I’m in biotech academia and it has changed things already. Yes the protein folding problem isn’t “solved” but no problem in biology ever is. Comparing to previous bio/chem Nobel winners like Crispr, touch receptors, quantum dots, click chemistry, I do think AlphaFold already has reached sufficient level of impact.
A gap between biological research and biological engineering is that, for bioengineering, the size of the potential solution space and the time and resources required to narrow it down are fundamental drivers of the cost of creating products - it turns out that getting a shitty answer quickly and cheaply is worth more than getting the right answer slowly.
I agree with you, though - they're two different answers. I've done a bunch of work in the metagenomics space, and you very quickly get outside areas where Alphafold can really help, because nothing you're dealing with is similar enough to already-characterized proteins for the algorithm to really have enough to draw on. At that point, an actual solution for protein folding that doesn't require a supercomputer would make a difference.
A proper protein structural model is an all-atom representation of the macromolecule at its global minimum energy conformation, and the expected end result of the folding process; both are equivalent and thus equally canonical. The “fast” part, i.e., the decrease in computational time comes mostly from the heuristics used for conformational space exploration. Structure prediction skips most of the folding pathway/energy funnel, but ends up at the same point as a completed folding simulation.
> At that point, an actual solution for protein folding that doesn't require a supercomputer would make a difference.
Or more representative sequences and enough variants by additional metagenomic surveys, for example. Of course, this might not be easily achievable.
Well, that's the hope, at least.
> Or more representative sequences and enough variants by additional metagenomic surveys, for example. Of course, this might not be easily achievable.
For sure, but for ostensibly profit-generating enterprises, it's pretty much out of the picture.
I think the reason an actual computational solution for folding is interesting is that the existing set of experimentally verified protein structures are for proteins we could isolate and crystalize (which is also the training set for AlphaFold, so that's pretty much the area its predictions are strongest, and even within that, it's only catching certain conformations of the proteins) - even if you can get a large set of metagenomic surveys and a large sample of protein sequences, the limitations on the methods for experimentally verifying the protein structure means we're restricted to a certain section of the protein landscape. A general purpose computationally tractable method for simulating protein folding under various conditions could be a solution for those cases where we can't actually physically "observe" the structure directly.
Attempting to predict structures using mechanism that simulate the physical folding process waste immense amount of energy and time sampling very uninteresting areas of space.
You don't want to use a supercomputer to simulate folding; it can be done with a large collection of embarassingly parallel machines much more cheaply and effectively. I proposed a number of approaches on supercomputers and was repeatedly told no because the codes didn't scale to the full supercomputer, and supercomputers are designed and built for codes that scale really well on non-embarassingly parallel problems. This is the reason I left academia for google- to use their idle cycles to simulate folding (and do protein design, which also works best using embarassingly parallel processing).
As far as I can tell, only extremely small and simple proteins (like ribonuclease) fold to somewhere close to their global energy minimum.
It seems to really accelerate productivity of researchers investigating bio molecules or molecules very similar to existing bio molecules. But not de novo stuff.
In a lot of cases deep learning is able to simulate complex system at a precision that is more than precise enough, that otherwise would not be tracktable (like is the case with alphafold), and again this is especially valuable if you can double check the output.
Ofc, in the field of language and vision and in a lot of other fields, deep learning is straight up the only solution.
A lot of bioinformatics tools using deep learning appeared around 2017-2018. But rather than being big breakthroughs like AlphaFold, most of them were just incremental improvements to various technical tasks in the middle of a pipeline.
Not many DL based tools I see these days regularly applied in genomics. Maybe: Tiara for 'high level' taxonomic classification, DeepVariant in some papers for SNP calling, that's about it? Some interesting gene prediction tools coming up like Tiberius. AlphaFold, of course.
Lots of papers but not much day-to-day usage from my POV.
There are a lot of differences between the cutting-edge methods that produce the best results, the established tools the average researcher is comfortable using, and whatever you are allowed to use in a clinical setting.
―https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp...
There's a lot of pharma companies and drug design startups that are actively trying to apply these methods, but I think the jury is still out for the impact it will finally have.
If I was the Nobel Committee, I would have waited a bit to see if this issue aged well. Also, in terms of giving credit, I think those who invented pairwise and multiple alignment dynamic programming algorithms deserved some recognition. AlphaFold built on top of those. They are the cornerstone of the entire field of biological sequence analysis. Interestingly, ESM was trained on raw sequences, not on multiple alignments. And while it performed worse, it generalizes better to unseen proteins like TCRs.
Protein folding ≠ protein structure prediction
> I think those who invented pairwise and multiple alignment dynamic programming algorithms deserved some recognition
I would add BLAST as well but that ship has sailed, I’m afraid.
For the curious, BLAST is very much like pairwise alignment but uses an index to speed up by avoiding attempting to align poorly scoring regions.
That's the key part, I think, being able to estimate how unique each alignment is without having to simulate the null distribution, as it was done before with FASTA.
The index also helps, but the speedup comes mostly from the other part.
The question is: has blast made more of an impact than alpha fold? I think so at the moment.
>> I do think AlphaFold already has reached sufficient level of impact.
A better one is seeing my grad-school friends with zero background in comp-sci or math, presenting their cell-biology results with AlphaFold in conferences and at lab meetings. They are not protein folding people either- just molecular biologists trying to present more evidence of docking partners, functional groups in their pathway of interest.
It reminds me of when Crispr came out. There were ways to edit DNA before Crispr, but its was tough to do right and required specialized knowledge. After Crispr came out, even non-specialists like me in tangential fields could get started.
There's an on-point blog-post "AI and Biology" (https://www.science.org/content/blog-post/ai-and-biology) which illustrates why AlphaFold's real breakthrough is not super actionable for creating further bio-medicinal applications in a similar vein.
Docking ligands doesn't make for particularly great structures, and snapshot structures really miss out on the important dynamics.
So it's hard for me to imagine how alphafold can help with small molecule development (alphafold2 doesn't even know what small molecules are). I agree it totally sounds plausible in principle, I've been in a team where such an idea was pushed before it flopped, but in practice I feel there's much less use to extract from there than one might think.
EDIT: To not be so purely negative: I'm sure real use can be found in tinkering with AlphaFold. But I really don't think it has or will become a big deal in small drug discovery workflows. My PoV is at least somewhat educated on the matter, but of course it does not reflect the breadth of what people are doing out there.
That people are arguing about the finer details of what it gets wrong is support for its value, not a detriment.
I mean, sure, prior to alphafold, the notion that sequence / structure relationship was "sufficient to predict" protein structure was merely a very confident theory that was used to regularly make the most reliable kind of structure predictions via homology modeling (it was also core to Rosetta, of course).
Now it is a very confident theory that is used to make a slightly larger subset of predictions via a totally different method, but still fails at the ones we don't know about. Vive la change!
3-mers and 9-mers, if I recall correctly. The fragment-based approach helped immensely with cutting down the conformational search space. The secondary structure of those fragments was enough to make educated guesses of the protein backbone’s, at a time where ab initio force field predictions struggled with it.
In order to create those fragment libraries, there was a step involving generation of multiple-sequence alignments, pruning the alignments, etc. Rosetta used sequence homology to generate structure. This wasn't a wild, untested theory.
Rosetta used remote sequence homology to generate the MSAs and find template fragments, which at the time was innovative. A similar strategy is employed for AlphaFold’s MSAs containing the evolutionary couplings.
But, the protein field has always played loose with the term "homology".