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In the field of molecular simulation for drug design, traditional molecular mechanic force fields and quantum chemical theories have been instrumental but limited in terms of scalability and computational efficiency. To overcome these limitations, machine learning force fields (MLFFs) have emerged as a powerful tool capable of balancing accuracy with efficiency. MLFFs rely on the relationship between molecular structures and potential energy, bypassing the need for a preconceived notion of interaction representations. Their accuracy depends on the machine learning models used, and the quality and volume of training data sets. With recent advances in equivariant neural networks and high‐quality datasets, MLFFs have significantly improved their performance. This review explores MLFFs, emphasizing their potential in drug design. It elucidates MLFF principles, provides development and validation guidelines, and highlights successful MLFF implementations. It also addresses potential challenges in developing and applying MLFFs. The review concludes by illuminating the path ahead for MLFFs, outlining the challenges to be overcome and the opportunities to be harnessed. This inspires researchers to embrace MLFFs in their investigations as a new tool to perform molecular simulations in drug design. [ABSTRACT FROM AUTHOR]

The standard GAFF2 force field parameterization has been refined for the fluorinated alcohols 2,2,2‐trifluoroethanol (TFE), 1,1,1,3,3,3‐hexafluoro‐2‐propanol (HFIP), and 1,1,1,3,3,3‐hexafluoropropan‐2‐one (HFA), which are commonly used to study proteins and peptides in biomimetic media. The structural and dynamic properties of both proteins and peptides are significantly influenced by the biomimetic environment created by the presence of these cosolvents in aqueous solutions. Quantum mechanical calculations on stable conformers were used to parameterize the atomic charges. Different systems, such as pure liquids, aqueous solutions, and systems formed by melittin protein and cosolvent/water solutions, have been used to validate the new models. The calculated macroscopic and structural properties are in agreement with experimental findings, supporting the validity of the newly proposed models. [ABSTRACT FROM AUTHOR]

Electron paramagnetic resonance (EPR) has become a powerful probe of conformational heterogeneity and dynamics of biomolecules. In this Review, we discuss different computational modeling techniques that enrich the interpretation of EPR measurements of dynamics or distance restraints. A variety of spin labels are surveyed to provide a background for the discussion of modeling tools. Molecular dynamics (MD) simulations of models containing spin labels provide dynamical properties of biomolecules and their labels. These simulations can be used to predict EPR spectra, sample stable conformations and sample rotameric preferences of label sidechains. For molecular motions longer than milliseconds, enhanced sampling strategies and de novo prediction software incorporating or validated by EPR measurements are able to efficiently refine or predict protein conformations, respectively. To sample large‐amplitude conformational transition, a coarse‐grained or an atomistic weighted ensemble (WE) strategy can be guided with EPR insights. Looking forward, we anticipate an integrative strategy for efficient sampling of alternate conformations by de novo predictions, followed by validations by systematic EPR measurements and MD simulations. Continuous pathways between alternate states can be further sampled by WE‐MD including all intermediate states. [ABSTRACT FROM AUTHOR]

The amyloid‐forming Aβ peptide is able to interact with metal cations to form very stable complexes that influence fibril formation and contribute to the onset of Alzheimer's disease. Multiple structures of peptides derived from Aβ in complex with different metals have been resolved experimentally to provide an atomic‐level description of the metal‐protein interactions. However, Aβ is intrinsically disordered, and hence more amenable to an ensemble description. Molecular dynamics simulations can now reach the timescales needed to generate ensembles for these type of complexes. However, this requires accurate force fields both for the protein and the protein‐metal interactions. Here we use state‐of‐the‐art methods to generate force field parameters for the Zn(II) cations in a set of complexes formed by different Aβ variants and combine them with the Amber99SB*‐ILDN optimized force field. Upon comparison of NMR experiments with the simulation results, further optimized with a Bayesian/Maximum entropy approach, we provide an accurate description of the molecular ensembles for most Aβ‐metal complexes. We find that the resulting conformational ensembles are more heterogeneous than the NMR models deposited in the Protein Data Bank. [ABSTRACT FROM AUTHOR]

Characterizing structural ensembles of intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) of proteins is essential for studying structure–function relationships. Due to the different neutron scattering lengths of hydrogen and deuterium, selective labeling and contrast matching in small‐angle neutron scattering (SANS) becomes an effective tool to study dynamic structures of disordered systems. However, experimental timescales typically capture measurements averaged over multiple conformations, leaving complex SANS data for disentanglement. We hereby demonstrate an integrated method to elucidate the structural ensemble of a complex formed by two IDRs. We use data from both full contrast and contrast matching with residue‐specific deuterium labeling SANS experiments, microsecond all‐atom molecular dynamics (MD) simulations with four molecular mechanics force fields, and an autoencoder‐based deep learning (DL) algorithm. From our combined approach, we show that selective deuteration provides additional information that helps characterize structural ensembles. We find that among the four force fields, a99SB‐disp and CHARMM36m show the strongest agreement with SANS and NMR experiments. In addition, our DL algorithm not only complements conventional structural analysis methods but also successfully differentiates NMR and MD structures which are indistinguishable on the free energy surface. Lastly, we present an ensemble that describes experimental SANS and NMR data better than MD ensembles generated by one single force field and reveal three clusters of distinct conformations. Our results demonstrate a new integrated approach for characterizing structural ensembles of IDPs. [ABSTRACT FROM AUTHOR]

Hydrogel is a three‐dimensional cross‐linked hydrophilic network that can imbibe a large amount of water inside its structure (up to 99% of its dry weight). Due to their unique characteristics of biocompatibility and flexibility, it has found applications in diversified fields, including tissue engineering, drug delivery, biosensors, and agriculture. Even though hydrogels are widely used in the biomedical field, their lower mechanical strength still limits their application to its full potential. Hydrogels can be reinforced with organic, inorganic, and metal‐based nanofillers to improve their mechanical strength. Due to improved computational power, computational‐based techniques are emerging as a leading characterization technique for nanocomposites and hydrogels. In nanomaterials, atomistic description governs the mechanical strength and thermal behavior that realized atomistic level simulations as an appropriate approach to capture the deformation governing mechanism. Among atomistic simulations, the molecular dynamics (MD)‐based approach is emerging as a prospective technique for simulating neat and nanocomposite‐based hydrogels' mechanical and thermal behavior. The success and accuracy of MD simulation entirely depend on the force field. This review article will compile the force field employed by the research community to capture the atomistic interactions in different nanocomposite‐based hydrogels. This article will comprehensively review the progress made in the atomistic approach to study neat and nanocomposite‐based hydrogels' properties. The authors have enlightened the challenges and limitations associated with the atomistic modeling of hydrogels. This article is categorized under:Structure and Mechanism > Computational Materials ScienceMolecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo MethodsSoftware > Molecular Modeling [ABSTRACT FROM AUTHOR]

