Multi-task learning for predicting SARS-CoV-2 antibody escape

Barak Gross, Roded Sharan

Research output: Contribution to journalArticlepeer-review


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 attack are variants that can evade the vaccine. Thus, a fundamental problem of growing importance 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.

Original languageEnglish
Article number886649
JournalFrontiers in Genetics
StatePublished - 11 Aug 2022


  • coronavirus
  • escape prediction
  • multi-task learning
  • neural network
  • receptor binding domain

All Science Journal Classification (ASJC) codes

  • Molecular Medicine
  • Genetics
  • Genetics(clinical)


Dive into the research topics of 'Multi-task learning for predicting SARS-CoV-2 antibody escape'. Together they form a unique fingerprint.

Cite this