10 ms·

I think I disagree with most of the comments here stating it’s premature to give the Nobel to AlphaFold. I’m in biotech academia and it has changed things alre

by eig 2y ago

I think I disagree with most of the comments here stating it’s premature to give the Nobel to AlphaFold. I’m in biotech academia and it has changed things already. Yes the protein folding problem isn’t “solved” but no problem in biology ever is. Comparing to previous bio/chem Nobel winners like Crispr, touch receptors, quantum dots, click chemistry, I do think AlphaFold already has reached sufficient level of impact.

singularity2001 2y ago
>> I do think AlphaFold already has reached sufficient level of impact. how so?
- cmavvv 2y ago
  https://www.pnas.org/doi/10.1073/pnas.2315002121 https://www.pnas.org/doi/10.1073/pnas.2315002121
  - deleted 2y ago
    [deleted]
- eig 2y ago
  Well I'm sure one could look at number of published papers etc, but that metric is a lot to do with hype and I see it as a lagging indicator. A better one is seeing my grad-school friends with zero background in comp-sci or math, presenting their cell-biology results with AlphaFold in conferences and at lab meetings. They are not protein folding people either- just molecular biologists trying to present more evidence of docking partners, functional groups in their pathway of interest. It reminds me of when Crispr came out. There were ways to edit DNA before Crispr, but its was tough to do right and required specialized knowledge. After Crispr came out, even non-specialists like me in tangential fields could get started.
  - deleted 2y ago
    [deleted]
  - tananan 2y ago
    In both academic and industrial settings, I've seen an initial spark of hope about AlphaFold's utility being replaced with a resignation that it's cool, but not really useful. Yet in both settings it continued as a playing card for generating interest. There's an on-point blog-post "AI and Biology" (https://www.science.org/content/blog-post/ai-and-biology https://www.science.org/content/blog-post/ai-and-biology) which illustrates why AlphaFold's real breakthrough is not super actionable for creating further bio-medicinal applications in a similar vein.
    - whimsicalism 2y ago
      That article explains why AI might not work so well further down the line biology discoveries, but I still think alphafold can really help with the development of small molecule therapies that bind to particular known targets and not to others, etc.
      - tananan 2y ago
        The thing with available ligand + protein recorded structures is that they are much, much more sparse than available protein structures themselves (which are already kinda sparse, but good enough to allow AlphaFold). Some of the commonly-used datasets for benchmarking structure-based affinity models are so biased you can get a decent AUC by only looking at the target or ligand in isolation (lol). Docking ligands doesn't make for particularly great structures, and snapshot structures really miss out on the important dynamics. So it's hard for me to imagine how alphafold can help with small molecule development (alphafold2 doesn't even know what small molecules are). I agree it totally sounds plausible in principle, I've been in a team where such an idea was pushed before it flopped, but in practice I feel there's much less use to extract from there than one might think. EDIT: To not be so purely negative: I'm sure real use can be found in tinkering with AlphaFold. But I really don't think it has or will become a big deal in small drug discovery workflows. My PoV is at least somewhat educated on the matter, but of course it does not reflect the breadth of what people are doing out there.
  - varelse 2y ago
    [dead]
  - adastra22 2y ago
    But Crispr actually edited genes. How much of this theoretical work was real, and how much was slop? Did the grad students actually achieve confirmation of their conformational predictions?
    - eig 2y ago
      Surprisingly, yes the predicted structures from AlphaFold had functional groups that fit with experimental data of binding partners and homologues. While I don't know whether it matched with the actual crystallization, it did match with those orthogonal experiments (these were cell biology, genetics, and molecular biology labs, not protein structure labs, so they didn't try to actually crystalize the proteins themselves).
fedeb95 2y ago
what has changed? I think people need more from a comment than blind trust.
- dekhn 2y ago
  It solidly answered the question: "Is evolutionary sequence relationship and structure data sufficient to predict a large fraction of the structures that proteins adopt". the answer, surprising few, is that the data we have indeed can be used to make general predictions (even outside of the training classes), and also surprising many, that we can do so with a minimum of evolutionary sequence data. That people are arguing about the finer details of what it gets wrong is support for its value, not a detriment.
  - timr 2y ago
    That's a bit like saying that the invention of the airplane proved that animals can fly, when birds are swooping around your head. I mean, sure, prior to alphafold, the notion that sequence / structure relationship was "sufficient to predict" protein structure was merely a very confident theory that was used to regularly make the most reliable kind of structure predictions via homology modeling (it was also core to Rosetta, of course). Now it is a very confident theory that is used to make a slightly larger subset of predictions via a totally different method, but still fails at the ones we don't know about. Vive la change!
    - dekhn 2y ago
      I think an important detail here is that Rosetta did something beyond traditional homology models- it basically shrank the size of the alignments to small (n=7 or so?) sequences and used just tiny fragments from the PDB, assembled together with other fragments. That's sort of fundamentally distinct from homology modelling which tends to focus on much larger sequences.
      - flobosg 2y ago
        > and used just tiny fragments from the PDB 3-mers and 9-mers, if I recall correctly. The fragment-based approach helped immensely with cutting down the conformational search space. The secondary structure of those fragments was enough to make educated guesses of the protein backbone’s, at a time where ab initio force field predictions struggled with it.
mhrmsn 2y ago
Crispr is widely used and there are even therapies approved based on it, you can actually buy TVs that use quantum dots and click chemistry has lots of applications (bioconjugation etc.), but I don't think we have seen that impact from AlphaFold yet. There's a lot of pharma companies and drug design startups that are actively trying to apply these methods, but I think the jury is still out for the impact it will finally have.