Machine learning was employed on the experimental crystal structures of the Cambridge Structural Database (CSD) to derive an intermolecular force field for all available types of atoms (general force field). The obtained pairwise interatomic potentials of the general force field allow for the fast and accurate calculation of intermolecular Gibbs energy. The approach is based on three postulates regarding Gibbs energy: the lattice energy must be below zero, the crystal structure must be a local minimum, and, if available, the experimental and the calculated lattice energy must coincide. The parametrized general force field was then validated regarding these three conditions. First, the experimental lattice energy was compared with the calculated energies. The observed errors were found to be in the order of experimental errors. Second, Gibbs lattice energy was calculated for all structures available in the CSD. Their energy values were found to be below zero in 99.86% of the cases. Finally, 500 random structures were minimized, and the change in density and energy was examined. The mean error in the case of density was below 4.06%, and for energy it was below 5.7%. The obtained general force field calculated Gibbs lattice energies of 259 041 known crystal structures within a few hours. Since Gibbs energy defines the reaction energy, the calculated energy can be used to predict chemical–physical properties of crystals, for instance, the formation of co‐crystals, polymorph stability and solubility. [ABSTRACT FROM AUTHOR]

This paper considers the narrow escape problem of a Brownian particle within a three‐dimensional Riemannian manifold under the influence of the force field. We compute an asymptotic expansion of mean sojourn time for Brownian particles. As a auxiliary result, we obtain the singular structure for the restricted Neumann Green's function which may be of independent interest. [ABSTRACT FROM AUTHOR]

In molecular modelling of novel solvents such as ionic liquids, it is common to scale atomic charges to improve the experiment‐simulation agreement for some selected properties. As these liquids are designed to solvate solutes, whether the solvation thermodynamics could be correctly described is of utmost importance. Therefore, we present a comprehensive large‐scale calculation of solvation free energies via nonequilibrium fast‐switching simulations for a spectrum of molecules in ionic liquids, the atomic charges of which are scaled to maximize the prediction‐experiment correlation. Further, the density‐derived choice is compared with the solvation‐thermodynamics‐derived one. When the scaling factor is decreased, the density exhibits a monotonically decreasing behavior due to weaker inter‐molecular interactions produced by scaled atomic charges. However, solvation free energies do not show consistent monotonic behaviors, which are caused by competing electrostatic and vdW responses to the scaling‐parameter variation. More intriguingly, the solvation‐free‐energy‐derived scaling factor is generally slightly higher than the density‐derived one. We further calculate partition coefficients ortransfer free energies of solutes from water to ionic liquids to provide another thermodynamic perspective of charge scaling. Another central result is the detailed evaluation of the widely used force fields for bonded and vdW terms, i.e., the GAFF derivatives. [ABSTRACT FROM AUTHOR]

Molecular docking is one of the most popular computational tools for the hit discovery step in drug design. However, there is ample room for improvement of docking's ability to identify correct binding modes and discriminate active from decoy compounds. Molecular dynamics (MD) simulations of protein–ligand docking structures have been shown to be effective in improving docking results. Here, we present CHARMM‐GUI high‐throughput simulator (HTS) that prepares MD simulation systems and inputs for multiple protein–ligand complex structures in a high‐throughput manner. HTS supports commonly used MD programs (NAMD, GROMACS, AMBER, OpenMM, GENESIS, Desmond, LAMMPS, and Tinker) along with various force field combinations for protein and ligand, including CHARMM36m, Amber (ff19SB/ff14SB), OPLS‐AA/M, CGenFF, GAFF2, and OpenFF. Validation tests using Miller and the directory of useful decoys‐enhanced (DUD‐E) datasets demonstrate that short MD simulations using HTS‐generated systems and simple ligand RMSD calculations consistently outperform docking results. Specifically, MD simulations can better identify correct ligand‐binding modes among top 10 binding poses as compared to docking scores. In addition, MD simulations can better discriminate active from decoy compounds in the DUD‐E dataset than docking scores for both soluble and membrane proteins. We expect that HTS can be a useful tool to facilitate the hit discovery process in drug design by improving docking results. [ABSTRACT FROM AUTHOR]

Deep eutectic solvents (DESs) are binary or ternary mixtures of compounds that possess significant melting point depressions relative to the pure isolated components. The discovery of DESs has been a major breakthrough with multiple fields benefitting from their low cost and tunable physiochemical properties. However, tailoring DESs for specific applications through their practically unlimited synthetic combinations can be as much a hindrance as a benefit given the expense and time‐required to perform large‐scale experimental measurements. This emphasizes the need for fast computational tools capable of making accurate predictions of DES physiochemical properties exclusively from molecular structure. Yet, these systems are not trivial to model or simulate at the atomic level given their exceedingly nonideal behaviors, asymmetry of components, and the complexity of their molecular electrostatic interactions. Despite the challenge, computational reports featuring quantum mechanical (QM) methods have provided significant understanding into the relationship between the melting point depression and the unique and complex hydrogen bond network present in DESs. Classical molecular dynamics (MD) methods have examined bulk‐phase solvent organization in conjunction with thermodynamic and transport properties. Machine learning (ML) algorithms have shown great potential as structure–property prediction tools. Overall, this review highlights computational accomplishments that have meaningfully advanced our understanding of DESs and strives to give the reader a sense of the overall strengths and drawbacks of the methodologies employed while hinting at promises of advances to come. This article is categorized under:Software > Simulation Methods [ABSTRACT FROM AUTHOR]

