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AlphaFold: a solution to a 50-year-old grand challenge in biology

deepmind.com

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Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#81

Post by entropicdrifter »

fabian2k wrote: post #59
derefr wrote: post #37

Earlier quoted context omitted.

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…

170,000 is three orders of magnitude less than the number of recorded protein sequences. I don't think it's dismissive to describe that as comparatively few.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#82

Post by dekhn »

comicjk wrote: post #60

CASP (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 C…

It's an improvement- and a big one- but not a solution to the problem. It mainly shows just how stuck the community had gotten with their techniques and how recently improvements in DNNs and information theory methods can be exploited if you have lots of TPU time.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#83

Post by IgniteTheSun »

xyzal wrote: post #6

Does it mean there is no point in playing fold.it anymore?

Considering the resource requirements for this AI approach mentioned in the article, its unlikely that its been tested on more than a few tens to hundreds of proteins. This may only work on a subset of the proteome so I would think it worth it to continue playing if you find it to be a fun past-time.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#84

Post by asdfasgasdgasdg »

dekhn wrote: post #78
asdfasgasdgasdg wrote: post #34

Earlier quoted context omitted.

I don't think anyone on HN is going to have more authority to qualify the results than the independent experts quoted in the linked article. Among whom are numbered a Nobel laureate, the president of the group that designs the tests of protein folding systems, and the former CEO of Genentech+current CEO of Calico.

Art's a smart guy and I have a lot of respect for his biological intuition, but his understanding of computational biology is very limited.

I would imagine that he is not assessing this advancement merely using his own personal expertise, but rather the combined expertise of the resources he represents. CEOs don't just look at problems and potential solutions. They have people who look at those things, and then tell them their opinion. In any case, you've picked a nit with one of the three people quoted. Any objections to the other two?

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#85

Post by WWWWH »

asdfasgasdgasdg wrote: post #34
unchocked wrote: post #22

Been out of the field for a while, could someone currently in it qualify these results? Hyperbolic title notwithstanding, they approach 90% median free modeling accuracy. The "other 90%" still remains to be solved...

I don't think anyone on HN is going to have more authority to qualify the results than the independent experts quoted in the linked article. Among whom are numbered a Nobel laureate, the president of the group that designs the tests of protein folding systems, and the former CEO of Genentech+current CEO of Calico.

Quite right. And the Nobel laureate in question is a structural biologist--so his expertise is directly relevant.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#87

Post by dekhn »

randcraw wrote: post #72
TrackerFF wrote: post #16

What are the immediate real-world applications of this? Just asking, because I have very little knowledge in this area.

For-profit corporations that value protein engineering will beat a path to DeepMind's door ASAP, like pharmas. Protein conformation prediction is essential when engineering new small-molecule drug compounds that must 'dock' with the specific proteins that regulate disease. Knowing how to create a protein with the precise shape to become biologically active has soaked up a lot of R&D funding toward pie-in-the-sky tech…

you give pharma too much credit. I had built a previous system to do something similar to this that produced excellent results and tried to give it away for free to Genentech, which ignored me. They said it didn't work for their purchasing department.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#88

Post by EgoIncarnate »

"AlphaFold achieves a median score of 87.0 GDT". Game changing, and a huge improvement, but not 100% solved. Also this is for static folding. Dynamic folding and interaction is a much harder problem. Those need to be tackled too before I would consider protein folding 'solved'.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#89

Post by derefr »

fabian2k wrote: post #59
derefr wrote: post #37

Earlier quoted context omitted.

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…

170k is "a few" compared to 180 million (i.e. the size of the PDB as soon as someone runs AlphaFold over everything in the UniProt.)

> In most cases the proteins were determined to be interesting by other experiments, and then people decided to try and solve their structure.

Yes, that's what we're doing right now, because structure is not a useful predictor, because we don't have structure available in advance of studies on the protein itself. There was no point to a "functional taxonomy" of proteins, because we were never trying to predict with protein-structure as the only data available.

In a world where protein structure is "on tap" in a data warehouse, part of the game of bioinformatics will become "structural analysis" of classes of known-function proteins, to find functional sub-units that do similar things among all studied proteins, allowing searches to be conducted for other proteins that express similar functional sub-units.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#90

Post by sanxiyn »

IgniteTheSun wrote: post #71
derefr wrote: post #37

Earlier quoted context omitted.

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…

Considering that this system "uses approximately 128 TPUv3 cores (roughly equivalent to ~100-200 GPUs) run over a few weeks" to determine a single protein structure, making predictions for all proteins encoded in a human genome seems impractical at this stage. With luck, this advance will help lead to discovery and definition of new folding rules and optimizations that will make protein folding predictions for the wh…

That's training time, not inference time.
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