Yes, exactly! This is an interesting perspective. Actually, running .ttt with msa_pth provided is effectively evotuning. We should highlight this connection better in the paper!

This is a joint work with @roman-bushuiev.bsky.social , and Olga Pimenova, Nikola Zadorozhny, Raman Samusevich, Elisabet Manaskova, @eunbelivable.bsky.social , @hannes-stark.bsky.social , Jiri Sedlar, @martinsteinegger.bsky.social , @pluskal-lab.org , @josef-sivic.bsky.social

We provide a user-friendly plug-and-play implementation github.com/anton-bushui...

Here, customization primarily benefits challenging, out-of-distribution proteins that are poorly represented in sequence databases (as measured by MSA size)

For example, ProteinTTT applied to ESMFold improves 19% of AlphaFold2-predicted viral protein structures in BFVD

This consistently improves performance of various protein language models across protein structure, fitness and function prediction, particularly on challenging targets

ProteinTTT enables *customizing* protein language models to one target protein at a time without assuming any additional data via on-the-fly self-supervised fine-tuning on the single protein