4 ms·

I think you’re suffering from a lack of imagination. It’ll be interesting to see where closed loop directed evolution[0], which is what I was alluding to above,

• timr 8y ago
  Ab initio structure prediction has nothing to do with directed evolution. It doesn't enable it or make it better. Directed evolution is a laboratory technique that depends on scale and cycle speed -- huge numbers of variants are screened rapidly against an assay during each round. Ab initio structure prediction adds nothing to the process. You might argue that you could then predict the structure of the best variants, but predicted structures are all but useless for drug discovery.
  • jf- 8y ago
    Think of this: You have a digital library of sequences that are fed into an AI that predicts structure. The structures are fed into an AI that predicts the desired function based on that structure. You use an optimisation algorithm to identify candidates. You synthesise the most promising candidates and use those as the basis for generating a physical library, and then screen as usual. You now have a way of navigating sequence space far more effectively, so you can explore more of it. You could also potentially use the results to feed back into the system regarding function, so it could become smarter over time.
    • natechols 8y ago
      You're describing what protein design researchers have been doing already for years already. Except for predicting the desired function, which AlphaFold doesn't do either - as the structural genomics projects of the 2000s found out, having the protein structure doesn't magically tell you what it does in vivo.
      • jf- 8y ago
        > You're describing what protein design researchers have been doing already for years already. If that’s so, can you link to any supplementary material about it? Particularly with respect to how machine learning is being used, how the candidate selection process works etc. I’m curious about the subject. > Except for predicting the desired function, which AlphaFold doesn't do either - as the structural genomics projects of the 2000s found out, having the protein structure doesn't magically tell you what it does in vivo. Protein function prediction is a real thing, and it requires knowing the structure. Good structure prediction is a step towards this.
        • timr 8y ago
          Protein function prediction does not require knowing the structure. In fact, nearly all function prediction is done on protein sequence alone, using alignment algorithms.
          • jf- 8y ago
            When I look up the subject I see several structural function prediction methods. This being so I don’t accept that having better structure does not assist in predicting function, at least when using these approaches.
            • timr 8y ago
              Are there structure-based methods? Yes. Does function prediction require structure? (i.e. what you said, above.) No.
        • natechols 8y ago
          Sorry, to be clear, there usually isn't any machine learning involved (at least not in the examples I'm familiar with), but the rest of the process is very similar. My point is just that it's not a new workflow and it's not something that the existing tools can't do; better predictions can reduce the search space and/or the number of iterations, but unless they're suddenly an order of magnitude more accurate, it's still an incremental improvement. It's difficult to guess from the CASP results how well this approach will reduce the number of false positives, which as I understand it is a big bottleneck in the design process - IMHO that's a much more interesting problem to solve than ab initio prediction, although they're closely related.