1. A Gaussian Evolutionary Method for Predicting Protein-Protein Interaction Sites.
- Author
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Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Rangan, C. Pandu, Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Marchiori, Elena, Moore, Jason H., Rajapakse, Jagath C., Liu, Kang-Ping, and Yang, Jinn-Moon
- Abstract
Protein-protein interactions play a pivotal role in modern 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 2nd 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. [ABSTRACT FROM AUTHOR]
- Published
- 2007
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