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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]

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 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]

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]

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]