- nextos 2y ago
  AlphaFold is excellent engineering, but I struggle calling this a breakthrough in science. Take T cell receptor (TCR) proteins, which are produced pseudo-randomly by somatic recombination, yielding an enormous diversity. AlphaFold's predictions for those are not useful. A breakthrough in folding would have produced rules that are universal. What was produced instead is a really good regressor in the space of proteins where some known training examples are closeby. If I was the Nobel Committee, I would have waited a bit to see if this issue aged well. Also, in terms of giving credit, I think those who invented pairwise and multiple alignment dynamic programming algorithms deserved some recognition. AlphaFold built on top of those. They are the cornerstone of the entire field of biological sequence analysis. Interestingly, ESM was trained on raw sequences, not on multiple alignments. And while it performed worse, it generalizes better to unseen proteins like TCRs.
  - flobosg 2y ago
    > A breakthrough in folding would have produced folding rules that are universal. Protein folding ≠ protein structure prediction > I think those who invented pairwise and multiple alignment dynamic programming algorithms deserved some recognition I would add BLAST as well but that ship has sailed, I’m afraid.
    - dekhn 2y ago
      The value in BLAST wasn't in its (very fast) alignment implementation but in the scoring function, which produced calibrated E-values that could be used directly to decide whether matches were significant or not. As a postdoc I did an extremely careful comparison of E-values to true, known similarities, and the E-values were spot on. Apparently, NIH ran a ton of evolution simulations to calibrate those parameters. For the curious, BLAST is very much like pairwise alignment but uses an index to speed up by avoiding attempting to align poorly scoring regions.
causal 2y ago
I agree. For those not in biotech, protein folding has been the holy grail for a long time, and AlphaFold represents a huge leap forward. Not unlike trying to find a way to reduce NP to P in CS. A leap forward there would be huge, even if it came short of a complete solution.
- flobosg 2y ago
  > Let me get the most important question out of the way: is AlphaFold’s advance really significant, or is it more of the same? I would characterize their advance as roughly two CASPs in one ―https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp13-what-just-happened/comment-page-1/ https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp...
divbzero 2y ago
I agree that it’s not premature, for two reasons: First, it’s been 6 years since AlphaFold first won CASP in 2018. This is not far from the 8 years it took from CRISPR’s first paper in 2012 to its Nobel Prize in 2020. Second, AlphaFold is only half the prize. The other half is awarded for David Baker’s work since the 1990s on Rosetta and RoseTTAFold.
pama 2y ago
Agreed. There are too many different directions of impact to point out explicitly, so I'll give a short vignette on one of the most immediate impacts, which was the use in protein crystallography. Many aspiring crystallographers correctly reorganized their careers following AlphaFold2, and everyone else started using it for molecular replacement as a way to solve the phase problem in crystallography; the models from AF2 allowed people to resolve new crystal structures from data measured years prior to the AF2 release.
- flobosg 2y ago
  Same with Rosetta, and even Foldit[1]! – https://www.nature.com/articles/nsmb.2119 https://www.nature.com/articles/nsmb.2119 [1]: https://en.wikipedia.org/wiki/Foldit https://en.wikipedia.org/wiki/Foldit
roughly 2y ago
It also proved that deep learning models are a valid approach to bioinformatics - for all its flaws and shortcomings, AlphaFold solves arbitrary protein structure in minutes on commodity hardware, whereas previous approaches were, well, this: https://en.wikipedia.org/wiki/Folding@home https://en.wikipedia.org/wiki/Folding@home A gap between biological research and biological engineering is that, for bioengineering, the size of the potential solution space and the time and resources required to narrow it down are fundamental drivers of the cost of creating products - it turns out that getting a shitty answer quickly and cheaply is worth more than getting the right answer slowly.
- flobosg 2y ago
  AlphaFold and Folding@home attempt to solve related, but essentially different, problems. As I already mentioned here, protein structure prediction is not fully equivalent to protein folding.
  - roughly 2y ago
    Yeah, this is what I mean by "a shitty answer fast" - structure prediction isn't a canonical answer, but it's a good enough approximation for good enough decision-making to make a bunch of stuff viable that wouldn't be otherwise. I agree with you, though - they're two different answers. I've done a bunch of work in the metagenomics space, and you very quickly get outside areas where Alphafold can really help, because nothing you're dealing with is similar enough to already-characterized proteins for the algorithm to really have enough to draw on. At that point, an actual solution for protein folding that doesn't require a supercomputer would make a difference.
    - flobosg 2y ago
      > this is what I mean by "a shitty answer fast" - structure prediction isn't a canonical answer A proper protein structural model is an all-atom representation of the macromolecule at its global minimum energy conformation, and the expected end result of the folding process; both are equivalent and thus equally canonical. The “fast” part, i.e., the decrease in computational time comes mostly from the heuristics used for conformational space exploration. Structure prediction skips most of the folding pathway/energy funnel, but ends up at the same point as a completed folding simulation. > At that point, an actual solution for protein folding that doesn't require a supercomputer would make a difference. Or more representative sequences and enough variants by additional metagenomic surveys, for example. Of course, this might not be easily achievable.
JangoSteve 2y ago
Interestingly, the award was specifically for the impact of AlphaFold2 that won CASP 14 in 2020 using their EvoFormer architecture evolved from the Transformer, and not for AlphaFold that won CASP 13 in 2018 with a collection of ML models each separately trained, and which despite winning, performed at a much lower level than AlphaFold2 would perform two years later.

