6 ms·

You don't get determinism when working within the body. Having a high enough accuracy will give you a "good idea" of the interactions it might have with other

by m00x 6y ago

You don't get determinism when working within the body. Having a high enough accuracy will give you a "good idea" of the interactions it might have with other proteins and substances, but can't account for the millions of other interactions they might have with other particles. Most of the bioinformatics aren't deterministic, but still rely on stochastic measures. DNA sequencing is done by sampling, then predicting the rest and as far as my own biology teachings go, it's categorized as "solved". Sequencing might get better, but we've accepted it as a solution for the moment. Arguing the term "solved" is just pedantic. We know it's not 100%, but the actual usefulness of improving the prediction of a few more Angstroms isn't going to make a huge practical difference. What matter is that we can start actually building tooling and lab tests on this method.

sdenton4 6y ago
Shouldn't the next step just be predicting a distribution of possible structures, instead of just the most likely? That doesn't seem crazy given where we're at on the machine learning side; predicting distributions is almost the default at this point, in many areas.
- Scandiravian 6y ago
  That is actually one of the "classic" ways of doing it. There's a bunch of different Bayesian models for protein structure - as an example, I implemented a model for structural changes in evolution for my master's thesis The big challenge for these kind of models is the curse of dimensionality. Since every atom in the structure can potentially interact with every other atom, it's tricky to make a joint distribution for the entire sequence and it's rare to have a model that's both accurate and parallelizable, so the field hasn't benefitted much from the advances in for instance GPU computing
dekhn 6y ago
The protein folding problem isn't part of the body- it's a reduction in which the protein is folded in regular water solvent with no other components. I'm not sure what you mean by "DNA sequencing is done by sampling then predicting the rest". DNA sequencing works by oversampling and then making a "call" about the specific base in a position given the evidence. Regions without data are described as N with estimated length M, rather than a "prediction".
- alextheparrot 6y ago
  The fragment amplification stage is definitely oversampling. I feel the original poster might have been referring to how the input DNA could’ve been heterogenous as it is from multiple cells. Could’ve also been confounding GWAS with sequencing, too, I suppose. Given the technical limitations of single cell techniques it seems you both could be technically correct regarding the sampling.
- nabla9 6y ago
  Protein folding is often aided by chaperones. Usually chaperones just accelerate the process, but some proteins requires chaperones to fold properly. chaperones. some of them prevent or correct damage caused by manifolding.
  - dekhn 6y ago
    The protein folding problem (as tested by the CASP competition, and "won" by DeepMind here) does not include proteins that require chaperones. It covers spontaneous reversible folders like ribonuclease A.

by m00x 6y ago

sdenton4 6y ago

Shouldn't the next step just be predicting a distribution of possible structures, instead of just the most likely? That doesn't seem crazy given where we're at on the machine learning side; predicting distributions is almost the default at this point, in many areas.

Scandiravian 6y ago
That is actually one of the "classic" ways of doing it. There's a bunch of different Bayesian models for protein structure - as an example, I implemented a model for structural changes in evolution for my master's thesis The big challenge for these kind of models is the curse of dimensionality. Since every atom in the structure can potentially interact with every other atom, it's tricky to make a joint distribution for the entire sequence and it's rare to have a model that's both accurate and parallelizable, so the field hasn't benefitted much from the advances in for instance GPU computing

dekhn 6y ago

The protein folding problem isn't part of the body- it's a reduction in which the protein is folded in regular water solvent with no other components. I'm not sure what you mean by "DNA sequencing is done by sampling then predicting the rest". DNA sequencing works by oversampling and then making a "call" about the specific base in a position given the evidence. Regions without data are described as N with estimated length M, rather than a "prediction".

alextheparrot 6y ago
The fragment amplification stage is definitely oversampling. I feel the original poster might have been referring to how the input DNA could’ve been heterogenous as it is from multiple cells. Could’ve also been confounding GWAS with sequencing, too, I suppose. Given the technical limitations of single cell techniques it seems you both could be technically correct regarding the sampling.
nabla9 6y ago
Protein folding is often aided by chaperones. Usually chaperones just accelerate the process, but some proteins requires chaperones to fold properly. chaperones. some of them prevent or correct damage caused by manifolding.
- dekhn 6y ago
  The protein folding problem (as tested by the CASP competition, and "won" by DeepMind here) does not include proteins that require chaperones. It covers spontaneous reversible folders like ribonuclease A.