Epitope-Based Peptide Vaccine Design against Fructose Bisphosphate Aldolase of Candida glabrata: An Immunoinformatics Approach

Autor: Essam Ammar Salih, Hozaifa Saif Osman, Mohamed Hassan, Athar A Alsafi, Amna A Mohamed, Reham M. Elhassan, Mohamed Bashair Hassan, Abeer Babiker Idris, Fatima A. Abdelrhman, Marwa Saad M Khalil, Lina Mohamed Elamin Elhasan, Asma Ibrahim H
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Article Subject
Immunology
Epitopes
T-Lymphocyte
Fructose-bisphosphate aldolase
Candida glabrata
Computational biology
Major histocompatibility complex
Epitope
Fungal Proteins
03 medical and health sciences
Immunogenicity
Vaccine
Antigen
Fructose-Bisphosphate Aldolase
MHC class I
Humans
Immunology and Allergy
Amino Acid Sequence
Homology modeling
Conserved Sequence
030304 developmental biology
0303 health sciences
biology
030306 microbiology
Histocompatibility Antigens Class I
Candidiasis
Histocompatibility Antigens Class II
Computational Biology
General Medicine
RC581-607
biology.organism_classification
Protein Structure
Tertiary
Molecular Docking Simulation
Drug Design
Vaccines
Subunit
Peptide vaccine
biology.protein
Epitopes
B-Lymphocyte
Fungal Vaccines
Immunologic diseases. Allergy
Epitope Mapping
Research Article
Zdroj: Journal of Immunology Research, Vol 2021 (2021)
Journal of Immunology Research
ISSN: 2314-8861
DOI: 10.1155/2021/8280925
Popis: Background. Candida glabrata is a human opportunistic pathogen that can cause life-threatening systemic infections. Although there are multiple effective vaccines against fungal infections and some of these vaccines are engaged in different stages of clinical trials, none of them have yet been approved by the FDA. 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) were 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, while the promising peptides which have 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 that the epitopes QTSNGGAAY and LFSSHMLDL have the lowest binding energy to MHC molecules. Conclusion. The epitope-based vaccines predicted by using immunoinformatics tools have remarkable advantages over the conventional vaccines in 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 candidate’s epitopes (QTSNGGAAY and LFSSHMLDL). To the best of our knowledge, this is the first study that has predicted B- and T-cell epitopes from the Fba1 protein by using in silico tools in order to design an effective epitope-based vaccine against C. glabrata.
Databáze: OpenAIRE
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