Reply to post: Grail seekers

DeepMind quits playing games with AI, ups the protein stakes with machine-learning code


Grail seekers

As anyone who has tried to make proteins will tell you, even when you have the right sequence of amino acids there is plenty of scope for the folding to go wrong. The energy differences between folded, misfolded and disordered proteins are small. Many proteins don't simply fold spontaneously - they need prior modification or "chaperone proteins" to get it right. There are cellular processes that modify the gene sequence before it is translated into amino acids. Etc etc.

To me this looks like the "big physics" approach: if you can't answer a question, throw a bigger machine at it. It works for some questions but I suspect that the result will be rather like previous attempts to computerise biology. It will get some good hits in the short term, then people will realise that most are actually rather similar to known cases - the "low hanging fruit" syndrome.

Meanwhile, rather like the back and forth between theoretical astrophysicists and astronomers, the biologists in the wet labs will need to collect more information on the hard cases mentioned: fibrous, disordered and membrane proteins.

There's no Holy Grail, just a lot more work.

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