Predictions from Automatic Servers
CAFASP3: The third critical assessment of fully automated structure prediction methods
Article first published online: 15 OCT 2003
DOI: 10.1002/prot.10538
Copyright © 2003 Wiley-Liss, Inc.
Issue
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Proteins: Structure, Function, and Bioinformatics
Supplement: Fifth Meeting on the Critical Assessment of Techniques for Protein Structure Prediction
Volume 53, Issue Supplement 6, pages 503–516, 2003
Additional Information
How to Cite
Fischer, D., Rychlewski, L., Dunbrack, R. L., Ortiz, A. R. and Elofsson, A. (2003), CAFASP3: The third critical assessment of fully automated structure prediction methods. Proteins, 53: 503–516. doi: 10.1002/prot.10538
Publication History
- Issue published online: 15 OCT 2003
- Article first published online: 15 OCT 2003
- Manuscript Accepted: 16 JUN 2003
- Manuscript Received: 23 FEB 2003
- Abstract
- Article
- References
- Cited By
Keywords:
- CAFASP;
- fully automated structure prediction;
- fold recognition;
- critical assessment;
- CASP;
- LiveBench
Abstract
We present the results of the fully automated CAFASP3 experiment, which was carried out in parallel with CASP5, using the same set of prediction targets. CAFASP participation is restricted to fully automatic structure prediction servers. The servers' performance is evaluated by using previously announced, objective, reproducible and fully automated evaluation methods. More than 60 servers participated in CAFASP3, covering all categories of structure prediction. As in the previous CAFASP2 experiment, it was possible to identify a group of 5–10 top performing independent servers. This group of top performing independent servers produced relatively accurate models for all the 32 “Homology Modeling” targets, and for up to 43% of the 30 “Fold Recognition” targets. One of the most important results of CAFASP3 was the realization of the value of all the independent servers as a group, as evidenced by the superior performance of “meta-predictors” (defined here as predictors that make use of the output of other CAFASP servers). The performance of the best automated meta-predictors was roughly 30% higher than that of the best independent server. More significantly, the performance of the best automated meta-predictors was comparable with that of the best 5–10 human CASP predictors. This result shows that significant progress has been achieved in automatic structure prediction and has important implications to the prospects of automated structure modeling in the context of structural genomics. Proteins 2003;53:503–516. © 2003 Wiley-Liss, Inc.

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