbonded parameters for amino acids. The improved FF proposed here successfully reproduces the radii of gyration of various IDPs, the binding sensitivity of several peripheral membrane proteins, and the dimerization free energies of several transmembrane helices. The new model also shows improved agreement with experiments on the free energy of peptide association in water. In addition, an extensive library of nonbonded interactions between proteins and lipids, including various glycerophospholipids, sphingolipids, and cholesterol, allows the study of specific interactions between lipids and peripheral and transmembrane proteins. Hence, the new SPICA FF (version 2) proposed herein is applicable with high accuracy for simulating a wide range of protein systems.

Molecular Dynamics Simulations of HDAC-Ligand Complexes Towards the Design of New Anticancer Compounds±.

Quantitative Structure-Activity Relationship (QSAR) studies gained a foothold in the mid-1960s to rationalise the biological activity of medicinally important compounds. Since then, the advancements in computer hardware and software added many new techniques and areas to this field of study. Molecular dynamics (MD) simulations are one such technique in direct drug design approaches. MD simulations have a special place in drug design studies because they decode the dynamics of intermolecular interactions between a biological target and its potential ligands/inhibitors. The trajectories from MD simulations provide different non-bonding interaction parameters to assess the compatibility of the protein-ligand complex and thereby facilitate the design of prospective compounds prior to their wet-lab exploration. Histone deacetylases (HDACs) play a key role in epigenetics and they are promising drug targets for cancer and various other diseases. This review attempts to shed some light on the modelling studies of HDAC inhibitors as anticancer agents. In view of the advantages of MD simulations in direct drug design, this review also discusses the fragment-based approach in designing new inhibitors of HDAC8 and HDAC2, starting from the interaction energies of ligand fragments obtained from the MD simulations of respective protein-ligand complexes. Here, the design of new anticancer compounds from largazole thiol, trichostatin A, vorinostat, and several other prototype compounds are reviewed. These studies may stimulate the interest of medicinal chemists in MD simulations as a direct drug design approach for new drug development.

Quantum mechanics based non-bonded force field functions for use in molecular dynamics simulations of materials and systems: The nitrogen and oxygen columns.

Accurate Force Fields (FFs) are essential for Molecular Dynamics (MD) simulations of the dynamics of realistic materials in terms of atomic-level interactions. The FF parameters of short-range valence interactions can be derived through Quantum Mechanical (QM) calculations on model systems practical for QM (<300 atoms). Similarly, the dynamic electrostatic interactions can be described with methods such as QEq or PQEq that allow charges and polarization to adjust dynamically. However, accurately extracting long-range van der Waals (vdW) interactions from QM calculations poses challenges due to the absence of a definitive method to distinguish between the different energetic components of electrostatics, polarization, vdW, hydrogen bonding, and valence interactions. To do this we use the Perdew-Burke-Ernzerhof flavor of Density Functional Theory, including empirical D3 vdW corrections, to predict the Equation of State for each element (keeping any covalent bonds fixed), from which we obtain the two-body vdW nonbond potential. Here, we extend these calculations to include non-bonded parameters for the N and O columns of the periodic table so that we now describe columns 15 (N), 16 (O), 17 (F), and 18 (Ne) of the periodic table. For these 20 elements, we find that the two-body vdW potentials can all be mapped to a single universal two-body curve, with just three scaling parameters: Re, De, and L. We refer to this as the Universal NonBond (UNB) potential. We expect this to be useful for new MD simulations and a helpful starting point to obtain UNB parameters for the remainder of the periodic table.

Development, Validation, and Applications of Nonbonded Interaction Parameters between Coarse-Grained Amino Acid and Water Models.

Interactions between amino acids and water play an important role in determining the stability and folding/unfolding, in aqueous solution, of many biological macromolecules, which affects their function. Thus, understanding the molecular-level interactions between water and amino acids is crucial to tune their function in aqueous solutions. Herein, we have developed nonbonded interaction parameters between the coarse-grained (CG) models of 20 amino acids and the one-site CG water model. The nonbonded parameters, represented using the 12-6 Lennard Jones (LJ) potential form, have been optimized using an artificial neural network (ANN)-assisted particle swarm optimization (PSO) (ANN-assisted PSO) method. All-atom (AA) molecular dynamics (MD) simulations of dipeptides in TIP3P water molecules were performed to calculate the Gibbs hydration free energies. The nonbonded force-field (FF) parameters between CG amino acids and the one-site CG water model were developed to accurately reproduce these energies. Furthermore, to test the transferability of these newly developed parameters, we calculated the hydration free energies of the analogues of the amino acid side chains, which showed good agreement with reported experimental data. Additionally, we show the applicability of these models by performing self-assembly simulations of peptide amphiphiles. Overall, these models are transferable and can be used to study the self-assembly of various biomaterials and biomolecules to develop a mechanistic understanding of these processes.

