14 ms·

Not knowing a lot about biotechnology, I read the article and it sounds great, but how big is this as a gamechanger? Can someone comment on how big are the impl

Not knowing a lot about biotechnology, I read the article and it sounds great, but how big is this as a gamechanger? Can someone comment on how big are the implications of this in, let’s say, 5 years from now, on day to day life? Does this mean that biotech is going to explode? Or just that drugs will come to market faster, perhaps cheaper for rare diseases, but from the same industry structure as always?

candiodari 6y ago
This will allow us to discover much more about the structure of the cell (of "life") at a before this unprecedented speed. We should find many, many more mechanisms and targets for medicine, but it takes 10-20 years to bring a new medicine to market. So in 5 years you'll see exactly zero new medicines pop up.
- piyh 6y ago
  No new medicines, but way more biotech tools. Higher yield GMO plants, foundational research into disease, science backed recommendations for lifestyle changes to avoid disease that previously eluded us, some crazy stuff happening in animal models. The progress in biotech the past 20 years makes moore's law look slow.
- pmastela 6y ago
  I agree. The main inhibitor of speed that products of this advancement will be deployed at will likely be determined by local policies. Though, given just how profound some of the impacts on medicine might be, the speed at which they can be deployed might become a matter of national security (a healthier population bodes well for a healthier economy which in turn strengthens national security). Hopefully this competition shortens the time-to-market for all these new medicines.
fabian2k 6y ago
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.
breck 6y ago
I never worked directly with protein folding or structure, but worked a bit in proteomics on teams measuring gene expression (which you could roughly think of as how much of each protein is found in this cell). IIRC there are 50,000 - potentially millions of "kinds" of proteins found in a human, and the "shape" of most of them is unknown, and that determines a lot about how they work. So imagine you gave an iPhone to someone in the 1800's, they wouldn't understand how most of it works, but this may be analogous to them finally figuring out some key aspects of the transistor. So it's another tool in the toolbelt and like all good tools will be used in all sorts of unpredictable ways. Someone else I'm sure could do a lot better at explaining how important shape is to understanding the function and behavior of proteins.
dekhn 6y ago
It seems unlikely there will be any large changes in life from solving protein folding. Knowing the structure of a protein (or really, its dynamics) is useful for identifying drugs that bind, but the real bottlenecks n drug discovery and biotech are elsewhere.
- ramraj07 6y ago
  If folding and docking, alongwith dynamics simulations, start getting commodified, that might change things significantly though. I can already start imagining project workflows that are significantly streamlined without much thought, god knows what other scientists would dream up when we reach those steps
comicjk 6y ago
The most accurate technique in computational drug discovery is protein-ligand binding prediction (https://blogs.sciencemag.org/pipeline/archives/2015/02/23/is_fep_ready_for_the_world https://blogs.sciencemag.org/pipeline/archives/2015/02/23/is...). Given the protein structure, you can predict which molecules will bind with it, even for molecules which have never been sythesized. Many protein targets have not been amenable to this because we don't know what the potential binding pockets look like. That set of proteins will now drastically shrink. We're going to have a lot of new drug candidates, and with any luck new drugs, come out of this.
nabla9 6y ago
Getting from DNA structure from tissue samples is relatively straight forward. DNA -> RNA -> unfolded protein is basically one-to-one mapping in most cases. How protein functions depends on how it folds into itself. Once you solve protein folding, you can take DNA sample and see the structure of the molecule without working in lab using crystallography techniques. Solving protein folding is huge, Nobel in chemistry scale achievement. It would be massive leap for biochemistry. It seems that Deep Mind solved competition benchmark and made huge leap, but it's just partial solution that works on limited set. After you have solved protein folding, there is still problem of solving chemical interactions between molecules accurately. Quantum chemistry is extremely compute intensive.
- ivalm 6y ago
  This is still for proteins that fold without chaperons, but I guess it does cover a lot.
shoguning 6y ago
IMO, this is huge. One of the biggest applications of ML to science that I know of for sure. People used to manually crystallize proteins at great effort to solve for structures. Of course, there is a caveat. The static, crystallized structure is only one aspect of a protein. The dynamic behavior dissolved in H2O, at different pH, different ionic strength, with different ligands/cofactors are all also important, and not (afaik) directly addressed by this research.
baybal2 6y ago
