Language models enable zero-shot prediction of the effects of mutations on protein function

🤖 Plain-English Summary

Abstract Modeling the effect of sequence variation on function is a fundamental problem for understanding and designing proteins. The conventional setting is limited, since a new model must be trained for each prediction task.

🔑 Key Findings

• Since evolution encodes information about function into patterns in protein sequences, unsupervised models of variant effects can be learned from sequence data.
• The approach to date has been to fit a model to a family of related sequences.
• The conventional setting is limited, since a new model must be trained for each prediction task.

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