Development of immunoinformatics program for amino acid sequence alignment and linear B-Cell epitope prediction based on spike proteins of SARS-CoV-2

Abstract

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.