A fairly large set of protein interactions are mediated by families of peptide binding domains, such as SH2, SH3, PDZ, and MHC etc. To identify their ligands by experimental screening is not only labor intensive but almost futile in screening low abundant species, due to the suppression by high abundant species. An ideal way of studying protein-protein interactions is to use high-throughput computational approaches to screen protein sequence databases, so as to direct the validating experiments towards the most promising peptides. Predictors with only good cross-validation were not good enough to screen protein databases. In the current study we built integrated machine learning systems using three novel coding methods, and screened the Swissprot and Genbank protein databases for potential ligands of 10 SH3 and 3 PDZ domains. A large fraction of predictions have already been experimentally confirmed by other independent research groups, indicating a satisfying generalization capability for future applications in identifying protein interactions.