Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

This article is part of the supplement: Selected articles from the Eleventh Asia Pacific Bioinformatics Conference (APBC 2013): Bioinformatics

Open Access Proceedings

Building Markov state models with solvent dynamics

Chen Gu1, Huang-Wei Chang1, Lutz Maibaum2, Vijay S Pande3, Gunnar E Carlsson4 and Leonidas J Guibas5*

Author Affiliations

1 Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305, USA

2 Department of Chemistry, University of Washington, Seattle, WA 98195, USA

3 Department of Chemistry, Stanford University, Stanford, CA 94305, USA

4 Department of Mathematics, Stanford University, Stanford, CA 94305, USA

5 Department of Computer Science, Stanford University, Stanford, CA 94305, USA

For all author emails, please log on.

BMC Bioinformatics 2013, 14(Suppl 2):S8  doi:10.1186/1471-2105-14-S2-S8

Published: 21 January 2013

Abstract

Background

Markov state models have been widely used to study conformational changes of biological macromolecules. These models are built from short timescale simulations and then propagated to extract long timescale dynamics. However, the solvent information in molecular simulations are often ignored in current methods, because of the large number of solvent molecules in a system and the indistinguishability of solvent molecules upon their exchange.

Methods

We present a solvent signature that compactly summarizes the solvent distribution in the high-dimensional data, and then define a distance metric between different configurations using this signature. We next incorporate the solvent information into the construction of Markov state models and present a fast geometric clustering algorithm which combines both the solute-based and solvent-based distances.

Results

We have tested our method on several different molecular dynamical systems, including alanine dipeptide, carbon nanotube, and benzene rings. With the new solvent-based signatures, we are able to identify different solvent distributions near the solute. Furthermore, when the solute has a concave shape, we can also capture the water number inside the solute structure. Finally we have compared the performances of different Markov state models. The experiment results show that our approach improves the existing methods both in the computational running time and the metastability.

Conclusions

In this paper we have initiated an study to build Markov state models for molecular dynamical systems with solvent degrees of freedom. The methods we described should also be broadly applicable to a wide range of biomolecular simulation analyses.