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Machine learning has rapidly become a key method for the analysis and organization of large-scale data in all scientific disciplines. In life sciences, the use of machine learning techniques is a particularly appealing idea since the enormous capacity of computational infrastructures generates terabytes of data through millisecond simulations of atomistic and molecular-scale biomolecular systems. Due to this explosion of data, the automation, reproducibility, and objectivity provided by machine learning methods are highly desirable features in the analysis of complex systems. In this review, we focus on the use of machine learning in biomolecular simulations. We discuss the main categories of machine learning tasks, such as dimensionality reduction, clustering, regression, and classification used in the analysis of simulation data. We then introduce the most popular classes of techniques involved in these tasks for the purpose of enhanced sampling, coordinate discovery, and structure prediction. Whenever possible, we explain the scope and limitations of machine learning approaches, and we discuss examples of applications of these techniques.

From the point of view of statistical mechanics, a full characterization of a molecular system requires an accurate determination of its possible states, their populations, and the respective interconversion rates. Toward this goal, well-established methods increase the accuracy of molecular dynamics simulations by incorporating experimental information about states using structural restraints and about populations using thermodynamics restraints. However, it is still unclear how to include experimental knowledge about interconversion rates. Here, we introduce a method of imposing known rate constants as constraints in molecular dynamics simulations, which is based on a combination of the maximum-entropy and maximum-caliber principles. Starting from an existing ensemble of trajectories, obtained from either molecular dynamics or enhanced trajectory sampling, this method provides a minimally perturbed path distribution consistent with the kinetic constraints, as well as modified free energy and committor landscapes. We illustrate the application of the method to a series of model systems, including all-atom molecular simulations of protein folding. Our results show that by combining experimental rate constants and molecular dynamics simulations, this approach enables the determination of transition states, reaction mechanisms, and free energies. We anticipate that this method will extend the applicability of molecular simulations to kinetic studies in structural biology and that it will assist the development of force fields to reproduce kinetic and thermodynamic observables. Furthermore, this approach is generally applicable to a wide range of systems in biology, physics, chemistry, and material science.

We present an approach to Langevin dynamics in the presence of holonomic constraints based on decomposition of the system into components representing geodesic flow, constrained impulse and constrained diffusion. We show that a particular ordering of the components results in an integrator that is an order of magnitude more accurate for configurational averages than existing alternatives. Moreover, by combining the geodesic integration method with a solvent–solute force splitting, we demonstrate that stepsizes of at least 8 fs can be used for solvated biomolecules with high sampling accuracy and without substantially altering diffusion rates, approximately increasing by a factor of two the efficiency of molecular dynamics sampling for such systems. The methods described in this article are easily implemented using the standard apparatus of modern simulation codes.

The statistical mechanics-based 3-dimensional reference interaction site model with the Kovalenko-Hirata closure (3D-RISM-KH) molecular solvation theory has proven to be an essential part of a multiscale modeling framework, covering a vast region of molecular simulation techniques. The successful application ranges from the small molecule solvation energy to the bulk phase behavior of polymers, macromolecules, etc. The 3D-RISM-KH successfully predicts and explains the molecular mechanisms of self-assembly and aggregation of proteins and peptides related to neurodegeneration, protein-ligand binding, and structure-function related solvation properties. Upon coupling the 3D-RISM-KH theory with a novel multiple time-step molecular dynamic (MD) of the solute biomolecule stabilized by the optimized isokinetic Nosé–Hoover chain thermostat driven by effective solvation forces obtained from 3D-RISM-KH and extrapolated forward by generalized solvation force extrapolation (GSFE), gigantic outer time-steps up to picoseconds to accurately calculate equilibrium properties were obtained in this new quasidynamics protocol. The multiscale OIN/GSFE/3D-RISM-KH algorithm was implemented in the Amber package and well documented for fully flexible model of alanine dipeptide, miniprotein 1L2Y, and protein G in aqueous solution, with a solvent sampling rate ~150 times faster than a standard MD simulation in explicit water. Further acceleration in computation can be achieved by modifying the extent of solvation layers considered in the calculation, as well as by modifying existing closure relations. This enhanced simulation technique has proven applications in protein-ligand binding energy calculations, ligand/solvent binding site prediction, molecular solvation energy calculations, etc. Applications of the RISM-KH theory in molecular simulation are discussed in this work.

