Comment by dekhn
2 years ago
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. Many people in my program could not graduate until they had a Nature or Science paper (my advisor was not an asshole, I graduated with just a paper in Biochemistry).
In a sense both of you are right- DeepMind is massively overplaying the value of what they did, trying to expand its impact far beyond what they actually achieved (this is common in competitive biology), but what they did was such an improvement over the state of the art that it's considered a major accomplishment. It also achieved the target of CASP- which was to make predictions whose scores are indistinguishable from experimentally determined structures.
I don't think academics thought CASP was unwinnable but most groups were very surprised that an industrial player using 5 year old tech did so well.
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.
8 replies →
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.