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• W2901566770 abstract "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." @default.
• W2901566770 created "2018-11-29" @default.
• W2901566770 creator A5005345147 @default.
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• W2901566770 date "2018-11-15" @default.
• W2901566770 modified "2023-10-17" @default.
• W2901566770 title "Unrolr: Structural analysis of protein conformations using stochastic proximity embedding" @default.
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• W2901566770 doi "https://doi.org/10.1002/jcc.25599" @default.
• W2901566770 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30447084" @default.
• W2901566770 hasPublicationYear "2018" @default.
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