Computational Framework Combining Quantum Mechanics, Molecular Dynamics, and Deep Neural Networks to Evaluate the Intrinsic Properties of Materials.

The design and evaluation of future nanomaterials with specific properties is a challenging task as the current traditional methods rely on trial and error approaches that are time-consuming and expensive. On the computational front, design tools such as molecular dynamics (MD) simulations help us reduce the costs and times. However, nonbonded potential parameters, the key input parameters for an MD simulation, are usually not available for designing and studying new materials. Resolving this, quantum mechanics (QM) calculations could be used to evaluate the system's energy as a function of the nonbonded distances, and the resulting data set could be fit to a generic potential equation to obtain the fitting constants (potential parameters). However, fitting this massive data set containing thousands of unknown parameters using traditional mathematical formulations is not feasible. Hence, most computational frameworks in the literature utilize several simplifications, leading to a severe loss of accuracy. Addressing this deficiency, in this work, we propose a multi-scale framework that couples QM calculations and MD with advanced deep neural networks to determine the potential parameters. This advanced framework has been extensively validated by employing it to predict properties such as the density, boiling point, and melting point of five different types of molecules that are well-understood, namely, the polar molecule H2O, ionic compound LiPF6, ethanol (C2H5OH), long-chain molecule C8H18, and the complex molecular system ethylene carbonate (EC).

Reparameterization of Non-Bonded Parameters for Copper Ions in Plastocyanin: An Adaptive Force Matching Study.

Molecular mechanics rely on existing experimental and theoretical inputs to confidently calculate the trajectories of molecular systems. These calculations, however, are often hindered by missing force field parameters. A notable subject of this problem is metal centers of proteins. This study parameterized, through an adaptive force matching (AFM) workflow, the copper cofactor of plastocyanin in its two oxidation states. New 12-6 Lennard-Jones (LJ) parameters and atomic partial charges were generated to complete the non-bonded description of the copper site. Our models show uniform distorted tetrahedral structures for reduced plastocyanin, Cu(I), and oxidized plastocyanin, Cu(II). These structures align with the QM/MM MD results and existing crystallography studies. TD-DFT calculations, meanwhile, showed that conformations with elongated axial Cu-SMet and shortened equatorial Cu-SCys bonds retain the experimental UV-Vis profile of blue copper (BC) proteins, thus signifying the importance of Cu-S interactions on BC proteins' unique spectroscopic properties.

Comparison of force fields to study the zinc-finger containing protein NPL4, a target for disulfiram in cancer therapy

Molecular dynamics (MD) simulations are a powerful approach to studying the structure and dynamics of proteins related to health and disease. Advances in the MD field allow modeling proteins with high accuracy. However, modeling metal ions and their interactions with proteins is still challenging. NPL4 is a zinc-binding protein and works as a cofactor for p97 to regulate protein homeostasis. NPL4 is of biomedical importance and has been proposed as the target of disulfiram, a drug recently repurposed for cancer treatment. Experimental studies proposed that the disulfiram metabolites, bis-(diethyldithiocarbamate)‑copper and cupric ions, induce NPL4 misfolding and aggregation. However, the molecular details of their interactions with NPL4 and consequent structural effects are still elusive. Here, biomolecular simulations can help to shed light on the related structural details. To apply MD simulations to NPL4 and its interaction with copper the first important step is identifying a suitable force field to describe the protein in its zinc-bound states. We examined different sets of non-bonded parameters because we want to study the misfolding mechanism and cannot rule out that the zinc may detach from the protein during the process and copper replaces it. We investigated the force-field ability to model the coordination geometry of the metal ions by comparing the results from MD simulations with optimized geometries from quantum mechanics (QM) calculations using model systems of NPL4. Furthermore, we investigated the performance of a force field including bonded parameters to treat copper ions in NPL4 that we obtained based on QM calculations.

COGITO: A Coarse-Grained Force Field for the Simulation of Macroscopic Properties of Triacylglycerides.

The use of molecular dynamics simulations is becoming ever more widespread; however, the application of this to pure triacylglyceride (TAG) systems is not. In this study, we are presenting the development, and validation, of a new force field (FF), which we have called the COarse-Grained Interchangeable Triacylglyceride-Optimized FF. The FF has been developed using both a bottom-up and top-down approach for different parameters, with the non-bonded parameters being optimized using a Bayesian optimization method. While the FF was developed using monounsaturated TAGs, results show that it is also suitable for fully saturated TAGs. Description of molecules which were not used during the development of the FF is carried out simply by interchanging the bead in the molecule topologies. Results show that the FF can reproduce the macroscopic properties (density and lattice parameters) of pure TAGs as both crystals and melt with high accuracy, as well as reproduce the differences in enthalpies.