by eig 2y ago

singularity2001 2y ago

>> I do think AlphaFold already has reached sufficient level of impact. how so?

cmavvv 2y ago
https://www.pnas.org/doi/10.1073/pnas.2315002121 https://www.pnas.org/doi/10.1073/pnas.2315002121
- deleted 2y ago
  [deleted]
eig 2y ago
Well I'm sure one could look at number of published papers etc, but that metric is a lot to do with hype and I see it as a lagging indicator. A better one is seeing my grad-school friends with zero background in comp-sci or math, presenting their cell-biology results with AlphaFold in conferences and at lab meetings. They are not protein folding people either- just molecular biologists trying to present more evidence of docking partners, functional groups in their pathway of interest. It reminds me of when Crispr came out. There were ways to edit DNA before Crispr, but its was tough to do right and required specialized knowledge. After Crispr came out, even non-specialists like me in tangential fields could get started.
- deleted 2y ago
  [deleted]
- tananan 2y ago
  In both academic and industrial settings, I've seen an initial spark of hope about AlphaFold's utility being replaced with a resignation that it's cool, but not really useful. Yet in both settings it continued as a playing card for generating interest. There's an on-point blog-post "AI and Biology" (https://www.science.org/content/blog-post/ai-and-biology https://www.science.org/content/blog-post/ai-and-biology) which illustrates why AlphaFold's real breakthrough is not super actionable for creating further bio-medicinal applications in a similar vein.
  - whimsicalism 2y ago
    That article explains why AI might not work so well further down the line biology discoveries, but I still think alphafold can really help with the development of small molecule therapies that bind to particular known targets and not to others, etc.
    - tananan 2y ago
      The thing with available ligand + protein recorded structures is that they are much, much more sparse than available protein structures themselves (which are already kinda sparse, but good enough to allow AlphaFold). Some of the commonly-used datasets for benchmarking structure-based affinity models are so biased you can get a decent AUC by only looking at the target or ligand in isolation (lol). Docking ligands doesn't make for particularly great structures, and snapshot structures really miss out on the important dynamics. So it's hard for me to imagine how alphafold can help with small molecule development (alphafold2 doesn't even know what small molecules are). I agree it totally sounds plausible in principle, I've been in a team where such an idea was pushed before it flopped, but in practice I feel there's much less use to extract from there than one might think. EDIT: To not be so purely negative: I'm sure real use can be found in tinkering with AlphaFold. But I really don't think it has or will become a big deal in small drug discovery workflows. My PoV is at least somewhat educated on the matter, but of course it does not reflect the breadth of what people are doing out there.
- varelse 2y ago
  [dead]
- adastra22 2y ago
  But Crispr actually edited genes. How much of this theoretical work was real, and how much was slop? Did the grad students actually achieve confirmation of their conformational predictions?
  - eig 2y ago
    Surprisingly, yes the predicted structures from AlphaFold had functional groups that fit with experimental data of binding partners and homologues. While I don't know whether it matched with the actual crystallization, it did match with those orthogonal experiments (these were cell biology, genetics, and molecular biology labs, not protein structure labs, so they didn't try to actually crystalize the proteins themselves).

