The 3D-partner is a web tool to predict interacting partners and binding models of a query protein sequence through structure complexes and a new scoring function. 3D-partner first utilizes IMPALA to identify homologous structures (templates) of a query from a heterodimer profile library. The interacting-partner sequence profiles of these templates are then used to search interacting candidates of the query from protein sequence databases (e.g. SwissProt) by PSI-BLAST. We developed a new scoring function, which includes the contact-residue interacting score (e.g. the steric, hydrogen bonds, and electrostatic interactions) and the template consensus score (e.g. couple-conserved residue and the template similarity scores), to evaluate how well the interfaces between the query and interacting candidates. Based on this scoring function, 3D-partner provides the statistic significance, the binding models (e.g. hydrogen bonds and conserved amino acids) and functional annotations of interacting partners. The correlation between experimental energies and predicted binding affinities of our scoring function is 0.91 on 275 mutated residues from the ASEdb. The average precision of the server is 0.72 on 563 queries and the execution time of this server for a query is ∼15 s on average. These results suggest that the 3D-partner server can be useful in protein-protein interaction predictions and binding model visualizations. The server is available online at: http://3D-partner.life.nctu.edu.tw.

BackgroundComprehensive exploration of protein-protein interactions is a challenging route to understand biological processes. For efficiently enlarging protein interactions annotated with residue-based binding models, we proposed a new concept "3D-domain interolog mapping" with a scoring system to explore all possible protein pairs between the two homolog families, derived from a known 3D-structure dimmer (template), across multiple species. Each family consists of homologous proteins which have interacting domains of the template for studying domain interface evolution of two interacting homolog families.ResultsThe 3D-interologs database records the evolution of protein-protein interactions database across multiple species. Based on "3D-domain interolog mapping" and a new scoring function, we infer 173,294 protein-protein interactions by using 1,895 three-dimensional (3D) structure heterodimers to search the UniProt database (4,826,134 protein sequences). The 3D- interologs database comprises 15,124 species and 283,980 protein-protein interactions, including 173,294 interactions (61%) and 110,686 interactions (39%) summarized from the IntAct database. For a protein-protein interaction, the 3D-interologs database shows functional annotations (e.g. Gene Ontology), interacting domains and binding models (e.g. hydrogen-bond interactions and conserved residues). Additionally, this database provides couple-conserved residues and the interacting evolution by exploring the interologs across multiple species. Experimental results reveal that the proposed scoring function obtains good agreement for the binding affinity of 275 mutated residues from the ASEdb. The precision and recall of our method are 0.52 and 0.34, respectively, by using 563 non-redundant heterodimers to search on the Integr8 database (549 complete genomes).ConclusionsExperimental results demonstrate that the proposed method can infer reliable physical protein-protein interactions and be useful for studying the protein-protein interaction evolution across multiple species. In addition, the top-ranked strategy and template interface score are able to significantly improve the accuracies of identifying protein-protein interactions in a complete genome. The 3D-interologs database is available at http://3D- interologs.life.nctu.edu.tw.

BackgroundThe protein-protein interaction (PPI) is one of the most important features to understand biological processes. For a PPI, the physical domain-domain interaction (DDI) plays the key role for biology functions. In the post-genomic era, to rapidly identify homologous PPIs for analyzing the contact residue pairs of their interfaces within DDIs on a genomic scale is essential to determine PPI networks and the PPI interface evolution across multiple species.ResultsIn this study, we proposed "pair Position Specific Scoring Matrix (pairPSSM)" to identify homologous PPIs. The pairPSSM can successfully distinguish the true protein complexes from unreasonable protein pairs with about 90% accuracy. For the test set including 1,122 representative heterodimers and 2,708,746 non-interacting protein pairs, the mean average precision and mean false positive rate of pairPSSM were 0.42 and 0.31, respectively. Moreover, we applied pairPSSM to identify ~450,000 homologous PPIs with their interacting domains and residues in seven common organisms (e.g. Homo sapiens, Mus musculus, Saccharomyces cerevisiae and Escherichia coli).ConclusionsOur pairPSSM is able to provide statistical significance of residue pairs using evolutionary profiles and a scoring system for inferring homologous PPIs. According to our best knowledge, the pairPSSM is the first method for searching homologous PPIs across multiple species using pair position specific scoring matrix and a 3D dimer as the template to map interacting domain pairs of these PPIs. We believe that pairPSSM is able to provide valuable insights for the PPI evolution and networks across multiple species.