One young lady I knew worked on neural algos recognition of X-ray images. They always had single digit, bizarre artifacts, where the program can't sometimes recognise the very data it was trained on with most minute differences. Other artifact was that the most "stereotypical cases" were least reliably recognised, and they hot a lot of flak for screwed up live demos, where a radiologist put a very, very obvious tumor shot onto the scanner, and it didn't work without a half an hour of wiggling the film, and a camera. The "bruteforce" solution may well be always, 80-85% off, but off consistently, and always. NN algo so far beat them, but fail with double digit frequencies on "artifacts" which they themselves can't do anything about. How well it deals with the later, is what I believe will measure its real world usefullness.
- tuatoru 6y ago
  I agree. The failures have to be explicable if we are to trust a model.
  - asah 6y ago
    Doesn't it depends on the application ? i.e. some applications can tolerate false positives/negatives ?
    - baybal2 6y ago
      May well be, but if you spend more compute, and human time checking for those corner cases than if you went with another, more consistent exhaustive search algorith, then the method looses to it economically. This is more the case the more close to bruteforce you come, like encryption cracking. Imagine, spending years of HPC cluster time, trying to break a password, while knowing you have a single digit chance to miss the right key, in a way which would be completely impossible with with a conventional solution.
- klmadfejno 6y ago
  I find this disingenuous. Yes, its important that the algos can perform well on real world data, but the framing of this post begins with an anecodote about one person who had a bad model, and implicitly extrapolates that these problems are generalized throughout all neural nets. One could say the same thing about programmers automating a task, or a number of other trivial examples. I would lean towards assuming deep mind has competent model validation teams vs. not, even if data science is hard.
xyzzyz 6y ago
My friend, who is working in crystallization lab, has told me that she’s gonna be claiming unemployment soon, and she was only half joking.
- dalke 6y ago
  She can still work on complexes, binding modes, and engineered biomolecules (eg, protein–drug conjugates and antisense oligonucleotide dimers) where the training data isn't really there.
_RPL5_ 6y ago
The industry process will not change. You still need industrial biologists to generate and validate AphaFold structures, interpret the results as part of the bigger picture, and to finally design the drugs. And, then, of course you still need to validate the drugs in experimental systems (first the test tube, then mice, then humans). So your second guess is correct - one of the steps is much cheaper now, which marginally improves the entire pipeline. As a result, drugs should now arrive to the market faster. As a side note, I am curious what happens to the field of structural biology in 10 to 15 years from now. Every research university has a large structural biology department with super expensive Xray/NRM/Cryo-EM machines, and armies of students who routinely spend 4-6 years of their PhD trying to solve a structure of a single protein. If AlphaFold works as advertised, NIH will gradually shift funding to other problems. (It was predicted that it'd be taxi drivers, not professors, that AI got first. Ironic.)
- dalke 6y ago
  > "armies of students who routinely spend 4-6 years of their PhD trying to solve a structure of a single protein" Back in the 1990s, when I worked on structure data, I remember that at least some crystallizations were easy enough they could be done as a rotation project. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6287266/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6287266/ suggests that life is now a lot easier than the 1990s. Quoting the abstract: > Macromolecular crystallography evolved enormously from the pioneering days, when structures were solved by “wizards” performing all complicated procedures almost by hand. In the current situation crystal structures of large systems can be often solved very effectively by various powerful automatic programs in days or hours, or even minutes. Such progress is to a large extent coupled to the advances in many other fields, such as genetic engineering, computer technology, availability of synchrotron beam lines and many other techniques, creating the highly interdisciplinary science of macromolecular crystallography. Due to this unprecedented success crystallography is often treated as one of the analytical methods and practiced by researchers interested in structures of macromolecules, but not highly competent in the procedures involved in the process of structure determination. Certainly some proteins are extremely hard to crystallize, and the new single-atom EM work will help a lot. But are there really "armies of students who routinely spend 4-6 years of their PhD trying to solve a structure of a single protein" these days? I honestly don't know. I'm sure some do. But if so, that army is pretty small compared to the vast numbers who more routinely use crystallography.
npunt 6y ago
In short, a core problem of biochem (the wagon) was just hitched to Moore's law (the horse). Our understanding of proteins will now grow exponentially not linearly, helping us to move up a level of abstraction to higher level biochemistry and biology problems.

