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.
Keywords
- Atomic solvation parameter
- Gaussian evolutionary method
- protein-protein interactions
- protein-protein binding site.
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Liu, KP., Yang, JM. (2007). A Gaussian Evolutionary Method for Predicting Protein-Protein Interaction Sites. In: Marchiori, E., Moore, J.H., Rajapakse, J.C. (eds) Evolutionary Computation,Machine Learning and Data Mining in Bioinformatics. EvoBIO 2007. Lecture Notes in Computer Science, vol 4447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71783-6_14
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DOI: https://doi.org/10.1007/978-3-540-71783-6_14
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