An integrative domain-based approach to predicting protein-protein interactions.
Protein-protein interactions (PPIs) are intrinsic to almost all cellular processes. Different computational methods offer new chances to study PPIs. To predict PPIs, while the integrative methods use multiple data sources instead of a single source, the domain-based methods often use only protein domain features. Integration of both protein domain features and genomic/proteomic features from multiple databases can more effectively predict PPIs. Moreover, it allows discovering the reciprocal relationships between PPIs and biological features of their interacting partners. We developed a novel integrative domain-based method for predicting PPIs using inductive logic programming (ILP). Two principal domain features used were domain fusions and domain-domain interactions (DDIs). Various relevant features of proteins were exploited from five popular genomic and proteomic databases. By integrating these features, we constructed biologically significant ILP background knowledge of more than 278,000 ground facts. The experimental results through multiple 10-fold cross-validations demonstrated that our method predicts PPIs better than other computational methods in terms of typical performance measures. The proposed ILP framework can be applied to predict DDIs with high sensitivity and specificity. The induced ILP rules gave us many interesting, biologically reciprocal relationships among PPIs, protein domains, and PPI-related genomic/proteomic features. Supplementary material is available at (http://www.jaist.ac.jp/~s0560205/PPIandDDI/).
Nguyen
Thanh-Phuong
TP
The Microsoft Research, University of Trento, Centre for Computational and Systems Biology, Povo (Trento), Italy. nguyen@cosbi.eu
Ho
Tu-Bao
TB
D000465:Algorithms; D019295:Computational Biology; D030541:Databases, Genetic; D030562:Databases, Protein; D023281:Genomics / Q000706:statistics & numerical data; D054730:Protein Interaction Domains and Motifs; D025941:Protein Interaction Mapping / Q000706:statistics & numerical data*; D040901:Proteomics / Q000706:statistics & numerical data