De novo determination of protein backbone structure from residual dipolar couplings using Rosetta

TitleDe novo determination of protein backbone structure from residual dipolar couplings using Rosetta
Publication TypeJournal Article
Year of Publication2002
AuthorsRohl, C. A., & Baker D.
JournalJournal of the American Chemical Society
Volume124
Issue11
Pagination2723-9
Date Published2002 Mar 20
ISSN0002-7863
KeywordsAlgorithms, Models, Chemical, Nuclear Magnetic Resonance, Biomolecular, Peptide Fragments, Peptide Library, Primary Publication, Protein Conformation, Protein Folding, Protein Structure, Secondary, Proteins
Abstract

As genome-sequencing projects rapidly increase the database of protein sequences, the gap between known sequences and known structures continues to grow exponentially, increasing the demand to accelerate structure determination methods. Residual dipolar couplings (RDCs) are an attractive source of experimental restraints for NMR structure determination, particularly rapid, high-throughput methods, because they yield both local and long-range orientational information and can be easily measured and assigned once the backbone resonances of a protein have been assigned. While very extensive RDC data sets have been used to determine the structure of ubiquitin, it is unclear to what extent such methods will generalize to larger proteins with less complete data sets. Here we incorporate experimental RDC restraints into Rosetta, an ab initio structure prediction method, and demonstrate that the combined algorithm provides a general method for de novo determination of a variety of protein folds from RDC data. Backbone structures for multiple proteins up to approximately 125 residues in length and spanning a range of topological complexities are rapidly and reproducibly generated using data sets that are insufficient in isolation to uniquely determine the protein fold de novo, although ambiguities and errors are observed for proteins with symmetry about an axis of the alignment tensor. The models generated are not high-resolution structures completely defined by experimental data but are sufficiently accurate to accelerate traditional high-resolution NMR structure determination and provide structure-based functional insights.

Alternate JournalJ. Am. Chem. Soc.
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