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Rosetta predictions in CASP5: Successes, failures, and prospects for complete automation
- Source :
- Proteins: Structure, Function, and Genetics. 53:457-468
- Publication Year :
- 2003
- Publisher :
- Wiley, 2003.
-
Abstract
- We describe predictions of the structures of CASP5 targets using Rosetta. The Ro- setta fragment insertion protocol was used to gener- ate models for entire target domains without detect- able sequence similarity to a protein of known structure and to build long loop insertions (and N-and C-terminal extensions) in cases where a struc- tural 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 pro- teins that were incorrectly parsed into domains and for topologically complex (high contact order) pro- teins 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 iden- tify the steps that remain to be automated. Proteins 2003;53:457- 468. © 2003 Wiley-Liss, Inc.
- Subjects :
- Models, Molecular
Protein Folding
Protein Conformation
Computer science
Context (language use)
Computational biology
computer.software_genre
Biochemistry
Protein Structure, Secondary
Bacterial Proteins
Fragment (logic)
Structural Biology
Animals
CASP
Molecular Biology
Native structure
business.industry
Computational Biology
Proteins
Reproducibility of Results
Methyltransferases
Protein structure prediction
Contact order
Automation
Protein Structure, Tertiary
De novo protein structure prediction
Ferredoxins
Data mining
business
computer
Algorithms
Subjects
Details
- ISSN :
- 10970134 and 08873585
- Volume :
- 53
- Database :
- OpenAIRE
- Journal :
- Proteins: Structure, Function, and Genetics
- Accession number :
- edsair.doi.dedup.....289c7077365094a553e33321b7c987db