Uncovering physical interactions among human and Mycobacterium tuberculosis proteins

Background: Pathogens usually evade and manipulate host immune pathways through host-pathogen protein interactions. Uncovering these interactions is crucial for determining the mechanisms underlying pathogen infection and the defense system. The growing prevalence of tuberculosis (TB) infection in the world necessitated advances in TB research. With the rising information from several divisions of biosciences, computational approaches are promising to analyze and interpret the data at the system level. Methods: In the present study, in silico two-hybrid systems is employed on model organisms to predict physical interactions among proteins of Human and Mycobacterium tuberculosis (Mtb). Consistent protein interactions are identified by the Interlog method. Co-expression analysis and functional annotations are performed to infer significant Human and Mtb protein physical interactions (HMIs). Results: The interactions identified in this study support the current TB research through an improved understanding of the pathogen infection and survival mechanism. A network of HMIs highlighted dnaK as the most highly interacting protein. Further, dnaK, eno, tuf, and gap proteins are found to trigger toll-like receptor signaling pathways and initiate pathogenesis. Conclusion: The interactions proteins identified in this study may incline the researchers to explore for novel therapeutic intervention strategies.