Epitope-Based Immunoinformatic Approach on Heat Shock 70 kDa Protein Complex of Cryptococcus neoformans var. grubii

Abstract

Introduction. Cryptococcosis is a ubiquitous opportunistic fungal disease caused by Cryptococcus neoformans var. grubii. It has high global morbidity and mortality among HIV patients and non-HIV carriers with 99% and 95%, respectively. Furthermore, the increasing prevalence of undesired toxicity profile of antifungal, multidrug-resistant organisms and the scarcity of FDA-authorized vaccines were the hallmark in the present days. This study was undertaken to design a reliable epitope-based peptide vaccine through targeting highly conserved immunodominant heat shock 70 kDa protein of Cryptococcus neoformans var. grubii that covers a considerable digit of the world population through implementing a computational vaccinology approach. Materials and Methods. A total of 38 sequences of Cryptococcus neoformans var. grubii’s heat shock 70 kDa protein were retrieved from the NCBI protein database. Different prediction tools were used to analyze the aforementioned protein at the Immune Epitope Database (IEDB) to discriminate the most promising T-cell and B-cell epitopes. The proposed T-cell epitopes were subjected to the population coverage analysis tool to compute the global population’s coverage. Finally, the T-cell projected epitopes were ranked based on their binding scores and modes using AutoDock Vina software. Results and Discussion. The epitopes (ANYVQASEK, QSEKPKNVNPVI, SEKPKNVNPVI, and EKPKNVNPVI) had shown very strong binding affinity and immunogenic properties to B-cell. (FTQLVAAYL, YVYDTRGKL) and (FFGGKVLNF, FINAQLVDV, and FDYALVQHF) exhibited a very strong binding affinity to MHC-I and MHC-II, respectively, with high population coverage for each, while FYRQGAFEL has shown promising results in terms of its binding profile to MHC-II and MHC-I alleles and good strength of binding when docked with HLA-C ∗ 12:03. In addition, there is massive global population coverage in the three coverage modes. Accordingly, our in silico vaccine is expected to be the future epitope-based peptide vaccine against Cryptococcus neoformans var. grubii that covers a significant figure of the entire world citizens.

Fungal Vaccine Development: State of the Art and Perspectives Using Immunoinformatics

“…The predicted peptide, including YMAADQFCL and ISYGTAMAV, had an average population coverage of 69.75% and 74.39%, thus demonstrating the importance of immunoinformatics analysis. Such data may help in the construction of a polypeptide vaccine aimed at the immunization or therapy of cryptococcosis [ 147 ].…”

“…In addition, the use of immunoinformatics has also shown epitopes of HSP70 T cells from C. neoformans , with the aim of identifying new vaccine candidates [ 147 , 161 ]. Initially, 10 promising epitopes were predicted and verified by using computational tools, all of which triggered both the cellular and humoral immune response.…”

“…These data were obtained through molecular docking, using HLA-C*12:03, HLA-DRB1*01:01 and Immunoglobulin G as a model. of the strength of the association between HLA binding sites and promiscuous peptides were also performed, thus highlighting the importance of HLA-based epitope predictions [ 147 ].…”

“…Studies involving epitopes from Coccidioides sp. in the process of immunizing mice and humans have already had their effectiveness evaluated as a vaccine for coccidioidomycosis [ 147 , 162 ]. Other studies have shown infection assays by inoculating spores of Coccidioides posadasii in HLA-DR4 transgenic mice (DRB1-0401 allele), which expresses human class II HLA, thus inducing the cellular response of T CD4 + lymphocytes.…”

Utilizing Immunoinformatics to Target Brain Tumors; An Aid to Current Neurosurgical Practice

“…Bioinformatics can expedite vaccine development. Several phases are involved in the design of therapeutic protein vaccines, including retrieval of amino acid sequences, prediction of helper T-lymphocytes (HTL) and cytotoxic T-lymphocytes (CTL) epitopes, prediction of B-cell epitopes, evaluation of population, physicochemical characteristics, and homology modeling, molecular docking with immune receptors, and molecular docking simulation (MDS) (9)(10)(11). A brief overview of immunoinformatics application to GBM is provided in Figure 1 (Created with BioRender.com).…”