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Proteins. 2014 Feb;82(2):278-87. doi: 10.1002/prot.24385. Epub 2013 Oct 17.

Protein models: the Grand Challenge of protein docking.

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Center for Bioinformatics, The University of Kansas, Lawrence, Kansas, 66047; United Institute of Informatics Problems, National Academy of Sciences, 220012, Minsk, Belarus.


Characterization of life processes at the molecular level requires structural details of protein-protein interactions (PPIs). The number of experimentally determined protein structures accounts only for a fraction of known proteins. This gap has to be bridged by modeling, typically using experimentally determined structures as templates to model related proteins. The fraction of experimentally determined PPI structures is even smaller than that for the individual proteins, due to a larger number of interactions than the number of individual proteins, and a greater difficulty of crystallizing protein-protein complexes. The approaches to structural modeling of PPI (docking) often have to rely on modeled structures of the interactors, especially in the case of large PPI networks. Structures of modeled proteins are typically less accurate than the ones determined by X-ray crystallography or nuclear magnetic resonance. Thus the utility of approaches to dock these structures should be assessed by thorough benchmarking, specifically designed for protein models. To be credible, such benchmarking has to be based on carefully curated sets of structures with levels of distortion typical for modeled proteins. This article presents such a suite of models built for the benchmark set of the X-ray structures from the Dockground resource ( by a combination of homology modeling and Nudged Elastic Band method. For each monomer, six models were generated with predefined C(α) root mean square deviation from the native structure (1, 2, …, 6 Å). The sets and the accompanying data provide a comprehensive resource for the development of docking methodology for modeled proteins.


benchmark sets; protein interactions; protein modeling; protein recognition; structure prediction

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