A Unified De Novo Approach for Predicting the Structures of Ordered and Disordered Proteins

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.