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.

Keywords

Atom (system on chip)Computer scienceArtificial intelligenceParallel computing

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Year
2007
Type
article
Volume
69
Issue
S8
Pages
118-128
Citations
208
Access
Closed

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Rhiju Das, Bin Qian, Srivatsan Raman et al. (2007). Structure prediction for CASP7 targets using extensive all-atom refinement with Rosetta@home. Proteins Structure Function and Bioinformatics , 69 (S8) , 118-128. https://doi.org/10.1002/prot.21636

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DOI
10.1002/prot.21636