IMMUNINFORMATICS STUDY IN PREDICTING CYTOTOXIC T LYMPHOCYTES (CTL) EPITOPES OF SARS-COV-2 SPIKE PROTEIN

Abstract

COVID-19 is a new disease due to a virus caused by a new coronavirus variant, namely SARSCoV-2. This virus has infected millions of people globally and caused social disruption to death in Indonesia. The development of artificial intelligence and bioinformatics is significant to realize each country's independence, especially ASEAN countries, towards the availability of vaccines in handling this pandemic outbreak. We aimed to predict the epitope that induces cytotoxic T lymphocytes (CTL) of the SARS-CoV-2 spike protein using an immunoinformatic approach used for COVID-19 vaccine design. SARS-CoV-2 spike protein sequences were obtained from local strains, and genetic sequences were stored in GISAID. Immunoinformatics analysis was carried out using NetMHCpan-4.0 on HLA-A found in Indonesian society (HLA-A02:01, HLA-A 11:01, and HLA-B 40:01), then BLAST alignment was performed using NCBI BLASTp to assess autoimmune potential (percent identity < 60%) Epitope Conservancy Analysis was performed using IEDB then the prediction of toxicity was made using ToxinPred and allergenicity using AllerTop 2.0 and homology modelling of epitopes for each HLA using MODELLER 10.0.Epitope prediction of spike protein resulted in several epitopes capable of producing a response to the CTL of 2 HLA-A and 1 HLA-B. Eight candidates for conservation epitopes were chosen considering that they do not potentially cause toxicity and allergenicity, and there are six new epitopes. We hope that the candidate epitopes that we have found can support and enhance learning in accelerating the design of universal vaccines, especially mRNA.