EVA:: evaluation of protein structure prediction servers

被引:123
作者
Koh, IYY
Eyrich, VA
Marti-Renom, MA
Przybylski, D
Madhusudhan, MS
Eswar, N
Graña, O
Pazos, F
Valencia, A
Sali, A
Rost, B
机构
[1] Columbia Univ, Ctr Computat Biol & Bioinformat, New York, NY 10032 USA
[2] Columbia Univ, Dept Biochem & Mol Biophys, CUBIC, New York, NY 10032 USA
[3] Univ Calif San Francisco, Dept Biopharmaceut Sci, San Francisco, CA 94143 USA
[4] Univ Calif San Francisco, Dept Pharmaceut Chem, San Francisco, CA 94143 USA
[5] Univ Calif San Francisco, Calif Inst Quantitat Biomed Res, San Francisco, CA 94143 USA
[6] Columbia Univ, Dept Phys, New York, NY 10027 USA
[7] CSIC, Ctr Nacl Biotecnol, Prot Design Grp, E-28049 Madrid, Spain
[8] Columbia Univ, Dept Biochem & Mol Biophys, NE Struct Genom Consortium, New York, NY 10032 USA
关键词
SECONDARY STRUCTURE PREDICTION; CORRELATED MUTATIONS; NEURAL NETWORKS; ALIGNMENT; INFORMATION; RECOGNITION; DATABASE; PROFILES; MODELS;
D O I
10.1093/nar/gkg619
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
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.
引用
收藏
页码:3311 / 3315
页数:5
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