Inferring Protein-Protein Interactions Based on Sequences and Interologs in Mycobacterium Tuberculosis

Abstract

Mycobacterium tuberculosis is a pathogenic bacterium that poses serious threat to human health. Inference of the protein interactions of M. tuberculosis will provide cues to understand the biological processes in this pathogen. In this paper, we constructed an integrated M. tuberculosis H37Rv protein interaction network by machine learning and ortholog-based methods. Firstly, we developed a support vector machine (SVM) method to infer the protein interactions by gene sequence information. We tested our predictors in Escherichia coli and mapped the genetic codon features underlying protein interactions to M. tuberculosis. Moreover, the documented interactions of other 14 species were mapped to the proteome of M. tuberculosis by the interolog method. The ensemble protein interactions were then validated by various functional linkages i.e., gene coexpression, evolutionary relationship and functional similarity, extracted from heterogeneous data sources.KeywordsProtein InteractionFunctional SimilarityProtein Interaction NetworkSemantic Similarity MeasureHeterogeneous Data SourceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.