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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

#211

Post by 6gvONxR4sf7o »

TheRealPomax wrote: post #176
6gvONxR4sf7o wrote: post #164

I hate headlines like “X has solved Y.” How often have we see computer vision and natural language solved at this point, whenever a model does well enough in a benchmark? Their own article doesn’t even have that headline. This is a massively cool thing that’s happened. Why ruin it with a massively hyperbolic headline?

Because only the experts in this field get to tell us, the laymen, what "solving the protein folding problem means", and they defined it not as "perfect" but as "more than good enough to be acceptable as correct result". Which this did. X has actually solved Y. That's not so much "massively cool", that's historical.

I think the “they” you’re referring to is only whatever PR person wrote the headline. Nowhere in the substance of this (PR!) post does it refer to it as anything but a great leap. When an expert in the field outside of deepmind says protein folding has been solved, I’ll believe it.

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

#212

Post by penagwin »

whimsicalism wrote: post #8
the8472 wrote: post #7

Earlier quoted context omitted.

> but many were skeptical as to the longevity of the performance DeepMind was able to achieve For a non-biologist, on what is this skepticism based? Just purely based on following ML news it looks like the trend for ML solutions has been that they've overtaken expert-systems once they've gained a solid foodhold in a field. Maybe this is some perception bias. Are there any cases where ML performed decently but then hi…

ML is a super overloaded term. There are definitely cases where machine learned statistical solutions do not perform as well as the systems tuned by the experts, but if you can define the task well and get the data for a deep solution, usually those will overtake.

This. I believe technically just linear regression could be considered "machine learning".

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

#213

Post by [deleted] »

cambalache wrote: post #125
aardvarkr wrote: post #101

Earlier quoted context omitted.

It’s officially recognized as a solution.

Well, it's not. Nature does not have a committee sorry. Proteins are delicate "machines" where even a a small change in the sequence (and thus the 3D structure) as small as a few amino-acids would change effectively the structure and the function of it. On top of that, proteins are dynamic beasts. In any case, it's a great advance, but DM, as many companies likes a little bit too much to tout its own horn.

[deleted]

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

#214

Post by kxs »

woeirua wrote: post #36

This is a big step forward, but the outstanding question as far as to whether or not this is useful for evaluating novel proteins, is going to be how good is the confidence metric at telling the user to trust or not trust the results. You can see from their examples, that AlphaFold is very good but not perfect. I imagine for some proteins it will still give misleading or erroneous results and if you can’t tell when t…

There are other experimental methods that are much cheaper that can be used to assist validation. Also the models look damn impressive, even down to the sidechain packing.

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

#215

Post by abecedarius »

jjk166 wrote: post #33

Now onto the much harder problem of doing the reverse: taking an arbitrary structure and determining an amino-acid sequence that will fold into it.

I think you have this backwards in practice. It was in the 80s that I first read a paper about a de-novo protein design engineered for a specific stable conformation. Natural proteins have no reason to be particularly predictable, just as genetic programming produces hard-to-understand programs relative to human-written ones. In fact making the structure especially stable against perturbations seems like it'd make it less responsive to changing evolutionary pressures.

(Am not a structural biologist.)

Added: 2019 article on de novo design: https://www.nature.com/articles/d41586-019-02251-x Not to say that better prediction won't also make design easier -- of course I expect it will.

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

#216

Post by tgbugs »

My conclusion reading this is that a gradient is a gradient is a gradient. If you can minimize one, you can minimize them all. The hard work would seem to be figuring out how to transform into a gradient that your hardware can solve. It will also be interesting to see the kinds of systematic errors that will come as a result of the biases in the training set, and whether it can be used to predict what the structures would look like under slightly different conditions (e.g. pH).

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

#217

Post by hedora »

dontbeevil1992 wrote: post #167
comicjk wrote: post #147

Earlier quoted context omitted.

Scientists can verify that an AlphaFold-predicted structure is correct, or at least useful, without being able to get the structure experimentally. For instance, we could use the AlphaFold-predicted structure to do protein-ligand binding calculations for a bunch of known molecules. If these calculations agree with experimental protein-ligand binding (which they generally do for proteins with known structures), then w…

does that mean that protein-folding is sort of in NP?

It’s probably not in NP, in that there is not a polynomial time algorithm that checks solutions for correctness.

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

#218

Post by garmaine »

vadansky wrote: post #128

Just to add to this whole "It's not solved! Yes it is!" discussion. Note that >According to Professor Moult, a score of around 90 GDT is informally considered to be competitive with results obtained from experimental methods. So if we go by >= 90 as solved: >In the results from the 14th CASP assessment, released today, our latest AlphaFold system achieves a median score of 92.4 GDT overall across all targets. they so…

That’s shifting goal posts. The hardest structures are also going to be harder experimentally.

What makes them hard to predict is the very close energies involved in different folding pathways. Those close energies mean there will be more variant structures which change by use the experimental approach too.

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

#219

Post by danaris »

The title here is not merely breathless clickbait, it also has very little to do with the headline of the actual article, which is "AlphaFold: a solution to a 50-year-old grand challenge in biology".

I thought the #1 criterion for titles was that they should match the original if at all reasonable...?

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

#220

Post by ramraj07 »

Rochus wrote: post #86
jjk166 wrote: post #33

Now onto the much harder problem of doing the reverse: taking an arbitrary structure and determining an amino-acid sequence that will fold into it.

What for?

The other comment mentioned the example of making proteins that bind a structure. Heres an extension - a general understanding of how an enzyme works to catalyze chemical reactions, is that it binds the reaction intermediate with higher affinity than the two substrates; thus if we have this reverse ability, we can start inventing enzymes that can catalyze any arbitrary chemical reaction, even ones that need energy input, so you could imagine for example enzyme systems that can convert plastic to fuel!
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