candiodari 6y ago

This will allow us to discover much more about the structure of the cell (of "life") at a before this unprecedented speed. We should find many, many more mechanisms and targets for medicine, but it takes 10-20 years to bring a new medicine to market. So in 5 years you'll see exactly zero new medicines pop up.

piyh 6y ago
No new medicines, but way more biotech tools. Higher yield GMO plants, foundational research into disease, science backed recommendations for lifestyle changes to avoid disease that previously eluded us, some crazy stuff happening in animal models. The progress in biotech the past 20 years makes moore's law look slow.
pmastela 6y ago
I agree. The main inhibitor of speed that products of this advancement will be deployed at will likely be determined by local policies. Though, given just how profound some of the impacts on medicine might be, the speed at which they can be deployed might become a matter of national security (a healthier population bodes well for a healthier economy which in turn strengthens national security). Hopefully this competition shortens the time-to-market for all these new medicines.

fabian2k 6y ago

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.

breck 6y ago

I never worked directly with protein folding or structure, but worked a bit in proteomics on teams measuring gene expression (which you could roughly think of as how much of each protein is found in this cell). IIRC there are 50,000 - potentially millions of "kinds" of proteins found in a human, and the "shape" of most of them is unknown, and that determines a lot about how they work. So imagine you gave an iPhone to someone in the 1800's, they wouldn't understand how most of it works, but this may be analogous to them finally figuring out some key aspects of the transistor. So it's another tool in the toolbelt and like all good tools will be used in all sorts of unpredictable ways. Someone else I'm sure could do a lot better at explaining how important shape is to understanding the function and behavior of proteins.

dekhn 6y ago

It seems unlikely there will be any large changes in life from solving protein folding. Knowing the structure of a protein (or really, its dynamics) is useful for identifying drugs that bind, but the real bottlenecks n drug discovery and biotech are elsewhere.

ramraj07 6y ago
If folding and docking, alongwith dynamics simulations, start getting commodified, that might change things significantly though. I can already start imagining project workflows that are significantly streamlined without much thought, god knows what other scientists would dream up when we reach those steps

comicjk 6y ago

The most accurate technique in computational drug discovery is protein-ligand binding prediction (https://blogs.sciencemag.org/pipeline/archives/2015/02/23/is_fep_ready_for_the_world https://blogs.sciencemag.org/pipeline/archives/2015/02/23/is...). Given the protein structure, you can predict which molecules will bind with it, even for molecules which have never been sythesized. Many protein targets have not been amenable to this because we don't know what the potential binding pockets look like. That set of proteins will now drastically shrink. We're going to have a lot of new drug candidates, and with any luck new drugs, come out of this.

nabla9 6y ago

Getting from DNA structure from tissue samples is relatively straight forward. DNA -> RNA -> unfolded protein is basically one-to-one mapping in most cases. How protein functions depends on how it folds into itself. Once you solve protein folding, you can take DNA sample and see the structure of the molecule without working in lab using crystallography techniques. Solving protein folding is huge, Nobel in chemistry scale achievement. It would be massive leap for biochemistry. It seems that Deep Mind solved competition benchmark and made huge leap, but it's just partial solution that works on limited set. After you have solved protein folding, there is still problem of solving chemical interactions between molecules accurately. Quantum chemistry is extremely compute intensive.

ivalm 6y ago
This is still for proteins that fold without chaperons, but I guess it does cover a lot.

shoguning 6y ago

IMO, this is huge. One of the biggest applications of ML to science that I know of for sure. People used to manually crystallize proteins at great effort to solve for structures. Of course, there is a caveat. The static, crystallized structure is only one aspect of a protein. The dynamic behavior dissolved in H2O, at different pH, different ionic strength, with different ligands/cofactors are all also important, and not (afaik) directly addressed by this research.

