In silico prediction of new antimicrobial peptides and proteins as druggable targets towards alternative anti-schistosomal therapy

Abstract

Schistosomiasis is a debilitating disease caused by a parasitic flatworm found in freshwater. With the exponential increase in prevalence, Praziquantel (PZQ) remains the only effective treatment drug, however, resistance to PZQ has been reported recently. Therefore, it is imperative to develop effective alternative anti-schistosomal compounds using bioinformatics-based tools utilizing the broad-spectrum therapeutic capabilities of antimicrobial peptides (AMPs). AMPs are essential components of the innate immune system and are responsible for complete destruction and immunomodulatory effects in the host defence against pathogens. Here, Hidden Markov model was used to identify six anti-microbial peptides (TAK1–TAK6) with potential anti-schistosomal capabilities. Also, glycosyltransferase and axonemal dynein intermediate chain protein were identified as important druggable Schistosome proteins. The 3-D structures of the AMPs and proteins were modelled using I-TASSER and it was shown that the six putative anti-schistosomal AMPs and the two proteins had low C-score, possibly due to lack of available templates for their modelling. Finally, PatchDock was employed to ascertain the interaction between the schistosome proteins and the putative AMPs. All the predicted putative AMPs have good binding affinity to the schistosomal proteins. Overall, the generated AMPs could serve as potential hits in schistosomiasis and could prove effective against drug-resistant schistosomes.

Keywords

• Schistosomiasis
• Glycosyltransferase
• Axonemal dynein intermediate chain
• Antimicrobial peptides
• Hidden markov model (HMMER)
• Praziquantel