Simultaneous prediction of protein folding and docking at high resolution

TitleSimultaneous prediction of protein folding and docking at high resolution
Publication TypeJournal Article
Year of Publication2009
AuthorsDas, R., André I., Shen Y., Wu Y., Lemak A., Bansal S., Arrowsmith C. H., Szyperski T., & Baker D.
JournalProceedings of the National Academy of Sciences of the United States of America
Volume106
Issue45
Pagination18978-83
Date Published2009 Nov 10
ISSN1091-6490
KeywordsComputer Simulation, Models, Molecular, Nuclear Magnetic Resonance, Biomolecular, Primary Publication, Protein Folding, Proteins, Software
Abstract

Interleaved dimers and higher order symmetric oligomers are ubiquitous in biology but present a challenge to de novo structure prediction methodology: The structure adopted by a monomer can be stabilized largely by interactions with other monomers and hence not the lowest energy state of a single chain. Building on the Rosetta framework, we present a general method to simultaneously model the folding and docking of multiple-chain interleaved homo-oligomers. For more than a third of the cases in a benchmark set of interleaved homo-oligomers, the method generates near-native models of large alpha-helical bundles, interlocking beta sandwiches, and interleaved alpha/beta motifs with an accuracy high enough for molecular replacement based phasing. With the incorporation of NMR chemical shift information, accurate models can be obtained consistently for symmetric complexes with as many as 192 total amino acids; a blind prediction was within 1 A rmsd of the traditionally determined NMR structure, and fit independently collected RDC data equally well. Together, these results show that the Rosetta "fold-and-dock" protocol can produce models of homo-oligomeric complexes with near-atomic-level accuracy and should be useful for crystallographic phasing and the rapid determination of the structures of multimers with limited NMR information.

Alternate JournalProc. Natl. Acad. Sci. U.S.A.
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das09A.pdf1.57 MB