141–150 of 683 posts
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Proteins which are coded by DNA.
So what? The DNA only codes for the RNA and amino acid sequence. Structure determination is yet another topic. When we determine the protein structure we already know the sequence. Neither DeepMind has to look at the DNA to train their DNN.
Structure determination is what allows you to see the purpose/effect of the sequence that the DNA encoded.
I am actually scared. This plus CRISPR means real nanotechnology is within reach.
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.
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.
>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.
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What for?
The forward folding problem lets you determine structures from a known genetic sequence. So for example you could very quickly sequence the genome of a virus and figure out how it worked much faster than current methods allow. The reverse folding problem lets you specify a structure and then make a genetic sequence to produce it. For example you could look at this virus to see how it infects its host, then design a c…
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…
I was wondering the same thing. But I also wonder if having good guesses makes the x-ray crystallography and other experiments to verify a given protein easier/cheaper/quicker? I don't know enough about the actual techniques to have an informed opinion but I would think it would be helpful.
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…
Now onto the much harder problem of doing the reverse: taking an arbitrary structure and determining an amino-acid sequence that will fold into it.
"AlphaFold achieves a median score of 87.0 GDT". Game changing, and a huge improvement, but not 100% solved. Also this is for static folding. Dynamic folding and interaction is a much harder problem. Those need to be tackled too before I would consider protein folding 'solved'.
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Given the DNA code for one of the "machines" that run cells, we can generate an atomic model of that machine. This means we can "compile" (one part of) the DNA code. It was already possible, but so slow that entire datacenters would spend months calculating this for a single protein and even then we can't use them on the really complex ones at all, necessitating things like neutron spectroscopy which are totally insa…
This is about proteins, not DNA.
It's true that in nature there are many steps between DNA and proteins (this list doesn't even include the steps that mediate the translation, ie. start it, stop it, slow it down, ...), but the structure of a protein is fully determined by the DNA code.
Protein folding is about you start from the DNA code that is fed into the ribosome ignoring all the meta information, and come up with an atomic model (VERY long list like "H atom at 3.27,2.17,12.18, C atom at 2.87, 2.19, 12.33, ..."). Now there's a million niceties we've discovered to make this problem simpler and nicer looking, but that's what it boils down to.