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Much discussion a few days ago: https://news.ycombinator.com/item?id=32262856 https://news.ycombinator.com/item?id=32262856
by macintux 4y ago
Much discussion a few days ago: https://news.ycombinator.com/item?id=32262856 https://news.ycombinator.com/item?id=32262856
- hedora 4y agoThe top comment there (from a structural biologist) is worth reading. Here's my opinion, as a computer scientist that worked in this area. A protein sequence is analogous to a computer program, but the "machine" is a mostly-water solution, and the instructions are interpreted by summing up all the intermolecular forces at play as the sequence is squirted out of a little extruder as a string (as in the stuff your clothes are made of, not text). Bits of the string repel and attract each other and it globs up in some biologically useful structure. The folding problem is the problem of predicting that structure from the string. Unlike the halting problem, there is no way to generate an execution trace saying a particular glob would be formed in reality. In fact, there is no way to perform a polynomial time check of the result, so we've already escaped the land of P and NP. Also, things like temperature and proximity to other proteins mean that there might not be a unique fold for a given sequence. Therefore, like the halting problem, we have unknown inputs, and we need to figure out which states an arbitrary program can reach. When someone claims to have "solved" folding, you should be as skeptical as you would be if someone claimed to have solved the halting problem for arbitrary machine code, and that they don't need any extra information about the machines that run that code. Although their program runs on conventional computers, it also works on programs written for quantum computers. (Edit: That's not to say this work isn't useful, or that this press release overclaims. I've been hearing some pretty wild claims about this work elsewhere...)
- COGlory 4y agoFor an unrelated reason, shortly after making that comment, I put 31 genes from a viral genome (the whole genome, assuming we have the reading frames correct and nothing else funky is going on) through AlphaFold. We're getting ready to do some proteomics to see what's in the capsid, and I wanted to inform the proteomics by doing some sequence analysis. Only three genes of the 31 came back with any sort of confidence. Two of the three were crystallized and solved by my group a few years back.
- westurner 4y agoIs the AlphaFold team winning Folding@home? (which started at Washington University in St. Louis, home of the Human Genome Project) https://foldingathome.org/ https://foldingathome.org/ FWIU, Folding@home has additional problems for AlphaFold, if not the AlphaFold team; > Install our software to become a citizen scientist and contribute your compute power to help fight global health threats like COVID19, Alzheimer’s Disease, and cancer. Our software is completely free, easy to install, and safe to use. Available for: Linux, Windows, Mac
- bpiche 4y agoRIP Folding@home on Playstation 3! Bring it back!
- flobosg 4y agoBoth projects tackle related problems, but each is trying to answer a different question: https://news.ycombinator.com/item?id=32264059 https://news.ycombinator.com/item?id=32264059
- westurner 4y ago> Folding@home answers a related but different question. While AlphaFold returns the picture of a folded protein in its most energetically stable conformation, Folding@home returns a video of the protein undergoing folding, traversing its energy landscape. Is there any NN architectural reason that AlphaFold could not learn and predict the Folding@home protein folding interactions as well? Is there yet an open implementation?
- dekhn 4y agoI think it would be much harder to do that, since it probably requires modelling physics at some level, while AlphaFold is really just mining statistical correlations of structures and sequences. Yes, there are open implementations of nearly-AlphaFold at this point.
- the__alchemist 4y agoIs it possible or likely that the folding process is more procedurally deterministic than it seems? (given sequence, temperature etc) The degrees of freedom perhaps seem intractable because we don't know what steps the structure takes between the linear extrusion and final fold. AlphaFold, if I understand correctly, doesn't attempt to solve this problem. Your comment implies we should be skeptical of it because it's solving a potentially-intractable problem; perhaps it's both tractable, and AlphaFold doesn't solve it. Let's say you have a car (or lego set etc). The number of possible ways the parts could go together are astronomical! Does that mean it's not possible to figure out how it fits together, or how you might build one?
- xvilka 4y agoGiven how quantum physics and chemistry works, highly unlikely.
- tsimionescu 4y agoYes, if you have a Lego set, or a series of car parts, there are many ways to put them together to make something. What AF is doing as far as I understand is essentially looking at a catalog of all Lego sets ever produced, or all car models ever produced, and choosing one that most closely matches the pieces it is seeing. But there is no reason to expect this process to produce the right end-result for a Lego set that has never been seen before.
- FartyMcFarter 4y agoDidn't AlphaFold win a competition based on folding proteins that had a secret result?
- simiones 4y agoYes, but that competition is using lots of proteins that are similar to other known proteins, as far as I understand. There is also a lot of sub-structure that helps - similar parts of proteins tend to fold in similar ways, so even if you don't have real predictive power on unknown sequences, you may do quite well for a protein that is 90% the same as one in the training set - you will be quite correct on ~90% of the folds, even if your pretty way off on the remaining 10%. Note that all of this is not to minimize the success of what AlphaFold achieved. I am just trying to explain how you can do well at this problem without having discovered some deeper deterministic structure in protein folds.
