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To add to this, the deep learning field has already moved on towards MSA-less structure prediction. None of this would be possible without building on top of the work open sourced by Deepmind.

https://www.biorxiv.org/content/10.1101/2022.07.21.500999v1 https://www.biorxiv.org/content/10.1101/2022.07.20.500902v1

To be overly dismissive is to lack imagination.

How do we know these "MSA-less" models aren't cheating (i.e. learning all MSAs implicitly from their training data)? If they are, they would similarly fail on any "novel" AA sequence (i.e. one without known/learned MSAs)

> What ramraj is talking about: if you go into a competitive grad program to get a PhD in structural biology, your advisor will probably expect that in 3-4 years you will: crystallize a protein of interest, collect enough data to make a model, and publish that model in a major journal.

All of that is generally applicable to molecular biology in general, and I don't see how the field of structural biology is especially egregious, the way ramraj is making it out to be.

Protein crystallization can be very difficult and there is no general solution. Kits that screen for crystal growth conditions usually help but optimization is needed in most cases. Then, that crystal must have certain properties that allow for good data acquisition at the X-ray facility. That’s another problem by itself and months or years can pass until you get a suitable protein crystal and X-ray diffraction dataset where you can model your structure.

I'm familiar with protein crystallization and the difficulties associated with it. What I don't agree with is the characterization of the field as especially difficult, above and beyond modern biology in general. Nor can I support the assertion that structural biology students are subject to special abuse that regular grad students are not.

> ... can be very difficult and there is no general solution

This is true of pretty much any graduate work in molecular biology.

> Nor can I support the assertion that structural biology students are subject to special abuse that regular grad students are not.

I didn’t say anything regarding that.

> This is true of pretty much any graduate work in molecular biology.

Just to elaborate my point: The process of protein cristallization is not understood at a level that allows the design of general and reproducible protocols. This inherent obscurity means that every new protein needs to undergo an ad hoc, heuristic, iterative process to obtain high quality crystals. This is an early methodological hurdle, at a stage where other routine procedures in biochemistry or molecular biology are usually successful.

I said that. We had a saying in grad school, "the very best protein structures are crystallized from postdoc tears".

As a current postdoc (genetics) I think postdoc tears are the fuel that academia runs on - as well as those of our significant others and kids.

> I didn’t say anything regarding that.

I know you didn't - this was one of the claims of ramraj I was responding to.

> The process of protein cristallization is not understood at a level that allows the design of general and reproducible protocols. This inherent obscurity means that every new protein needs to undergo an ad hoc, heuristic, iterative process to obtain high quality crystals. This is an early methodological hurdle, at a stage where other routine procedures in biochemistry or molecular biology are usually successful.

I don't disagree, though I would suggest that there's just as much grunt work, frustration, and hand wringing in other fields of molecular biology at the graduate level and above. Even if other fields have reproducible protocols established, that's not what gets papers published. With the possible exception of clinical samples, more often than not we have no clue if the analyses we're doing will yield anything, and the high risk zone is where all grad students live.

In most other sub fields you don’t get to not publish if exactly one endpoint never comes to pass. I know I didn’t have something like that, and most of my non crystallographer friends didn’t.

There’s a lot of structural biology apologists here in this thread. Happy to crap on DeepMind but not ready to take criticism of their own field.

For anyone outside of the field wanting to learn more, check out this documentary: https://en.m.wikipedia.org/wiki/Naturally_Obsessed

> In most other sub fields you don’t get to not publish if exactly one endpoint never comes to pass. I know I didn’t have something like that, and most of my non crystallographer friends didn’t.

How is this a problem unique to structural biology? In every subfield we're hoping to publish interesting results, and that endpoint is defined by the nature of the field. As a geneticist, in the early 90s, sequencing & characterizing a single bacterial gene would have been the focus of an ambitious PhD thesis and would yield multiple papers. Sequencing at that time period had a dozen points of failure and high risk to set as the goal for a thesis. Today, sequencing a whole genome is unlikely to yield a single publication. If you're setting the ability to crystallize as the single point of failure endpoint, that logic applies to every subfield. We all have something that could potentially derail our plans, and I fail to see how structural biology is unique in that respect.

> There’s a lot of structural biology apologists here in this thread. Happy to crap on DeepMind but not ready to take criticism of their own field.

I'm not a structural biologist - I'm a Geneticist who disagrees with your characterization of SB. The issues you've mentioned are not unique to SB, but apply to pretty much all subfields. I see grad students in general lament their life choices when their cell culture fails, their mice die, protocols just don't work, or their results just don't make sense.

> If you're setting the ability to crystallize as the single point of failure endpoint, that logic applies to every subfield.

I agree that there are other fields with similar issues. What baffles me is how long protein crystallization has been a problem.

I’ll use your example:

Nowadays, sequencing a gene is unlikely to yield a single publication by itself but is no early point of failure. It’s a solved problem with protocols that have been thoroughly developed and explained to boredom. New early points of failure arise (sample related, maybe?).

Nowadays, determining the structure of a protein is unlikely to yield a single publication by itself but has a clear, early, unsolved point of failure. No understandable protocol other than buying $creening plate$, fetching cat whiskers, drawing a theoretical phase diagram that tells you nothing, and pray that your crystallization tray doesn’t show a scrambled egg tomorrow or in six weeks. This has been an issue for more than fifty years and almost 200k published structures. The jump you mentioned in sequencing hasn’t happened yet in protein crystallography and might never happen because our understanding of macromolecular crystallization is lacking and thus we cannot predict proper crystallization conditions.

Sure, I agree that crystallization in particular has faced this particular bottleneck for a long time. The field of SB, however, has still managed to advance massively too. For example, Cryo-EM can do things we could barely imagine a decade ago.

The point I'm trying to make is that from the perspective of a grad student, no field is devoid of risk, and it's surprisingly easy to be stuck by something that's a solved problem on paper. For example, I know of a grad student that's been trying to develop a mouse line for about a year now, and has now discovered that this strain just won't work for what they have in mind - and must now recreate the mutant combinations in a different strain that's at least a year's work - if it even works. I've heard stories of entire mouse lines die, and you're back to square one - years of work lost.

The other thing that complicates some of these fields is the massive pace of innovation they're undergoing that it is very hard for an individual lab to keep up to date. Grad students are using techniques that were published less than 5 years ago, and there's no locally available expertise to tap into. What remains the same is the level of grunt work grad students and postdocs have to do, even if the techniques get more sophisticated over time.

I did rotations in multiple types of lab as part of my program and I can't say I ever found that students in regular molecular biology labs had nearly as hard a time as structural biologists; SB is its own class of hell. Given the number of papers published in molecular biology that turn out to be "gel was physically cut and reasssembled to show the results the authors desired" (it's much harder to cheat on a protein structure)...

I think this is highly subjective and that every field has its own special hells. For example, in computational biology it's a lot easier to generate results (when things actually work) but conversely it's a lot harder to convince journals. The burden of proof required to publish is sometimes ridiculously high - I had a paper spend almost 3 years in review.

Hear, hear. This is probably the best take.