Title: Epitope based peptide vaccine design against fructose bisphosphate aldolase of candida glabrata: An immunoinformatics approach

Abstract

Background: Candida glabrata is a human opportunistic pathogen that can cause life-threatening systemic infections [1-2]. There are multiple effective vaccines against fungal infections, some of these vaccines were engaged in different stages of clinical trials, none of them yet approved by (FDA) [3]. Aim: Using Immunoinformatics approach to predict the most conserved and immunogenic B- and T-cell epitopes from the Fructose Bisphosphate aldolase (Fba1) protein of C. glabrata. Material and Method: 13 C. glabrata Fructose bisphosphate aldolase protein sequences (361 amino acids) retrieved from NCBI and presented in several tools on the IEDB server for prediction of the most promising epitopes. Homology modeling and molecular docking were performed. Result: The promising B-cell Epitopes were AYFKEH, VDKESLYTK, and HVDKESLYTK (Figure 1). While, promising peptides which have the high affinity to MHC I binding were: AVHEALAPI, KYFKRMAAM, QTSNGGAAY, RMAAMNQWL and YFKEHGEPL. Two peptides LFSSHMLDL and YIRSIAPAY were noted to have the highest affinity to MHC class II that interact with 9 alleles. The molecular Docking revealed the epitopes QTSNGGAAY and LFSSHMLDL have the lowest binding energy to MHC molecules. Conclusion: Epitope-based vaccines predicted by using Immunoinformatics tools have advantages over the conventional vaccines that they are more specific, less time consuming, safe, less allergic and more antigenic. Further in vivo and in vitro experiments are needed to prove the effectiveness of the best candidates' epitopes (QTSNGGAAY and LFSSHMLDL). In the best of our knowledge, this is the first study that has predicted B- and T- cells epitopes from Fba1 protein by using immunoinformatics to design an effective epitope-based vaccine against C. galabrata.