4 ms·

I'm not really "in the field" but I did some computational work on protein folding in the past. I have a hunch on how they are doing this. From the blog post

I'm not really "in the field" but I did some computational work on protein folding in the past. I have a hunch on how they are doing this. From the blog post it appears that the network is using angles and distances of aminoacids in a given sequence in know structures to predict good starting point(s) for regular molecular dynamics-based structural optimization (what is called the "gradient descent step" in the post). If I'm wrong then I'll just have to try this approach myself one day...

Cybiote 8y ago
Yes, it seems you're very much on the right track according to page 12, direct structure optimization: http://predictioncenter.org/casp13/doc/CASP13_Abstracts.pdf http://predictioncenter.org/casp13/doc/CASP13_Abstracts.pdf The document also answers some of my questions. Some confusion from the fact that they are using multiple methods in parallel. The scoring networks are resnets. The database was used in training one of the networks. The generative network section is still unclear, but it looks like it was used in one of their methods, fragment assembly step and network architecture is DRAW (trained with DB). Network generated Fragments inserted using simulated annealing, ranked by either just their conv-net or both existing methods and conv-net. Simulated Annealing hyperparameters were optimized with evolutionary search. Their third approach is what is most similar to what you describe. It looks like they combine lots of approaches, including features, convnet scores and predictions from other methods. Still needs a lot of detail to be filled in such as more detail on how they integrate memory and how gradient descent is actually done directly on protein chain structures. It's possible their computational advantage made a large difference in being able to arrive at this result; existing researchers need not feel too bad.
natechols 8y ago
gradient descent != molecular dynamics; the latter is simulating forces on atoms, not attempting to optimize a target function. You can use molecular dynamics for optimization too - it can be very powerful, just slow - but I don't think that's what they're describing here.
- electricslpnsld 8y ago
  I read it as they are using a molecular dynamics package to define a function (potential energy of the protein structure) and are using gradient descent to (slowly) reach stable extrema (static configurations of the protein).
  - natechols 8y ago
    That makes more sense, but the energy function isn't really a feature of molecular dynamics, anything that does molecular optimization or modeling will have this information built in.
  - deleted 8y ago
    [deleted]
- hmartiniano 8y ago
  You're absolutely right. My bad, mixed too many ideas at once :). Gradient descent (or similar techniques) can be used for structural optimization with the same atomistic models used in molecular dynamics.
sjg007 8y ago
I mean if you can have gradient descent minimize the free energy of the system then it should work.

Cybiote 8y ago

Yes, it seems you're very much on the right track according to page 12, direct structure optimization: http://predictioncenter.org/casp13/doc/CASP13_Abstracts.pdf http://predictioncenter.org/casp13/doc/CASP13_Abstracts.pdf The document also answers some of my questions. Some confusion from the fact that they are using multiple methods in parallel. The scoring networks are resnets. The database was used in training one of the networks. The generative network section is still unclear, but it looks like it was used in one of their methods, fragment assembly step and network architecture is DRAW (trained with DB). Network generated Fragments inserted using simulated annealing, ranked by either just their conv-net or both existing methods and conv-net. Simulated Annealing hyperparameters were optimized with evolutionary search. Their third approach is what is most similar to what you describe. It looks like they combine lots of approaches, including features, convnet scores and predictions from other methods. Still needs a lot of detail to be filled in such as more detail on how they integrate memory and how gradient descent is actually done directly on protein chain structures. It's possible their computational advantage made a large difference in being able to arrive at this result; existing researchers need not feel too bad.

natechols 8y ago

gradient descent != molecular dynamics; the latter is simulating forces on atoms, not attempting to optimize a target function. You can use molecular dynamics for optimization too - it can be very powerful, just slow - but I don't think that's what they're describing here.

electricslpnsld 8y ago
I read it as they are using a molecular dynamics package to define a function (potential energy of the protein structure) and are using gradient descent to (slowly) reach stable extrema (static configurations of the protein).
- natechols 8y ago
  That makes more sense, but the energy function isn't really a feature of molecular dynamics, anything that does molecular optimization or modeling will have this information built in.
- deleted 8y ago
  [deleted]
hmartiniano 8y ago
You're absolutely right. My bad, mixed too many ideas at once :). Gradient descent (or similar techniques) can be used for structural optimization with the same atomistic models used in molecular dynamics.

sjg007 8y ago

I mean if you can have gradient descent minimize the free energy of the system then it should work.