Fold Recognition: Assessment
CASP5 assessment of fold recognition target predictions
Article first published online: 15 OCT 2003
DOI: 10.1002/prot.10557
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 395–409, 2003
Additional Information
How to Cite
Kinch, L. N., Wrabl, J. O., Krishna, S. S., Majumdar, I., Sadreyev, R. I., Qi, Y., Pei, J., Cheng, H. and Grishin, N. V. (2003), CASP5 assessment of fold recognition target predictions. Proteins, 53: 395–409. doi: 10.1002/prot.10557
Publication History
- Issue published online: 15 OCT 2003
- Article first published online: 15 OCT 2003
- Manuscript Accepted: 17 JUN 2003
- Manuscript Received: 28 FEB 2003
- Abstract
- Article
- References
- Cited By
Keywords:
- protein fold prediction;
- structure comparison;
- alignment quality;
- threading;
- domain structure;
- CASP5
Abstract
We present an overview of the fifth round of Critical Assessment of Protein Structure Prediction (CASP5) fold recognition category. Prediction models were evaluated by using six different structural measures and four different alignment measures, and these scores were compared to those assigned manually over a diverse subset of target domains. Scores were combined to compare overall performance of participating groups and to estimate rank significance. The methods used by a few groups outperformed all other methods in terms of the evaluated criteria and could be considered state-of-the-art in structure prediction. We discuss a few examples of difficult fold recognition targets to highlight the progress of ab initio-type methods on difficult structure analogs and the difficulties of predicting multidomain targets and selecting prediction models. We also compared the results of manual groups to those of automatic servers evaluated in parallel by CAFASP, showing that the top performing automated server structure predictions approached those of the best manual predictors. Proteins 2003;53:395–409. © 2003 Wiley-Liss, Inc.

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