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 molecular-specific programmable environment to prepare, handle, simulate, visualize, and analyze molecular systems.
HTMD is based on Python, so that scientists can easily extend it to their needs. With HTMD, it is possible to do very complex protocols in just a few lines.
In a single script, it is possible to plan an entire computational experiment, from manipulating PDBs, building, executing and analyzing simulations, computing Markov state models, kinetic rates, affinities and pathways.
See more information on https://www.htmd.org.
HTMD Forum: https://forum.htmd.org
We are also on Github: https://github.com/Acellera/htmd
Report issues on: https://github.com/Acellera/htmd/issues