Molecular dynamics (MD) simulation has been widely used in the field of biomedicine to study the conformational transition of proteins caused by mutation or ligand binding/unbinding. It provides some perspectives those are difficult to find in traditional biochemical or pathological experiments, for example, detailed effects of mutations on protein structure and protein–protein/ligand interaction at the atomic level. In this review, a broad overview on conformation changes and drug discovery by MD simulation is given. We first discuss the preparation of protein structure for MD simulation, which is a key step that determines the accuracy of the simulation. Then, we summarize the applications of commonly used force fields and MD simulations in scientific research. Finally, enhanced sampling methods and common applications of these methods are introduced. In brief, MD simulation is a powerful tool and it can be used to guide experimental study. The combination of MD simulation and experimental techniques is an a priori means to solve the biomedical problems and give a deep understanding on the relationship between protein structure and function. [ABSTRACT FROM AUTHOR]

Molecular dynamics (MD) simulation is a powerful tool for exploring the conformational energy landscape of proteins, and the reliability of MD results is crucially dependent on the underlying force field (FF). An accurate FF capable of producing balanced distributions of diverse conformations at multiple levels has been a long‐sought goal. Towards this, several decades of joint efforts have been made to address FF deficiencies, manifested by conformational biases at different levels (local conformations, secondary structures, and global extendedness of polypeptide chain). We first present the major FF biases, then review the strategies to address them separately. Specifically, both nonresidue‐specific and residue‐specific strategies for torsional parameter optimization have been applied to achieve local conformation and secondary structure balances. Significant improvements can be gained with residue‐specific torsional parameters especially when explicit dihedral couplings are considered. Further, the additional balance between protein–protein and protein–water interactions has been optimized via multiple ways to reproduce the global extendedness of polypeptide chains, especially for unfolded or disordered proteins. This review aims to summarize the most valuable experience and lessons gained from the past, which, we hope, can facilitate further improvements of both classical FFs and more sophisticated models such as polarizable FFs. This article is categorized under:Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo MethodsMolecular and Statistical Mechanics > Molecular Mechanics [ABSTRACT FROM AUTHOR]

The calibration of torsional interaction terms by fitting relative gas‐phase conformational energies against their quantum‐mechanical values is a common procedure in force‐field development. However, much less attention has been paid to the optimization of third‐neighbor nonbonded interaction parameters, despite their strong coupling with the torsions. This article introduces an algorithm termed LLS‐SC, aimed at simultaneously parametrizing torsional and third‐neighbor interaction terms based on relative conformational energies. It relies on a self‐consistent (SC) procedure where each iteration involves a linear least‐squares (LLS) regression followed by a geometry optimization of the reference structures. As a proof‐of‐principle, this method is applied to obtain torsional and third‐neighbor interaction parameters for aliphatic chains in the context of the GROMOS 53A6 united‐atom force field. The optimized parameter set is compared to the original one, which has been fitted manually against thermodynamic properties for small linear alkanes. The LLS‐SC implementation is freely available under http://github.com/mssm-labmmol/profiler. [ABSTRACT FROM AUTHOR]

Discovery of natural antioxidants has been carried out for decades relying mainly on experimental approaches that are commonly associated with time and cost demanding biochemical assays. The maturation of quantitative structure activity relationship (QSAR) modelling has provided an alternative approach for searching and designing antioxidant compounds with alleviated costs. As a contribution to this approach, this work aimed to establish a fragment‐based 3D‐QSAR procedure to discover and design potential antioxidants based on tryptophyllin L structures isolated from the red tree frog Litoria rubella. A force field and a Gaussian 3D‐QSAR model were built to screen for potential antioxidants from tripeptide fragments covering all sequences of tryptophyllin L database. Among those, PWY(NH2) corresponding tryptophyllin L 4.1 was predicted to have the highest 2,2′‐azino‐bis(3‐ethylbenzothiazoline‐6‐sulphonic acid) radical cation (ABTS+·) scavenging capability. Two newly designed peptides PYW and PYW(NH2) together with PWY(NH2), tryptophyllin L 4.1, and the reference peptide PWY were synthesized and subjected to two antioxidant assays including ABTS scavenging and ferric reducing antioxidant power assays. Although the experimental TEAC values of the five peptides were roughly similar to those from predictions, the activity order was not in agreement with the predictions. The dissimilarities were accounted by the difference in the experimental procedures, the deviation of modelling regression, and the synergetic effect of structural and experimental features. The ABTS radical scavenging assays revealed that all the tested peptides were strong ABTS+· scavengers with the antioxidant capabilities approximately twice as high as trolox and higher than glutathione. The ferric reducing activities of the peptides were, on the other hand, much weaker than that of trolox suggesting different antioxidant mechanisms inserted by trolox and the peptides. This work was a demonstration that 3D‐QSAR methods can be employed in conjunction with experimental methods to effectively detect and design antioxidant peptides. [ABSTRACT FROM AUTHOR]

Docking studies play a critical role in the current workflow of drug discovery. However, limitations may often arise through factors including inadequate ligand sampling, a lack of protein flexibility, scoring function inadequacies (e.g., due to metals, co‐factors, etc.), and difficulty in retaining explicit water molecules. Herein, we present a novel CHARMM‐based induced fit docking (CIFDock) workflow that can circumvent these limitations by employing all‐atom force fields coupled to enhanced sampling molecular dynamics procedures. Self‐guided Langevin dynamics simulations are used to effectively sample relevant ligand conformations, side chain orientations, crystal water positions, and active site residue motion. Protein flexibility is further enhanced by dynamic sampling of side chain orientations using an expandable rotamer library. Steps in the procedure consisting of fixing individual components (e.g., the ligand) while sampling the other components (e.g., the residues in the active site of the protein) allow for the complex to adapt to conformational changes. Ultimately, all components of the complex—the protein, ligand, and waters—are sampled simultaneously and unrestrained with SGLD to capture any induced fit effects. This modular flexible docking procedure is automated using CHARMM scripting, interfaced with SLURM array processing, and parallelized to use the desired number of processors. We validated the CIFDock procedure by performing cross‐docking studies using a data set comprised of 21 pharmaceutically relevant proteins. Five variants of the CHARMM‐based SWISSDOCK scoring functions were created to quantify the results of the final generated poses. Results obtained were comparable to, or in some cases improved upon, commercial docking program data. [ABSTRACT FROM AUTHOR]

