Inferring Protein-Protein Interactions Using a Hybrid Genetic Algorithm/Support Vector Machine Method
Authors: Wang, Bing; Chen, Peng; Zhang, Jun; Zhao, Guangxin; Zhang, Xiang
Source: Protein and Peptide Letters, Volume 17, Number 9, September 2010 , pp. 1079-1084(6)
Publisher: Bentham Science Publishers
Abstract:Identifying protein-protein interaction is crucial for understanding the biological systems and processes, as well as mutant design. This paper proposes a novel hybrid Genetic Algorithm/Support Vector Machine (GA/SVM) method to predict the interactions between proteins intermediated by the protein-domain relations. A protein domain is a structural and/or functional unit of the protein. Every protein can be characterized by a distinct domain or a sequential combination of multiple domains. In our method, the protein was first represented by its domains where the effects of domain duplication were also considered. Transformation of the domain composition was taken to simulate the combination of different domains using genetic algorithm (GA). The optimal transformation was discovered using a predictor constructed by a support vector machines (SVM) method. Compared with random predictor, the prediction performance of our method is more effective and efficient with 0.85 sensitivity, 0.90 specificity and 0.88 accuracy.
Document Type: Research Article
Publication date: September 1, 2010
- Protein & Peptide Letters publishes short papers in all important aspects of protein and peptide research, including structural studies, recombinant expression, function, synthesis, enzymology, immunology, molecular modeling, drug design etc. Manuscripts must have a significant element of novelty, timeliness and urgency that merit rapid publication. Reports of crystallisation, and preliminary structure determinations of biologically important proteins are acceptable. Purely theoretical papers are also acceptable provided they provide new insight into the principles of protein/peptide structure and function.