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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

#501

Post by dekhn »

beowulfey wrote: post #254
treis wrote: post #143

Earlier quoted context omitted.

That is an impressive improvement, but I think you've missed the most important point: >a score of around 90 GDT is informally considered to be competitive with results obtained from experimental methods So DeepMind is to the point where it's a question of whether their generated model or the experimentally determined structure is closest to the actual physical structure.

I don’t think you can say DeepMind could ever be more accurate to the true physical structure since it was built on the same experimental structures that it is being compared to. The limit of accuracy is the experimental data. However, I think we can say that a DeepMind prediction could at least be as good as a new experimental structure.

DM is merging several experimental data: known x-ray structures, and evolutionary data. The experimental method (xray) doesn't take advantage of the evolutionary data. And it also doesn't model the underlying protein behavior accurately (xray basically assumes a single static model with atoms fluctuating in little gaussian "puffs" around the atomic centers, but that's not how most proteins behave).

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

#502

Post by ac42 »

softwaredoug wrote: post #487
cs702 wrote: post #118

Two years ago, after DeepMind submitted its first set of predictions to CASP (Critical Assessment of protein Structure Prediction), Mohammed AlQuraishi, an expert in the field, asked, "What just happened?" https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp... Now that the problem of static protein structure prediction has been solved (prediction errors are below the threshold that is considered acceptable i…

> I don’t think we would do ourselves a service by not recognizing that what just happened presents a serious indictment of academic science. Much like other fields, I do begin to question the academic structure to making advances. It appears something is rotten in the state of academia. Oddly it's academia doing incremental improvements to existing methods but industry making novel leaps and bounds... The other majo…

I think so, too. Linear algebra, control theory and quantum mechanics haven't gotten us anywhere and ivory towers prevail as this machine learning solution to a problem in biological chemistry clearly demonstrates. /s

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

#503

Post by dekhn »

ramraj07 wrote: post #438
timr wrote: post #405

Earlier quoted context omitted.

"It looks like DeepMind invented a completely new method for this round that's not just an extension of their previous work, showing how much you can gain if you don't shoebox yourself into just trying to improve existing methods. That all the scientists were highly skeptical about the scope of ML (and these are computer scientists to begin with mind you) just shows how little they knew of what they did know of what…

We don't know too much about the exact model they made but it looks sufficiently generalizable to be able to give a candidate protein structure for any given sequence. It doesn't automatically cure cancer and inject the drug but that by itself is an amazing tool that if available to everyone will revolutionize biology experimentation. I will definitely blame the protein structure field in multiple levels though. It w…

The reason for your second paragraph is pretty straightforward. There has been an immense amount of support for proteins as "the workhorses of the cell" for hundred+ years. I call it the "protein bias". We've seen in many times- for example when it was first hypothesized and then proved that DNA, rather than protein, is the heredity-encoding material, and seen many times, for example in the denial that RNA could act as an enzyme or the functional core of the ribosome could be a ribozyme.

I think what basically happened is a very influential group of scientists mainly in Cambridge around the 50s and 60s convinced everytbody that reductionist molecular biology would be able to crystallize proteins and "understand precisely how they function" by inspecting the structures carefully enough.

I learned, after reading all those breathless papers about individual structures and how they explain the function of protein is that in the vast majority of cases, they don't have enough data to speculate responsibility about the behavior of proteins and how they implement their functions. There are definiteyl cases of where an elucidated structure immediately led to an improved understanding of function:

"It has not escaped our notice (12) that the specific pairing we have postulated immediately suggests a possible copying mechanism for the genetic material."

but most papers about how cytochrome "works" aren't really illuminating at all.

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

#504

Post by FredFS456 »

derefr wrote: post #37
fabian2k wrote: post #26

Earlier 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…

There are post-translational modifications to proteins. This means that for many (most?) proteins, the amino acid chain sequence is different from what you would predict from the DNA. These modifications are dependent on the state of the cell at the time of translation, and so cannot be predicted from the DNA alone. Even with a 100% accurate folding model, we cannot simply know the shapes of all the proteins inside the human body based on the genome.

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

#505

Post by mFixman »

treis wrote: post #143
mabbo wrote: post #30

Sometimes announcements like this are a bit over-the-top. But what really, to me, cements the 'big-deal' of this is the "Median Free-Modelling Accuracy" graph half way down the page. Scores of 30-45 for 15 years. Now scores of 87-92. This isn't a minor improvement, it's a leap forward.

That is an impressive improvement, but I think you've missed the most important point: >a score of around 90 GDT is informally considered to be competitive with results obtained from experimental methods So DeepMind is to the point where it's a question of whether their generated model or the experimentally determined structure is closest to the actual physical structure.

