Main Article Content
Corona Virus Disease 2029 (COVID-19), the current outbreak caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has been declared as a global pandemic. Insights into B-cell epitope profiles of SARS-CoV-2 will greatly contribute to a better design of a vaccine to combat COVID-19. With an advance in the immunoinformatics, B-cell epitope prediction combining multiple tools has been previously shown to be an effective and accurate method to identify potential B-cell epitopes from the SARS-CoV-2 S protein sequence. However, extracting the potential peptides from each prediction tool as well as finalizing the putative B-cell epitopes are still time-consuming and labor-intensive. In this work, we thus aim to develop a program and interface for a rapid and accurate prediction of B-cell epitopes by using SARS-CoV-2 S as a test sequence. To fill the gaps in B-cell epitope prediction combining multiple prediction tools, a new program was developed with a special feature that it can assemble the prediction results obtained from distinct tools and then generate potential B-cell epitopes. To run the program, a full-length sequence of the target protein in FASTA format is input together with CSV files of the prediction results obtained from B-cell epitope and coil structure prediction using BepiPred - 2.0 tool, and from predictions for accessibility, hydrophilicity and antigenicity using the methods of Emini, Parker, and Kolaskar & Tongaonkar, respectively. The program runs in 2 phases. In phase I, peptides are extracted from each tool based on the input thresholds and predicted peptides from all tools are next aligned. In phase II, the program creates the final putative B-cell epitopes depending on the users’ criteria. We provide a program with a solution addressing problems associated with B-cell epitope prediction using multiple tools and criteria for epitope identification, thus enabling a more rapid and accurate B-cell epitope prediction.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
© Journal of Asian Medical Students’ Association (JAMSA). Released under a Creative Commons license.
P. A. Karplus and G. E. Schulz, “Prediction of chain flexibility in proteins: a tool for the selection of peptide antigen,” Naturwissenschaften, vol. 72, no. 4, pp. 212-213, 1985.
E. A. Emini, J. V. Hughes, D. S. Perlow, and J. Boger, “Induction of hepatitis A virus-neutralizing antibody by a virusspecific synthetic peptide,” Journal of Virology, vol. 55, no. 3, pp. 836–839, 1985.
L. Pellequer, E. Westhof, and M. H. V. Van Regenmortel, “Correlation between the location of antigenic sites and the prediction of turns in proteins,” Immunology Letters, vol. 36, no. 1, pp. 83–99, 1993.
T. P. Hopp and K. R. Woods, “Prediction of protein antigenic determinants from amino acid sequences,” Proceedings of the National Academy of Sciences of the United States of America, vol. 78, no. 6, pp. 3824–3828,
WHO Director-General’s opening remarks at the media briefing on COVID19 -March 2020
Xinyang Li,"Clinical determinants of the severity of COVID-19: A systematic review and meta-analysis"
Muhammad Adnan Shereen,"COVID-19 infection: Origin, transmission, and characteristics of human coronaviruses"
Jiao Zhang,"Asymptomatic carriers of COVID-19 as a concern for disease prevention and control: more testing, more follow-up"
Tarek A.Ahmad,"B-cell epitope mapping for the design of vaccines and effective diagnostics
Author links open overlay panel"
Anne S. De Groot,"Epitope-Based Immunome-Derived Vaccines: A Strategy for Improved Design and Safety"
Kanokporn Polyiam,"Immunodominant linear B cell epitopes in the spike and membrane proteins of SARS-CoV-2 identified by immunoinformatics prediction and immunoassay"
Jian Zhang,"Conformational B-Cell Epitopes Prediction from Sequences Using Cost-Sensitive Ensemble Classifiers and Spatial Clustering"
Jose L. Sanchez-Trincado,"Peptide-Based Immunotherapeutics and Vaccines 2017"
Lenka Potocnakova,"An Introduction to B-Cell Epitope Mapping and In Silico Epitope Prediction"