Sampling bottlenecks in de novo protein structure prediction

TitleSampling bottlenecks in de novo protein structure prediction
Publication TypeJournal Article
Year of Publication2009
AuthorsKim, D. E., Blum B., Bradley P., & Baker D.
JournalJournal of molecular biology
Volume393
Issue1
Pagination249-60
Date Published2009 Oct 16
ISSN1089-8638
KeywordsAlgorithms, Computational Biology, Computer Simulation, Models, Molecular, Primary Publication, Protein Conformation, Protein Structure, Tertiary, Proteins
Abstract

The primary obstacle to de novo protein structure prediction is conformational sampling: the native state generally has lower free energy than nonnative structures but is exceedingly difficult to locate. Structure predictions with atomic level accuracy have been made for small proteins using the Rosetta structure prediction method, but for larger and more complex proteins, the native state is virtually never sampled, and it has been unclear how much of an increase in computing power would be required to successfully predict the structures of such proteins. In this paper, we develop an approach to determining how much computer power is required to accurately predict the structure of a protein, based on a reformulation of the conformational search problem as a combinatorial sampling problem in a discrete feature space. We find that conformational sampling for many proteins is limited by critical "linchpin" features, often the backbone torsion angles of individual residues, which are sampled very rarely in unbiased trajectories and, when constrained, dramatically increase the sampling of the native state. These critical features frequently occur in less regular and likely strained regions of proteins that contribute to protein function. In a number of proteins, the linchpin features are in regions found experimentally to form late in folding, suggesting a correspondence between folding in silico and in reality.

Alternate JournalJ. Mol. Biol.
AttachmentSize
kim09A.pdf1.12 MB