Primary Publication
HTMD: High-Throughput Molecular Dynamics for Molecular Discovery (2016)  View

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.

Related Publications
On-the-Fly Learning and Sampling of Ligand Binding by High-Throughput Molecular Simulations (2014)  View

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.