Structure prediction for CASP7 targets using extensive all-atom refinement with Rosetta@home

Rhiju Das, Bin Qian, Srivatsan Raman, Robert Vernon, James Thompson, Philip Bradley, Sagar Khare, Michael D. Tyka, Divya Bhat, Dylan Chivian, David E. Kim, William H. Sheffler, Lars Malmström, Andrew M. Wollacott, Chu Wang, Ingemar Andre, David Baker

Research output: Contribution to journalArticlepeer-review

156 Scopus citations

Abstract

We describe predictions made using the Rosetta structure prediction methodology for both template-based modeling and free modeling categories in the Seventh Critical Assessment of Techniques for Protein Structure Prediction. For the first time, aggressive sampling and all-atom refinement could be carried out for the majority of targets, an advance enabled by the Rosetta@home distributed computing network. Template-based modeling predictions using an iterative refinement algorithm improved over the best existing templates for the majority of proteins with less than 200 residues. Free modeling methods gave near-atomic accuracy predictions for several targets under 100 residues from all secondary structure classes. These results indicate that refinement with an all-atom energy function, although computationally expensive, is a powerful method for obtaining accurate structure predictions.

Original languageEnglish (US)
Pages (from-to)118-128
Number of pages11
JournalProteins: Structure, Function and Genetics
Volume69
Issue numberSUPPL. 8
DOIs
StatePublished - 2007
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Structural Biology
  • Biochemistry
  • Molecular Biology

Keywords

  • All-atom refinement
  • CASP
  • Fragment insertion
  • Free modeling
  • Protein structure prediction
  • Rosetta
  • Template-based modeling

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