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A billion summations and multiplications. You can fight me over this.
But like… the helium gets all wavy man…
I mean… modern CPUs can do multiple billions of summations and multiplications in a single second (let alone GPUs), and yet protein folding can take a long time.
A quantum computer is MUCH faster for finding viable solutions, though they’re MUCH harder to set up even for individual problems, and still need verification of the results.
I did not yet see a single quantum algorithm able to tackle the protein folding problem.
Sure, quantum computers could be faster at solving graph related problems, but I did not see an approach able to reduce protein folding to a graph problem.
On the other hand neural networks have been successfully applied to the protein folding problem, and they do that quite quickly.
Not a perfect solution indeed, quantum computers may be much better at that; but I still do not see a theoretical framework which justifies claims as to the applicability of quantum computers to the protein folding problem.
Ehhhhhh that’s purely a problem of the “setting up” a quantum computer (ok ok and how many qubits are involved). They SHOULD be extremely capable of it. Literally able to get a ‘correct’ answer in one cycle.
It’s just very unfortunate that quantum computers benefit greatly from slower cycles, and the setup of the computations are a real nightmare. As you say, I’m unaware of any that can solve protein folding in a single cycle, and the more cycles there are involved, the more subject it is to the algorithm implemented and setup steps taken.
Maybe one day, we’ll have billion-qubit quantum computers solving all sorts of problems reliably, but that day is definitely not today!
As far as I understand it a billion qbit computer is not possible because the time to setup the system would be too long to retain coherence.
But maybe new techniques will be developed, who knows.
I still do not see a theoretical framework which justifies claims as to the applicability of quantum computers to the protein folding problem.
The quantum phase estimation (QPE) algorithm which calculates ground states of molecules is theoretically predicted to have exponential speedup over classical methods on unstructured problems. Protein folding is a ground state problem.
Not saying it will be best in practice, but that’s at least a theoretical framework.
Hello, thank you for the lead. I tried replying the other day but I had just woken up drunk in the campment of a homeless guy and I was not in the best conditions to go through the math.
I took a look at this worked example: https://dojo.qulacs.org/en/qp_main/notebooks/7.1_quantum_phase_estimation_detailed.html
As far as I understand they use the iterative approach because it requires less qbits and are able to decompose the eigenvalues of a Hamiltonian in more or less a single step.
If it is as I understand it this would be quite huge, since you’d be able to directly apply this to the Hartree-Fock equation or Density Functional Theory without having to come up with new ways to represent molecules.
One thing which appears quite critical is:
Prepare an initial state with sufficient overlap with the ground state
What does sufficient overlap mean? Could we take an AlphaFold model and that’s sufficient to then determine the ground state?
I guess this is easy with helium when you have 2 atoms, but when you have hundred of thousands it becomes a difficult task even to get to that point.
Moreover, I’m not exactly sure what they’re calculating: they plot an error; but it appears to be an error over the computed energy and not atom positions.
We already have reliable ways, and moderately fast, to compute the energy of a system. What we’re missing is a quick way to explore different spatial conformations of atoms to identify the one which leads to the lowest energy.
Another problem which I could not determine is whether the amount of required qbits scales with the dimensions of the molecular system. I suppose it does. In that case, could we estimate how many qbits would be required for a protein or at least a peptide?
Why are we protein folding? The largest quantum computer — the Universe — already solved it because we’re standing.
Do you recommend we go back to trial and error on humans, then? Do you want to volunteer as a guinea pig? If not, then STFU.
Because checking a billion proteins for the shape we want is a hardware problem, and the hardware for doing that is very slow and expensive. If we can fold proteins using software, we can try billions of proteins far faster and cheaper. We could probably work backwards too, reducing the search space from billions to millions.
Agreed, tho to clarify, gonna need more than a billion. Like Alpha Fold is the closest I’ve read about… Dunno how many summations and multiplications it is, but I know it’s more than a billion
But I’m convinced configuring a quantum computer with enough qbits to solve it is just going to be so much more difficult than throwing few quintillion more billion summations and multiplications.
Wait I thought protein folding was “solved”? Is it just significantly improved or was I lied to?
Not solved, no. Definitely much much better than before. The difference AlphaFold made is significant: we’re talking about getting a decent model in a couple minutes using a PC compared to several months of calculations before.
