Multi-task learning for predicting SARS-CoV-2 antibody escape - Gross, Barak; Sharan, Roded (Frontiers in Genetics, 2022)
The coronavirus pandemic has revolutionized our world with vaccination proving to be a key tool in fighting the disease. However, a major threat to this line of attach are variants that can evade the vaccine. Thus, a fundamental problem in this field is the identification of mutations of concern with high escape probability. In this paper we develop a computational framework that harnesses systematic mutation screens in the receptor binding domain of the viral Spike protein for escape prediction. The framework analyzes data on escape from multiple antibodies simultaneously, creating a latent representation of mutations that is shown to be effective in predicting escape and binding properties of the virus. We use this representation to validate the escape potential of current SARS-COV-2 variants.