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Nucleic Acids Research, 2003, Vol. 31, No. 13 3311-3315
© 2003 Oxford University Press

EVA: evaluation of protein structure prediction servers

Ingrid Y. Y. Koh*,1, Volker A. Eyrich2, Marc A. Marti-Renom3, Dariusz Przybylski2,4, Mallur S. Madhusudhan3, Narayanan Eswar3, Osvaldo Graña5, Florencio Pazos5, Alfonso Valencia5, Andrej Sali3 and Burkhard Rost1,2,6

1 Columbia University Center for Computational Biology and Bioinformatics (C2B2), Russ Berrie Pavilion, 1150 St Nicholas Avenue, New York, NY 10032, USA 2 CUBIC, Department of Biochemistry and Molecular Biophysics, Columbia University, 650 West 168th Street BB217, New York, NY 10032, USA 3 Departments of Biopharmaceutical Sciences and Pharmaceutical Chemistry, and California Institute for Quantitative Biomedical Research, University of California, San Francisco, CA 94143, USA 4 Department of Physics, Columbia University, 538 West 120th Street, New York, NY 10027, USA 5 Protein Design Group, Centro Nacional de Biotecnologia (CNB-CSIC), Cantoblanco, Madrid 28049, Spain 6 North East Structural Genomics Consortium (NESG), Department of Biochemistry and Molecular Biophysics, Columbia University, 650 West 168th Street BB217, New York, NY 10032, USA

*To whom correspondence should be addressed. Tel: +1 2123054018; Fax: +1 2123057932; Email: koh{at}cubic.bioc.columbia.edu

EVA (http://cubic.bioc.columbia.edu/eva/) is a web server for evaluation of the accuracy of automated protein structure prediction methods. The evaluation is updated automatically each week, to cope with the large number of existing prediction servers and the constant changes in the prediction methods. EVA currently assesses servers for secondary structure prediction, contact prediction, comparative protein structure modelling and threading/fold recognition. Every day, sequences of newly available protein structures in the Protein Data Bank (PDB) are sent to the servers and their predictions are collected. The predictions are then compared to the experimental structures once a week; the results are published on the EVA web pages. Over time, EVA has accumulated prediction results for a large number of proteins, ranging from hundreds to thousands, depending on the prediction method. This large sample assures that methods are compared reliably. As a result, EVA provides useful information to developers as well as users of prediction methods.


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