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Protein folding is a big and important problem, so this is certainly big news if it works as well as it seems. But I wouldn't assume that this changes everythin

Protein folding is a big and important problem, so this is certainly big news if it works as well as it seems. But I wouldn't assume that this changes everything, we can already determine how proteins fold by experimental work. The disadvantage is that this is a lot of work, though the methods there also improved a lot. One question is how robust the predictions are that DeepMind produces. I would also assume that right now it can't e.g. determine protein structures in the present of other small molecules, or protein complexes. A lot of the interesting stuff lies in the interactions between molecules. And in general in life sciences any new development will take at least a decade until it hits day to day life, likely even more. We're living with a exception to this rule right now due to the pandemic, but in general things take quite a bit of time in that space.

derefr 6y ago
We can already determine how a few proteins (170k — which sounds like a lot, but which is only 0.09% of all currently-catalogued protein sequences) fold by experimental work. What an accurate model of protein folding allows us to do, is to take our big database of DNA, predict protein foldings for all of it, and then stand up a search index for this database, keying each amino-acid "row" by the "words" of its predicted protein's structural features. We could then, with a simple search query that executes in O(log n) time, find DNA targets that produce molecules with interesting structures that might be worthy of study. This would, for example, be a game-changer in how biopharmaceutical macromolecule-therapy R&D is conducted. Right now we have to notice that some bacterium or another produces some interesting protein, and then engineer a bioreactor to get more of that protein. With this tech, we can work backward from an entirely hyothetical, under-specified "interesting protein", to figure out what catalogued-but-unstudied DNA sequences produce never-before-catalogued proteins that fit that particular functional "shape", and therefore might do the interesting thing. Then we can either directly synthesize that same DNA, or find the organism we originally sampled it from and study it more.
- fabian2k 6y ago
  "A few" does appear quite dismissive of the enormous amounts of effort in structural biology so far. There are more than 170,000 structures in the PDB right now. To determine potential targets for drugs we have to understand what the proteins do. Having the structure is not really enough for that, it doesn't tell you the purpose of the protein (though it certainly can give you some hints). In most cases the proteins were determined to be interesting by other experiments, and then people decided to try and solve their structure. So the structures we already solved are also biased towards the more biologically relevant proteins.
  - entropicdrifter 6y ago
    170,000 is three orders of magnitude less than the number of recorded protein sequences. I don't think it's dismissive to describe that as comparatively few.
    - ClumsyPilot 6y ago
      I don't know the field, and I understood 'a few' as like a dozen, certainly not in the thousands. Anyone uninitiated with think the same, and thise already informed. Well, they are already informed.
      - ALittleLight 6y ago
        I also don't know the field and the opposite concern is that 170,000 sounds like a lot, but, apparently, it's a relatively small amount compared to the number of proteins there are. It makes sense to me to refer to it as a small number - e.g. "That hard drive is tiny." "No, it stores several million bytes..."
    - flobosg 6y ago
      Structure is much, much more conserved than sequence. In other words, protein sequences with low sequence identity can fold similarly due to the physical constraints that guide protein folding.
  - derefr 6y ago
- ghostpepper 6y ago
  This does indeed sound like a game changer then, if true
- IgniteTheSun 6y ago