Understanding the ways of interaction of a drug with a biological target is crucial in the pharmacology field. One methodology that allows the simulation of new drugs under in vitro conditions is the classical molecular dynamics (MD) method. However, MD simulations in systems containing metallodrugs are challenging due to the lack of specific parameters found in the literature. In this work, we propose and model a new possible anticancer platinum(II) complex: cis‐dichloro(2‐aminomethylpyridine)platinum(II) bonded to 2‐(4′‐amino‐2′‐hydroxyphenyl)benzothiazole (AHBT). The developed model consists of a new set of AMBER force field parameters based on density functional theory (DFT) calculations. The extensive validation of the parameter set shows that it adequately describes the structural properties of the complex. Overall, we expect this work to contribute significantly to future MD simulations of Pt(II) complexes in biological targets that are still not well explored, mainly due to the few parameters found in the literature. [ABSTRACT FROM AUTHOR]

The possible effect of plastic nanoparticles of waste origin on biological systems is still unclear and could pose a severe threat. Model studies at the molecular level are urgently needed in order to help reveal the interplay between these particles and biological systems, and thereby to indicate the direction of further research. In the present study, simulated annealing molecular dynamics is adjusted and applied to generate an array of conformations for a sample peptide oligoalanine possibly binding to polyethylene and nylon 6,6 nanoplastics. The resulting structures, with a diameter of up to 5 nm, were investigated with the aid of static quantum chemical calculations. The obtained data unequivocally show that both plastic nanoparticles influence the relative stability of α‐helix, β‐hairpin, and other conformations strongly. Polyethylene nanoparticle increases the stability of the helical foldamer. Nylon 6,6 nanoplastic offers strong plastic‐peptide interactions on its surface, making the unfolding of the peptide thermodynamically highly favorable. These results further underscore that nanoplastics can do significant molecular‐level damage to living organisms via facilitating the misfolding and denaturation of proteins. Furthermore, it is apparent that plastics can have very different effects on living matter depending upon their composition, and hence experiments with any single kind of plastics (eg, polystyrene) should not be considered generally valid for all nanoplastics. [ABSTRACT FROM AUTHOR]

FFLUX is a biomolecular force field under construction, based on Quantum Chemical Topology (QCT) and machine learning (kriging), with a minimalistic and physically motivated design. A detailed analysis of the forces within the kriging models as treated in FFLUX is presented, taking as a test example a liquid water model. The energies of topological atoms are modeled as 3Natoms‐6 dimensional potential energy surfaces, using atomic local frames to represent the internal degrees of freedom. As a result, the forces within the kriging models in FFLUX are inherently N‐body in nature where N refers to Natoms. This provides a fuller picture that is closer to a true quantum mechanical representation of interactions between atoms. The presented computational example quantitatively showcases the non‐negligible (as much as 9%) three‐body nature of bonded forces and angular forces in a water molecule. We discuss the practical impact on the pressure calculation with N‐body forces and periodic boundary conditions (PBC) in molecular dynamics, as opposed to classical force fields with two‐body forces. The equivalence between the PBC‐related correction terms in the general virial equation is shown mathematically. [ABSTRACT FROM AUTHOR]

The AutoDock suite provides a comprehensive toolset for computational ligand docking and drug design and development. The suite builds on 30 years of methods development, including empirical free energy force fields, docking engines, methods for site prediction, and interactive tools for visualization and analysis. Specialized tools are available for challenging systems, including covalent inhibitors, peptides, compounds with macrocycles, systems where ordered hydration plays a key role, and systems with substantial receptor flexibility. All methods in the AutoDock suite are freely available for use and reuse, which has engendered the continued growth of a diverse community of primary users and third‐party developers. [ABSTRACT FROM AUTHOR]

Flavin containing molecules form a group of important cofactors that assist a wide range of enzymatic reactions. Flavins use the redox‐active isoalloxazine system, which is capable of one‐ and two‐electron transfer reactions and can exist in several protonation states. In this work, molecular mechanics force field parameters compatible with the CHARMM36 all‐atom additive force field were derived for biologically important flavins, including riboflavin, flavin mononucleotide, and flavin adenine dinucleotide. The model was developed for important protonation and redox states of the isoalloxazine group. The partial charges were derived using the CHARMM force field parametrization strategy, where quantum mechanics water–solute interactions are used to target optimization. In addition to monohydrate energies and geometries, electrostatic potential around the compound was used to provide additional restraints during the charge optimization. Taking into account the importance of flavin‐containing molecules special attention was given to the quality of bonded terms. All bonded terms, including stiff terms and torsion angle parameters, were parametrized using exhaustive potential energy surface scans. In particular, the model reproduces well the butterfly motion of isoalloxazine in the oxidized and reduced forms as predicted by quantum mechanics in gas phase. The model quality is illustrated by simulations of four flavoproteins. Overall, the presented molecular mechanics model will be of utility to model flavin cofactors in different redox states. © 2019 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

The parametrization of classical nonbonded models of metal ions has been widely addressed in the recent years. Despite the continuous development of novel and more physically inspired functional forms, the 12‐6 Lennard‐Jones plus Coulomb potential is still the most adopted force field in molecular dynamics (MD) codes, owing to its simple form and easy implementation. However, due to the integer formal charge, unpolarizable force fields of ions may suffer from overestimated interatomic electrostatic interactions, leading to nonphysical clustering or repulsion between such full charges. The electronic continuum correction (ECC) can fix this problem through a simple inclusion of solvent polarization effects via ionic charge rescaling. In this work, the development of novel nonbonded models for mono, divalent, and highly charged metal ions is presented. For each metal species, the ionic charge has been scaled, according to the ECC. Lennard‐Jones parameters have been optimized using experimental structural and thermodynamic properties as target quantities. Performances of the proposed models are discussed and compared with the literature data, while transferability attitudes among different and well‐known water models are evaluated. © 2019 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

The topmost polyamide (PA) layer of the thin‐film‐composite reverse osmosis (RO) membrane is the most important part in the membrane‐based RO technology. With the aid of molecular dynamics simulations, many PA layer‐related features in the RO process can be revealed. With many novel types of PA RO membranes out of trimesoyl chloride/m‐phenylenediamine monomers developed in the laboratory, a convenient model building tool for these PA layer systems is urgently needed to conduct the theoretical analysis. Here, we develop a new universal toolkit for constructing PA RO membranes, named as MembrFactory, which combines flexibility of force fields and membrane compositions. A key characteristic of our approach is the use of monomers as the starting state, and the final membrane model was obtained automatically by stepwise reaction between the functional groups on the monomers. The reliability of MembrFactory has been validated by constructing several common PA RO membranes. © 2019 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

