Integrative in silico approaches for therapeutic target identification in the human pathogen corynebacterium diphtheriae

Abstract

Corynebacterium diphtheriae (Cd) is a gram-positive human pathogen responsible for diphtheria infection and once regarded for high mortalities worldwide. The fatality gradually decreased with improved living standards and further alleviated when many immunization programs were introduced. However, numerous drug-resistant strains emerged recently that consequently decreased the efficiency of current therapeutics and vaccines, thereby obliging the scientific community to start investigating new therapeutic targets in pathogenic microorganisms. In 2014, our research group introduced the word panmodelome for the first time in the scientific world (Hassan et al., 2014). Inspired by panmodelomics approach, in this study, our group aimed to contribute including the prediction of modelome of thirteen C. diphtheriae strains, using the MHOLline workflow. Considering the quality of the models and using in-house scripts, a set of 465 conserved proteins were selected by combining the results of pangenomics based on core genome and core-modelome analyses. Further, using subtractive proteomics and modelomics approaches for target identification, a set of 23 proteins were selected as essential proteins for bacteria. Considering human as a host, 8 of these proteins (glpX, nusB, rpsH, hisE, smpB, bioB, DIP1084 and DIP0983) were seen as essential and non-host homologs. These proteins were subjected to virtual screening using three different compound libraries (extracted from ZINC database, plant-derived natural compounds, and Di-terpenoid Iso-steviol derivatives). The proposed drug molecules showing favorable interactions, lowered energy values and high complementarity with the predicted targets have also been reported in the present study. Our proposed approach expedites the rapid and efficient selection of C. diphtheriae putative proteins for developing a broad spectrum of novel drugs and vaccines because some of these targets have already been identified and validated in other organisms. Furthermore, we adopted a different approach using same number of genomes of C. diphtheriae to identify drug/vaccine targets based on the druggable pocketome. As a result, we identify 10 targets in which interestingly 3 (hisE-phosphoribosyl-ATP pyrophosphatase, glpX-fructose 1,6-bisphosphatase II, and rpsH 30S ribosomal protein S8) targets were common with our first study. Further, we are working on characterization of hisE-phosphoribosyl-ATP pyrophosphatase, glpX-fructose 1,6-bisphosphatase II, and rpsH 30S ribosomal protein S8 in C. diphtheriae strain NCTC13129. The selection of these proteins (hisE, glpX and rpsH) were made on the bases of their identification through two different computational approaches. Our proposed approaches expedite the selection of C. diphtheriae putative proteins for broad-spectrum development of novel drugs and vaccines, owing to the fact that our study is computational and need experimental validation