Originally published In Press as doi:10.1074/mcp.M500346-MCP200 on March 29, 2006.
Molecular & Cellular Proteomics 5:1224-1232, 2006.
© 2006 by The American Society for Biochemistry and Molecular Biology, Inc.
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 GenBankTM 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.
To whom correspondence should be addressed. Tel.: 86-010-6521-2284; Fax: 86-010-6521-2284; E-mail: gaoyouhe{at}pumc.edu.cn
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Copyright © 2006 by the American Society for Biochemistry and Molecular Biology.
