Background:
Clostridioides difficile (CD) is a multi-drug resistant, enteric pathogenic bacterium. The CD associated infections are the leading cause of nosocomial diarrhea that can further lead to pseudomembranous colitis up to a toxic mega-colon or sepsis with greater mortality and morbidity risks. The CD infection possess higher rates of recurrence due to its greater resistance against antibiotics. Considering its higher rates of recurrence, it has become a major burden on the healthcare facilities. Therefore, there is a dire need to identify novel drug targets to combat with the antibiotic resistance of Clostridioides difficile.
Objective:
To identify and propose new and novel drug targets against the Clostridioides difficile.
Methods:
In the current study, a computational subtractive genomics approach was applied to obtain a set of potential drug targets that exists in the multi-drug resistant strain of Clostridioides difficile. Here, the uncharacterized proteins were studied as potential drug targets. The methodology involved several bioinformatics databases and tools. The druggable proteins sequences were retrieved based on non-homology with host proteome and essentiality for the survival of the pathogen. The uncharacterized proteins were functionally characterized using different computational tools and sub-cellular localization was also predicted. The metabolic pathways were analyzed using KEGG database. Eventually, the druggable proteome has been fetched using sequence similarity with the already available drug targets present in DrugBank database. These druggable proteins were further explored for the structural details to identify drug candidates.
Results :
A priority list of potential drug targets was provided with the help of the applied method on complete proteome set of the C. difficile. Moreover, the drug like compounds have been screened against the potential drug targets to prioritize potential drug candidates. To facilitate the need for drug targets and therapies, the study proposed five potential protein drug targets out of which three proposed drug targets were subjected to homology modeling to explore their structural and functional activities.
Conclusion:
In conclusion, we proposed three unique, unexplored drug targets against C. difficile. The structure-based methods were applied and resulted in a list of top scoring compounds as potential inhibitors to proposed drug targets.
Generation and Analysis of Large-Scale Data-Driven Mycobacterium tuberculosis Functional Networks for Drug Target Identification
