Earlier post on this with direct results: https://news.ycombinator.com/item?id=25253488
AlphaFold: a solution to a 50-year-old grand challenge in biology
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Re: AlphaFold: a solution to a 50-year-old grand challenge in biology
#52At Sun back in the day our workstations tended to have fairly promiscuous login settings, so one of my coworkers took the liberty to launch folding@home on every machine in the org. Listing running processes one day, I saw this thing pegging my CPU; asked around and others had it too. A virus!?! Then he fessed up. Kinda miffed at first but ultimately really cool, so we let the thing keep running. That was my introduc…
I ran Folding@Home at Google on hundreds of thousands of fast Xeon cores for over a year. I concluded at the end that unbiased MD simulations are not an effective use of computer time.
Re: AlphaFold: a solution to a 50-year-old grand challenge in biology
#53Pretty interesting that they only used about $15k worth of resources (retail price) to achieve this. It's not a technique that would have been out of reach for other organizations based only on not being able to afford the compute.
Re: AlphaFold: a solution to a 50-year-old grand challenge in biology
#54So the median accuracy went from ~58% (2018) to 84% (2020) in 2 years? Does 84% == solved? Also, any low hanging frut implications for longevity tech?
100% accuracy is "solved".
Re: AlphaFold: a solution to a 50-year-old grand challenge in biology
#55This is amazing, if we can simulate multi-protein interactions, you could imagine in our lifetimes being able to see a fully computation driven simulation of a human blood cell. That would be a huge breakthrough.
Re: AlphaFold: a solution to a 50-year-old grand challenge in biology
#56This sounds wonderful and frightening. On the one hand, now we can engineer drugs at light speed. But wasn't protein folding supposed to be NP-hard? Can deep learning find the cracks in P vs NP? Perhaps making clever guesses at prime factors because it learned some weird structural fact that has eluded mathematicians. If we break crypto, there goes the modern world. Banks, bitcoin, privacy, Internet, the whole sheban…
> But wasn't protein folding supposed to be NP-hard?
Yeah, at least some variations of it are NP-hard. SAT is THE NP-complete problem, but there are some really good SAT solvers around. This basically means: They have a solution that mostly does very well on most instances. But because (probably) P != NP, you will never have a polynomial time algorithm for this.
Re: AlphaFold: a solution to a 50-year-old grand challenge in biology
#57This sounds wonderful and frightening. On the one hand, now we can engineer drugs at light speed. But wasn't protein folding supposed to be NP-hard? Can deep learning find the cracks in P vs NP? Perhaps making clever guesses at prime factors because it learned some weird structural fact that has eluded mathematicians. If we break crypto, there goes the modern world. Banks, bitcoin, privacy, Internet, the whole sheban…
Far from an expert on complexity theory, but NP-hard problems can be approximated in polynomial time. With Deep Learning you are doing approximation. So this is nothing ground breaking in that respect.
there are also a variety of problems that are hard to approximate.
Re: AlphaFold: a solution to a 50-year-old grand challenge in biology
#58What are the immediate real-world applications of this? Just asking, because I have very little knowledge in this area.
Given the DNA code for one of the "machines" that run cells, we can generate an atomic model of that machine. This means we can "compile" (one part of) the DNA code. It was already possible, but so slow that entire datacenters would spend months calculating this for a single protein and even then we can't use them on the really complex ones at all, necessitating things like neutron spectroscopy which are totally insane, and only work on like 1% of proteins.
This is useful because for example chemical simulation tools don't run on DNA code, but on atomic models. And also to produce "images" of the molecules (images between quotes because most proteins are too small to interact with reasonable photons, and no interaction with photons means you can't see them in any way)
DNA has other parts that are really important but we don't understand at all yet, where this doesn't help at all. This applies to sections of DNA sent to ribosomes, to produce actual molecules. Besides that, there are pieces of DNA that "index" the DNA, pointers (from one gene to another), triggers (that for instance start production of an enzyme based on some external influence, like detection of a marker molecule) and export markers (that tell you what to do once the protein is produced, for example, mark a protein to be removed from the cell, incorporated into the cell membrane, or for instance used inside the cell nucleus, and there's also one that essentially says "at this point stop producing a protein and instead couple the rest of the DNA code to the end of the protein you just made").
Re: AlphaFold: a solution to a 50-year-old grand challenge in biology
#59Earlier quoted context omitted.
Protein folding is a big and important problem, so this is certainly big news if it works as well as it seems. But I wouldn't assume that this changes everything, we can already determine how proteins fold by experimental work. The disadvantage is that this is a lot of work, though the methods there also improved a lot. One question is how robust the predictions are that DeepMind produces. I would also assume that ri…
We can already determine how a few proteins (170k — which sounds like a lot, but which is only 0.09% of all currently-catalogued protein sequences) fold by experimental work. What an accurate model of protein folding allows us to do, is to take our big database of DNA, predict protein foldings for all of it, and then stand up a search index for this database, keying each amino-acid "row" by the "words" of its predict…
"A few" does appear quite dismissive of the enormous amounts of effort in structural biology so far. There are more than 170,000 structures in the PDB right now.
To determine potential targets for drugs we have to understand what the proteins do. Having the structure is not really enough for that, it doesn't tell you the purpose of the protein (though it certainly can give you some hints).
In most cases the proteins were determined to be interesting by other experiments, and then people decided to try and solve their structure. So the structures we already solved are also biased towards the more biologically relevant proteins.
Re: AlphaFold: a solution to a 50-year-old grand challenge in biology
#60CASP (Critical Assessment of protein Structure Prediction) is calling it a solution. To quote from the article:
"We have been stuck on this one problem – how do proteins fold up – for nearly 50 years. To see DeepMind produce a solution for this, having worked personally on this problem for so long and after so many stops and starts, wondering if we’d ever get there, is a very special moment."
--Professor John Moult Co-founder and chair of CASP