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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

#551

Post by ZitchDog »

14 wrote: post #494
pmastela wrote: post #371

Earlier quoted context omitted.

Like the old Arthur C. Clark quote goes: “Any sufficiently advanced technology is indistinguishable from magic” -- unless it might be cheating in which case throw them a curve ball. Kudos to the DeepMind team for making magic happen.

I am happy you mention this. I was reading the article and thinking “wow the amount of scientific knowledge these guys need to know to understand what they are doing is way beyond me”. I work in health care and I always talk to clients about all the cool things they witnessed in their life. Cell phones, TVs, microwaves are some obvious ones I like to talk about. I sit and wonder what are the things my generation will…

I think mRNA vaccines will be a big one too.

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

#552

Post by 015UUZn8aEvW »

lolive wrote: post #543
sudosysgen wrote: post #540

Earlier quoted context omitted.

I just had a discussion with a friend about this! It's indeed a very difficult question. We ended on the conclusion that God can't possibly exist outside of time and space in the Abrahamic tradition because he precedes the creation of the Universe, but I'm sure there's a twist we missed somewhere.

“God is dead” Marilyn Manson.

First as tragedy, then as farce.

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

#553

Post by thamer »

There's something I don't understand about protein shapes. There are tons of software solutions – on the web and offline – to visualize the shape of proteins from their sequence of amino acids. How do these work then, if we don't know how the atoms might be arranged in space?

For example, this[1] is the code for SARS-Cov-2's Spike(S) protein. From what I understand of this page it's pretty short, only ~1,757 proteins (corresponding to ~3,821 bases in RNA).

And here[2] is a visualization of it in 3D. You'll likely recognize the characteristic mushroom shape that's been portrayed in 3D models of SARS-CoV-2 in the media. How does this software work if there's no real way to tell how the protein is arranged?

[1] https://www.ncbi.nlm.nih.gov/nuccore/NC_045512 (search for spike glycoprotein on that page)

[2] https://3dmol.csb.pitt.edu/viewer.html?pdb=6X6P&style=stick&...

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

#554

Post by emmelaich »

IfOnlyYouKnew wrote: post #401
Seanambers wrote: post #179

Earlier quoted context omitted.

I particularly like the rant on pharmaceuticals companies lack of basic research. My impression has been that medical progression have been slow for quite some time, nice to see that there are some truth to that. In the end software and tech companies might just eat up the pharmaceutical industry as well. - It's all just code at some level. The Deepmind team did this with ; "We trained this system on publicly availab…

In a prior(/n) life I worked on Protein folding, and participated in CASP. This was a/the "holy grail" problem of molecular biology, long thought to be an automatic Nobel. It's somewhat unfair to characterise developments prior to this as insignificant. In fact by the time I was working on it, that "automatic Nobel" was no longer assumed, because the field had made quite a bit of progress, in many tiny steps by many…

The game was https://fold.it/ presumably.

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

#555

Post by ampdepolymerase »

gulperxcx wrote: post #247
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…

but how would this affect day to day life, though? Not how long you think it will.

Entire classes of diseases may become history. Creutzfeldt-Jakob and other prion diseases can now be completely understood. Precision targeting of cancerous cells will become trivial (in theory). Minimal life projects (simplest cell possible) will require less trial and error. In general, it will provide a magnitude level improvement to biotechnology, akin to moving from Aristotelian physics to Newtonian.

Some possibilities: artificial muscles for robots, man-made blood substitutes, designer enzymes to break down plastic and other compounds. Software defined biology, where the pipeline from DNA code to actual protein can now be modeled in silico ahead of time. The biology classes of the future may be less observation of animals and more training in usage of whatever the equivalent of Autodesk for biology will be. Healthcare economics in developing nations will be changed as biochemistry itself may finally become deterministic (to some extent). Orphan drug development price would drop (and if you take into account right to try laws and ignore ethics in favor of progress, then people with rare disease may be cured en masse without bankrupting the health insurance company).

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

#556

Post by deeviant »

timr wrote: post #459
ramraj07 wrote: post #438

Earlier quoted context omitted.

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…

"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." They say on their own press-release page that side-chains are a future research p…

> Nothing about this changes the fact that protein crystallography is a gold-standard method for determining a protein structure.

Yes it does. Protein crystallography is/was the gold-standard. Once this result is verified and accepted by the scientific community as a whole, that changes.

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

#557

Post by [deleted] »

deeviant wrote: post #556
timr wrote: post #459

Earlier quoted context omitted.

"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." They say on their own press-release page that side-chains are a future research p…

> Nothing about this changes the fact that protein crystallography is a gold-standard method for determining a protein structure. Yes it does. Protein crystallography is/was the gold-standard. Once this result is verified and accepted by the scientific community as a whole, that changes.

[deleted]

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

#558

Post by deeviant »

garmaine wrote: post #333
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…

> Are there any cases where ML performed decently but then hit a ceiling while expert systems kept improving? Yes, this describes entire history of AI including several boom-bust cycles. In particular the 80's come to mind. Yes the practitioners think that there's no technical barriers stopping them from eating the world, but that's exactly what people thought about other so-called revolutionary advances. Although to…

> We appear to be at the tail end of the maximum hype part of the boom-bust cycle. Thinking that the rapid gains being made by the current deep learning approaches will soon hit a wall is a reasonable outside-view prediction to make: nearly every time we've had a similarly transformative technology in the AI space and elsewhere, hitting the wall is exactly what happened. The onus would be on practitioners to show that this time really is different.

What a take. Neural networks just took a huge bite out of protein folding and your take is: This just in, the Deep Learning boom is about to go bust! Asinine.

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

#559

Post by jassany »

thamer wrote: post #553

There's something I don't understand about protein shapes. There are tons of software solutions – on the web and offline – to visualize the shape of proteins from their sequence of amino acids. How do these work then, if we don't know how the atoms might be arranged in space? For example, this[1] is the code for SARS-Cov-2's Spike(S) protein. From what I understand of this page it's pretty short, only ~1,757 proteins…

I work suggest trying to understand how the haemoglobin protein works... The shape of it is not too confusing too look at.

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

#560

Post by paraschopra »

thamer wrote: post #553

There's something I don't understand about protein shapes. There are tons of software solutions – on the web and offline – to visualize the shape of proteins from their sequence of amino acids. How do these work then, if we don't know how the atoms might be arranged in space? For example, this[1] is the code for SARS-Cov-2's Spike(S) protein. From what I understand of this page it's pretty short, only ~1,757 proteins…

Al such proteins have been crystallised and we know their shape experimentally.
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