Title
Discerning novel drug targets for treating Mycobacterium auium ss. paratuberculosis-associated autoimmune disorders: an in silico approach
Abstract
Mycobacterium avium subspecies paratuberculosis (MAP) exhibits 'molecular mimicry' with the human host resulting in several autoimmune diseases such as multiple sclerosis, type 1 diabetes mellitus (T1DM), Hashimoto's thyroiditis, Crohn's disease (CD), etc. The conventional therapy for autoimmune diseases includes immunosuppressants or immunomodulators that treat the symptoms rather than the etiology and/or causative mechanism(s). Eliminating MAP-the etiopathological agent might be a better strategy to treat MAP-associated autoimmune diseases. In this case study, we conducted a systematic in silico analysis to identify the metabolic chokepoints of MAP's mimicry proteins and their interacting partners. The probable inhibitors of chokepoint proteins were identified using DrugBank. DrugBank molecules were stringently screened and molecular interactions were analyzed by molecular docking and 'off-target' binding. Thus, we identified 18 metabolic chokepoints of MAP mimicry proteins and 13 DrugBank molecules that could inhibit three chokepoint proteins viz. katG, rpoB and narH. On the basis of molecular interaction between drug and target proteins finally eight DrugBank molecules, viz. DB00609, DB00951, DB00615, DB01220, DB08638, DB08226, DB08266 and DB07349 were selected and are proposed for treatment of three MAP-associated autoimmune diseases namely, T1DM, CD and multiple sclerosis. Because these molecules are either approved by the Food and Drug Administration or these are experimental drugs that can be easily incorporated in clinical studies or tested in vitro. The proposed strategy may be used to repurpose drugs to treat autoimmune diseases induced by other pathogens.
Year
DOI
Venue
2021
10.1093/bib/bbaa195
BRIEFINGS IN BIOINFORMATICS
Keywords
DocType
Volume
Molecular mimicry, autoimmunity, mimicry interaction partners, metabolic pathways, chokepoints, drug repurposing
Journal
22
Issue
ISSN
Citations 
3
1467-5463
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Anjali Garg100.34
Neelja Singhal200.34
manish kumar340169.07