Maybe I was wrong for expecting the impossible here, but I was excited to see this specific structure and it appears that there is still work to do. Nevertheless, kudos to Deepmind on their amazing achievement and contributions to the field!
Another famous one would be R-domain of CFTR, which was not resolved in experimental structure determination, and AlphaFold models correctly show disorder there. Nothing to be done in those cases except perform molecular simulation or other experiments to assess dynamic ensembles, https://alphafold.ebi.ac.uk/entry/P13569
> A discussion of the applications that AlphaFold DB may enable and the possible impact of the resource on science and society
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After decades of effort, 17% of the total residues in human protein sequences are covered by an experimentally-determined structure1. Here we dramatically expand structural coverage by applying the state-of-the-art machine learning method, AlphaFold2, at scale to almost the entire human proteome (98.5% of human proteins). The resulting dataset covers 58% of residues with a confident prediction, of which a subset (36% of all residues) have very high confidence.
https://www.nature.com/articles/s41586-021-03828-1
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The metric they use (residues) is a bit unusual (I would have used number of proteins instead), but I assume they wanted to account for ambiguity (such as proteins with partial structures).
If so it might be better to link to the paper instead: https://www.nature.com/articles/s41586-021-03828-1
1. How likely is it that alphafold learned to accurately predict protein structure in the narrow domain of proteins that have been experimentally synthesized and whose structure has been measured? in other words will AlphaFold's results generalize to proteins which cannot yet be synthesized in the laboratory.
2. If Alphafold's accuracy holds, what type of commercial applications does this open up?
> After decades of effort, 17% of the total residues in human protein sequences are covered by an experimentally-determined structure. Here we dramatically expand structural coverage by applying the state-of-the-art machine learning method, AlphaFold2, at scale to almost the entire human proteome (98.5% of human proteins).
But at least it inspired someone to make and release this
I think many people believe that given infinite computer time the protein folding simulations would produce the same output as the static prediction (modulo a number of complex details) but use far, far more computer time to get there.
The fundamental observation from the DM AF2 paper that I've been able to glean (which I kind of sort of already believed) is that careful multiple sequence alignments of 30-100 evolutionarily related proteins is enough to produce coarse distance constraints that can be used to guide a structure prediction to a good answer quickly. And that depended on new ML technology that didn't exist before.
refers to structures determined by means of physical examination, with like crystallography, not to attempts at predictive computational analysis prior to AlphaFold, which were not accurate compared to AlphaFold.
I have some experience with recombinant yeast and PTMs. Degree of glycosylation actually vary a lot depending on strain used and has a huge effect of protein activity. And of course these PTMs affects the crystal structure.
This is my first big boy project and I’m driving solo so it takes me a while to make any progress. But at least now I have this db and genbank to model after
More info: https://deepmind.com/blog/article/putting-the-power-of-alpha...
I think I was primed for a knee-jerk reaction because when Alphafold's results were announced back in Dec. 2020, with expressions of what a boon it would be for researchers around the globe, I anticipated there would be a timeline announced for exposing a tool or for the open-sourcing. (The Github repo has only just been released about 6 days ago ...)
With all the work on SARS-CoV-2's 'interactome', as well as human proteins & enzymes involved in pharmacology of antiviral drugs under development / repurposing , it's easy to imagine that drug developers would have liked to exercise Alphafold as soon as it was announced. (I myself have wanted a structure for human enzyme OATP1A2 that wasn't available on the PDB for such a drug pharmacology study - quite glad it is available at hand now.. .:) ).
Anyway I'm sure good arguments will be made about the need to really 'get it right' before releasing, or internal deliberations on how much to open up vs charging for it.
But 7 months lead time during a pandemic is a long time...
In all cases thanks again for this innovation's availability now. :)
https://news.ycombinator.com/item?id=27894060
The colab provides a slightly-less-accurate version that operates in the cloud. For the real mccoy it seems one must set up one’s own environment and leverage the git repo.
