281–290 of 683 posts
This is a lot bigger than people are assuming if protein folding can be done quickly and cheaply it will trickle down to a lot more than medicine. It is going to advance bio fuels, food production and a lot more.
Imagine protein computers or protein metamaterials
The same information we get through x-ray diffraction will now be available 100x or even 1000x cheaper, and using this model can even aid the interpretation of xray diffraction data!
What excites me most isn't doing what we can do now, for cheaper (which will surely lead to more effective research methods), but the potential to gain a systematic view of protein structures, either across the genome, species, or through time which will give us a deeper and more fundamental understanding of biology.
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I think this is the interesting part because there aren't going to be the same regulatory hurdles for using ribosomes to manufacture technology as there are for medicines. Synthetic organelles that weave fibers, build metamaterials, etc could lead to pretty magical advances in our capability.
Perhaps we'll live to see The Diamond Age
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Plastic degradation is a thing already in naturally occurring bacteria that evolved a PETase: https://science.sciencemag.org/content/351/6278/1196/tab-fig...
But producing fuel as the fellow suggested would then be another function to be added to the bacterium; and maybe it should work on different kinds of plastic.
No, they didn't. They approximated a solution to protein folding. The two are different concepts -- this isn't the typical HN pedantry. "Solving" the problem would entail developing an interpretable algorithm for taking a string of amino acids and determining the 3D structure once folded. Approximating a solution would entail simulating that algorithm, which is what their neural network is doing. It is of course usua…
This is the absolute definition of it.
No, they didn't. They approximated a solution to protein folding. The two are different concepts -- this isn't the typical HN pedantry. "Solving" the problem would entail developing an interpretable algorithm for taking a string of amino acids and determining the 3D structure once folded. Approximating a solution would entail simulating that algorithm, which is what their neural network is doing. It is of course usua…
The distinction you're making between "solved" and "closely approximated" makes logical sense to me. However, if I'm interpreting the AlphaFold results correctly, this distinction isn't practically significant, right? If you can approximate an algorithm with error that is "below the threshold that is considered acceptable in experimental measurements" (to quote another HN comment), then you have something as good as…
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How do you define enormous? "It uses approximately 128 TPUv3 cores (roughly equivalent to ~100-200 GPUs) run over a few weeks". Also last time it took about a year for good replications to pop up.
A couple of hundred GPU's is well within the reach of many even moderately well heeled research institutes. It'd seem that about 3 weeks of compute time with 128 TPU v3's would be about $170,311.68.
Been out of the field for a while, could someone currently in it qualify these results? Hyperbolic title notwithstanding, they approach 90% median free modeling accuracy. The "other 90%" still remains to be solved...
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…
The article did claim:
> According to Professor Moult, a score of around 90 GDT is informally considered to be competitive with results obtained from experimental methods.
So perhaps their score of 87 GDT is pretty significant. But “competitive with” is not the same as “always in agreement with”, as you point out. Could be the failure modes are problematic.