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Unrolr: Structural analysis of protein conformations using stochastic proximity embedding
Author(s) -
Eberhardt Jérôme,
Stote Roland H.,
Dejaegere Annick
Publication year - 2018
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.25599
Subject(s) - computer science , embedding , dihedral angle , dimensionality reduction , molecular dynamics , cluster analysis , algorithm , curse of dimensionality , metric (unit) , data mining , chemistry , computational chemistry , machine learning , artificial intelligence , molecule , hydrogen bond , operations management , organic chemistry , economics
Molecular dynamics (MD) simulations are widely used to explore the conformational space of biological macromolecules. Advances in hardware, as well as in methods, make the generation of large and complex MD datasets much more common. Although different clustering and dimensionality reduction methods have been applied to MD simulations, there remains a need for improved strategies that handle nonlinear data and/or can be applied to very large datasets. We present an original implementation of the pivot‐based version of the stochastic proximity embedding method aimed at large MD datasets using the dihedral distance as a metric. The advantages of the algorithm in terms of data storage and computational efficiency are presented, as well as the implementation realized. Application and testing through the analysis of a 200 ns accelerated MD simulation of a 35‐residue villin headpiece is discussed. Analysis of the simulation shows the promise of this method to organize large conformational ensembles. © 2018 Wiley Periodicals, Inc.