In Silico T Cell Epitope Identification for SARS-CoV-2: Progress and Perspectives
29 Pages Posted: 4 Nov 2020 Last revised: 31 Dec 2020
Date Written: October 24, 2020
Growing evidence suggests that T cells may play a critical role in combating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Hence, COVID-19 vaccines that can elicit a robust T cell response may be particularly important. The design, development and experimental evaluation of such vaccines is aided by an understanding of the landscape of T cell epitopes of SARS-CoV-2, which is largely unknown. Due to the challenges of identifying epitopes experimentally, many studies have proposed the use of in silico methods. Here, we present a review of the in silico methods that have been used for the prediction of SARS-CoV-2 T cell epitopes. These methods employ a diverse set of technical approaches, often rooted in machine learning. A performance comparison is provided based on the ability to identify a specific set of immunogenic epitopes that have been determined experimentally to be targeted by T cells in convalescent COVID-19 patients, shedding light on the relative performance merits of the different approaches adopted by the in silico studies. The review also puts forward perspectives for future research directions.
Note: Funding: The authors were supported by the General Research Fund of the Hong Kong Research Grants Council (RGC) [Grant No. 16204519 and 16201620]. S.F.A. was additionally supported by the Hong Kong Ph.D. Fellowship Scheme (HKPFS).
Declaration of Interests: None to declare.
Keywords: coronavirus, COVID-19, computational prediction, SARS-CoV, peptide-HLA binding, immunogenicity, allergenicity, toxicity, reverse vaccinology, immunoinformatics
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