標題: A Gaussian Evolutionary Method for predicting protein-protein interaction sites
作者: Liu, Kang-Ping
Yang, Jinn-Moon
生物資訊及系統生物研究所
Institude of Bioinformatics and Systems Biology
關鍵字: atomic solvation parameter;Gaussian evolutionary method;protein-protein interactions;protein-protein binding site
公開日期: 2007
摘要: Protein-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.
URI: http://hdl.handle.net/11536/5602
ISBN: 978-3-540-71782-9
ISSN: 0302-9743
期刊: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, Proceedings
Volume: 4447
起始頁: 143
結束頁: 154
Appears in Collections:Conferences Paper