We propose a general strategy to remediate force‐field artifacts in describing pairwise interactions among similar molecules M in the vicinity of another chemical species, C, like water molecules interacting at short distance from a monoatomic ion. This strategy is based on introducing a three‐body potential energy term that alters the pairwise interactions among M‐type molecules when they lie at short range from the species C. In other words the species C is the center of a space domain where the pairwise interactions among the molecules M is altered. Here, we apply it to improve the description of the water interactions provided by the polarizable water model TCPE/2013 in the vicinity of halides, from F− to At−, and of the prototypical carboxylate anion CH3COO−. We show the accuracy and the transferability of such an approach to investigate not only the hydration process of single anions but also of a salt solution NH4+/Cl‐ in aqueous phase. This strategy can be used to remediate the drawbacks of any kind of force fields. © 2019 Wiley Periodicals, Inc. A general strategy is proposed to remediate force‐field artifacts in describing solvent pairwise interactions in the vicinity of a solute, based on introducing a three‐body potential energy term that alters the solvent pairwise interactions in a space domain centered at the solute. It is used to improve the description of the water interactions provided by the polarizable water model TCPE/2013 in the vicinity of halides, from F− to At−, and of the carboxylate anion CH3COO−. [ABSTRACT FROM AUTHOR]

Available estimates indicate that 30–40 % of all proteins need at least one metal ion to perform their biological function. Therefore, they are called metalloproteins. The correct biosynthesis of metalloproteins requires living organisms to be able to cope with issues such as the limited bioavailability or the potential cytotoxicity of several essential metals. Thus, organisms have developed complex machineries that guarantee the proper intracellular concentration and distribution among compartments of each metal, i.e. metal homeostasis. To understand how the different proteins responsible for metal homeostasis carry out their function, it is necessary to investigate their three‐dimensional (3D) structure and mobility at the atomic level. Nuclear magnetic resonance spectroscopy is one of the main experimental techniques providing this information. Computer simulations of molecular dynamics (MD) complement experimental information by showing how the 3D structure fluctuates over time and as a function of environmental conditions, with the possibility of exploring a wider range of timescales and conditions than usually amenable to experiment. Here we review numerous applications of MD for the investigation of the structure and dynamics of metalloproteins, and we also mention some technical aspects related to the parametrization of metals in commonly used force fields. Molecular dynamics simulations complement experimental information providing an atomistic view on biological functions. Here we discuss some applications to the study of metalloproteins, from the determination of their 3D structures, to their role in metal storage, transport and metal‐dependent transcriptional regulation. Some technical aspects related to the parametrization of metal ions are also mentioned. [ABSTRACT FROM AUTHOR]

We present a new force field for zinc ion to be used in classical Molecular Dynamics. Such model has been developed according to an optimization procedure by fitting experimental quantities taken from the literature. The metal ion electrostatic charge has been scaled by a factor 0.75, according to a recently proposed polarization model which considers the electronic screening effects due to the surrounding environment. Zinc interatomic interactions have been modeled by means of the Waldman‐Hagler combination rule. It is demonstrated that the proposed force field for zinc is transferable from water solution to protein catalytic sites, offering in both the considered cases good performances in the reproduction of experimental quantities usually addressed in soft matter computer simulations. A new force field for zinc ion, based on the standard Lennard‐Jones plus Coulomb potential, is presented. The ion point charge has been scaled to consider the electronic screening effects due to the environment. Structural and thermodynamic properties in water solution and protein metal centers have been calculated by means of molecular simulations, showing a fair agreement with experimental data. [ABSTRACT FROM AUTHOR]

The key to nanoscale control of physical, chemical, and biological processes lies in well‐founded models of noncovalent binding. Atomistic simulations probe the free‐energy surface underlying molecular assembly processes in solution. Two examples of noncovalent binding studied by molecular dynamics simulations are discussed, the dimerization of a water‐soluble perylene bisimide derivative in aqueous solution with a focus on the influence of solvent composition on the aggregation strength and the binding of 1‐butanol to α‐cyclodextrin at infinite dilution with the focus on the determination of method‐independent binding free energies. The association of molecular building blocks in a given solvent depends in a complex manner on the solvent composition. Molecular dynamics simulations provide a molecular‐level understanding of the various driving forces. In this work, two examples of noncovalent binding are studied and some aspects of force field validation are illustrated. [ABSTRACT FROM AUTHOR]

Intrinsically disordered proteins (IDPs) are closely related to various human diseases. Because IDPs lack certain tertiary structure, it is difficult to use X‐ray and NMR methods to measure their structures. Therefore, molecular dynamics simulation is a useful tool to study the conformer distribution of IDPs. However, most generic protein force fields were found to be insufficient in simulations of IDPs. Here, we report our development for the CHARMM community. Our residue‐specific IDP force field (CHARMM36IDPSFF) was developed based on the base generic force field with CMAP corrections for all 20 naturally occurring amino acids. Multiple tests show that the simulated chemical shifts with the newly developed force field are in quantitative agreement with NMR experiment and are more accurate than the base generic force field. Comparison of J‐couplings with previous work shows that CHARMM36IDPSFF and its corresponding base generic force field have their own advantages. In addition, CHARMM36IDPSFF simulations also agree with experiment for SAXS profiles and radii of gyration of IDPs. Detailed analysis shows that CHARMM36IDPSFF can sample more diverse and disordered conformers. These findings confirm that the newly developed force field can improve the balance of accuracy and efficiency for the conformer sampling of IDPs. [ABSTRACT FROM AUTHOR]

