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Background   Mycobacterium tuberculosis  is an infectious bacterium posing serious threats to human health. Due to the difficulty in performing molecular biology experiments to detect protein interactions, reconstruction of a protein interaction map of  M. tuberculosis  by computational methods will provide crucial information to understand the biological processes in the pathogenic microorganism, as well as provide the framework upon which new therapeutic approaches can be developed.    Results  In this paper, we constructed an integrated  M. tuberculosis  protein interaction network by machine learning and ortholog-based methods. Firstly, we built a support vector machine (SVM) method to infer the protein interactions of  M. tuberculosis  H37Rv by gene sequence information. We tested our predictors in  Escherichia coli  and mapped the genetic codon features underlying its protein interactions to  M. tuberculosis . Moreover, the documented interactions of 14 other species were mapped to the interactome of  M. tuberculosis  by the interolog method. The ensemble protein interactions were validated by various functional relationships, i.e., gene coexpression, evolutionary relationship and functional similarity, extracted from heterogeneous data sources. The accuracy and validation demonstrate the effectiveness and efficiency of our framework.    Conclusions  A protein interaction map of  M. tuberculosis  is inferred from genetic codons and interologs. The prediction accuracy and numerically experimental validation demonstrate the effectiveness and efficiency of our method. Furthermore, our methods can be straightforwardly extended to infer the protein interactions of other bacterial species.

Inferring a protein interaction map of Mycobacterium tuberculosis based on sequences and interologs