Solvents represent the quiet majority in biomolecular systems, yet modeling their influence with both speed and ri:gor remains a central challenge. This study maps the state of the art in implicit solvent theory and practice, spanning classical continuum electrostatics (PB/GB; DelPhi, APBS), modern nonpolar and cavity/dispersion treatments, and quantum–continuum models (PCM, COSMO/COSMO-RS, SMx/SMD). We highlight where these methods excel and where they falter, namely, around ion specificity, heterogeneous interfaces, entropic effects, and parameter sensitivity. We then spotlight two fast-moving frontiers that raise both accuracy and throughput: machine learning-augmented approaches that serve as PB-accurate surrogates, learn solvent-averaged potentials for MD, or supply residual corrections to GB/PB baselines, and quantum-centric workflows that couple continuum solvation methods, such as IEF-PCM, to sampling on real quantum hardware, pointing toward realistic solution-phase electronic structures at emerging scales. Applications across protein–ligand binding, nucleic acids, and intrinsically disordered proteins illustrate how implicit models enable rapid hypothesis testing, large design sweeps, and long-time sampling. Our perspective argues for hybridization as a best practice, meaning continuum cores refined by improved physics, such as multipolar water, ML correctors with uncertainty quantification and active learning, and quantum–continuum modules for chemically demanding steps.

The formation of biomolecular materials via dynamical interfacial processes, such as self-assembly and fusion, for diverse compositions and external conditions can be efficiently probed using ensemble Molecular Dynamics (MD). However, this approach requires many simulations when investigating a large composition phase space. In addition, there is difficulty in predicting whether each simulation will yield biomolecular materials with the desired properties or outcomes and how long each simulation will run. These difficulties can be overcome by rules-based management systems, including intermittent inspection, variable sampling, and premature termination or extension of the individual MD simulations. Automating such a management system can significantly improve runtime efficiency and reduce the burden of organizing large ensembles of MD simulations. To this end, a computational framework, the Pipelines for Automating Compliance-based Elimination and Extension (PACE 2 ), is proposed for high-throughput ensemble biomolecular materials simulations. The PACE 2 framework encompasses Candidate pipelines, where each pipeline includes temporally separated simulation and analysis tasks. When a MD simulation is completed, an analysis task is triggered, which evaluates the MD trajectory for compliance. Compliant simulations are extended to the next MD phase with a suitable sample rate to allow additional, detailed analysis. Non-compliant simulations are eliminated, and their computational resources are reallocated or released. The framework is designed to run on local desktop computers and high-performance computing resources. Preliminary scientific results enabled by the use of PACE 2 framework are presented, which demonstrate its potential and validates its function. In the future, the framework will be extended to address generalized workflows and investigate composition-structure-property relations for other classes of materials.

Objective The purpose of this work is to optimize the processing of molecular dynamics (MD) trajectory data obtained for large biomolecular systems. Two popular software tools were chosen as the reference: the tng and the xdrfile libraries. Current implementation of tng algorithms and library is either fast or storage efficient and xdrfile is storage efficient but slow. Our aim was to combine speed and storage efficiency through the xdrfile’s code modification. Results Here we present libxtc, a ready-to-use library for reading MD trajectory files in xtc format. The effectiveness of libxtc is demonstrated for several biomolecular systems of various sizes (~ 2 × 10 4 to ~ 2 × 10 5 atoms). In sequential mode, the performance of libxtc is up to 1.8 times higher and 1.4 times lower than xdrfile and tng, respectively. In parallel mode, libxtc is about 3 and 1.3 times faster than xdrfile and tng. At the same time, MD data stored in the xtc format require about 1.3 times less disk space than those treated with the tng algorithm in the fastest reading mode, which is a noticeable saving especially when the MD trajectory is long and the number of atoms is large—this applies to most biologically relevant systems.

