The genome-wide identification of pairs of interacting proteins is an important step in the elucidation of cell regulatory mechanisms 1,2 . Much of our present knowledge derives from high-throughput techniques such as the yeast two-hybrid assay and affinity purification 3 , as well as from manual curation of experiments on individual systems 4 . A variety of computational approaches based, for example, on sequence homology, gene co-expression and phylogenetic profiles, have also been developed for the genome-wide inference of proteinprotein interactions (PPIs) 5,6 . Yet comparative studies suggest that the development of accurate and complete repertoires of PPIs is still in its early stages 7-9 . Here we show that three-dimensional structural information can be used to predict PPIs with an accuracy and coverage that are superior to predictions based on non-structural evidence. Moreover, an algorithm, termed PrePPI, which combines structural information with other functional clues, is comparable in accuracy to high-throughput experiments, yielding over 30,000 highconfidence interactions for yeast and over 300,000 for human. Experimental tests of a number of predictions demonstrate the ability of the PrePPI algorithm to identify unexpected PPIs of considerable biological interest. The surprising effectiveness of three-dimensional structural information can be attributed to the use of homology models combined with the exploitation of both close and remote geometric relationships between proteins. Until now, structural information has had relatively little impact in constructing protein-protein interactomes, primarily because there is a marked difference between the number of proteins with known sequences and those with an experimentally known structure. For example, as of early 2010, the Protein Data Bank (PDB) provided structures for ,600 of the total complement of ,6,500 yeast proteins (,10%), whereas the structural coverage of protein-protein complexes is even more sparse, with only about 300 structures available out of the approximately 75,000 PPIs (,0.5%) recorded in publically available databases. However, ,3,600 additional yeast proteins have homology models in either the ModBase 10 or SkyBase 11 databases. Moreover, there were about 37,000 protein-protein complexes derived from multiple organisms in the PDB and Protein Quaternary Structure 12 (PQS) databases, which might be used as 'templates' to model PPIs. Clearly, if structure is to be useful on a large scale, it is essential that modelling of individual proteins and of complexes be exploited. A number of studies have used structurally characterized complexes as templates to construct models of the complexes that might be formed between proteins that have been classified as having sequence and/or structural relationships to the proteins in the template [13] [14] [15] . We searched more broadly for templates, using geometric relationships between groups of secondary structure elements as revealed by structural alignment, independently of how they are classified. It has been demonstrated that even distantly related proteins often use regions of their surface with similar arrangements of secondary structure elements to bind to other proteins 16-18 , suggesting the possibility of considerably expanding the number of putative PPIs that can be identified. It is likely that further expansion can be achieved if interactions involving unstructured regions of proteins are taken into account, but these are not considered in this work. Our approach to the prediction of PPIs is embodied in an algorithm we have named PrePPI (predicting protein-protein interactions), which combines structural and non-structural interaction clues using Bayesian statistics (see Fig. 1 and Methods for details). The structural component of PrePPI involves a number of steps. Briefly, given a pair of query proteins (QA and QB), we first use sequence alignment to identify structural representatives (MA and MB) that correspond to either their experimentally determined structures or to homology models. We then use structural alignment to find both close and remote structural neighbours (NA i and NB j ) of MA and MB (an average of ,1,500 neighbours are found for each structure). Whenever two (for example, NA 1 and NB 3 ) of the over 2 million pairs of neighbours of MA and MB form a complex reported in the PDB, this defines a template for modelling the interaction of QA and QB. Models of the complex are created by superimposing the representative structures on their corresponding structural neighbours in the template (that is, MA on NA 1 and MB on NB 3 ). This procedure produces about 550 million 'interaction models' for about 2.4 million PPIs involving about 3,900 yeast proteins, and about 12 billion models for about 36 million PPIs involving about 13,000 human proteins. Note that an interaction model is based on structure-based sequence alignments of query proteins to their individual templates ( Supplementary Fig. 1 ) and that we do not construct a three-dimensional model of each complex because the scoring of so many individual complexes would be prohibitively time consuming using standard energy functions (for example, as used in docking 19 ). Once an interaction model has been created, it is evaluated using a combination of five empirical scores that measure properties derived from alignments of the individual monomers to their templates ( Supplementary Fig. 1 ). The first score, SIM, depends on the structural similarity between models of the two query proteins (that is, MA and MB) and those in the template complex (that is, NA 1 and NB 3 ). The next two scores determine whether the interface in the template complex actually exists in the model. They are calculated as SIZ, the number, and COV, the fraction, of interacting residue pairs in the template (for example, NA 1 -NB 3 ) that align to some pair of residues in the model (MA-MB). The final two scores reflect whether the residues that appear in the model interface have properties consistent with those that mediate known PPIs (for example, residue type, evolutionary conservation, or statistical propensity to be in protein-protein interfaces). This information is obtained from three publically available servers that predict interfacial residues based on the sequence and structure of the
Also Naïve Bayes classification method in REF gave a comprehensive study and prediction of PPI on yeast and humans via three-dimensional structural information.
Structure-based prediction of protein–protein interactions on a genome-wide scale
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