Protein models docking benchmark 2

被引:18
作者
Anishchenko, Ivan [1 ,2 ]
Kundrotas, Petras J. [1 ]
Tuzikov, Alexander V. [2 ]
Vakser, Ilya A. [1 ,3 ]
机构
[1] Univ Kansas, Ctr Bioinformat, Lawrence, KS 66047 USA
[2] Natl Acad Sci, United Inst Informat Problems, Minsk 220012, BELARUS
[3] Univ Kansas, Dept Mol Biosci, Lawrence, KS 66045 USA
基金
美国国家科学基金会;
关键词
protein recognition; protein modeling; structure prediction; protein interactions; modeling of protein complexes; PREDICTION; RECOGNITION; ROTATION; SETS;
D O I
10.1002/prot.24784
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
Structural characterization of protein-protein interactions is essential for our ability to understand life processes. However, only a fraction of known proteins have experimentally determined structures. Such structures provide templates for modeling of a large part of the proteome, where individual proteins can be docked by template-free or template-based techniques. Still, the sensitivity of the docking methods to the inherent inaccuracies of protein models, as opposed to the experimentally determined high-resolution structures, remains largely untested, primarily due to the absence of appropriate benchmark set(s). Structures in such a set should have predefined inaccuracy levels and, at the same time, resemble actual protein models in terms of structural motifs/packing. The set should also be large enough to ensure statistical reliability of the benchmarking results. We present a major update of the previously developed benchmark set of protein models. For each interactor, six models were generated with the model-to-native C RMSD in the 1 to 6 angstrom range. The models in the set were generated by a new approach, which corresponds to the actual modeling of new protein structures in the real case scenario, as opposed to the previous set, where a significant number of structures were model-like only. In addition, the larger number of complexes (165 vs. 63 in the previous set) increases the statistical reliability of the benchmarking. We estimated the highest accuracy of the predicted complexes (according to CAPRI criteria), which can be attained using the benchmark structures. The set is available at http://dockground.bioinformatics.ku.edu.. Proteins 2015; 83:891-897. (c) 2015 Wiley Periodicals, Inc.
引用
收藏
页码:891 / 897
页数:7
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