Protein-protein docking benchmark version 3.0

Bioinformatics Program, Boston University, Boston, Massachusetts 02215, USA.
Proteins Structure Function and Bioinformatics (Impact Factor: 2.92). 11/2008; 73(3):705-9. DOI: 10.1002/prot.22106
Source: PubMed

ABSTRACT We present version 3.0 of our publicly available protein-protein docking benchmark. This update includes 40 new test cases, representing a 48% increase from Benchmark 2.0. For all of the new cases, the crystal structures of both binding partners are available. As with Benchmark 2.0, Structural Classification of Proteins (Murzin et al., J Mol Biol 1995;247:536-540) was used to remove redundant test cases. The 124 unbound-unbound test cases in Benchmark 3.0 are classified into 88 rigid-body cases, 19 medium-difficulty cases, and 17 difficult cases, based on the degree of conformational change at the interface upon complex formation. In addition to providing the community with more test cases for evaluating docking methods, the expansion of Benchmark 3.0 will facilitate the development of new algorithms that require a large number of training examples. Benchmark 3.0 is available to the public at


Available from: Joël Janin, Mar 13, 2015
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    ABSTRACT: Interaction sites on protein surfaces mediate virtually all biological activities, and their identification holds promise for disease treatment and drug design. Novel algorithmic approaches for the prediction of these sites have been produced at a rapid rate, and the field has seen significant advancement over the past decade. However, the most current methods have not yet been reviewed in a systematic and comprehensive fashion. Herein, we describe the intricacies of the biological theory, datasets, and features required for modern protein-protein interaction site (PPIS) prediction, and present an integrative analysis of the state-of-the-art algorithms and their performance. First, the major sources of data used by predictors are reviewed, including training sets, evaluation sets, and methods for their procurement. Then, the features employed and their importance in the biological characterization of PPISs are explored. This is followed by a discussion of the methodologies adopted in contemporary prediction programs, as well as their relative performance on the datasets most recently used for evaluation. In addition, the potential utility that PPIS identification holds for rational drug design, hotspot prediction, and computational molecular docking is described. Finally, an analysis of the most promising areas for future development of the field is presented.
    Algorithms for Molecular Biology 12/2015; 10(1):7. DOI:10.1186/s13015-015-0033-9 · 1.86 Impact Factor
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    ABSTRACT: Prediction of protein-protein interfaces is a relevant problem to understand protein function and to solve problems such as design of new therapeutic drugs. Most of the predictors currently developed receive a set of features and produces a binary result indicating whether each residue is an inter-face or not. This paper describes a new protein-protein in-terface residue predictor that incorporates a classifier based on Bayesian networks that is used in a second classifica-tion stage. This classifier receives as input the predictions generated by some initial classifier (as in (Yan, Dobbs, and Honavar 2004)) in addition to some extra features, and pro-duces a binary classification result. The result of several ex-periments suggest that classification performance may be im-proved by using this two-stage classification approach when features of several closest neighbor residues is used.