Automated prediction of CASP-5 structures using the Robetta server

被引:228
作者
Chivian, D
Kim, DE
Malmström, L
Bradley, P
Robertson, T
Murphy, P
Strauss, CEM
Bonneau, R
Rohl, CA
Baker, D
机构
[1] Univ Washington, HHMI, Seattle, WA 98195 USA
[2] Univ Washington, Dept Biochem, Seattle, WA 98195 USA
[3] Los Alamos Natl Lab, Los Alamos, NM USA
[4] Inst Syst Biol, Seattle, WA USA
[5] Univ Calif Santa Cruz, Santa Cruz, CA 95064 USA
关键词
automated protein structure prediction server; CASP; CAFASP; rosetta; fragment insertion; fragment assembly; ab initio modeling; de novo modeling; template-based modeling; domain parsing; homology modeling; comparative modeling; sequence alignment;
D O I
10.1002/prot.10529
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
Robetta is a fully automated protein structure prediction server that uses the Rosetta fragment-insertion method. It combines template-based and de novo structure prediction methods in an attempt to produce high quality models that cover every residue of a submitted sequence. The first step in the procedure is the automatic detection of the locations of domains and selection of the appropriate modeling protocol for each domain. For domains matched to a homolog with an experimentally characterized structure by PSI-BLAST or Pcons2, Robetta uses a new alignment method, called K*Sync, to align the query sequence onto the parent structure. It then models the variable regions by allowing them to explore conformational space with fragments in fashion similar to the de novo protocol, but in the context of the template. When no structural homolog is available, domains are modeled with the Rosetta de novo protocol, which allows the full length of the domain to explore conformational space via fragment-insertion, producing a large decoy ensemble from which the final models are selected. The Robetta server produced quite reasonable predictions for targets in the recent CASP-5 and CAFASP-3 experiments, some of which were at the level of the best human predictions. (C) 2003 Wiley-Liss, Inc.
引用
收藏
页码:524 / 533
页数:10
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