Structure prediction for CABP7 targets using extensive all-atom refinement with Rosetta@home

被引:133
作者
Das, Rhiju
Bin Qian
Raman, Srivatsan
Vernon, Robert
Thompson, James
Bradley, Philip
Khare, Sagar
Tyka, Michael D.
Bhat, Divya
Chivian, Dylan
Kim, David E.
Sheffler, William H.
Malmstrom, Lars
Wollacott, Andrew M.
Wang, Chu
Andre, Ingemar
Baker, David
机构
[1] Univ Washington, Dept Biochem, Seattle, WA 98195 USA
[2] Univ Washington, Dept Genome Sci, Seattle, WA 98195 USA
[3] Univ Washington, Dept Biomol Struct & Design, Seattle, WA 98195 USA
[4] Lawrence Berkeley Natl Lab, Phys Biosci Div, Berkeley, CA 94720 USA
关键词
protein structure prediction; fragment insertion; Rosetta; CASP; all-atom refinement; template-based modeling; free modeling;
D O I
10.1002/prot.21636
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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.
引用
收藏
页码:118 / 128
页数:11
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