AlphaFold: a solution to a 50-year-old grand challenge in biology

This was a/the "holy grail" problem of molecular biology, long thought to be an automatic Nobel. It's somewhat unfair to characterise developments prior to this as insignificant. In fact by the time I was working on it, that "automatic Nobel" was no longer assumed, because the field had made quite a bit of progress, in many tiny steps by many different groups, and the assumption was it would continue in this slog until reaching some state of sufficiency for practical applications without ever seeing the sort of singular achievement that would be worthy of praise and prize.

Far more went into this breakthrough, obviously, than those TPU-hours: the development of those TPUs, for example, and assembling a team that can make use of them. The protein folding problem requires very little knowledge of biology or physics to understand and was always pre-destined for some outsider to sweep. Indeed, there was game that allowed people to solve structures by intuition alone, and, IIRC, some 13-year old Mexican kid cleaned everyone's clock some years back.

Why didn't some research group do this first? Most of them just don't have the budget. We were five people, total, IIRC, and felt pretty rich because we were computer-people getting the same budget for materials as everyone at our institution, which was all wetlab, otherwise. So I was a student being paid $20/h but with a $50,000/p.a. hardware budget. How many false start does it take before you do that run with 128TPUs "for a few weeks" that works? If you blow your budget on one gigantic Google invoice, what's going to happen to you when it doesn't pan out, and the whole institute laughs at you? Etc...

There are quite a few rather good things this problem has inspired over the years, though. Among them is CASP itself: the idea of instituting a yearly competition that gives unequivocal feedback on the state of the field and every group working on it is rather rare, I believe, and it's been successful. Indeed, it would seem that CASP was necessary to attract outside groups like Deepmind, i. e. deep-pocketed industry groups striving to prove themselves on a clearly defined problem. Chess, Jeopardy, CASP: maybe it would be worthwhile to explore not , but as a superior strategy in some cases.

There was also folding@home, pioneering the distributed-donated-computing model, and the aforementioned gamification of the problem, and hundreds of the most intricate, custom-tailed, more-or-less insane ideas people devoted months and/or careers and/or careers of their most promising post-docs to that didn't pan out.

Like cellular automata. They don't work for this, trust me. (Great hit for interactive poster sessions, though)

Back in the 1990s, when I worked on structure data, I remember that at least some crystallizations were easy enough they could be done as a rotation project.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6287266/ suggests that life is now a lot easier than the 1990s. Quoting the abstract:

> Macromolecular crystallography evolved enormously from the pioneering days, when structures were solved by “wizards” performing all complicated procedures almost by hand. In the current situation crystal structures of large systems can be often solved very effectively by various powerful automatic programs in days or hours, or even minutes. Such progress is to a large extent coupled to the advances in many other fields, such as genetic engineering, computer technology, availability of synchrotron beam lines and many other techniques, creating the highly interdisciplinary science of macromolecular crystallography. Due to this unprecedented success crystallography is often treated as one of the analytical methods and practiced by researchers interested in structures of macromolecules, but not highly competent in the procedures involved in the process of structure determination.

Certainly some proteins are extremely hard to crystallize, and the new single-atom EM work will help a lot. But are there really "armies of students who routinely spend 4-6 years of their PhD trying to solve a structure of a single protein" these days?

I honestly don't know. I'm sure some do. But if so, that army is pretty small compared to the vast numbers who more routinely use crystallography.

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.

`.cgi`... we've come full circle