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Sampling from the equilibrium distribution has always been a major problem in molecular simulations due to the very high dimensionality of the conformational space. Over several decades, many approaches have been used to overcome the problem. In particular, we focus on unbiased simulation methods such as parallel and adaptive sampling. Here, we recast adaptive sampling schemes on the basis of multi-armed bandits and develop a novel adaptive sampling algorithm under this framework, AdaptiveBandit. We test it on multiple simplified potentials and in a protein folding scenario. We find that this framework performs similarly to or better than previous methods in every type of test potential. Furthermore, it provides a novel framework to develop new sampling algorithms with better asymptotic characteristics.

Motivation An important step in structure-based drug design consists in the prediction of druggable binding sites. Several algorithms for detecting binding cavities, those likely to bind to a small drug compound, have been developed over the years by clever exploitation of geometric, chemical and evolutionary features of the protein. Results Here we present a novel knowledge-based approach that uses state-of-the-art convolutional neural networks, where the algorithm is learned by examples. In total, 7622 proteins from the scPDB database of binding sites have been evaluated using both a distance and a volumetric overlap approach. Our machine-learning based method demonstrates superior performance to two other competitive algorithmic strategies. Availability and implementation DeepSite is freely available at www.playmolecule.org. Users can submit either a PDB ID or PDB file for pocket detection to our NVIDIA GPU-equipped servers through a WebGL graphical interface. Supplementary information Supplementary data are available at Bioinformatics online.

Recent advances in molecular simulations have allowed scientists to investigate slower biological processes than ever before. Together with these advances came an explosion of data that has transformed a traditionally computing-bound into a data-bound problem. Here, we present HTMD, a programmable, extensible platform written in Python that aims to solve the data generation and analysis problem as well as increase reproducibility by providing a complete workspace for simulation-based discovery. So far, HTMD includes system building for CHARMM and AMBER force fields, projection methods, clustering, molecular simulation production, adaptive sampling, an Amazon cloud interface, Markov state models, and visualization. As a result, a single, short HTMD script can lead from a PDB structure to useful quantities such as relaxation time scales, equilibrium populations, metastable conformations, and kinetic rates. In this paper, we focus on the adaptive sampling and Markov state modeling features.

HTMD is a programmable scientific platform intended to facilitate simulation-based research in molecular systems. This paper presents the functionalities of HTMD for the preparation of a molecular dynamics simulation starting from PDB structures, building the system using well-known force fields, and applying standardized protocols for running the simulations. We demonstrate the framework's flexibility for high-throughput molecular simulations by applying a preparation, building, and simulation protocol with multiple force-fields on all of the seven hundred eukaryotic membrane proteins resolved to-date from the orientation of proteins in membranes (OPM) database. All of the systems are available on www.playmolecule.org .

High-throughput molecular dynamics (MD) simulations are a computational method consisting of using multiple short trajectories, instead of few long ones, to cover slow biological time scales. Compared to long trajectories this method offers the possibility to start the simulations in successive batches, building a knowledgeable model of the available data to inform subsequent new simulations iteratively. Here, we demonstrate an automatic, iterative, on-the-fly method for learning and sampling molecular simulations in the context of ligand binding for the case of trypsin-benzamidine binding. The method uses Markov state models to learn a simplified model of the simulations and decide where best to sample from, achieving a converged binding affinity in approximately one microsecond, 1 order of magnitude faster than classical sampling. This method demonstrates for the first time the potential of adaptive sampling schemes in the case of ligand binding.