Comment by renonce | Hacker News Reader

Comment by renonce | Hacker News Reader

> What is different about the new AlphaFold3 model compared to AlphaFold2?

> AlphaFold3 can predict many biomolecules in addition to proteins. AlphaFold2 predicts structures of proteins and protein-protein complexes. AlphaFold3 can generate predictions containing proteins, DNA, RNA, ions,ligands, and chemical modifications. The new model also improves the protein complex modelling accuracy. Please refer to our paper for more information on performance improvements.

AlphaFold 2 generally produces looping “ribbon-like” predictions for disordered regions. AlphaFold3 also does this, but will occasionally output segments with secondary structure within disordered regions instead, mostly spurious alpha helices with very low confidence (pLDDT) and inconsistent position across predictions.

So the criticism towards AlphaFold 2 will likely still apply? For example, it’s more accurate for predicting structures similar to existing ones, and fails at novel patterns?

I am not aware of anybody currently criticiszing AF2's abilities outside of its training set. In fact the most recent papers (written by crystallographers) they are mostly arguing about atomic-level details of side chains at this point.

problem is biomolecules, are "chaperoned" to fold properly, only specific regions such as, alpha helix, or beta pleatedsheet will fold de novo.

Chaperone (protein)

https://en.wikipedia.org/wiki/Chaperone_(protein)

In principle couldn’t we just incorporate knowledge about chaperones into the model?

In a way it is already incorporated. Broadly speaking, chaperones function by restricting the available conformational sampling space for the protein to fold. Some researchers even consider the ribosome as a chaperone of sorts for the nascent protein chain it synthetizes.

Protein structure prediction methods do the same: they find ways of restricting the conformational space to explore, in hopes of finding the global minimum-energy conformation representing the native structure of the protein.

Then it’s not clear to me exactly why should chaperones be a problem, though I get the gist intuitively

if you want a protien or any other bio molecule to fold properly, a chaperone system must be either designed or elucidated.

the primary sequence is not the only consideration for proper folding.

chaperones allow higher energy folding events to occur and be maintained until subsequent modification stabilizes high energy structural motif.

chaperones also enforce an A before B before C regime of folding so that the sequence doesnt just crumple up according to energy of hydrostatic interactions

Sure, I get the mechanics. My question is, if we can incorporate knowledge about chaperones into the models as explicit or latent variables, so to speak, then why can’t the models predict something like “probability of molecule a given the presence of chaperone b”?

it sure can, given enough computation, chaperones are often protiens themselves but can be otherwise; they are subject to the same forces so twist and turn fold and conform.

they often interact with each other, and must exert influence at proper stage of modification.

other effects beyond foldingoccur, such as addition or elimination of prosthetic groups.

the take home message is fallacy of oversimplifying the process of many molecules plus ionic enironment, interacting to influence a single molecule

the order of folding is crucial, this is a progression of folding events.

the difference is akin to origami vs a crumpled ball

Chaperones exist - however many proteins will quite happily fold in isolation without any external help.

most protiens require chaperones, to fold properly.

Most? Evidence?

Almost all the 3D structures that Alphafold was trained on were generated from crystals of pure protein.

ie made without chaperones.

>So the criticism towards AlphaFold 2 will likely still apply? For example, it’s more accurate for predicting structures similar to existing ones, and fails at novel patterns?

Yes, and there is simply no way to bridge that gap with this technique. We can make it better and better at pattern matching, but it is not going to predict novel folds.

alphafold has been shown to accurately predict some novel folds. The technique doesn't entirely depend on whole-domain homology.

> but it is not going to predict novel folds

https://www.nature.com/articles/s42003-022-03357-1

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