What's next for AlphaFold and the AI protein-folding revolution
nature.com
nature.com
It is sometimes important to remind oneself that the selection of protein structures that exist in nature and that we determined experimentally is biased. Nature doesn't like proteins that misfold because they can easily cause trouble. And proteins with less defined structures are generally harder to solve with the usual methods like X-ray crystallography. The list of protein structures we know isn't a representative sample of all possible protein structures, it's mostly structures that are useful in nature and that we can determine with the methods we have available.
What do you mean by 'proteins with less defined structures'? I'm not familiar with what this phrase could mean, could you please expand on this concept?
There are also intrinsically disordered proteins that have no defined structure when they are on their own, that's essentially like a piece of string that is almost completely flexible. Those proteins can still adopt a specific well-defined structure if they bind to something else.
So for the intrinsically disordered ones, are you mainly talking about the secondary or tertiary structures? My assumption based on your statement is that we're keeping the same primary structure (order of amino acids) but they don't have many (if any at all) intermolecular interactions? Would it be safe to assume that you're referring to shorter polypeptide rather than large proteins?
A lot of crystallography is focused on enzymatic reactions where you bind a ligand that sits there for the sake of introducing some conformation that you can study. The ligands generally approximate the natural substrate at either the beginning, end, or some intermediate step in enzyme catalyzed synthesis.
I would consider most enzymes to be intrinsically disordered at their active sites.
The active site is structured to stabilize the transition state of the affected molecule and move it from one state to the next in the chemical reaction. That requires very specific shapes and correlated changes. But of course, this being biology, you can remove all 3 active site residues in a serine protease catalytic triad, and still see proteolysis because the protein, when it binds the substrate, forces the subtrate into its transition pathway.
People have been working on these things for quite some time- I saw talks about time-resolved crystallography of active sites, and while they say "significant structure changes", they really only mean localized breathing-like motions, not massive rotations of entire domains.
put another way, is it possible to a priori determine if a protein is ID or ordered?
for instance, you said enzymes are highly ordered. is this based on experimental observations (which could later be wrong if imaging techniques improve) or is there some principle that allows us to treat this as a fact?
thanks in advance for your time.
There’s software that attempts to predict intrinsic disorder based on sequence alone, but in general, in the absence of homolog (evolutionarily related) proteins with known structure you would still need to check experimentally for disorder.
EDIT:
> if the goal is to reliably assess certain viral proteins as ID or ordered, experimental methods are the only methods for achieving this?
If you don’t find homologs with solved structures, experimental characterization is the way to go.
There is an absolutely enormous amount of experimental data about enzyme structure, but frankly I think the simplest is to just understand that the modern ideas about the reversible protein folding process came from ribonuclease, a protein that cuts RNA: https://en.wikipedia.org/wiki/Anfinsen%27s_dogma
There may also be intrinsically disorderd enzymes, I'm not really sure how they would work, but of course, in biology, there's always a weird example that violates normal expectations because evolution once randomly tried somethign a billion years ago and got stuck with it.
the goal is to reliably characterize certain viral proteins as ID or ordered. would you happen to have any advice on this?
Sounds to me like you're doing some bioinformatics stuff to understand how some virus works, or am I totally off base on that guess?
if ordered proteins are not in fact easier to understand than ID ones, it would be helpful to understand why.
thanks for the feedback.
You could predict functionality based on homology with evolutionary related sequences, but they would work equally well for all proteins (ordered or not).
to clarify, the statement isn't that it is straightforward -- simply that it's easier to analyze something that doesn't change shape unpredictably than something that does (fewer variables to consider).
whether predictions based on evolutionary related sequences are 100% accurate in human cells is another matter, especially when it comes to viruses.
It’s not that surprising given the conceptual background of the method. Since it’s relying on evolutionarily coupled residues, AlphaFold is looking at sets of complementary mutations that keep or rescue a determined structure, i.e. the complete opposite of structural disruption.
> The list of protein structures we know isn't a representative sample of all possible protein structures
And the same goes for protein sequences.
> "Baker’s team gets AlphaFold and RoseTTAFold to “hallucinate” new proteins. The researchers have altered the AI code so that, given random sequences of amino acids, the software will optimize them until they resemble something that the neural networks recognize as a protein. In December 2021, Baker and his colleagues reported expressing 129 of these hallucinated proteins in bacteria, and found that about one-fifth of them folded into something resembling their predicted shape."
20% is not that great but it has potential. One long-standing goal is the de novo design of protein-based industrial catalysts for specific chemical transformations. Proteins from bacteria that live in boiling sulfur vents etc. have been used to some extent, but the idea is that similar proteins could be designed for a much wider variety of industrial processes. As the article notes, specificity remains a challenge (and designed proteins don't approach the efficiency of the evolutionary selected proteins), but it still seems promising.
P.S. I'm a bit more skeptical about the drug-design programs. It's not so much that novel drugs can't be designed that bind to the desired targets, it's that they might bind to a whole lot of undesired targets as well, leading to nasty side effects. Now if you could screen against the whole proteome, perhaps.
20% success rate is in line with other protein design methods, though.
This sounds interesting. Can you talk more about this? Efficiency in what sense, energy efficiency in the actual protein assembly in biological systems, or efficiency in actual performance of the protein while it's functioning in a biological system?
See my work in this area: https://pubmed.ncbi.nlm.nih.gov/24345941/ which is explicitly attempting to simulate an approximate folding pathway(s).
For the 'CATH' hierarchical classification, the 'Topology' level is something like the organization of secondary structure in an 'Architecture'. This has some relationship to topology in the general sense, but is a narrower definition.
For me, the 'fold' is what happens after 'folding' occurs, but I take the point that it is confusing.
Strictly speaking most of them are not mathematical knots either, since the ends are not connected. Some have salt bridges, I seem to recall.
Generally, any non-covalent bonds aren't considered to be topological connections in proteins, although salt bridges can definitely ahve bond energies not far from covalent bonds.
> For me, the 'fold' is what happens after 'folding' occurs, but I take the point that it is confusing.
Same here.
There’s an AlphaFold variant that can predict quaternary structure: https://www.biorxiv.org/content/10.1101/2021.10.04.463034v2
> They could have named it AlphaTS.
TS looks more like “transition state” to me.
As for your own project I’m sorry for you: there are no more 20% projects, like in the old times :(
That being said, I'm perfectly happy to just be proud of what DeepMind has achieved. They've been great to interact with when there were shared challenges, and I - and to my knowledge all colleagues around me - aren't very interested in a question of credit assignment.
Google's infrastructure and DeepMind's internal (not cloud) access to it has been absolutely critical. In many ways, DM is leading Google in terms of software development, while Google is leading in providing unique ML hardware capabilities.
They arent often given for techniques or computation. But the results are outstanding.
https://venturebeat.com/2021/10/10/ai-lab-deepmind-becomes-p...