However we still need experimental data: in many occasions AlphaFold gives an incorrect model. With some experimental data that model can be improved, but we still have no reliable way to know what the structure of a protein is starting from the amino acidic sequence without extensive experimentation.
That’s the big promise of quantum computers, there are however two major problems in my opinion:
- There’s still no theoretical framework which explains how once we have a quantum computer we may tackle protein folding
- Plenty quantum computing companies closed shortly after AlphaFold was published since they lost all funding because protein folding was “solved”
Didn’t google shut down alphafold recently?
90% of quantum computing is a hype scam anyway.
Agreed, I still do hope they can maintain some of their promises. However until now, I have not really seen any real advances towards making something useful.
I do not have a deep knowledge of quantum computers, but I know plenty people working on them and often get to talk about it.
I know people working on chemical problems who are basically approximating atoms to point charges. And either way those calculations are slower than on a CPU. For the uninformed, in chemistry the interactions between electronic orbitals is fundamental; this is in no way an approximation useful to obtain any kind of information.
This is fine, I understand methodologies take time to develop; however as far as I understand it those techniques they’re using are mathematically limited to using point charges: no matter how much they improve them that’ll be the highest level of accuracy.
I hope someone finds a way to handle such things better: as much as you can make a great machine learning model you’re always depending on available data.
That’s really intereting. Thank you for the informative comment.
I once did a internship where we solved protein structures by using Cristalisation and X-ray fracturing. Is this what you’re referring to with extensive research or has the methodology advanced?
Yes, X-ray is the gold standard. Technology has advanced in the sense that the protein crystallization is now more standardized and automated, as well as the analysis of the results.
It is not the only technique, for example there are cheaper ones based on mass spectrometry which do not resolve the full structure but allow to understand which amino acids are spatially near; such information is useful when developing a protein model and to validate whether a model is plausible.
The other two major techniques for structure resolution are NMR spectra analysis and the fairly novel technique of cryo electro microscopy.
These in general do not resolve the protein structure to the same resolution as X-ray but have other advantages: they allow you to observe the protein structure when in solution, which may be significantly different from the crystallized structure.
Gotcha, thank you ❤️
We traded it in for something that will hallucinate a solution.
AlphaFold is incorrect in plenty occasions.
More incorrect than a text prediction algorithm?
I doubt there is a comparable correctness metric between LLMs and protein structure prediction models.
You can measure how many times they correctly predict a thing, but results will greatly change according to what your objective is. Those are only comparable when you’re trying to predict the same thing.
As such my reply would be: sometimes more incorrect sometimes more correct. However, in general, a mishandled incorrect protein structure prediction is way more expensive than an LLM hallucination.
I mean… machine learning extends far beyond the stupid lying machines, and can produce far more trustworthy results than the dipshits at “Open” AI, but results will still always require validation, as all results require a lot of validation in science before they should be trusted.
Ah, yes I can imagine that being the case
How do you solve protein folding when there are so many different ways for proteins to arrange?
A reliable way to simulate any kind of protein fold. As long as you have a method you could hypothetically “solve” the issue
You dont solve anything. You generate models. That’s it. Without experimental validation they are just cartoons.
Then why has modelling been so focused on?
Because the models become a database you can search for certain characteristics.
(This is a guess I consider reasonable.)
Its somewhat complex. Protein structures are usually solved with x-ray crystallography. You get a 2-dimensional diffraction pattern. There are different methods to “solve” the diffraction pattern and get the Protein structure. Most are a lot of work, including biochemical lab work. But if you have a model it is relatively easy. The modern computer generated models have revolutionized protein x-ray crystallography.
There actually aren’t. However, some of the most interesting proteins have no structure at all.
The interesting part would be to a reliable and computationally accessible way to handle disordered proteins. Seeing how they can move could be quite revolutionary.
I had to work on some disordered proteins and you’re pretty much just guessing, plausibly you’re better off going to a casino blindfolded and play blackjack.
My mind thought about this ancient meme about katanas and how they’re folded 1000 times
Maybe samurai just needed bigger pockets
Question: Is folding protien like folding paper, and you can only get 11 creases in it? 🤔
It’s a lot more like smushing a shoestring into a tangled ball
