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  Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions

Nedialkova, L. V., Amat, M. A., Kevrekidis, I. G., & Hummer, G. (2014). Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions. The Journal of Chemical Physics, 141(11), 114102-114117. doi:10.1063/1.4893963.

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 Creators:
Nedialkova, Lilia V. 1, Author
Amat, Miguel A. 1, Author
Kevrekidis, Ioannis G. 2, Author
Hummer, Gerhard3, Author                 
Affiliations:
1Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544, USA , ou_persistent22              
2Department of Chemical and Biological Engineering and Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544, USA, ou_persistent22              
3Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max Planck Society, ou_2068292              

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 Abstract: Using the helix-coil transitions of alanine pentapeptide as an illustrative example, we demonstrate the use of diffusion maps in the analysis of molecular dynamics simulation trajectories. Diffusion maps and other nonlinear data-mining techniques provide powerful tools to visualize the distribution of structures in conformation space. The resulting low-dimensional representations help in partitioning conformation space, and in constructing Markov state models that capture the conformational dynamics. In an initial step, we use diffusion maps to reduce the dimensionality of the conformational dynamics of Ala5. The resulting pretreated data are then used in a clustering step. The identified clusters show excellent overlap with clusters obtained previously by using the backbone dihedral angles as input, with small—but nontrivial—differences reflecting torsional degrees of freedom ignored in the earlier approach. We then construct a Markov state model describing the conformational dynamics in terms of a discrete-time random walk between the clusters. We show that by combining fuzzy C-means clustering with a transition-based assignment of states, we can construct robust Markov state models. This state-assignment procedure suppresses short-time memory effects that result from the non-Markovianity of the dynamics projected onto the space of clusters. In a comparison with previous work, we demonstrate how manifold learning techniques may complement and enhance informed intuition commonly used to construct reduced descriptions of the dynamics in molecular conformation space.

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Language(s): -
 Dates: 20142014-06-142014-08-042014-11-15
 Publication Status: Published online
 Pages: 15
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1063/1.4893963
 Degree: -

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Title: The Journal of Chemical Physics
  Other : J. Chem. Phys.
Source Genre: Journal
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Publ. Info: Woodbury, N.Y. : American Institute of Physics
Pages: 15 Volume / Issue: 141 (11) Sequence Number: - Start / End Page: 114102 - 114117 Identifier: ISSN: 0021-9606
CoNE: https://pure.mpg.de/cone/journals/resource/954922836226