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Comment by dekhn | Hacker News Reader

how could they do ab initio? They depend on multiple sequence alignments.

If I'm mistaken about this then I'll happily take back what I said, but there's no way that AF2 could work wihtout MSAs, therefore, it is not ab initio.

Ah, OK checked the paper again. They're working on the "template" category which means there is structure-sequence information... maybe CASP organizers consider this ab initio ? The paper never mentions anything about ab initio predicitons. Is that what you're saying, that template methods are ab initio?

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Just in case there is a confusion: there is a difference between available sequences (~300 million in standard protein sequence repositories) and structures (~170k structures in the PDB, perhaps about ~120k that are structurally non-redundant). A large amount of CASP14 targets have no available templates; in fact, many of them represented previously unseen topologies. However, all of them had some (in most cases, many) available sequences.

The commonly accepted definition of homology modelling implies using a known structure ("template") as a scaffold to model the protein's topology. Since there are many CASP14 targets without appropriate templates, AlphaFold 2 simply cannot "just do homology modelling".

I do take the point that the correct term is "free modelling" (it does not have, or does not use, any good structure as a template), and not "ab initio modelling" (it uses physics to fold the protein), though. A deep enough MSA is generally a requirement.

Again, it's entirely possible I missed some very subtle point in AF2's system, but my understanding is that each target AF2 predicted had an underlying structural template covering the majority of the domain and the mapping was established through the MSA.

IE, any MSAs would always include alignments to known protein structures. Are you saying their MSAs don't include alignments to known protein structures?

(the reason I'm asking all this is because if I'm mistaken, then AF2 did do something "interesting", but everything in the paper says that everything they did is template based. If they are just folding proteins using MSAs without alignments to protein structures, that's far more interesting. I don't think they did that.

edit: I've now reread the paper again, and I believe their claim of making predictions where there is no structural homology is incorrect from a technical perspective. I've communicated this to both the CASP organizers (whom I know) and DeepMind.

Yes: they predict structures using MSAs, without alignments to known protein structures in a majority of the cases.

OK if that's truly accurate, then they did make a significant accomplishment. However, I'm 99% certain (from reading the paper) that they actually do have alignments to structures, but the similarity is very low.

It would help if you coould point to one of the alignmennts they made that has no underlying structure (even a template fragment) support.

I reread the methods section, https://static-content.springer.com/esm/art%3A10.1038%2Fs415...

They train jointly on the results of genetic search and template search (template search). Can you show an example of a prediction made using only genetic search and not template search. Those templates are fastas made from PDB files, which, while not homology modelling, is definitely not "ab initio".

I've been in communication with several different teams and leaders at CASP and I've confirmed that this does appear to be the case.

I'm going to be a bit skeptical but if that's the case, then it really is a significant improvement. Glad to see that with just the idea, the academic community was able to reach near parity in a short time, demonstrating there was nothing unique to DM except their huge amount of compute, storage, and talent, and this would have happened in the next CASP anyway.

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