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 |
顯示於類別: | 會議論文 |