  Considering that this system "uses approximately 128 TPUv3 cores (roughly equivalent to ~100-200 GPUs) run over a few weeks" to determine a single protein structure, making predictions for all proteins encoded in a human genome seems impractical at this stage. With luck, this advance will help lead to discovery and definition of new folding rules and optimizations that will make protein folding predictions for the whole human genome more tractable.
  - sanxiyn 6y ago
    That's training time, not inference time.
  - deleted 6y ago
    [deleted]
  - foota 6y ago
    My reading based on context was that this was time to train, not time to predict.
  - mrDmrTmrJ 6y ago
    I think it is possible to make predictions for all proteins encoded in the human genome. Perhaps you misread a very long and confusing sentence? Background, Neural networks have two modes 1) training - where you learn all the model weights and 2) inference - where you run the model once on new data. Training takes takes a long time, because you're computing derivatives to implement updates rules on millions or billions of parameters based on iteratively examining massive datasets. Inference is extremely fast because you're just running matrix multiplies of those parameters on new data. And TPUs/GPUs are specially designed to compute matrix multiplies. The article said: "We trained this system [...] over a few weeks." I searched for, but did not see them identify the inference time. I do expect inference time to be well under one second, though I'm not personally experienced with running inference on this type of network architecture. For comparison, GPT-3 and AlphaStar have month long training times and real-time (sub-second) inference times.
  - Rochus 6y ago
    Still much faster than synthesizing the protein and then doing NMR or cristallography to solve the structure puzzle what easily takes half a year or more (and very expensive equipment).
- carlob 6y ago
  Here is another interesting approach in synthetic protein building: https://science.sciencemag.org/content/369/6502/440.abstract https://science.sciencemag.org/content/369/6502/440.abstract
- btilly 6y ago
  We can already determine how a few proteins fold by experimental work. Where "a few" is around 0.1% of the known 180 million proteins. So a relative few and a whole lot. But the catch is which proteins could we figure out by experiment, and which not. In particular membrane proteins are hard to experimentally determine. But knowing how they fold is very important for figuring out how to get things to react with or get through membranes such as cell walls. Which is an important problem for everything from understanding how viruses work to targeted delivery of drugs. We now have a way to find those structures.
- FredFS456 6y ago
  There are post-translational modifications to proteins. This means that for many (most?) proteins, the amino acid chain sequence is different from what you would predict from the DNA. These modifications are dependent on the state of the cell at the time of translation, and so cannot be predicted from the DNA alone. Even with a 100% accurate folding model, we cannot simply know the shapes of all the proteins inside the human body based on the genome.
gulperxcx 6y ago
but how would this affect day to day life, though? Not how long you think it will.
- ampdepolymerase 6y ago
  Entire classes of diseases may become history. Creutzfeldt-Jakob and other prion diseases can now be completely understood. Precision targeting of cancerous cells will become trivial (in theory). Minimal life projects (simplest cell possible) will require less trial and error. In general, it will provide a magnitude level improvement to biotechnology, akin to moving from Aristotelian physics to Newtonian. Some possibilities: artificial muscles for robots, man-made blood substitutes, designer enzymes to break down plastic and other compounds. Software defined biology, where the pipeline from DNA code to actual protein can now be modeled in silico ahead of time. The biology classes of the future may be less observation of animals and more training in usage of whatever the equivalent of Autodesk for biology will be. Healthcare economics in developing nations will be changed as biochemistry itself may finally become deterministic (to some extent). Orphan drug development price would drop (and if you take into account right to try laws and ignore ethics in favor of progress, then people with rare disease may be cured en masse without bankrupting the health insurance company).

