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Uncovering Large-Scale Conformational Change in Molecular Dynamics without Prior Knowledge

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Department of Physics, Wake Forest University, Winston-Salem, North Carolina 27109, United States
Department of Mathematics & Statistics, Wake Forest University, Winston-Salem, North Carolina 27109, United States
*E-mail: [email protected]. Phone: +1 (336) 758-4975. Fax: +1 (336) 758-6142.
Cite this: J. Chem. Theory Comput. 2016, 12, 12, 6130–6146
Publication Date (Web):November 1, 2016
https://doi.org/10.1021/acs.jctc.6b00757
Copyright © 2016 American Chemical Society
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Abstract

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As the length of molecular dynamics (MD) trajectories grows with increasing computational power, so does the importance of clustering methods for partitioning trajectories into conformational bins. Of the methods available, the vast majority require users to either have some a priori knowledge about the system to be clustered or to tune clustering parameters through trial and error. Here we present non-parametric uses of two modern clustering techniques suitable for first-pass investigation of an MD trajectory. Being non-parametric, these methods require neither prior knowledge nor parameter tuning. The first method, HDBSCAN, is fast—relative to other popular clustering methods—and is able to group unstructured or intrinsically disordered systems (such as intrinsically disordered proteins, or IDPs) into bins that represent global conformational shifts. HDBSCAN is also useful for determining the overall stability of a system—as it tends to group stable systems into one or two bins—and identifying transition events between metastable states. The second method, iMWK-Means, with explicit rescaling followed by K-Means, while slower than HDBSCAN, performs well with stable, structured systems such as folded proteins and is able to identify higher resolution details such as changes in relative position of secondary structural elements. Used in conjunction, these clustering methods allow a user to discern quickly and without prior knowledge the stability of a simulated system and identify both local and global conformational changes.

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The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jctc.6b00757.

  • Additional cluster visualizations of MutSα, villin headpiece, thrombin, F10, and thrombin aptamer; comparison of K-Means and Amorim–Hennig; correlation of RMSF to clustering results (PDF)

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