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Connecting Molecular Energy Landscape Analysis with Markov Model-based Analysis of Equilibrium Structural Dynamics

9 pagesPublished: March 18, 2019

Abstract

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.

Keyphrases: energy landscape, Markov state models, molecular structural dynamics

In: Oliver Eulenstein, Hisham Al-Mubaid and Qin Ding (editors). Proceedings of 11th International Conference on Bioinformatics and Computational Biology, vol 60, pages 181--189

Links:
BibTeX entry
@inproceedings{BiCOB2019:Connecting_Molecular_Energy_Landscape,
  author    = {Kazi Lutful Kabir and Nasrin Akhter and Amarda Shehu},
  title     = {Connecting Molecular Energy Landscape Analysis with Markov Model-based Analysis of Equilibrium Structural Dynamics},
  booktitle = {Proceedings of 11th International Conference on Bioinformatics and Computational Biology},
  editor    = {Oliver Eulenstein and Hisham Al-Mubaid and Qin Ding},
  series    = {EPiC Series in Computing},
  volume    = {60},
  pages     = {181--189},
  year      = {2019},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {https://easychair.org/publications/paper/nv7M},
  doi       = {10.29007/tmgc}}
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