Background: Mycetoma is a distinct flesh eating and destructive neglected tropical disease. It is endemic in many tropical and subtropical countries. Mycetoma is caused by bacterial infections (actinomycetoma) such as Streptomyces somaliensis and Nocardiae or true fungi (eumycetoma) such as Madurella mycetomatis. Until date, treatments fail to cure the infection and the available marketed drugs are expensive and toxic upon prolonged usage. Moreover, no vaccine was prepared yet against mycetoma.The aim of this study is to predict effective epitope-based vaccine against fructose-bisphosphate aldolase enzymes of M. mycetomatis using immunoinformatics approaches.
Methods and Materials:Fructose-bisphosphate aldolase of Madurella mycetomatis Sequence was retrieved from NCBI. Different prediction tools were used to analyze the nominee's epitopes in Immune Epitope Database for B-cell, T-cell MHC class II & I. Then the proposed peptides were docked using Autodock 4.0 software program.
Results and Conclusions:The proposed and promising peptides KYLQ shows a potent binding affinity to B-cell, FEYARKHAF with a very strong binding affinity to MHC1 alleles and FFKEHGVPL that show a very strong binding affinity to MHC11and MHC1 alleles. This indicates a strong potential to formulate a new vaccine, especially with the peptide FFKEHGVPL which is likely to be the first proposed epitope-based vaccine against Fructose-bisphosphate aldolase of Madurella mycetomatis. This study recommends an in-vivo assessment for the most promising peptides especially FFKEHGVPL.
Keywords:Immunoinformatics, Fructose-bisphosphate aldolase (FBA), Epitope-based vaccine, Madurella mycetomatis.
Materials and Methods:The Sequence of Fructose-bisphosphate aldolase (FBA) was retrieved from NCBI Database (https://www.ncbi.nlm.nih.gov/protein) [18] in a FASTA format as of September 2017 for further analysis, then the candidate epitopes were analyzed using different prediction tools of Immune Epitope Database IEDB analysis resource (http://www.iedb.org/) [19] .
B-cell epitope prediction:Candidate epitopes were analyzed by several B-cell prediction methods that determine the antigenicity, hydrophilicity, flexibility and surface accessibility. The linear predicted epitopes were obtained by using BepiPred test from immune epitope database (http://tools.iedb.org/bcell/result/) [20] with a threshold value of 0.149 and a window size 6.Furthermore, surface accessible epitopes were predicted with a threshold value of 1.0 and a window size of 6.0 using the Emini surface accessibility prediction tool [21] .The Antigenicity methods of Kolaskar and Tongaonker (http://tools.iedb.org/bcell/result/) were proposed to determine the sites of antigenic epitopes with a default threshold value of 1.030 and a window size of 6.0 [22] .
MHC class I binding predictions:Analysis of peptide binding to MHC1 molecules was assessed by the IEDB MHC I prediction tool at http://tools.iedb.org/mhc1. The attachment of cleaved peptides to MHC molecules was predicted using artificial neural networ...