完整後設資料紀錄
DC 欄位語言
dc.contributor.authorLiu, Kang-Pingen_US
dc.contributor.authorYang, Jinn-Moonen_US
dc.date.accessioned2014-12-08T15:07:08Z-
dc.date.available2014-12-08T15:07:08Z-
dc.date.issued2007en_US
dc.identifier.isbn978-3-540-71782-9en_US
dc.identifier.issn0302-9743en_US
dc.identifier.urihttp://hdl.handle.net/11536/5602-
dc.description.abstractProtein-protein interactions play a pivotal role in modem molecular biology. Identifying the protein-protein interaction sites is great scientific and practical interest for predicting protein-protein interactions. In this study, we proposed a Gaussian Evolutionary Method (GEM) to optimize 18 features, including ten atomic solvent and eight protein 2(nd) structure features, for predicting protein-protein interaction sites. The training set consists of 104 unbound proteins selected from PDB and the predicted successful rate is 65.4% (68/104) proteins in the training dataset. These 18 parameters were then applied to a test set with 50 unbound proteins. Based on the threshold obtained from the training set, our method is able to predict the binding sites for 98% (49/50) proteins and yield 46% successful prediction and 42.3% average specificity. Here, a binding-site prediction is considered successful if 50% predicted area is indeed located in protein-protein interface (i.e. the specificity is more than 0.5). We believe that the optimized parameters of our method are useful for analyzing protein-protein interfaces and for interfaces prediction methods and protein-protein docking methods.en_US
dc.language.isoen_USen_US
dc.subjectatomic solvation parameteren_US
dc.subjectGaussian evolutionary methoden_US
dc.subjectprotein-protein interactionsen_US
dc.subjectprotein-protein binding siteen_US
dc.titleA Gaussian Evolutionary Method for predicting protein-protein interaction sitesen_US
dc.typeArticleen_US
dc.identifier.journalEvolutionary Computation, Machine Learning and Data Mining in Bioinformatics, Proceedingsen_US
dc.citation.volume4447en_US
dc.citation.spage143en_US
dc.citation.epage154en_US
dc.contributor.department生物資訊及系統生物研究所zh_TW
dc.contributor.departmentInstitude of Bioinformatics and Systems Biologyen_US
dc.identifier.wosnumberWOS:000246102100014-
顯示於類別:會議論文