Fig. 1: Guiding evolution with protein language models.

a,b, Two possible models for relating the space of mutations with high evolutionary plausibility (for example, mutations seen in antibodies) to the space with high fitness under specific selection pressures (for example, mutations that result in high binding affinity to a specific antigen). Both models assume that mutations with high fitness make up a rare subset of the full mutational space and that, in general, high-fitness mutations are also evolutionarily plausible. Under the first model (a), mutations with high fitness are rare within the subset of mutations that are evolutionarily plausible. Under the second model (b), when restricted to the regime of plausible mutations, improvements to fitness become much more common. c, Protein language models, trained on millions of natural protein sequences learn amino acid patterns that are likely to be seen in nature. We hypothesized that most mutations with high language model likelihood would also be evolutionarily plausible. Assuming that this is true, and if the second model (b) better describes nature, then a language model with no information about specific selection pressures can still efficiently guide evolution.