Neurodegenerative amyloidogenesis begins with the aggregation of intrinsically disordered proteins (IDPs), which is the first step in a cascade of assembly events that can lead to insoluble fibrous deposits in brain tissue. IDP conformations that promote formation of toxic oligomers remain poorly understood, and are the most fundamental target of putative treatments for neurodegenerative disease. Rapid advances in theory, simulation and experimental methods, hold the promise of reversing protein aggregation by identifying and developing inhibitors of the transient amyloidogenic IDP conformations. To make meaningful progress it is important to appreciate the benefits and limitations of the latest developments in computational methods of conformational and ensemble modeling, and their integration and validation with experiments. Integrated studies are beginning to provide significant conceptual and mechanistic insights, including identification of the properties of amyloidogenic IDPs in their free, unbound form. At the same time, contradicting viewpoints have emerged concerning convergence of IDP ensemble signatures and properties from parallel studies, and there also remains a pressing need to develop physical models that can deliver reliable predictions across different IDP families. Focussing on the four most common amyloidogenic IDPs of Amyloid β, Tau, α‐synuclein and Prions, improvements are proposed for next‐generation models and experiments that can potentially identify drug treatments for neurodegenerative disease via incorporation of the extended cellular environment. This article is categorized under: Molecular and Statistical Mechanics > Molecular Mechanics Structure and Mechanism > Computational Biochemistry and Biophysics Structure and Mechanism > Molecular Structures [ABSTRACT FROM AUTHOR]

Abstract: Structural characterization of liquid pyrrole has been obtained by performing classical molecular dynamics simulations with a new parameterization of electrostatic interactions. Despite the relatively simple molecular structure of pyrrole, a correct and accurate representation of its intermolecular interactions in bulk phase is a challenging task, since these are affected at short range by the quadrupole–quadrupole term. This new parameterization permits not only to correctly describe the liquid structure but also to obtain macroscopic properties in excellent agreement with experiments. [ABSTRACT FROM AUTHOR]

Abstract: Recently, stereoinversions and isomerizations of amino acid residues in the proteins of living beings have been observed. Because isomerized amino acids cause structural changes and denaturation of proteins, isomerizations of amino acid residues are suspected to cause age‐related diseases. In this study, AMBER molecular force field parameters were tested by using computationally generated nonapeptides and tripeptides including stereoinverted and/or isomerized amino acid residues. Energy calculations by using density functional theory were also performed for comparison. Although the force field parameters were developed by parameter fitting for l‐α‐amino acids, the accuracy of the computational results for d‐amino acids and β‐amino acids was comparable to those for l‐α‐amino acids. The conformational energies for tripeptides calculated by using density functional theory were reproduced more accurately than those for nonapeptides calculated by using the molecular mechanical force field. The evaluations were performed for the ff99SB, ff03, ff12SB, and the latest ff14SB force field parameters. [ABSTRACT FROM AUTHOR]

On their way through the heliosphere, galactic cosmic rays (GCRs) are modulated by various effects before they can be detected at Earth. This process can be described by the Parker equation, which calculates the phase space distribution of GCRs depending on the main modulation processes: convection, drifts, diffusion, and adiabatic energy changes. A first-order approximation of this equation is the force field approach, reducing it to a one-parameter dependency, the solar modulation potential ϕ. Utilizing this approach, it is possible to reconstruct ϕ from ground-based and spacecraft measurements. However, it has been shown previously that ϕ depends not only on the local interstellar spectrum (LIS) but also on the energy range of interest. We have investigated this energy dependence further, using published proton intensity spectra obtained by PAMELA and heavier nuclei measurements from IMP-8 and ACE/CRIS. Our results show severe limitations at lower energies including a strong dependence on the solar magnetic epoch. Based on these findings, we will outline a new tool to describe GCR proton spectra in the energy range from a few hundred MeV to tens of GeV over the last solar cycles. In order to show the importance of our modification, we calculate the global production rates of the cosmogenic radionuclide 10Be which is a proxy for the solar activity ranging back thousands of years. [ABSTRACT FROM AUTHOR]

Free-energy calculations based on molecular simulations provide access to a wide range of thermodynamic properties such as the solubility and partitioning of a molecule in and between various phases. It is demonstrated how molecular dynamics free-energy simulations may be used to obtain the solubilities of primary alcohols and n-alkanes in water and binding affinities of primary alcohols to α-cyclodextrin. The equivalence of two distinct routes to calculate binding free energies is shown leading to the conclusion that host-guest binding affinities may be used to probe the underlying molecular force field. [ABSTRACT FROM AUTHOR]

We re-investigate the image charge model of Iori and Corni (Iori and Corni, J. Comput. Chem. 2008, 29, 1656). We find that a simple symmetrization of their model allows to obtain quantitatively correct results for the electrostatic interaction of a water molecule with a metallic surface. This symmetrization reduces the magnitude of the electrostatic interaction to less than 10% of the total interaction energy. © 2017 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

An accurate van der Waals force field (VDW FF) was derived from highly precise quantum mechanical (QM) calculations. Small molecular clusters were used to explore van der Waals interactions between gas molecules and porous materials. The parameters of the accurate van der Waals force field were determined by QM calculations. To validate the force field, the prediction results from the VDW FF were compared with standard FFs, such as UFF, Dreiding, Pcff, and Compass. The results from the VDW FF were in excellent agreement with the experimental measurements. This force field can be applied to the prediction of the gas density (H2, CO2, C2H4, CH4, N2, O2) and adsorption performance inside porous materials, such as covalent organic frameworks (COFs), zeolites and metal organic frameworks (MOFs), consisting of H, B, N, C, O, S, Si, Al, Zn, Mg, Ni, and Co. This work provides a solid basis for studying gas adsorption in porous materials. © 2017 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Conventional classical force fields by construction do not explicitly partition intermolecular interactions to include polarization and charge transfer effects, whereas fully quantum mechanical treatments allow a means to effect this dissection (although not uniquely due to the lack of a charge transfer operator). Considering the importance of polarization in a variety of systems, a particular class of classical models, charge equilibration models, have been extensively developed to study those systems; since these types of interaction models are inherently based on movement of charge throughout a system, they are natural platform for including polarization and charge transfer effects within the context of molecular simulations. Here, we present two bond-space charge equilibration models we term as QE2 and mixed QE2 treat charge transfer in classical molecular mechanical calculations those provide practical solutions to two major drawbacks of charge equilibration models: (a) a nonvanishing amount of charge transfer between two heteroatoms at large separations, and (b) superlinear polarizability scaling during bond dissociation due to charge transfer over unphysical, large distances. To control charge transfer during dissociation of a bond in a molecular system, we introduce a distance-dependent scaling function (QE2 model) which, controls and recovers physical behavior of the homonuclear and heteronuclear charge transfer between two atoms at small and large values of internuclear separation; and the mixed QE2 model in which we combine the QE2 model under allow and disallow charge transfer situations that describe both charge transfer and polarizability in a distance-dependent manner. We demonstrate the utility of both models in the case of a water dimer, and compare the results with other existing models, and further, we perform short molecular dynamics simulations for few water clusters with the QE2 model to show the charge transfer and internuclear separation are correlated in dynamics. © 2017 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

