Rosetta predictions in CASP5: successes, failures, and prospects for complete automation

TitleRosetta predictions in CASP5: successes, failures, and prospects for complete automation
Publication TypeJournal Article
Year of Publication2003
AuthorsBradley, P., Chivian D., Meiler J., Misura K. M. S., Rohl C. A., Schief W. R., Wedemeyer W. J., Schueler-Furman O., Murphy P., Schonbrun J., Strauss C. E. M., & Baker D.
Volume53 Suppl 6
Date Published2003
KeywordsAlgorithms, Animals, Bacterial Proteins, Computational Biology, Ferredoxins, Methyltransferases, Models, Molecular, Primary Publication, Protein Conformation, Protein Folding, Protein Structure, Secondary, Protein Structure, Tertiary, Proteins, Reproducibility of Results

We describe predictions of the structures of CASP5 targets using Rosetta. The Rosetta fragment insertion protocol was used to generate models for entire target domains without detectable sequence similarity to a protein of known structure and to build long loop insertions (and N-and C-terminal extensions) in cases where a structural template was available. Encouraging results were obtained both for the de novo predictions and for the long loop insertions; we describe here the successes as well as the failures in the context of current efforts to improve the Rosetta method. In particular, de novo predictions failed for large proteins that were incorrectly parsed into domains and for topologically complex (high contact order) proteins with swapping of segments between domains. However, for the remaining targets, at least one of the five submitted models had a long fragment with significant similarity to the native structure. A fully automated version of the CASP5 protocol produced results that were comparable to the human-assisted predictions for most of the targets, suggesting that automated genomic-scale, de novo protein structure prediction may soon be worthwhile. For the three targets where the human-assisted predictions were significantly closer to the native structure, we identify the steps that remain to be automated.

Alternate JournalProteins
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