AlphaFold: a solution to a 50-year-old grand challenge in biology • Hacker News Board

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

#241

Post by Rochus » Mon, Nov 30, 2020, 5:47 PM UTC

jjk166 wrote: post #227

Rochus wrote: post #208

Earlier quoted context omitted.

No, the genome of the host is much smaller than the theoretical number of combinations. There are about 20 to 30k different proteins in a human cell (about 20k directly encoded on the DNA).

If you are designing proteins, you're not limited to those that are already encoded in the host's DNA.

Right, but you made the example with the virus docking at a known organism. If you do synthetic biology and modify bacteria to produce any proteins then the situation is different of course.

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

#242

Post by shoguning » Mon, Nov 30, 2020, 5:48 PM UTC

harperlee wrote: post #12

Not knowing a lot about biotechnology, I read the article and it sounds great, but how big is this as a gamechanger? Can someone comment on how big are the implications of this in, let’s say, 5 years from now, on day to day life? Does this mean that biotech is going to explode? Or just that drugs will come to market faster, perhaps cheaper for rare diseases, but from the same industry structure as always?

IMO, this is huge. One of the biggest applications of ML to science that I know of for sure. People used to manually crystallize proteins at great effort to solve for structures.

Of course, there is a caveat. The static, crystallized structure is only one aspect of a protein. The dynamic behavior dissolved in H2O, at different pH, different ionic strength, with different ligands/cofactors are all also important, and not (afaik) directly addressed by this research.

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

#243

Post by mrDmrTmrJ » Mon, Nov 30, 2020, 5:48 PM UTC

ClumsyPilot wrote: post #127

aardvarkr wrote: post #101

Earlier quoted context omitted.

It’s officially recognized as a solution.

I am not sure we are talking about the same thing -i.e. there is a solution for hunger, but it's not a solved problem.

This benchmark maybe solved, but simultaneously, there remain other open problems relating to protein folding which are unsolved and which may not even have benchmarks yet :)

Said differently, there's vast space between having a great result on a specific benchmark (this) and solving all interesting problems in a scientific field.

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

#244

Post by ramraj07 » Mon, Nov 30, 2020, 5:48 PM UTC

mwcampbell wrote: post #173

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…

How far does the similarity extend? Specifically, the big question for me is whether AlphaFold will be freely available like ImageNet, or proprietary.

The competition requires enough revealing about the methodology for other teams to replicate it so open implementations are going to be available for sure.

It also looks like they came up with a brand new jiggling algorithm which is probably just V1 now, this really changes things in a significant way!

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

#245

Post by flobosg » Mon, Nov 30, 2020, 5:49 PM UTC

Rochus wrote: post #231

ramraj07 wrote: post #220

Earlier quoted context omitted.

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 in…

Ok, then this is about enzymes which do not yet exist in the organism. You could then modify bacteria so they produce this enzyme and feed on plastic, I see.

Plastic degradation is a thing already in naturally occurring bacteria that evolved a PETase: https://science.sciencemag.org/content/351/6278/1196/tab-fig...

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

#246

Post by enchiridion » Mon, Nov 30, 2020, 5:51 PM UTC

empiricus wrote: post #17

I am actually scared. This plus CRISPR means real nanotechnology is within reach.

My thought as well. I wonder what the world will look like in 20 years because of this.

I'm willing to bet it will be staggeringly different than what most people are expecting.

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

#247

Post by gulperxcx » Mon, Nov 30, 2020, 5:51 PM UTC

fabian2k wrote: post #26

harperlee wrote: post #12

Not knowing a lot about biotechnology, I read the article and it sounds great, but how big is this as a gamechanger? Can someone comment on how big are the implications of this in, let’s say, 5 years from now, on day to day life? Does this mean that biotech is going to explode? Or just that drugs will come to market faster, perhaps cheaper for rare diseases, but from the same industry structure as always?

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.

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

#248

Post by baybal2 » Mon, Nov 30, 2020, 5:52 PM UTC

harperlee wrote: post #12

Not knowing a lot about biotechnology, I read the article and it sounds great, but how big is this as a gamechanger? Can someone comment on how big are the implications of this in, let’s say, 5 years from now, on day to day life? Does this mean that biotech is going to explode? Or just that drugs will come to market faster, perhaps cheaper for rare diseases, but from the same industry structure as always?

One young lady I knew worked on neural algos recognition of X-ray images.

They always had single digit, bizarre artifacts, where the program can't sometimes recognise the very data it was trained on with most minute differences.

Other artifact was that the most "stereotypical cases" were least reliably recognised, and they hot a lot of flak for screwed up live demos, where a radiologist put a very, very obvious tumor shot onto the scanner, and it didn't work without a half an hour of wiggling the film, and a camera.

The "bruteforce" solution may well be always, 80-85% off, but off consistently, and always. NN algo so far beat them, but fail with double digit frequencies on "artifacts" which they themselves can't do anything about.

How well it deals with the later, is what I believe will measure its real world usefullness.

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

#249

Post by beowulfey » Mon, Nov 30, 2020, 5:52 PM UTC

Seanambers wrote: post #179

flobosg wrote: post #171

Earlier quoted context omitted.

AlQuraishi described the progress made in CASP13 (2018) as “two CASPs in one”. This one is an even bigger breakthrough.

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…

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.

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

#250

Post by Seanambers » Mon, Nov 30, 2020, 5:52 PM UTC

tonfa wrote: post #226

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…

> So it wasn't out of reach for academia, pharmaceuticals, or others with a bit of resources. How much does hiring a deepmind-like team cost though? (massively more than the TPU resources?) Still within reach of pharmaceutical industry I guess, but maybe not so easy for academia.

From what I can gather, Google bought Deepmind for 500 million USD in 2014, they have outstanding debt to its parent company as of 2019 of 1.3 billion USD.

And they had income around 100 million in 2019 but it's all against Google, so looks like a 2 billion +/- 0.5 operation so far, and who knows if they pay for compute.

Other articles place the runrate at 500 million per year in 2019.

Which means 500 million * 6 years = 3 bn + 0.5 purchase price. = 3.5 bn. So somewhere in the 2.5 - 3.5 billion range its seems likely as total cost so far.

Nevertheless doesn't seem out of reach for a multinational.