Computation of Antigenicity Predicts SARS-CoV-2 Vaccine Breakthrough Variants - Hu, Ye-Fan; Hu, Jing-Chu; Gong, Hua-Rui; Danchin, Antoine; Sun, Ren; Chu, Hin; Hung, Ivan Fan-Ngai; Yuen, Kwok Yung; To, Kelvin Kai-Wang; Zhang, Bao-Zhong; Yau, Thomas; Huang, Jian-Dong (Frontiers in Immunology, 2022)
It has been reported that multiple SARS-CoV-2 variants of concerns (VOCs) including Alpha, Beta, Gamma, and Delta can reduce neutralisation by antibodies, resulting in vaccine breakthrough infections. Virus-antiserum neutralisation assays are typically performed to monitor potential vaccine breakthrough strains. However, experimental-based methods took several weeks whether newly emerging variants can break through current vaccines or therapeutic antibodies. To address this, we sought to establish a computational model to predict the antigenicity of SARS-CoV-2 variants by sequence alone. In this study, we firstly identified the relationship between the antigenic difference transformed from the amino acid sequence and the antigenic distance from the neutralisation titres. Based on this correlation, we obtained a computational model for the receptor binding domain (RBD) of the spike protein to predict the fold decrease in virus-antiserum neutralisation titres with high accuracy (~0.79). Our predicted results were comparable with experimental neutralisation titres of variants, including Alpha, Beta, Delta, Gamma, Epsilon, Iota, Kappa, and Lambda, as well as SARS-CoV. Here, we firstly predicted the fold of decrease of Omicron as 17.4-fold less susceptible to neutralisation. We visualised all 1521 SARS-CoV-2 lineages to indicate variants including Mu, B.1.630, B.1.633, B.1.649, and C.1.2, which can induce vaccine breakthrough infections in addition to reported VOCs Beta, Gamma, Delta, and Omicron. Our study offers a quick approach to predict the antigenicity of SARS-CoV-2 variants as soon as they emerge. Furthermore, this approach can facilitate future vaccine updates to cover all major variants.