derefr 6y ago

We can already determine how a few proteins (170k — which sounds like a lot, but which is only 0.09% of all currently-catalogued protein sequences) fold by experimental work. What an accurate model of protein folding allows us to do, is to take our big database of DNA, predict protein foldings for all of it, and then stand up a search index for this database, keying each amino-acid "row" by the "words" of its predicted protein's structural features. We could then, with a simple search query that executes in O(log n) time, find DNA targets that produce molecules with interesting structures that might be worthy of study. This would, for example, be a game-changer in how biopharmaceutical macromolecule-therapy R&D is conducted. Right now we have to notice that some bacterium or another produces some interesting protein, and then engineer a bioreactor to get more of that protein. With this tech, we can work backward from an entirely hyothetical, under-specified "interesting protein", to figure out what catalogued-but-unstudied DNA sequences produce never-before-catalogued proteins that fit that particular functional "shape", and therefore might do the interesting thing. Then we can either directly synthesize that same DNA, or find the organism we originally sampled it from and study it more.

fabian2k 6y ago
"A few" does appear quite dismissive of the enormous amounts of effort in structural biology so far. There are more than 170,000 structures in the PDB right now. To determine potential targets for drugs we have to understand what the proteins do. Having the structure is not really enough for that, it doesn't tell you the purpose of the protein (though it certainly can give you some hints). In most cases the proteins were determined to be interesting by other experiments, and then people decided to try and solve their structure. So the structures we already solved are also biased towards the more biologically relevant proteins.
- entropicdrifter 6y ago
  170,000 is three orders of magnitude less than the number of recorded protein sequences. I don't think it's dismissive to describe that as comparatively few.
  - ClumsyPilot 6y ago
    I don't know the field, and I understood 'a few' as like a dozen, certainly not in the thousands. Anyone uninitiated with think the same, and thise already informed. Well, they are already informed.
    - ALittleLight 6y ago
      I also don't know the field and the opposite concern is that 170,000 sounds like a lot, but, apparently, it's a relatively small amount compared to the number of proteins there are. It makes sense to me to refer to it as a small number - e.g. "That hard drive is tiny." "No, it stores several million bytes..."
  - flobosg 6y ago
    Structure is much, much more conserved than sequence. In other words, protein sequences with low sequence identity can fold similarly due to the physical constraints that guide protein folding.
- derefr 6y ago
ghostpepper 6y ago
This does indeed sound like a game changer then, if true
IgniteTheSun 6y ago
Considering that this system "uses approximately 128 TPUv3 cores (roughly equivalent to ~100-200 GPUs) run over a few weeks" to determine a single protein structure, making predictions for all proteins encoded in a human genome seems impractical at this stage. With luck, this advance will help lead to discovery and definition of new folding rules and optimizations that will make protein folding predictions for the whole human genome more tractable.
- sanxiyn 6y ago
  That's training time, not inference time.
- deleted 6y ago
  [deleted]
- foota 6y ago
  My reading based on context was that this was time to train, not time to predict.
- mrDmrTmrJ 6y ago
  I think it is possible to make predictions for all proteins encoded in the human genome. Perhaps you misread a very long and confusing sentence? Background, Neural networks have two modes 1) training - where you learn all the model weights and 2) inference - where you run the model once on new data. Training takes takes a long time, because you're computing derivatives to implement updates rules on millions or billions of parameters based on iteratively examining massive datasets. Inference is extremely fast because you're just running matrix multiplies of those parameters on new data. And TPUs/GPUs are specially designed to compute matrix multiplies. The article said: "We trained this system [...] over a few weeks." I searched for, but did not see them identify the inference time. I do expect inference time to be well under one second, though I'm not personally experienced with running inference on this type of network architecture. For comparison, GPT-3 and AlphaStar have month long training times and real-time (sub-second) inference times.
- Rochus 6y ago
  Still much faster than synthesizing the protein and then doing NMR or cristallography to solve the structure puzzle what easily takes half a year or more (and very expensive equipment).
carlob 6y ago
Here is another interesting approach in synthetic protein building: https://science.sciencemag.org/content/369/6502/440.abstract https://science.sciencemag.org/content/369/6502/440.abstract
btilly 6y ago
We can already determine how a few proteins fold by experimental work. Where "a few" is around 0.1% of the known 180 million proteins. So a relative few and a whole lot. But the catch is which proteins could we figure out by experiment, and which not. In particular membrane proteins are hard to experimentally determine. But knowing how they fold is very important for figuring out how to get things to react with or get through membranes such as cell walls. Which is an important problem for everything from understanding how viruses work to targeted delivery of drugs. We now have a way to find those structures.
FredFS456 6y ago
There are post-translational modifications to proteins. This means that for many (most?) proteins, the amino acid chain sequence is different from what you would predict from the DNA. These modifications are dependent on the state of the cell at the time of translation, and so cannot be predicted from the DNA alone. Even with a 100% accurate folding model, we cannot simply know the shapes of all the proteins inside the human body based on the genome.

gulperxcx 6y ago

but how would this affect day to day life, though? Not how long you think it will.

ampdepolymerase 6y ago
Entire classes of diseases may become history. Creutzfeldt-Jakob and other prion diseases can now be completely understood. Precision targeting of cancerous cells will become trivial (in theory). Minimal life projects (simplest cell possible) will require less trial and error. In general, it will provide a magnitude level improvement to biotechnology, akin to moving from Aristotelian physics to Newtonian. Some possibilities: artificial muscles for robots, man-made blood substitutes, designer enzymes to break down plastic and other compounds. Software defined biology, where the pipeline from DNA code to actual protein can now be modeled in silico ahead of time. The biology classes of the future may be less observation of animals and more training in usage of whatever the equivalent of Autodesk for biology will be. Healthcare economics in developing nations will be changed as biochemistry itself may finally become deterministic (to some extent). Orphan drug development price would drop (and if you take into account right to try laws and ignore ethics in favor of progress, then people with rare disease may be cured en masse without bankrupting the health insurance company).