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J Chem Theory Comput. 2016 Apr 12;12(4):1845-52. doi: 10.1021/acs.jctc.6b00049. Epub 2016 Mar 16.

HTMD: High-Throughput Molecular Dynamics for Molecular Discovery.

Author information

1
Computational Biophysics Laboratory (GRIB-IMIM), Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB) , C/Doctor Aiguader 88, 08003 Barcelona, Spain.
2
Acellera, Barcelona Biomedical Research Park (PRBB) , C/Doctor Aiguader 88, 08003 Barcelona, Spain.
3
Department of Mathematics, Computer Science and Bioinformatics, Free University of Berlin , Berlin, Germany.
4
Institució Catalana de Recerca i Estudis Avançats (ICREA) , Passeig Lluis Companys 23, Barcelona 08010, Spain.

Abstract

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.

PMID:
26949976
DOI:
10.1021/acs.jctc.6b00049

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