Suppose you want to choose which changes to an enzyme to test in the lab. A protein language model can help rank candidates, but extracting an embedding does not by itself give you a prediction of enzyme activity. An embedding is a vector of numbers representing the sequence. ESM can return one vector per amino acid, or average those into one vector for the whole protein. To predict a measured property from those vectors, you still need a predictor trained on labelled examples. A different output is a mutation score. A masked model compares how likely the replacement amino acid is with how likely the original amino acid is, given the surrounding sequence. That can rank substitutions without training on your experiment’s labels. It is not a measurement of activity or a probability that the enzyme will work. The practical distinction: use embeddings as inputs to a trained predictor, or use the model’s documented mutation-scoring procedure. Evaluate the ranking against the property you actually measured; the two routes do not produce interchangeable outputs. Upstream documentation and examples: https://github.com/facebookresearch/esm Mutation-scoring paper: https://proceedings.neurips.cc/paper/2021/hash/f51338d736f95dd42427296047067694-Abstract.html Guide: https://rewirebio.io/blog/a-protein-embedding-is-not-an-explanation/ Disclosure: rewirebio.io is my site. The guide, this post and the explanatory diagram are AI-assisted. This describes documented methods, not a new experiment. https://preview.redd.it/p8l7dmyyqfvh1.png?width=1400&format=png&auto=webp&s=e121d5095033beace8e25ccf44b846256d8a964b submitted by /u/Fair-Rain3366
Originally posted by u/Fair-Rain3366 on r/ArtificialInteligence