Staphylococcus protein A (SpA) is found in the cell wall of Staphylococcus aureus bacteria. Its ability to bind to the constant Fc regions of antibodies means it is useful for antibody extraction, and further integration with inorganic materials can lead to the development of diagnostics and therapeutics. We have investigated the adsorption of SpA on inorganic surface models such as experimentally relevant negatively charged silica, as well as positively charged and neutral surfaces, by use of fully atomistic molecular dynamics simulations. We have found that SpA, which is itself negatively charged at pH7, is able to adsorb on all our surface models. However, adsorption on charged surfaces is more specific in terms of protein orientation compared to a neutral Au (111) surface, while the protein structure is generally well maintained in all cases. The results indicate that SpA adsorption is optimal on the siloxide-rich silica surface, which is negative at pH7 since this keeps the Fc binding regions free to interact with other species in solution. Due to the dominant role of electrostatics, the results are transferable to other inorganic materials and pave the way for new diagnostic and therapeutic designs where SpA might be used to conjugate antibodies to nanoparticles.

We present the estimation of solvation free energies of small solutes in water, n -octanol and hexane using molecular dynamics simulations with two MARTINI models at different resolutions, viz. the coarse-grained (CG) and the hybrid all-atom/coarse-grained (AA/CG) models. From these estimates, we also calculate the water/hexane and water/octanol partition coefficients. More than 150 small, organic molecules were selected from the Minnesota solvation database and parameterized in a semi-automatic fashion. Using either the CG or hybrid AA/CG models, we find considerable deviations between the estimated and experimental solvation free energies in all solvents with mean absolute deviations larger than 10 kJ/mol, although the correlation coefficient is between 0.55 and 0.75 and significant. There is also no difference between the results when using the non-polarizable and polarizable water model, although we identify some improvements when using the polarizable model with the AA/CG solutes. In contrast to the estimated solvation energies, the estimated partition coefficients are generally excellent with both the CG and hybrid AA/CG models, giving mean absolute deviations between 0.67 and 0.90 log units and correlation coefficients larger than 0.85. We analyze the error distribution further and suggest avenues for improvements.

Cofactor-independent urate oxidase (UOX) is an ∼137 kDa tetrameric enzyme essential for uric acid (UA) catabolism in many organisms. UA is first oxidized by O 2 to dehydroisourate (DHU) via a peroxo intermediate. DHU then undergoes hydration to 5-hydroxyisourate (5HIU). At different stages of the reaction both catalytic O 2 and water occupy the `peroxo hole' above the organic substrate. Here, high-resolution neutron/X-ray crystallographic analysis at room temperature has been integrated with molecular dynamics simulations to investigate the hydration step of the reaction. The joint neutron/X-ray structure of perdeuterated Aspergillus flavus UOX in complex with its 8-azaxanthine (8AZA) inhibitor shows that the catalytic water molecule (W1) is present in the peroxo hole as neutral H 2 O, oriented at 45° with respect to the ligand. It is stabilized by Thr57 and Asn254 on different UOX protomers as well as by an O—H...π interaction with 8AZA. The active site Lys10–Thr57 dyad features a charged Lys10–NH 3 + side chain engaged in a strong hydrogen bond with Thr57 OG1 , while the Thr57 OG1–HG1 bond is rotationally dynamic and oriented toward the π system of the ligand, on average. Our analysis offers support for a mechanism in which W1 performs a nucleophilic attack on DHU C5 with Thr57 HG1 central to a Lys10-assisted proton-relay system. Room-temperature crystallography and simulations also reveal conformational heterogeneity for Asn254 that modulates W1 stability in the peroxo hole. This is proposed to be an active mechanism to facilitate W1/O 2 exchange during catalysis.