The fully polarizable, multipolar, and atomistic force field protein FFLUX is being built from machine learning (i.e., kriging) models, each of which predicts an atomic property. Each atom of a given protein geometry needs to be assigned such a kriging model. Such a knowledgeable atom needs to be informed about a sufficiently large environment around it. The resulting complexity can be tackled by collecting the 20 natural amino acids into a few groups. Using substituted deca-alanines, we present the proof-of-concept that a given atom's charge can be modeled by a few kriging models only. © 2017 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

The force field conversion from one MD program to another one is exhausting and error-prone. Although single conversion tools from one MD program to another exist not every combination and both directions of conversion are available for the favorite MD programs A mber, C harmm, D l-P oly, G romacs, and L ammps. We present here a general tool for the force field conversion on the basis of an XML document. The force field is converted to and from this XML structure facilitating the implementation of new MD programs for the conversion. Furthermore, the XML structure is human readable and can be manipulated before continuing the conversion. We report, as testcases, the conversions of topologies for acetonitrile, dimethylformamide, and 1-ethyl-3-methylimidazolium trifluoromethanesulfonate comprising also Urey-Bradley and Ryckaert-Bellemans potentials. © 2017 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

We present a systematic test of the performance of three popular united-atom force fields-OPLS-UA, GROMOS and TraPPE-at predicting hydrophobic solvation, more precisely at describing the solvation of alkanes in alkanes. Gibbs free energies of solvation were calculated for 52 solute/solvent pairs from Molecular Dynamics simulations and thermodynamic integration making use of the IBERCIVIS volunteer computing platform. Our results show that all force fields yield good predictions when both solute and solvent are small linear or branched alkanes (up to pentane). However, as the size of the alkanes increases, all models tend to increasingly deviate from experimental data in a systematic fashion. Furthermore, our results confirm that specific interaction parameters for cyclic alkanes in the united-atom representation are required to account for the additional excluded volume within the ring. Overall, the TraPPE model performs best for all alkanes, but systematically underpredicts the magnitude of solvation free energies by about 6% (RMSD of 1.2 kJ/mol). Conversely, both GROMOS and OPLS-UA systematically overpredict solvation free energies (by ∼13% and 15%, respectively). The systematic trends suggest that all models can be improved by a slight adjustment of their Lennard-Jones parameters. © 2016 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Intrinsically disordered proteins are essential for biological processes such as cell signalling, but are also associated to devastating diseases including Alzheimer's disease, Parkinson's disease or type II diabetes. Because of their lack of a stable three-dimensional structure, molecular dynamics simulations are often used to obtain atomistic details that cannot be observed experimentally. The applicability of molecular dynamics simulations depends on the accuracy of the force field chosen to represent the underlying free energy surface of the system. Here, we use replica exchange molecular dynamics simulations to test five modern force fields, OPLS, AMBER99SB, AMBER99SB*ILDN, AMBER99SBILDN-NMR and CHARMM22*, in their ability to model Aβ42, an intrinsically disordered peptide associated with Alzheimer's disease, and compare our results to nuclear magnetic resonance (NMR) experimental data. We observe that all force fields except AMBER99SBILDN-NMR successfully reproduce local NMR observables, with CHARMM22* being slightly better than the other force fields. [ABSTRACT FROM AUTHOR]

Intrinsically disordered proteins are proteins which lack of specific tertiary structure and unable to fold spontaneously without the partner binding. These intrinsically disordered proteins are found to associate with various diseases, such as diabetes, cancer, and neurodegenerative diseases. However, current widely used force fields, such as ff99SB, ff14SB, OPLS/AA, and Charmm27, are insufficient in sampling the conformational characters of intrinsically disordered proteins. In this study, the CMAP method was used to correct the φ/ ψ distributions of disorder-promoting amino acids. The simulation results show that the force filed parameters ( ff14IDPs) can improve the φ/ ψ distributions of the disorder-promoting amino acids, with RMSD less than 0.10% relative to the benchmark data of intrinsically disordered proteins. Further test suggests that the calculated secondary chemical shifts under ff14IDPs are in quantitative agreement with the data of NMR experiment for five tested systems. In addition, the simulation results show that ff14IDPs can still be used to model structural proteins, such as tested lysozyme and ubiquitin, with better performance in coil regions than the original general Amber force field ff14SB. These findings confirm that the newly developed Amber ff14IDPs is a robust model for improving the conformation sampling of intrinsically disordered proteins. [ABSTRACT FROM AUTHOR]

ABSTRACT Calcium ions (Ca2+) play key roles in various fundamental biological processes such as cell signaling and brain function. Molecular dynamics (MD) simulations have been used to study such interactions, however, the accuracy of the Ca2+ models provided by the standard MD force fields has not been rigorously tested. Here, we assess the performance of the Ca2+ models from the most popular classical force fields AMBER and CHARMM by computing the osmotic pressure of model compounds and the free energy of DNA-DNA interactions. In the simulations performed using the two standard models, Ca2+ ions are seen to form artificial clusters with chloride, acetate, and phosphate species; the osmotic pressure of CaAc2 and CaCl2 solutions is a small fraction of the experimental values for both force fields. Using the standard parameterization of Ca2+ ions in the simulations of Ca2+-mediated DNA-DNA interactions leads to qualitatively wrong outcomes: both AMBER and CHARMM simulations suggest strong inter-DNA attraction whereas, in experiment, DNA molecules repel one another. The artificial attraction of Ca2+ to DNA phosphate is strong enough to affect the direction of the electric field-driven translocation of DNA through a solid-state nanopore. To address these shortcomings of the standard Ca2+ model, we introduce a custom model of a hydrated Ca2+ ion and show that using our model brings the results of the above MD simulations in quantitative agreement with experiment. Our improved model of Ca2+ can be readily applied to MD simulations of various biomolecular systems, including nucleic acids, proteins and lipid bilayer membranes. © 2016 Wiley Periodicals, Inc. Biopolymers 105: 752-763, 2016. [ABSTRACT FROM AUTHOR]

