Accurate extraction of functional associations between proteins based on common interaction partners and common domains.
BACKGROUND
Genomic and proteomic approaches have accumulated a huge amount of data which provide clues to protein function. However, interpreting single omic data for predicting uncharacterized protein functions has been a challenging task, because the data contain a lot of false positives. To overcome this problem, methods for integrating data from various omic approaches are needed for more accurate function prediction.


RESULTS
In this paper, we have developed a method which extracts functionally similar proteins with high confidence by integrating protein-protein interaction data and domain information. We used this method to analyze publicly available data from Saccharomyces cerevisiae. We identified 1042 functional associations, involving 765 proteins of which 98 (12.8%) had no previously ascribed function. Our method extracts functionally similar protein pairs more accurately than conventional methods, and predicting function for previously uncharacterized proteins can be achieved. Our method can of course be applied to protein-protein interaction data for any species.
Okada
Kinya
K
Graduate School of Information Sciences, Nara Institute of Science and Technology, Ikoma, Japan. okada-k@cb.k.u-tokyo.ac.jp
Kanaya
Shigehiko
S
Asai
Kiyoshi
K
D000465:Algorithms; D000595:Amino Acid Sequence; D001665:Binding Sites; D030562:Databases, Protein; D005786:Gene Expression Regulation / Q000502:physiology; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D029701:Saccharomyces cerevisiae Proteins / Q000737:chemistry* / Q000378:metabolism*; D020539:Sequence Analysis, Protein / Q000379:methods*; D015398:Signal Transduction / Q000502:physiology; D013329:Structure-Activity Relationship
D011506:Proteins; D029701:Saccharomyces cerevisiae Proteins