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.
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.
> 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.
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.
> 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.
No problem, shame though!
It seems to me there must be scope for using AI to improve this process given the results it achieves in other domains, and the alphafold result is very encouraging. Maybe that order of magnitude improvement will eventually be possible.
To add to the GP, there are at least two more fundamental problems with the computational approach:
- chaperones. Not all proteins fold by themselves, quite a few bind to an additional protein that helps them fold in the desired shape. It means that the final state is impossible to achieve from the "initial" state by gradient descent.
- proteins don't necessarily exist in the minimum potential energy state. Moreover, sometimes the state flips on addition of a ligand (e.g. myosin's relationship with ATP) and that's crucial for the protein function.
So static folding only gets you so far. Unfortunately, nature is hideously complicated and "entangled", so there is a tremendous gap between even perfect protein folding and real in vitro results.
In molecular modeling, at some point you're limited either by quantity or quality of input data (for example known protein structures), by the accuracy of your energy function, or by the inability to simulate at long timescales. AI alone won't magically solve the problem without fixing the others too. (AI combined with quantum computing, maybe?)
I am much more excited about the application of AI to more complex problems like metabolic engineering/synthetic biology, literature mining, and genome-wide association studies. It's a shame the training data are such an incomplete mess, but that'll improve slowly.
Protein function prediction does not require knowing the structure. In fact, nearly all function prediction is done on protein sequence alone, using alignment algorithms.
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.
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.