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

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.

Figure 1

Estimated probability density function for the Random Forest scores in each class.

Figure 2

ROC curve for the Random Forest prediction of benchmark set interfaces using 10-fold cross-validation. The high prediction accuracy is shown by an area under the curve as high as 0.945.

Figure 3

Correct predicted α₂β₂ structure for a bacterial nitrile hydratase from a PDB structure (entry 1UGQ) that is incorrectly annotated as a heterodimer.

Figure 4

Venn diagram of the number of common Pfam-A domain family contacts in the predicted biological complexes, in the 3DID database, and in the iPfam database (version 21.0).

Figure 5

Schematic graph representation of the predicted homohexamer complex for E. coli phosphopantetheine adenylyltransferase (PDB entry 1B6T). Nodes represent subunits, denoted by their chain name and symmetry transformation, and edges represent inter-subunit contacts in the complex.