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Open Access Highly Accessed Research article

Comprehensive inventory of protein complexes in the Protein Data Bank from consistent classification of interfaces

Andrew J Bordner12 and Andrey A Gorin1

Author Affiliations

1 Computational Science and Mathematics Division and BioEnergy Science Center, Oak Ridge National Laboratory, P.O. Box 2008, MS 6173, Oak Ridge, TN 37831, USA

2 Mayo Clinic, 13400 East Shea Boulevard, Scottsdale, AZ 85259, USA

BMC Bioinformatics 2008, 9:234  doi:10.1186/1471-2105-9-234

Published: 12 May 2008

Abstract

Background

Protein-protein interactions are ubiquitous and essential for all cellular processes. High-resolution X-ray crystallographic structures of protein complexes can reveal the details of their function and provide a basis for many computational and experimental approaches. Differentiation between biological and non-biological contacts and reconstruction of the intact complex is a challenging computational problem. A successful solution can provide additional insights into the fundamental principles of biological recognition and reduce errors in many algorithms and databases utilizing interaction information extracted from the Protein Data Bank (PDB).

Results

We have developed a method for identifying protein complexes in the PDB X-ray structures by a four step procedure: (1) comprehensively collecting all protein-protein interfaces; (2) clustering similar protein-protein interfaces together; (3) estimating the probability that each cluster is relevant based on a diverse set of properties; and (4) combining these scores for each PDB entry in order to predict the complex structure. The resulting clusters of biologically relevant interfaces provide a reliable catalog of evolutionary conserved protein-protein interactions. These interfaces, as well as the predicted protein complexes, are available from the Protein Interface Server (PInS) website (see Availability and requirements section).

Conclusion

Our method demonstrates an almost two-fold reduction of the annotation error rate as evaluated on a large benchmark set of complexes validated from the literature. We also estimate relative contributions of each interface property to the accurate discrimination of biologically relevant interfaces and discuss possible directions for further improving the prediction method.