fedeb95 2y ago

what has changed? I think people need more from a comment than blind trust.

dekhn 2y ago
It solidly answered the question: "Is evolutionary sequence relationship and structure data sufficient to predict a large fraction of the structures that proteins adopt". the answer, surprising few, is that the data we have indeed can be used to make general predictions (even outside of the training classes), and also surprising many, that we can do so with a minimum of evolutionary sequence data. That people are arguing about the finer details of what it gets wrong is support for its value, not a detriment.
- timr 2y ago
  That's a bit like saying that the invention of the airplane proved that animals can fly, when birds are swooping around your head. I mean, sure, prior to alphafold, the notion that sequence / structure relationship was "sufficient to predict" protein structure was merely a very confident theory that was used to regularly make the most reliable kind of structure predictions via homology modeling (it was also core to Rosetta, of course). Now it is a very confident theory that is used to make a slightly larger subset of predictions via a totally different method, but still fails at the ones we don't know about. Vive la change!
  - dekhn 2y ago
    I think an important detail here is that Rosetta did something beyond traditional homology models- it basically shrank the size of the alignments to small (n=7 or so?) sequences and used just tiny fragments from the PDB, assembled together with other fragments. That's sort of fundamentally distinct from homology modelling which tends to focus on much larger sequences.
    - flobosg 2y ago
      > and used just tiny fragments from the PDB 3-mers and 9-mers, if I recall correctly. The fragment-based approach helped immensely with cutting down the conformational search space. The secondary structure of those fragments was enough to make educated guesses of the protein backbone’s, at a time where ab initio force field predictions struggled with it.

mhrmsn 2y ago

Crispr is widely used and there are even therapies approved based on it, you can actually buy TVs that use quantum dots and click chemistry has lots of applications (bioconjugation etc.), but I don't think we have seen that impact from AlphaFold yet. There's a lot of pharma companies and drug design startups that are actively trying to apply these methods, but I think the jury is still out for the impact it will finally have.

nextos 2y ago
AlphaFold is excellent engineering, but I struggle calling this a breakthrough in science. Take T cell receptor (TCR) proteins, which are produced pseudo-randomly by somatic recombination, yielding an enormous diversity. AlphaFold's predictions for those are not useful. A breakthrough in folding would have produced rules that are universal. What was produced instead is a really good regressor in the space of proteins where some known training examples are closeby. If I was the Nobel Committee, I would have waited a bit to see if this issue aged well. Also, in terms of giving credit, I think those who invented pairwise and multiple alignment dynamic programming algorithms deserved some recognition. AlphaFold built on top of those. They are the cornerstone of the entire field of biological sequence analysis. Interestingly, ESM was trained on raw sequences, not on multiple alignments. And while it performed worse, it generalizes better to unseen proteins like TCRs.
- flobosg 2y ago
  > A breakthrough in folding would have produced folding rules that are universal. Protein folding ≠ protein structure prediction > I think those who invented pairwise and multiple alignment dynamic programming algorithms deserved some recognition I would add BLAST as well but that ship has sailed, I’m afraid.
  - dekhn 2y ago
    The value in BLAST wasn't in its (very fast) alignment implementation but in the scoring function, which produced calibrated E-values that could be used directly to decide whether matches were significant or not. As a postdoc I did an extremely careful comparison of E-values to true, known similarities, and the E-values were spot on. Apparently, NIH ran a ton of evolution simulations to calibrate those parameters. For the curious, BLAST is very much like pairwise alignment but uses an index to speed up by avoiding attempting to align poorly scoring regions.

