Understanding In-Silico Approaches to Design Synthetic Antifungal Peptides to Combat Fungal Infections like Mucormycosis

Abstract

Antimicrobial peptides are the small sized molecule ranging in size from 2 to 9 kDa with expansive range of antimicrobial activity against bacteria, fungi, viruses etc. They are also used as first line of defense against various pathogens. With the emergence of various fungal infections in the present day and uprising antifungal resistance has made the choice of antifungal drugs very limited, the conventional drugs are slowly becoming ineffective to these fungal pathogens. Researchers have turned to these naturally occurring molecules which represent diverse range of functions and structural features but these naturally occurring peptides exhibit high toxicity, instability and low specificity towards the target which can be combatted by using various in silico and computational approaches to design and modify these AMPs in such a way that their efficiency is increased. In this article, we have specifically focused on Mucormycosis infection because of its high mortality rates and a very few synthetic AMPs have been produced against Mucorales considering the severity of this disease and the rapid surge in Mucormycosis cases emerged in the country. In this paper we will discuss about the present scenario of the disease, AMPs as antifungal therapy, role, classification of antifungal peptides, mechanism of action, advantages and limitations of natural AMPs, important physicochemical properties taken into account while designing synthetic AMPs (SAMPs) and the workflow pipeline to characterize and predict potential synthetic AMPs by using the existing web servers, databases and bioinformatics tools to develop new alternatives of conventional drugs available in the market against fungal infections.