Efficient evolution of human antibodies from general protein language models

Natural evolution must explore a vast landscape of possible sequences for desirable yet rare mutations, suggesting that learning from natural evolutionary strategies could guide artificial evolution. Here we report that general protein language models can efficiently evolve human antibodies by suggesting mutations that are evolutionarily plausible, despite providing the model with no information about the target antigen, binding specificity or protein structure. We performed language-model-guided affinity maturation of seven antibodies, screening 20 or fewer variants of each antibody across only two rounds of laboratory evolution, and improved the binding affinities of four clinically relevant, highly mature…

• DR
  Damon Runyon Cancer Research Foundation

  Award: DRG-2301-17
• NI
  National Institutes of Health

  Awards: DP1AI158125, K99HD104924
• NI
  National Institute of Child Health and Human Development

  Award: K99HD104924
• SM
  Stanford Maternal and Child Health Research Institute