I will definitely blame the protein structure field in multiple levels though. It was always frustrating to me to open up Nature or Science and see it filled with papers about structure - like they are innovating so much that half of the top science magazines every week have papers in that field, yet it's not going anywhere? Or is it simply just a bunch of professors tooting their own horns about ostensible progress in a field that's archaic by decades if not years? The overall protein structure field internalised some dogmas in self defeating ways to everyone's detriment and finally events like this (and Cryo em, maybe) will jolt them out or make them fully irrelevant so we can move on. it's only doubly ironic that this came from a team in a company with minimal academic ties showing how toxic that entire system is. I only feel pity for the graduate students still trying to crystallize proteins in this day and age.
I think what basically happened is a very influential group of scientists mainly in Cambridge around the 50s and 60s convinced everytbody that reductionist molecular biology would be able to crystallize proteins and "understand precisely how they function" by inspecting the structures carefully enough.
I learned, after reading all those breathless papers about individual structures and how they explain the function of protein is that in the vast majority of cases, they don't have enough data to speculate responsibility about the behavior of proteins and how they implement their functions. There are definiteyl cases of where an elucidated structure immediately led to an improved understanding of function:
"It has not escaped our notice (12) that the specific pairing we have postulated immediately suggests a possible copying mechanism for the genetic material."
but most papers about how cytochrome "works" aren't really illuminating at all.
They say on their own press-release page that side-chains are a future research problem, and nothing about their method description makes me believe they've innovated on all-atom modeling. This software seems able to generate good models of protein backbones; these kinds of models certainly have uses, but a backbone model is not enough for drug design.
This is certainly an advancement, but you're exaggerating the scope of the accomplishment.
" I only feel pity for the graduate students still trying to crystallize proteins in this day and age."
Nothing about this changes the fact that protein crystallography is a gold-standard method for determining a protein structure. CryoEM has made it possible to obtain good structures for classes of proteins we could never achieve before, and it's certainly interesting if we can run a computer for a few days to get a 1Å ab initio model for a protein sequence, but we could already do that for a large class of proteins with homology modeling. These predicted structures still aren't generally that useful for drug design, where tiny details of molecular interactions matter.
To put it in perspective: protein energetics are measured on the scale of tens of kcal / mol. Protein-drug interactions are measured in fractions of a kcal. A single hydrogen bond or cation-pi interaction or displaced water molecule can make the difference between a drug candidate and an abandoned lead. Tiny changes in backbone position make the difference between a good structure and a bad one. Alphafold isn't doing that kind of modeling.
I understand the self preservation instincts that kick in when there's a suggestion that the entire field has been in a dark age for a while, but I hope you can see that there might be something fundamentally wrong with how research is done in academia and that is to blame for why this didn't happen sooner, and why it's so hard for many to embrace it.
Regarding your comments on the inapplicability of this current solution for docking, I'm sure that's the next project they're taking up, and let's see where that goes.
This is exactly the same type of progression that happened with Go, where when their software bet a professional player everyone's like "yeah but I bet he wasn't that good". A few years later and Lee Sedol just decided to retire. I am interested to see what happens to that entire academic field in a similar vein, though my interests are more in knowing how science can advance from more people thinking this way.
Yes it does. Protein crystallography is/was the gold-standard. Once this result is verified and accepted by the scientific community as a whole, that changes.
My PhD (now over a decade ago...yikes) was in applying much simpler ML methods to these kinds of problems (I started in protein folding, finished in protein / nucleic acid recognition, but my real interest was always protein design). Even back then, it was clear that ML methods had a lot more potential for structural biology (pun unintended) than for which they were being given credit. But it was hard to get interest from a research community that cared little about non-physical solutions. No matter how well you did, people would dismiss it as a "black box solution", and that pretty much limited your impact.
Some of this is understandable: even today, it's not at all clear that a custom-built ML model for protein folding is of much use to anyone -- particularly a model that doesn't consider all of the atoms in the protein. The traditional justification for research in this area is that if you could develop a sufficiently general model of protein physics, it would also allow you to do all sorts of other stuff that is much more interesting: rational protein design, drug binding, etc.
The alphafold model is not really useful for any of this, so in a way, it's kind of like the weinermobile of science: cool and impressive when done well ("hey! a giant hot dog on wheels!"), but not really useful outside of the niche for which it was designed. So it's hard to blame researchers in this field -- who generally have to chase funding and justify their existence -- from pursuing the application of deep learning to this one, narrow problem domain.
Obviously there will now be a wave of follow-on research, and it's impossible to know what methods this will spawn. Maybe this will revolutionize computational structural biology, maybe not. But I think it's a little unfair to demonize the entire field. Protein folding just traditionally hasn't been a very useful or interesting area, and like all "pure science", it leads to a lot of small-stakes, tribal thinking amongst the few players who can afford to compete. This is right out of Thomas Kuhn: a newcomer sweeps into a field, glances at the work of the past, then bashes it over the head, dismissively.