ABSTRACT Specialized to simulate proteins in molecular dynamics (MD) simulations with explicit solvation, FF12MC is a combination of a new protein simulation protocol employing uniformly reduced atomic masses by tenfold and a revised AMBER forcefield FF99 with (i) shortened CH bonds, (ii) removal of torsions involving a nonperipheral sp3 atom, and (iii) reduced 1-4 interaction scaling factors of torsions ϕ and ψ. This article reports that in multiple, distinct, independent, unrestricted, unbiased, isobaric-isothermal, and classical MD simulations FF12MC can (i) simulate the experimentally observed flipping between left- and right-handed configurations for C14-C38 of BPTI in solution, (ii) autonomously fold chignolin, CLN025, and Trp-cage with folding times that agree with the experimental values, (iii) simulate subsequent unfolding and refolding of these miniproteins, and (iv) achieve a robust Z score of 1.33 for refining protein models TMR01, TMR04, and TMR07. By comparison, the latest general-purpose AMBER forcefield FF14SB locks the C14-C38 bond to the right-handed configuration in solution under the same protein simulation conditions. Statistical survival analysis shows that FF12MC folds chignolin and CLN025 in isobaric-isothermal MD simulations 2-4 times faster than FF14SB under the same protein simulation conditions. These results suggest that FF12MC may be used for protein simulations to study kinetics and thermodynamics of miniprotein folding as well as protein structure and dynamics. Proteins 2016; 84:1490-1516. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

The cover image, by Yuan‐Ping Pang., is based on the FF12MC: A revised AMBER forcefield and new protein simulation protocol, DOI: 10.1002/prot.25094. [ABSTRACT FROM AUTHOR]

New equations for torque and atomic force are derived for use in flexible molecule force fields with atomic multipoles. The expressions are based on Cartesian tensors with arbitrary multipole rank. The standard method for rotating Cartesian tensor multipoles and calculating torque is to first represent the tensor with n indexes and 3 n redundant components. In this work, new expressions for directly rotating the unique ( n + 1)( n + 2)/2 Cartesian tensor multipole components Θ pqr are given by introducing Cartesian tensor rotation matrix elements X( R). A polynomial expression and a recursion relation for X( R) are derived. For comparison, the analogous rotation matrix for spherical tensor multipoles are the Wigner functions D( R). The expressions for X( R) are used to derive simple equations for torque and atomic force. The torque and atomic force equations are applied to the geometry optimization of small molecule crystal unit cells. In addition, a discussion of computational efficiency as a function of increasing multipole rank is given for Cartesian tensors. © 2016 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

A non-polarizable force field based on atomic multipoles fit to reproduce experimental crystal properties and ab initio gas-phase dimers is described. The Ewald method is used to calculate both long-range electrostatic and 1/ r6 dispersion energies of crystals. The dispersion energy of a crystal calculated by a cutoff method is shown to converge slowly to the exact Ewald result. A method for constraining space-group symmetry during unit-cell optimization is derived. Results for locally optimizing 4427 unit cells including volume, cell parameters, unit-cell r.m.s.d. and CPU timings are given for both flexible and rigid molecule optimization. An algorithm for randomly generating rigid molecule crystals is described. Using the correct experimentally determined space group, the average and maximum number of random crystals needed to find the correct experimental structure is given for 2440 rigid single component crystals. The force field energy rank of the correct experimental structure is presented for the same set of 2440 rigid single component crystals assuming the correct space group. A complete crystal prediction is performed for two rigid molecules by searching over the 32 most probable space groups. [ABSTRACT FROM AUTHOR]

Computational protein design depends on an energy function and an algorithm to search the sequence/conformation space. We compare three stochastic search algorithms: a heuristic, Monte Carlo (MC), and a Replica Exchange Monte Carlo method (REMC). The heuristic performs a steepest-descent minimization starting from thousands of random starting points. The methods are applied to nine test proteins from three structural families, with a fixed backbone structure, a molecular mechanics energy function, and with 1, 5, 10, 20, 30, or all amino acids allowed to mutate. Results are compared to an exact, 'Cost Function Network' method that identifies the global minimum energy conformation (GMEC) in favorable cases. The designed sequences accurately reproduce experimental sequences in the hydrophobic core. The heuristic and REMC agree closely and reproduce the GMEC when it is known, with a few exceptions. Plain MC performs well for most cases, occasionally departing from the GMEC by 3-4 kcal/mol. With REMC, the diversity of the sequences sampled agrees with exact enumeration where the latter is possible: up to 2 kcal/mol above the GMEC. Beyond, room temperature replicas sample sequences up to 10 kcal/mol above the GMEC, providing thermal averages and a solution to the inverse protein folding problem. © 2016 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

The currently available force field parameters for modified RNA residues in AMBER show significant deviations in conformational properties from experimental observations. The examination of the transferability of the recently revised torsion parameters revealed that there was an overall improvement in the conformational properties for some of the modifications but the improvements were still insufficient in describing the sugar pucker preferences ( J. Chem. Inf. Model. 2014, 54, 1129-1142). Here, we report an approach for the development and fine tuning of the AMBER force field parameters for 2-thiouridine, 4-thiouridine, and pseudouridine with diverse conformational preferences. The χ torsion parameters were reparameterized at the individual nucleoside level. The effect of combining the revised γ torsion parameter and modifying the Lennard-Jones σ parameters were also tested by directly comparing the conformational preferences obtained from our extensive molecular dynamics simulations with those from experimental observations. © 2016 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

The prediction of mutation-induced free-energy changes in protein thermostability or protein-protein binding is of particular interest in the fields of protein design, biotechnology, and bioengineering. Herein, we achieve remarkable accuracy in a scan of 762 mutations estimating changes in protein thermostability based on the first principles of statistical mechanics. The remaining error in the free-energy estimates appears to be due to three sources in approximately equal parts, namely sampling, force-field inaccuracies, and experimental uncertainty. We propose a consensus force-field approach, which, together with an increased sampling time, leads to a free-energy prediction accuracy that matches those reached in experiments. This versatile approach enables accurate free-energy estimates for diverse proteins, including the prediction of changes in the melting temperature of the membrane protein neurotensin receptor 1. [ABSTRACT FROM AUTHOR]