baybal2 6y ago

One young lady I knew worked on neural algos recognition of X-ray images. They always had single digit, bizarre artifacts, where the program can't sometimes recognise the very data it was trained on with most minute differences. Other artifact was that the most "stereotypical cases" were least reliably recognised, and they hot a lot of flak for screwed up live demos, where a radiologist put a very, very obvious tumor shot onto the scanner, and it didn't work without a half an hour of wiggling the film, and a camera. The "bruteforce" solution may well be always, 80-85% off, but off consistently, and always. NN algo so far beat them, but fail with double digit frequencies on "artifacts" which they themselves can't do anything about. How well it deals with the later, is what I believe will measure its real world usefullness.

tuatoru 6y ago
I agree. The failures have to be explicable if we are to trust a model.
- asah 6y ago
  Doesn't it depends on the application ? i.e. some applications can tolerate false positives/negatives ?
  - baybal2 6y ago
    May well be, but if you spend more compute, and human time checking for those corner cases than if you went with another, more consistent exhaustive search algorith, then the method looses to it economically. This is more the case the more close to bruteforce you come, like encryption cracking. Imagine, spending years of HPC cluster time, trying to break a password, while knowing you have a single digit chance to miss the right key, in a way which would be completely impossible with with a conventional solution.
klmadfejno 6y ago
I find this disingenuous. Yes, its important that the algos can perform well on real world data, but the framing of this post begins with an anecodote about one person who had a bad model, and implicitly extrapolates that these problems are generalized throughout all neural nets. One could say the same thing about programmers automating a task, or a number of other trivial examples. I would lean towards assuming deep mind has competent model validation teams vs. not, even if data science is hard.

xyzzyz 6y ago

My friend, who is working in crystallization lab, has told me that she’s gonna be claiming unemployment soon, and she was only half joking.

dalke 6y ago
She can still work on complexes, binding modes, and engineered biomolecules (eg, protein–drug conjugates and antisense oligonucleotide dimers) where the training data isn't really there.

_RPL5_ 6y ago

The industry process will not change. You still need industrial biologists to generate and validate AphaFold structures, interpret the results as part of the bigger picture, and to finally design the drugs. And, then, of course you still need to validate the drugs in experimental systems (first the test tube, then mice, then humans). So your second guess is correct - one of the steps is much cheaper now, which marginally improves the entire pipeline. As a result, drugs should now arrive to the market faster. As a side note, I am curious what happens to the field of structural biology in 10 to 15 years from now. Every research university has a large structural biology department with super expensive Xray/NRM/Cryo-EM machines, and armies of students who routinely spend 4-6 years of their PhD trying to solve a structure of a single protein. If AlphaFold works as advertised, NIH will gradually shift funding to other problems. (It was predicted that it'd be taxi drivers, not professors, that AI got first. Ironic.)

dalke 6y ago
> "armies of students who routinely spend 4-6 years of their PhD trying to solve a structure of a single protein" Back in the 1990s, when I worked on structure data, I remember that at least some crystallizations were easy enough they could be done as a rotation project. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6287266/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6287266/ suggests that life is now a lot easier than the 1990s. Quoting the abstract: > Macromolecular crystallography evolved enormously from the pioneering days, when structures were solved by “wizards” performing all complicated procedures almost by hand. In the current situation crystal structures of large systems can be often solved very effectively by various powerful automatic programs in days or hours, or even minutes. Such progress is to a large extent coupled to the advances in many other fields, such as genetic engineering, computer technology, availability of synchrotron beam lines and many other techniques, creating the highly interdisciplinary science of macromolecular crystallography. Due to this unprecedented success crystallography is often treated as one of the analytical methods and practiced by researchers interested in structures of macromolecules, but not highly competent in the procedures involved in the process of structure determination. Certainly some proteins are extremely hard to crystallize, and the new single-atom EM work will help a lot. But are there really "armies of students who routinely spend 4-6 years of their PhD trying to solve a structure of a single protein" these days? I honestly don't know. I'm sure some do. But if so, that army is pretty small compared to the vast numbers who more routinely use crystallography.

npunt 6y ago

In short, a core problem of biochem (the wagon) was just hitched to Moore's law (the horse). Our understanding of proteins will now grow exponentially not linearly, helping us to move up a level of abstraction to higher level biochemistry and biology problems.