I don't have a background in biology, and that quote confused me.

What's an experimental method for protein folding and why is it so good? Are they talking about creating an actual, physical protein in a lab and observing how it folds?

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

#507

Post by flobosg »

mFixman wrote: post #505
treis wrote: post #143

Earlier quoted context omitted.

That is an impressive improvement, but I think you've missed the most important point: >a score of around 90 GDT is informally considered to be competitive with results obtained from experimental methods So DeepMind is to the point where it's a question of whether their generated model or the experimentally determined structure is closest to the actual physical structure.

I don't have a background in biology, and that quote confused me. What's an experimental method for protein folding and why is it so good? Are they talking about creating an actual, physical protein in a lab and observing how it folds?

> Are they talking about creating an actual, physical protein in a lab and observing how it folds?

Exactly. Researches purify the folded protein and then use methods such as X-ray crystallography, nuclear magnetic resonance, and cryo-electron microscopy to determine its three-dimensional atomic structure.

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

#508

Post by ashtonbaker »

The_rationalist wrote: post #492
ashtonbaker wrote: post #468

Earlier quoted context omitted.

> but experimental methods have not solved protein folding either. I might be missing something here, but isn't "experimental methods" just shorthand for "our best knowledge of a protein's structure, obtained via NMR or X-ray crystallography"? In that case, I'm not sure what "solving" protein folding even means - literally zero mean error? We can't know/solve anything beyond our best knowledge, that's tautological. >…

It's pretty clear what solving means, it means to have an exact representation of the 3D structure. Our partial knowledge obtained from such techniques is what it is, partial. We need new metrology that increase the observability accuracy and completeness OR better deterministic models from sequences. "We can't know/solve anything beyond our best knowledge, that's tautological." yes it is indeed tautological if you a…

> It's pretty clear what solving means, it means to have an exact representation of the 3D structure.

That's not clear at all, because perfect measurement doesn't exist. I agree that improving is always a worthy goal, but clearly we don't need 100% accuracy to consider something "solved" for the purposes of science. Also, "3D structure" of a protein is not a fixed truth, the parts are in motion all the time and may even have multiple semi-stable conformations. Rather than focusing on X,Y,Z perfection, I would imagine getting the angles between bonds, or the general topological conformation right would be more valuable.

> if you assume that experimental methods can't get better ...

I'm saying that if your definition for "solved" is "perfect knowledge", then we might as well not discuss whether method X or Y solves the problem, because they obviously do not.

The more I think about it, the more I think we should just drop the whole debate over the word "solved". Clearly different experiments and different proteins will have different requirements which may or may not be met by this or by other techniques - I agree that I would be interested to hear an expert weigh in on those requirements.

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

#509

Post by shawnz »

Rochus wrote: post #488
shawnz wrote: post #484

Earlier quoted context omitted.

Nobody is suggesting that this research has anything to do with gene expression or anything like that. Their point was simply that we now have better tools to actually see the meaning/effect of a given DNA sequence. Also, there is no need to passive-agressively highlight your credentials. I already researched them before replying.

I rather think most people comment without even having a look at the referenced article. And since when is the reference to a qualification considered aggressive? If your doctor hangs his doctor's certificate on the wall, is he "passive-aggressive"? Pretty weird. > that we now have better tools to actually see the meaning/effect of a given DNA sequence Note that the "meaning/effect" of a DNA segment encoding a protei…

> Note that the "meaning/effect" of a DNA segment encoding a protein [...]

The "meaning" of a DNA segment is not to encode a protein. The "meaning" is to describe a mechanism in the host organism (by way of encoding a protein). That is a complex process which involves gene expression AND protein folding.

For example would you say that the "meaning" of some Java code is to generate bytecode? Of course not, the "meaning" is to run some algorithm on the computer that executes it

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

#510

Post by Ericson2314 »

elcritch wrote: post #270
beowulfey wrote: post #249

Earlier quoted context omitted.

I mean, credit where credit is due. Google employs some of the greatest names in artificial intelligence and the DeepMind team had a huge chunk of them working on this problem. While the resources may have been available, I don’t think any other single institution had the level of brain power.

It also makes one reconsider the notion that monopolies are entirely bad. This essentially appears to be a vanity project for Google. Though of course they'll benefit from it in many ways, but it's not like they're doing this as the core product of their service. It's a pretty awesome achievement.

You've just describe why many Socialists 100 years were very skeptical of anti-trust as trying to sacrifice modernity to proper up a romanticized notion of the past as disaggregated pure-petit-bourgeois capitalism. Really not that different than the critism of the Luddites 100 years before that.

See https://ilr.law.uiowa.edu/print/volume-100-issue-5/all-i-rea...

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