SAPPHIRE-based clustering.

TitleSAPPHIRE-based clustering.
Publication TypeJournal Article
Year of Publication2020
AuthorsCocina F., Vitalis A., Caflisch A.
JournalJournal of Chemical Theory and Computation
Volume16
Start Page6383
Issue10
Pagination6383–6396
Date Published2020 Sep 09
Type of ArticleResearch Article
ISSN1549-9626
KeywordsAlgorithms, clustering, data analysis, markov state models, molecular dynamics
Abstract

Molecular dynamics simulations are a popular means to study biomolecules, but it is often difficult to gain insights from the trajectories due to their large size, in both time and the number of features. The SAPPHIRE (States And Pathways Projected with HIgh REsolution) plot allows a direct visual inference of the dominant states visited by high-dimensional systems and how they are interconnected in time. Here, we extend this visual inference into a clustering algorithm. Specifically, the automatic procedure derives from the SAPPHIRE plot states that are kinetically homogeneous, structurally annotated, and of tunable granularity. We provide a relative assessment of the kinetic fidelity of this SAPPHIRE-based partitioning in comparison to popular clustering methods. This assessment is carried out on trajectories of a toy model and two polypeptides. We conclude with an application of our approach to a recent 100-microsecond trajectory of the main protease of SARS-CoV-2.

DOI10.1021/acs.jctc.0c00604
pubindex

0259

Alternate JournalJ. Chem. Theory Comput.
PubMed ID32905698