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Molecular & Cellular Proteomics 5:1224-1232, 2006.
© 2006 by The American Society for Biochemistry and Molecular Biology, Inc.

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Research

An Integrated Machine Learning System to Computationally Screen Protein Databases for Protein Binding Peptide Ligands^*,S

Ling Zhang, Chen Shao, Dexian Zheng and Youhe Gao

From the Proteomics Research Center, National Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences/Peking Union Medical College, 5 Dong Dan San Tiao, 100005 Beijing, China

A fairly large set of protein interactions is mediated by families of peptide binding domains, such as Src homology 2 (SH2), SH3, PDZ, major histocompatibility complex, etc. To identify their ligands by experimental screening is not only labor-intensive but almost futile in screening low abundance species due to the suppression by high abundance species. An ideal way of studying protein-protein interactions is to use high throughput computational approaches to screen protein sequence databases to direct the validating experiments toward 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 Swiss-Prot and GenBank^TM protein databases for potential ligands of 10 SH3 and three PDZ domains. A large fraction of predictions has already been experimentally confirmed by other independent research groups, indicating a satisfying generalization capability for future applications in identifying protein interactions.

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