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Updated: Nov 4, 2025
A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes
Published on: March 25, 2014
Epitope-Based Peptide Vaccine Design against Fructose Bisphosphate Aldolase of Candida glabrata: An Immunoinformatics
Lina Mohamed Elamin Elhasan1, Mohamed B Hassan2, Reham M Elhassan3
1Faculty of Science and Technology, Department of Biotechnology, Omdurman Islamic University, Khartoum, Sudan.
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.
Insights
This study identified conserved and immunogenic epitopes from the Fba1 protein of Candida glabrata using immunoinformatics. These predicted epitopes show promise for developing a novel, effective epitope-based vaccine against C. glabrata infections.
Area of Science:
- * Mycology and Immunology
- * Computational Biology and Bioinformatics
Background:
- * Candida glabrata is an opportunistic human pathogen causing severe systemic infections.
- * Despite advancements in vaccine development, no FDA-approved fungal vaccines are currently available.
Purpose of the Study:
- * To predict conserved and immunogenic B-cell and T-cell epitopes from the Fba1 protein of C. glabrata using an immunoinformatics approach.
- * To identify potential candidates for an epitope-based vaccine against C. glabrata.
Main Methods:
- * Retrieved 13 C. glabrata Fba1 protein sequences from NCBI.
- * Utilized IEDB server tools for epitope prediction.
- * Performed homology modeling and molecular docking.
Main Results:
- * Identified promising B-cell epitopes: AYFKEH, VDKESLYTK, HVDKESLYTK.
- * Predicted high-affinity MHC class I binding peptides: AVHEALAPI, KYFKRMAAM, QTSNGGAAY, RMAAMNQWL, YFKEHGEPL.
- * Identified high-affinity MHC class II binding peptides: LFSSHMLDL, YIRSIAPAY. QTSNGGAAY and LFSSHMLDL showed lowest binding energy to MHC molecules.
Conclusions:
- * Epitope-based vaccines offer advantages such as specificity, safety, and reduced allergenicity compared to conventional vaccines.
- * The predicted epitopes QTSNGGAAY and LFSSHMLDL warrant further in vivo and in vitro validation.
- * This study is the first to predict B- and T-cell epitopes from C. glabrata Fba1 protein using in silico methods for vaccine design.