- dmix 4y agoNow I really want to read a long form book like this comment ‘A Computer Scientists Guide to an intuitive understanding of biochemistry’ I’ve found it extremely hard to have a casual understanding of biology, unlike math where I feel like I have a solid high level sampling of the field. I’ve done a few bio and chemistry courses and books but it’s so deep and ill suited for a programmer who is used to asking how things work underneath at every level (you have to constantly stop yourself from asking why something does what it does and just go with it until it starts to connect later, which is more of a commitment than I could give). Anyway thanks for your comment
- inciampati 4y agoI would suggest carefully reading a deep textbook on biology like Molecular Biology of the Cell. You can't get a casual but realistic understanding of biology without a significant effort. That's a big problem in modern society. Biology is subtle and yet ever-important to us earth-bound organisms. The vast majority of people have only the most trivial understanding of biology, but scientifically we have a rather complete perspective and mental model that, due to its recent development, hasn't yet become common.
- feet 4y agoGreat book suggestion! Absolutely agree as someone in the field Biology and biochemistry is unbelievably complicated and difficult to grasp without truly going deep into the fundamentals
- sseagull 4y agoSlightly OT, but I am a computational chemist (PhD) looking to learn more about molecular biology (to say, and undergraduate or beginning graduate level). I am looking to learn more to see ways in which advances in computational chemistry tools could be applicable outside of our usual domains. I am looking at Molecular Biology of the Cell (Alberts) and Cell Biology (Pollard). Both were recommended to me, but wondering what the pros and cons of each are (if you are familiar with both of them).
- the__alchemist 4y agoI propose this as a thought experiment: Nature has solved this. How? Some lines of reasoning: #1: The quantum interactions of electrons that are the basis for chemical bonds behave in ways our computers and intuition are incapable of simulating #2 It's a matter of degree, not kind, and nature is more sophisticated than our computers, reasoning and thought processes. #3 Nature is magic, whatever you define that to be #4 When stipulating the degrees of freedom involved (ie from dihedral angles), the possibility of additional information we haven't discovered is being overlooked. Is there a recipe or algorithm that could help?
- hexmiles 4y agoCould not be just bruteforce? Nature operates on a much bigger temporal scale than us.
- the__alchemist 4y agoCould be! Are you thinking thermodynamic fluctuations from surrounding water molecules jostling things around into many combinations? In this view, do you think the final protein would be found by chance, or through intermediate assemblies?
- simiones 4y agoIsn't #1 the most likely, given that most quantum interactions take exponential time to simulate on classical computers with any known algorithm?
- GuB-42 4y agoI'd go with #1. Especially considering that there are quantum approaches to protein folding. But nature hasn't really "solved" the problem, it is just doing its thing, but the way it does things is completely different from what our computers do. It is like trying to reproduce a guitar sound using a synthesizer. A guitar solves to problem of sounding like a guitar, but it doesn't mean it is more sophisticated than a synthesizer, in fact, a synthesizer can do much more, it is just that the process by which the guitar makes sounds are hard to simulate.
- 4y ago
- heavenlyblue 4y agoWhy did we escape the land of NP?
- FartyMcFarter 4y agoNP problems are ones whose positive solutions are verifiable in polynomial time. For example, the problem "is there a route in this graph that visits all nodes and has length <= L" can be quickly verified with a classical computer, as long as you're given a "yes" answer accompanied by such a route. Finding the answer from scratch might be much slower, but checking it is quick.
- sebzim4500 4y ago>When someone claims to have "solved" folding, you should be as skeptical as you would be if someone claimed to have solved the halting problem for arbitrary machine code That's absurd. The halting problem is provably impossible with either conventional computers or Quantum computers. This is clearly not true for protein folding, although it is possible that it is computationally intractable with a conventional computer.
- rch 4y agoI think the parent comment is saying that it's impossible to arrive at a specific folding endpoint because that state is dependent on continuously changing environmental variables. Take a look at the configs for Amber (molecular dynamics simulation -- https://ambermd.org https://ambermd.org). QC might help map the space of inputs that would converge, but it probably couldn't identify a hypothetical 'done folding' state for any given protein.
- antioppressor 4y ago
- dekhn 4y agoI don't think it's super valuable to spend time thinking about the computational class protein folding (or structure prediction) is in. It's clear now that approaches that approximate the expensive physics and extended sampling using every bit of additional information available are going to be much more successful in providing data that people need from structures.
- Protostome 4y agoDisclaimer: I'm a professional (computational) structural biologist. My opinion is slightly different than the researcher that commented on the linked post. I didn't see any claim by DeepMind that protein structure prediction is a solved problem. I think these guys are pretty diligent when it comes to communicating their science. What you may have seen, is a non-scientist reporter making inaccurate claims. The problem with the structure prediction problem is not a loss/energy function problem, even if we had an accurate model of all the forces involved we'd still not have an accurate protein structure prediction algorithm. Protein folding is a chaotic process (similar to the 3 body problem). There's an enormous number of interactions involved - between different amino acids, solvent and more. Numerical computation can't solve chaotic systems because floating point numbers have a finite representation, which leads to rounding errors and loss of accuracy. Besides, Short range electro static and van der waals interactions are pretty well understood and before alphafold many algorithms (like Rosetta) were pretty successful in a lot of protein modeling tasks. Therefore, we need a *practical* way to look at protein structure determination that is akin to AlphaFold2.