AlphaFold reveals the structure of the protein universe
Before my comment gets dismissed, I will disclaim I am a professional structural biologist that works in this field every day.<p>These threads are always the same: lots of comments about protein folding, how amazing DeepMind is, how AlphaFold is a success story, how it has flipped an entire field on it&#x27;s head, etc. The language from Google is so deceptive about what they&#x27;ve actually done, I think it&#x27;s actually intentionally disingenuous.<p>At the end of the day, AlphaFold is amazing homology modeling. I love it, I think it&#x27;s an awesome application of machine learning, and I use it frequently. But it&#x27;s doing the same thing we&#x27;ve been doing for 2 decades: pattern matching sequences of proteins with unknown structure to sequences of proteins with known structure, and about 2x as well as we used to be able to.<p>That&#x27;s extremely useful, but it&#x27;s not knowledge of protein folding. It can&#x27;t predict a fold de novo, it can&#x27;t predict folds that haven&#x27;t been seen (EDIT: this is maybe not strictly true, depending on how you slice it), it fails in a number of edge cases (remember, in biology, edge cases are everything) and again, I can&#x27;t stress this enough, we have no new information on how proteins fold. We know all the information (most of at least) for a proteins final fold is in the sequence. But we don&#x27;t know much about the in-between.<p>I like AlphaFold, it&#x27;s convenient and I use it (although for anything serious or anything interacting with anything else, I still need a real structure), but I feel as though it has been intentionally and deceptively oversold. There are 3-4 other deep learning projects I think have had a much greater impact on my field.<p>EDIT: 
See below: 
<a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=32265662" rel="nofollow">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=32265662</a> for information on predicting new folds.