The martini 3 ecosystem for coarse-grained simulations.

Coarse-grained molecular dynamics (MD) simulations using the Martini force-field have become a powerful tool in studying biomolecular systems at near to all-atom resolution. About two years ago we put forward a complete reparameterization of the original Martini force-field. The Martini 3 force-field comprises rebalanced non-bonded interaction parameters as well as an improved parametrization strategy for generating molecular parameters. So far Martini 3 has been successfully applied in numerous studies ranging from protein ligand binding for computer-aided drug design to simulations of large-scale systems such as the SARS-Cov2 viral envelope. As exemplified by these studies, the trend goes towards high-throughput sampling studies as well as large-scale biomolecular systems with the set goal of simulating whole cells. To prepare the Martini universe for the next simulation era, advancement in three aspects is of fundamental importance: 1) development of automated workflows that enable fast and efficient setup of complex systems; 2) extension and improvement of available molecule and force-field parameters; 3) management of feedback from the research community. To this end we created the vermouth python library as a unified framework for both program development and parameter management. Based on vermouth, tools such as martinize2, polyply, or fast-forward have been created, which facilitate setting up Martini simulations. In addition, improved parameters for small molecules and carbohydrates have been released with updates for other classes of biomolecules such as lipids underway. Lastly, by openly hosting all programs and parameters on GitHub, we give the research community an opportinuty to provide feedback and contribute to developments. Here we present how these individual developments make up the Martini 3 ecosystem for coarse-grained simulations and our current goals for future advances.

The reconciliation between the experimental and calculated octanol-water partition coefficient of 1,2-dipalmitoyl-sn-glycero-3-phosphatidylcholine using atomistic molecular dynamics: an open question

The octanol-water partition coefficient of 1,2-dipalmitoyl-sn-glycero-3-phosphatidylcholine (DPPC) was investigated using atomistic molecular dynamics simulations via thermodynamic integration and multistate Bennett acceptance ratio methods. The GAFF and CHARMM36 force fields were used with six water models widely used in molecular dynamics simulations. The OPC4 water model provided the best agreement with the experimental octanol-water partition coefficient of DPPC using the two force fields. However, there is still plenty of room for improvement in water models with correct estimation of surface tension that uses better and suitable non-bonded interaction parameters between water-water and water-DPPC. The Gibbs free energy of transferring DPPC from octanol to water phase was calculated to be 19.8 ± 0.3 and 20.2 ± 0.3 kcal mol−1, giving a partition coefficient of 14.5 ± 0.4 and 14.8 ± 0.3 for the GAFF and CHARMM36 force fields, respectively. This study reinforces the importance of developing new water models that reproduce experimental surface tensions to reconcile the water-water and water-DPPC non-bonded interactions and the existing discrepancy between experimental measurements of amphiphilic molecules that are important in many areas of scientific applications and industry such as biophysics, surfactant, colloids, membranes, medicine, nanotechnology, and food and pharmaceutical industries, and so on. It raises two important open questions: Is the experimental octanol-water partition coefficient of DPPC reliable? Or is its calculation accurate using the OPC4 water model? With respect to the experimental measurements, there may be non-treated aspects such as the formation of aggregates in aqueous phase and limit of detection of the applied method. And, in the calculation, some effects are not possible to be considered in a correct way or viable time such as calculating quantum effects, sampling all conformations, considering phase transitions, and correctly evaluating the intermolecular forces to estimate an accurate surface tension. Communicated by Ramaswamy H. Sarma

Identifying Systematic Force Field Errors Using a 3D-RISM Element Counting Correction.

Hydration free energies of small molecules are commonly used as benchmarks for solvation models. However, errors in predicting hydration free energies are partially due to the force fields used and not just the solvation model. To address this, we have used the 3D reference interaction site model (3D-RISM) of molecular solvation and existing benchmark explicit solvent calculations with a simple element count correction (ECC) to identify problems with the non-bond parameters in the general AMBER force field (GAFF). 3D-RISM was used to calculate hydration free energies of all 642 molecules in the FreeSolv database, and a partial molar volume correction (PMVC), ECC, and their combination (PMVECC) were applied to the results. The PMVECC produced a mean unsigned error of 1.01±0.04kcal/mol and root mean squared error of 1.44±0.07kcal/mol, better than the benchmark explicit solvent calculations from FreeSolv, and required less than 15 s of computing time per molecule on a single CPU core. Importantly, parameters for PMVECC showed systematic errors for molecules containing Cl, Br, I, and P. Applying ECC to the explicit solvent hydration free energies found the same systematic errors. The results strongly suggest that some small adjustments to the Lennard-Jones parameters for GAFF will lead to improved hydration free energy calculations for all solvent models.