Using protein language models to accelerate their artificial evolution works; the interesting part is why.

The paper also gives a good explanation about their thinking process. If you only have a few minutes (but you should use more), you can start with Fig 1 in the paper. Their hypothesis was that evolutionary pressures favor protein characteristics that are useful in application settings, which means that sampling from evolutionary plausible mutations would favor high-utility proteins.

This is a very neat idea! It pays, though, to try to clarify this even more — there’s much here that’s assumed or implied in domain knowledge or elsewhere in the paper, and translating this idea elsewhere benefits from understanding each individual step.

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