MUCORMYCOSIS VACCINE DESIGN USING BIOINFORMATIC TOOLS

Abstract

Mucormycosis or black fungus is an uncommon and serious disease initiated by a set of molds known as mucormycetes. The molds are present all over the environment. This disease generally occurs in immunocompromised individuals who ingest medicines to decrease their immune system’s ability to fight against pathogens or individuals suffering from health issues. This disease usually affects lungs or sinuses after the spores have entered the body. A rise in muormycosis cases was seen with the rise of coronavirus infections in India. Hence, there is a vital requirement for a mucormycosis vaccine due to the presence covid-19 cases. Vaccines are biological products that can be used to prevent or treat diseases. Vaccine development is a lengthy and expensive process. To decrease the cost and duration of vaccine development, bioinformatic methods can be used. In this project we have used these techniques to design and develop a mucormycosis vaccine. Epitope screening using computational methods predicts that the CotH1, CotH2, CotH3, Fet3 and Ftr1 proteins can be utilized for constructing vaccines. Molecular docking tests predict that the epitopes (AADVISYRVSTAVWHVSWGD, ADVISYRVSTAVWHVSWGDP, DTQRKTVKDVYLTAAQRVSV, EWHLESGLAVVFVEAPDIAQ, SGASFNFPGDFDSFIPNVES, IWALPEGQFMANRNWFKIRH, SFLVAIAHAASVQFNLIAPS, IGSNYVHSFANVSFGIHGAG, FLVAITHAASIKFNVIAPNA, QNADKKATFEKYLTETVRVL) have low energy minimization score which show sufficient binding with the MHC class II molecules and give stability to the complex. More wet lab tests are further required to use of these peptides as a vaccine against Mucormycosis.