Open AccessA Unified De Novo Approach for Predicting the Structures of Ordered and Disordered Proteins
Author(s) -
John J. Ferrie,
E. James Petersson
Publication year - 2020
Publication title -
the journal of physical chemistry. b
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.864
H-Index - 392
eISSN - 1520-6106
pISSN - 1520-5207
DOI - 10.1021/acs.jpcb.0c02924
Subject(s) - intrinsically disordered proteins , monte carlo method , molecular dynamics , computer science , sequence (biology) , statistical physics , computational biology , chemistry , physics , biology , biophysics , computational chemistry , mathematics , biochemistry , statistics
As recognition of the abundance and relevance of intrinsically disordered proteins (IDPs) continues to grow, demand increases for methods that can rapidly predict the conformational ensembles populated by these proteins. To date, IDP simulations have largely been dominated by molecular dynamics (MD) simulations, which require significant compute times and/or complex hardware. Recent developments in MD have afforded methods capable of simulating both ordered and disordered proteins, yet to date, accurate fold prediction from a sequence has been dominated by Monte Carlo (MC)-based methods such as Rosetta. To overcome the limitations of current approaches in IDP simulation using Rosetta while maintaining its utility for modeling folded domains, we developed PyRosetta-based algorithms that allow for the accurate de novo prediction of proteins across all degrees of foldedness along with structural ensembles of disordered proteins. Our simulations have accuracy comparable to state-of-the-art MD with vastly reduced computational demands.