8 ms·

Jeez. Clickbait title, tons of fallacy, like goalpost moving. Some context on myself: I have 15+ years of postgraduate chemistry/molecular bio/biophysics/bio

Jeez. Clickbait title, tons of fallacy, like goalpost moving. Some context on myself: I have 15+ years of postgraduate chemistry/molecular bio/biophysics/biochemistry experience, then quit to go into tech, worked for AI hardware and now AI-driven software startups, mostly as backend (not implementing ML models, but I know how to do that and have done some small models for myself). I'm a pessimist about this AI innovation cycle's prospects in general AI and in particular drug discovery. Has protein folding been solved? Yes. As a practitioner, what I want to be able to do is pull a sequence that I've retrieved from DNA, drop it into a computer, and get the structure out. Intuitive insight can FOLLOW from those results. For example: In one of my projects, I was able to look at the structure (a homolog had been solved, so I just did a dumb alignment and threading), identify that it was acting as an NPN transistor (unpublished details), fix the electron flow through the enzyme and improve the yield (https://link.springer.com/article/10.1186/1754-1611-7-17 https://link.springer.com/article/10.1186/1754-1611-7-17). Later, I looked at the structure of the enzyme, modified the surface charge in one particular part of it, and improved electron throughput again (https://www.mdpi.com/1422-0067/16/1/2020 https://www.mdpi.com/1422-0067/16/1/2020). This was with primitive "protein folding through homology" tools, now there's an good chance I could do these sorts of things with proteins I don't have homologous structure for. These are the sorts of things that protein folding enables. One more thought -- I bet that DeepMind can do things like make it obvious where there are certain posttranslation sites (like FeS clusters) or make it obvious where there is a cryptic phosphorylation or glycosylation site (sequence holdover from a previous mutant that no longer has its expected functionality) because it's been buried. Will there be corner cases? Yes. Probably deepmind will have difficulty solving the fold of amyloid fibrils. Probably deepmind will have some difficulty with super-strange post-translational modifications (think things like GFP's core fluorophore), or if you design a drug where you splice in an unnatural or D-amino acid, or oddities like that. > we are not at the point where this AI tool can be used for drug discovery I disagree. Sure, it probably won't be able to find a small molecule binding site. It almost certainly won't be able to design a drug that has a long-range allosteric effect (think Gleevec's super strange mechanism of action). But, deepmind WILL be able to help design biologics that, for example, can interact with bump-hole mutations. There was never an expectation that "protein folding" solves every problem in the drug discovery pipeline. That's out of scope for the basic problem. As for this: > AI methods rely on learning the rules of protein folding from existing protein structures. Come on. There is no method that doesn't rely on learning the rules of protein folding from existing structures. Even de novo MD-modeling has tweaking fudge factors (we could call it "dark biochemical fields" -- think: what is the expected dielectric constant around a tyrosine residue?? No way we're calculating that from the schroedinger equation) that are empirically derived to get your results.

choeger 6y ago
Could you shed some light on the 66% part, too? As a layman and general sceptic of hype topics, this one is the biggest obstacle for me. How are we going to procede with models that are 33% wrong? In other engineering areas, uncertainty is usually covered by the modeler. But how would that work here?
- dnautics 6y ago
  who knows. You can't generalize 1 error out of 3 to 66%. Statistics of small numbers. Knowing how ML works, I'm surprised it didn't give any indication of low confidence. Not to give DeepMind too much credit, but it's also entirely possible that "both are correct". By the nature of how targets are selected for CASP, year over year there is higher likelihood that "pathologically difficult" proteins are presented for the competition, for example - a protein that exhibits polymorphic structure where the technique (say Xtal vs NMR) biases the "solved" fold in a huge way. I believe CASP tries to weed out proteins that we know are polymorphic, but you can never be sure, and again, as time marches on, those are the proteins that fundamentally are harder to solve, so it's likely they will be enriched in the test pool. An extreme example is insulin. The structure of insulin is has been solved for 60? I think years, but when it's in the environment of its receptor, it looks TOTALLY different (solved 5? years ago). Having said that, doubtful that DeepMind could ascertain that structure, since the environment is super-super different. I think that the modestly high error rate is an indication that deepmind is mostly interpolating and has solved the broad protein folding heuristic. It probably will get better.
  - Voloskaya 6y ago
    > You can't generalize 1 error out of 3 to 66% Right but we also can't generalize from 2 out 3, and without knowing this figure it's really hard to say how useful this is, no? > Knowing how ML works, I'm surprised it didn't give any indication of low confidence. This is actually a hard problem in ML, for example in NLP, many people assume a high log prob score means a high confidence but it is not true at all.
    - 6y ago

choeger 6y ago

Could you shed some light on the 66% part, too? As a layman and general sceptic of hype topics, this one is the biggest obstacle for me. How are we going to procede with models that are 33% wrong? In other engineering areas, uncertainty is usually covered by the modeler. But how would that work here?

dnautics 6y ago
who knows. You can't generalize 1 error out of 3 to 66%. Statistics of small numbers. Knowing how ML works, I'm surprised it didn't give any indication of low confidence. Not to give DeepMind too much credit, but it's also entirely possible that "both are correct". By the nature of how targets are selected for CASP, year over year there is higher likelihood that "pathologically difficult" proteins are presented for the competition, for example - a protein that exhibits polymorphic structure where the technique (say Xtal vs NMR) biases the "solved" fold in a huge way. I believe CASP tries to weed out proteins that we know are polymorphic, but you can never be sure, and again, as time marches on, those are the proteins that fundamentally are harder to solve, so it's likely they will be enriched in the test pool. An extreme example is insulin. The structure of insulin is has been solved for 60? I think years, but when it's in the environment of its receptor, it looks TOTALLY different (solved 5? years ago). Having said that, doubtful that DeepMind could ascertain that structure, since the environment is super-super different. I think that the modestly high error rate is an indication that deepmind is mostly interpolating and has solved the broad protein folding heuristic. It probably will get better.
- Voloskaya 6y ago
  > You can't generalize 1 error out of 3 to 66% Right but we also can't generalize from 2 out 3, and without knowing this figure it's really hard to say how useful this is, no? > Knowing how ML works, I'm surprised it didn't give any indication of low confidence. This is actually a hard problem in ML, for example in NLP, many people assume a high log prob score means a high confidence but it is not true at all.
  - 6y ago