causal 2y ago

I agree. For those not in biotech, protein folding has been the holy grail for a long time, and AlphaFold represents a huge leap forward. Not unlike trying to find a way to reduce NP to P in CS. A leap forward there would be huge, even if it came short of a complete solution.

flobosg 2y ago
> Let me get the most important question out of the way: is AlphaFold’s advance really significant, or is it more of the same? I would characterize their advance as roughly two CASPs in one ―https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp13-what-just-happened/comment-page-1/ https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp...

divbzero 2y ago

I agree that it’s not premature, for two reasons: First, it’s been 6 years since AlphaFold first won CASP in 2018. This is not far from the 8 years it took from CRISPR’s first paper in 2012 to its Nobel Prize in 2020. Second, AlphaFold is only half the prize. The other half is awarded for David Baker’s work since the 1990s on Rosetta and RoseTTAFold.

pama 2y ago

Agreed. There are too many different directions of impact to point out explicitly, so I'll give a short vignette on one of the most immediate impacts, which was the use in protein crystallography. Many aspiring crystallographers correctly reorganized their careers following AlphaFold2, and everyone else started using it for molecular replacement as a way to solve the phase problem in crystallography; the models from AF2 allowed people to resolve new crystal structures from data measured years prior to the AF2 release.

flobosg 2y ago
Same with Rosetta, and even Foldit[1]! – https://www.nature.com/articles/nsmb.2119 https://www.nature.com/articles/nsmb.2119 [1]: https://en.wikipedia.org/wiki/Foldit https://en.wikipedia.org/wiki/Foldit

roughly 2y ago

It also proved that deep learning models are a valid approach to bioinformatics - for all its flaws and shortcomings, AlphaFold solves arbitrary protein structure in minutes on commodity hardware, whereas previous approaches were, well, this: https://en.wikipedia.org/wiki/Folding@home https://en.wikipedia.org/wiki/Folding@home A gap between biological research and biological engineering is that, for bioengineering, the size of the potential solution space and the time and resources required to narrow it down are fundamental drivers of the cost of creating products - it turns out that getting a shitty answer quickly and cheaply is worth more than getting the right answer slowly.

flobosg 2y ago
AlphaFold and Folding@home attempt to solve related, but essentially different, problems. As I already mentioned here, protein structure prediction is not fully equivalent to protein folding.
- roughly 2y ago
  Yeah, this is what I mean by "a shitty answer fast" - structure prediction isn't a canonical answer, but it's a good enough approximation for good enough decision-making to make a bunch of stuff viable that wouldn't be otherwise. I agree with you, though - they're two different answers. I've done a bunch of work in the metagenomics space, and you very quickly get outside areas where Alphafold can really help, because nothing you're dealing with is similar enough to already-characterized proteins for the algorithm to really have enough to draw on. At that point, an actual solution for protein folding that doesn't require a supercomputer would make a difference.
  - flobosg 2y ago
    > this is what I mean by "a shitty answer fast" - structure prediction isn't a canonical answer A proper protein structural model is an all-atom representation of the macromolecule at its global minimum energy conformation, and the expected end result of the folding process; both are equivalent and thus equally canonical. The “fast” part, i.e., the decrease in computational time comes mostly from the heuristics used for conformational space exploration. Structure prediction skips most of the folding pathway/energy funnel, but ends up at the same point as a completed folding simulation. > At that point, an actual solution for protein folding that doesn't require a supercomputer would make a difference. Or more representative sequences and enough variants by additional metagenomic surveys, for example. Of course, this might not be easily achievable.

JangoSteve 2y ago

Interestingly, the award was specifically for the impact of AlphaFold2 that won CASP 14 in 2020 using their EvoFormer architecture evolved from the Transformer, and not for AlphaFold that won CASP 13 in 2018 with a collection of ML models each separately trained, and which despite winning, performed at a much lower level than AlphaFold2 would perform two years later.