title: Persistent topology and metastable state in conformational dynamics.
abstract: The large amount of molecular dynamics simulation data produced by modern computational models brings big opportunities and challenges to researchers. Clustering algorithms play an important role in understanding biomolecular kinetics from the simulation data, especially under the Markov state model framework. However, the ruggedness of the free energy landscape in a biomolecular system makes common clustering algorithms very sensitive to perturbations of the data. Here, we introduce a data-exploratory tool which provides an overview of the clustering structure under different parameters. The proposed Multi-Persistent Clustering analysis combines insights from recent studies on the dynamics of systems with dominant metastable states with the concept of multi-dimensional persistence in computational topology. We propose to explore the clustering structure of the data based on its persistence on scale and density. The analysis provides a systematic way to discover clusters that are robust to perturbations of the data. The dominant states of the system can be chosen with confidence. For the clusters on the borderline, the user can choose to do more simulation or make a decision based on their structural characteristics. Furthermore, our multi-resolution analysis gives users information about the relative potential of the clusters and their hierarchical relationship. The effectiveness of the proposed method is illustrated in three biomolecules: alanine dipeptide, Villin headpiece, and the FiP35 WW domain.
authors.affiliation: Institute for Computational and Mathematical Engineering, Stanford University, Stanford, California, United States of America. huangwei@stanford.edu
authors.forename: Huang-Wei
authors.initials: HW
authors.lastname: Chang
authors.forename: Sergio
authors.initials: S
authors.lastname: Bacallado
authors.forename: Vijay S
authors.initials: VS
authors.lastname: Pande
authors.forename: Gunnar E
authors.initials: GE
authors.lastname: Carlsson
mesh_terms: D000409:Alanine / Q000737:chemistry; D016000:Cluster Analysis; D004151:Dipeptides / Q000737:chemistry; D008840:Microfilament Proteins / Q000737:chemistry; D008968:Molecular Conformation; D056004:Molecular Dynamics Simulation
chemical_list: D004151:Dipeptides; D008840:Microfilament Proteins; C026615:villin; D000409:Alanine
references.citation: Proc Natl Acad Sci U S A. 2012 Oct 30;109(44):17807-13
references.pmid: 22778442
references.citation: Methods. 2009 Oct;49(2):197-201
references.pmid: 19410002
references.citation: J Chem Theory Comput. 2011 Oct 11;7(10):3412-3419
references.pmid: 22125474
references.citation: J Chem Phys. 2007 Apr 21;126(15):155101
references.pmid: 17461665
references.citation: J Chem Phys. 2009 Sep 28;131(12):124101
references.pmid: 19791846
references.citation: Philos Trans A Math Phys Eng Sci. 2005 Feb 15;363(1827):357-75; discussion 375-7
references.pmid: 15664888
references.citation: Science. 2011 Oct 28;334(6055):517-20
references.pmid: 22034434
references.citation: Curr Opin Struct Biol. 2008 Apr;18(2):154-62
references.pmid: 18378442
references.citation: J Chem Phys. 2005 Nov 22;123(20):204909
references.pmid: 16351319
references.citation: J Chem Phys. 2011 May 7;134(17):174105
references.pmid: 21548671
references.citation: Proteins. 2008 Mar;70(4):1185-95
references.pmid: 17847092
references.citation: Curr Opin Struct Biol. 2013 Feb;23(1):58-65
references.pmid: 23237705
references.citation: BMC Bioinformatics. 2007 May 03;8 Suppl 2:S7
references.pmid: 17493256