Furthermore, the authors must engage a lot of human curation to ensure the sequences they generate are active. First, they pick an easy target. Second, they employ by-hand classical bioinformatics techniques on their predicted sequences after they are generated. For example, they manually align them and select those which contain specific important amino acids at specific positions which are present in 100% of functional proteins of that class, and are required for function. This is all done by a human bioinformatics expert (or automated) before they test the generated sequences. This is the protein equivalent of cherry-picking great examples of, for example, ChatGPT responses and presenting them as if the model only made predictions like that.
One other comment, in protein science, a sequence with 40% identity to another sequence is not “very different” if it is homologous. Since this model is essentially generating homologs from a particular class, it’s no surprise at a pairwise amino acid level, the generated sequences have this degree of similarity. Take proteins in any functional family and compare them. They will have the same overall 3-D structure—called their “fold”—yet have pairwise sequence identities much lower than 30–40%. This “degeneracy”, the notion that there are many diverse sequences that all fold into the same shape, is both a fundamental empirical observation in protein science as well as a grounded physical theory.
Not to be negative. I really enjoyed reading this paper and I think the work is important. Some related work by Meta AI is the ESM series of models [1] trained on the same data (the UniProt dataset [2]).
One thing I wonder is about the vocabulary size of this model. The number of tokens is 26 for the 20 amino acids and some extras, whereas for a LLM like Meta’s LLaMa the vocab size is 32,000. I wonder how that changes training and inference, and how we can adopt the transformer architecture for this scenario.