Open AccessConnecting Molecular Energy Landscape Analysis with Markov Model-based Analysis of Equilibrium Structural Dynamics
Author(s) -
Kazi Lutful Kabir,
Nasrin Akhter,
Amarda Shehu
Publication year - 2019
Publication title -
epic series in computing
Language(s) - English
Resource type - Conference proceedings
ISSN - 2398-7340
DOI - 10.29007/tmgc
Subject(s) - markov chain , computer science , energy landscape , theoretical computer science , statistical physics , state space , molecular dynamics , cluster analysis , markov process , identification (biology) , macro , graph , mathematics , artificial intelligence , machine learning , physics , computational chemistry , chemistry , ecology , statistics , biology , thermodynamics , programming language
Molecular dynamics simulation software now provides us with a view of the structure space accessed by a molecule. Increasingly, Markov state models are proposed to integrate various simulations of a molecule and extract its equilibrium structural dynamics. The approach relies on organizing the structures accessed in simulation into states as an at- tempt to identify thermodynamically-stable and semi-stable (macro)states among which transitions can then be quantified. Typically, off-the-shelf clustering algorithms are used for this purpose. In this paper, we investigate two additional complementary approaches to state identification that rely on graph embeddings of the structures. In particular, we show that doing so allows revealing basins in the energy landscape associated with the accessed structure space. Moreover, we demonstrate that basins, directly tied to stable and semi-stable states, yield to a better model of dynamics on a proof-of-concept application.