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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

#231

Post by Rochus »

ramraj07 wrote: post #220
Rochus wrote: post #86

Earlier quoted context omitted.

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 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.

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

#233

Post by xral »

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…

AlQuraishi's tweet [0] about this:

> CASP14 #s just came out and they’re astounding—DeepMind looks to have solved protein structure prediction. Median GDT_TS went from 68.5 (CASP13) to 92.4!!!! Cf. their 2nd best CASP13 struct scored 92.8 (out of 100). Median RMSD is 2.1Å. I think it's over https://predictioncenter.org/casp14/zscores_final.cgi

[0]: https://twitter.com/MoAlQuraishi/status/1333383634649313280

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

#236

Post by ramraj07 »

mncharity wrote: post #64

Additional commentary in Science: https://www.sciencemag.org/news/2020/11/game-has-changed-ai-... (submitted by furcyd : https://news.ycombinator.com/item?id=25254888 ).

The most amazing part:

> The organizers even worried DeepMind may have been cheating somehow. So Lupas set a special challenge: a membrane protein from a species of archaea, an ancient group of microbes. For 10 years, his research team tried every trick in the book to get an x-ray crystal structure of the protein. “We couldn’t solve it.”

> But AlphaFold had no trouble. It returned a detailed image of a three-part protein with two long helical arms in the middle. The model enabled Lupas and his colleagues to make sense of their x-ray data; within half an hour, they had fit their experimental results to AlphaFold’s predicted structure. “It’s almost perfect,” Lupas says. “They could not possibly have cheated on this. I don’t know how they do it.”

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

#237

Post by SubiculumCode »

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.

Something like this comes up in assessing the accuracy of automated segmentation results of brain regions e.g. the hippocampus. Human-machine reliability is approaching the human to human reliability, so it becomes harder to improve the automated methods.

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

#240

Post by marcosdumay »

asdfasgasdgasdg wrote: post #183
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 have a related question about this. If experimental methods produce results around a score of 90, what is the baseline we are comparing the DeepMind results against? If the experimental error is equal to the observed DeepMind error, how can we say which one is actually more erroneous?

Finding the energy of each configuration should be much easier than finding the lowest-energy configuration. Can that be calculated ab-initio or it is still too expensive?
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