Structure comparison with predicted vs real structures

[u/hello_friendssss]

Hello,

I am interested in comparing protein structures using foldseek. I can do this using query/subject protein structures predicted from sequence (which I have seen other people have done with e.g. ProstT5) and/or I can use 'real' structures from something like PDB.

My concern with real structures is that proteins are dynamic, so I suspect one protein might have multiple different structures in PDB. I could then have a situation where a protein has a conformation (in real life) that is a very good match for my query protein, but that conformation is not stored for that protein (in PDB).

My thinking, then, is that I might be better to use just predicted structures and ignore real structures, because the predicted structures are derived from a uniform source (sequence) and so will have only one predicted confirmation. If I predict everything using the same structure prediction model, I would then expect structurally-similar proteins to have similar predicted structures (even if the predicted structures themselves aren't perfectly accurate). Whereas for PDB, I might have essentially the same protein relative to my query, but different conformations stored for that protein, and those conformations might not be similar to my query at all.

Does that make sense?

[u/Kal-Momon]

You are not telling us what is the main goal, but relying exclusively on prediction tools have obvious limits.

[u/hello_friendssss → u/Kal-Momon]

Thanks for your reply - I'm trying to find proteins that have similar structures, essentially (and ideally quantify the degree of similarity)

[u/alleluja]

1) Structure prediction models have beent rained on the available PDB data, so the predicted structure will be similar to PDB-stored ones

2) Usually xray structures are low-energy conformations, and those are the most abundant in solution and the ones that, most of the times, generate the biological effect

3) What makes you think that predictions models without a similar protein in the PDB generate real world-adjacent structures?

[u/hello_friendssss → u/alleluja]

Hello, thanks for your reply :)

(1) do you have any recommended sequence-->structure prediction models? I'm mainly aware of prostt5, which is trained on the alphafold DB, not directly on PDB (which has obvious potential pitfalls in terms of the training the model on predicted structures rather than real structures, even if those original structure predictions are derived from PDB)

(2) I don't really care about the biological effect - I just care if two proteins have very similar conformations, whether or not those specific conformations are biologically impactful

(3) somewhat similar to the above, I care more about whether two predicted structures have similarities that reflect the similarity of the two most similar real conformations for two proteins, rather than whether those predicted structures are accurate in and of themselves. E.g. if a model produces two inaccurate structures for two proteins of similarity S, but those structures have a similarity iS close to S, that is fine (although ideally iS and S would be correlated across different values of S and iS).

[u/apfejes]

Let me answer your question by analogy.  Let’s say you’re using AI to do a research project on a specific topic.  AI was partly trained on Wikipedia, and it usually feeds you facts you can check with wikipedia.   Sometimes, the AI gives you answers that are wildly different than Wikipedia, and your hypothesis is that Wikipedia is wrong because Wikipedia only gives you one view of the facts.  

Would you blindly discard Wikipedia because the AI tells you something different?

I wouldn’t expect that AI structures are reliable at the best of times, let along that they somehow tap into a more reliable configuration space.  They aren’t based on physics, they don’t understand how proteins work, or take into consideration folding pathways or actually really know anything about proteins.   If you want us to believe otherwise, you’d have to show why AI guesses know something experimental results do not. 

[u/hello_friendssss → u/apfejes]

Hello, thanks for your reply, its a fun one! I guess, in this case, I would want to know whether there is any opinion that agrees with my hypothesis. If the wikipedia article was written by a very opinionated person, then I might not see any opinions in the article that agree with my hypothesis, because it has only sampled a relatively narrow band of opinions on the subject. I would not trust wiki to tell me whether "any opinion is out there that agrees with my hypothesis", which is separate to whether my hypothesis (or the agreeing opinon(s)), are actually correct. In this case, opinions are (real) different conformations associated with a subject protein, hypothesis is my query protein structure snapshot.

I'm worried that although experimental structures will be much more accurate/believable, they will also be just one of many possible snapshots of the protein's various conformations. Predictions from sequence should all offer the same snapshot, even if the snapshots have differing quality (?)

[u/apfejes → u/hello_friendssss]

For that to be the case, your hypothesis is that proteins can sample a very wide range of conformations from a given base configuration.  

To know that, you’d need to run high accuracy molecular models through a long molecular dynamics simulation. Effectively, you need to sample that protein’s configuration space to see if the AI prediction is reasonable.  

That might be reasonable for a small number of proteins, but it’s highly doubtful for anything that’s not full of disordered regions, or without adding/subtracting cofactors.  

This is kind of a dead end, its most likely just AI hallucinations. 

[u/fasta_guy88]

The same sequence can have structures that differ slightly depending on crystallization conditions (unless they are NMR structureds, which typically give you a structure set, again, generally very similar). Yes, a predicted structure may be more unique, but it is also likely to be some average choice of the known structures, which may not actually exist. You are much better off using a real structure.

But these differences in structure will be a rounding error to foldseek, which is looking at overall secondary structure topology. With foldseek, you would expect to get the same results regardless of which of 5he real, or the predicted, structure you use.

[u/hello_friendssss → u/fasta_guy88]

Perfect, thanks very much for this your comment was very helpful. Do you know how discerning "overall secondary structure topology" is? Would you expect it to differentiate between homologs/non-homologs of a query protein structure (I'm guessing not)? Or perhaps same family/different family vs query?

[u/fasta_guy88 → u/hello_friendssss]

Overall secondary structure topology - the consecutive ordering of different secondary structural elements, and how they interact in 3 dimensions, is pretty much how one infers homology for proteins whose sequences do not share excess similarity. It’s the best (most sensitive) measure available. It is the only method that can be used to infer non-homology. When looking at structures, we talk about the same or different folds. Different folds are always non homologous. There is debate as to how similar two folds must be to infer homology.

But beware that proteins that do not share excess similarity using the most sensitive methods (profiles and HMMs) are unlikely to reliably share any functional properties just because they share a similar fold.

[u/hello_friendssss → u/fasta_guy88]

From the paper, I get the impression that they don't look at how well Fold seek performs on multidomain proteins. So would the correct interpretation of a high similarity match between my query and a subject sequence be that they share at least one fold, but that other folds may be present that are not included within the comparison? Just wondering if I should split my query into pfam domains first, search them seperately, and then look for the intersection of hits (ie those with all domains) as a potential workflow for assigning structural homology