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. <a href="https://medium.com/@marcelorinesi/using-protein-language-models-to-accelerate-their-artificial-evolution-works-the-interesting-part-3c2501f21a3f">Read More</a>