19 ms·

AlphaFold 2 is here: what’s behind the structure prediction miracle

241 points by couteiral 5y ago

londons_explore 5y ago
> Like most bioinformatics programs, AlphaFold 2 comes equipped with a “preprocessing pipeline”, which is the discipline’s lingo for “a Bash script that calls some other codes”. Having Bioinformatics people requiring to stray a long way from their core competency to learn a scripting language from the 80's to write glue code seems... suboptimal. How many hours of expert time has been wasted figuring out how to split a string in bash? Can us software people build a better tool to eliminate the need for this?
- johnnycerberus 5y ago
  I think the tools already exist just that people are conservative with their choices.
  - ethbr0 5y ago
    (Obligatory xkcd; you know the one)
    - FartyMcFarter 5y ago
      Not really?
      - londons_explore 5y ago
        https://xkcd.com/927/ https://xkcd.com/927/
  - pbronez 5y ago
    MLops is a pretty hot area right now. The industry is trying to figure out how to engineer these things in a robust way. It’s not ubiquitous at all yet. There are lots of tools that wrap K8s and help you train models, but trying in DataOps in a robust way…. I haven’t seen the definitive answer yet. Seriously please tell me if you are founding this company so I can invest.
- cinntaile 5y ago
  Pretty much anyone that works with data has to clean their data, learning to use the tools to do that is important. Whether that's bash, perl, R, python, ... doesn't really matter that much. If they already know bash then bash is a good tool since they can now focus on their data instead of wasting time on learning a new tool to do the same thing.
- culopatin 5y ago
  It’s sad. Bio people really need friends in software to boost their research. Unfortunately they do a codeacademy python course for 5 min and try to get their projects going. Sometimes they succeed, sometimes they fail. But they don’t really have much time to dedicate to properly learn software dev and it’s not what they are into anyway, it’s a necessity. I think we could create something like a GitHub of bio projects that need help and people assist with hopes of getting their names in a paper
- ackbar03 5y ago
  I think DeepMind went one step further and solved the entire problem for them. They don't even have to touch their keyboards anymore
- stingraycharles 5y ago
  Sure there are better alternatives, but the advantage of bash / shell scripting is that it’s very easy to glue a whole collection of tools together, and that expertise in this transfers well between domains. They probably could have achieved the same by invoking things in Python, but it would have been slower and not achieved a lot, other than “not using shell scripts”. And once you go down the path of optimizing this enough, you’ll end up reinventing shell scripts altogether.
  - sanxiyn 5y ago
    Well, AlphaFold 2 generates MSA by invoking things in Python: https://github.com/deepmind/alphafold/blob/main/alphafold/data/tools/jackhmmer.py https://github.com/deepmind/alphafold/blob/main/alphafold/da.... So the article is actually mistaken on this point.
- knuthsat 5y ago
  From looking at the code, Bash looks pretty clean. I also use Bash and AWK for preprocessing a lot.
  - laichzeit0 5y ago
    I used to be that guy as well, till a college convinced me that anything I can do in Bash, Awk I could probably do easier in Perl. Then everyone sort of drifted to Python. I get that if you never used Perl it’s pointless to learn it if you’re already in the Python stack, but… damn Perl’s regular expressions and how it’s so baked into the syntax of the language makes using regex in Python seem like going back to the Stone Age.
- volta83 5y ago
  Bash is easier to explain and use than, eg, explaining ppl how to use Python subprocess Module Launch different apps, capture their output, etc. I find astonishing how bad Python is as a bash replacement. I often rather write an argument parser in bash than use Python if I have to invoke a bunch of commands.
  - hortense 5y ago
    Python is bad, but bash is worse as soon as you need any kind of logic. > explaining ppl how to use Python subprocess Module Launch different apps, capture their output There's no shame in using `os.system`.
    - maliker 5y ago
      Well, technically subprocess.check_call if you need to capture output.
- couteiral 5y ago
  There is a growing trend to include Docker (or Singularity, which is more compatible with HPC architectures common in bioinformatics) images alongside codes. In particular, AlphaFold 2 does provide a Dockerfile, and they even include a Python "launcher script" hiding all the details of running the code. Sadly, this is very uncommon in the community. In a bioinformatics meeting, the sentence "I spent X days setting up Y software" will not raise many eyebrows
  - maliker 5y ago
    I work in power systems, and the situation is similar. Maybe worse because paper authors often come up with new computational techniques but don’t implement them in code (much less code with a dockerfile). I look with much jealousy over at the computer science field where papers often include code, multiple versions under version control, automated tests, setup/docker scripts, and demonstration workflows and interfaces.
- dfas231 5y ago
  * Can us software people build a better tool to eliminate the need for this? Most probably, not. Bash is currently the sweet spot. It is actually the best tool for this job. Any other option comes with increased complexity, will make the whole software less stable.
- cabalamat 5y ago
  > How many hours of expert time has been wasted figuring out how to split a string in bash? Probably a good many more than would be needed to learn how to split a string in Python.
- 6gvONxR4sf7o 5y ago
  Among the bioinformatics folks I know, bash is already a core competency. If you’re using your average biologist as your mental model, you’re thinking of the wrong people.
- jweather 5y ago
  I don't see this as a bad thing. Shell scripts are a great way to prototype text processing pipelines using multiple smaller components.
Quarrel 5y ago
Awesome. I wrote a thesis on protein structure prediction in 1995. We weren't very good at it then. Amazing to see this.
- VSerge 5y ago
  I remember that the scientific game "Fold It" was also quite exciting when it came out 12-13 years ago or so, since populations of players could get results beyond what either specialists or computer systems could achieve. I guess one could argue that an AI such as this could be compared to a large automated population of trained players trying to solve a 3D puzzle.
- kevstev 5y ago
  I did some undergraduate research on this around 1999. At the time we were trying to prove that we could throw more firepower at the problem by building a beowulf cluster to solve the problem. After a bit of tweaking, we were able to get more performance than a single machine, but soon seti@home was released and to me at least the writing was on the wall that we were not taking the most optimal approach. In hindsight though, we were so far off from both an algorithmic perspective and a hardware perspective to actually achieving meaningful results. I am glad, 20 years later it seems real progress is being made. I haven't really followed the folding@home project in many many years, but its not clear to me much came out of it that was all that useful, at least not in practical terms.
  - ac29 5y ago
    Not sure about folding@home, but the lab that runs rosetta@home released a paper earlier this month claiming they have a new algorithm with comparable results to AlphaFold2: https://science.sciencemag.org/content/early/2021/07/19/science.abj8754 https://science.sciencemag.org/content/early/2021/07/19/scie... I don't believe this new approach runs on their distributed compute network, but its cool to see some good competition.
bamboozled 5y ago
Thank you Google...thank you!
- maliker 5y ago
  Why did they open source it? Wouldn’t this model be very valuable to the pharma industry?
  - gostsamo 5y ago
    It is written in the article. A few reasons could be pressure from the publishing magazine, emerging open source implementations of the same idea, and the fact that this is still far from easy to comercialize.
    - londons_explore 5y ago
      Don't forget internal pressure. A lot of people will say "Unless you opensource my work and that of my colleagues, I quit". When faced with all your best people threatening to quit, you might just opensource that work. It turns out you still have an advantage by being ~1 year ahead on applying it to anything, and having all the people who know how it works on your staff.
    - credit_guy 5y ago
      Another reason could be that whoever wants to run it will very likely run it in the cloud, and there's a chance they'd run it in the Google Cloud. A machine similar to the one they mention on the alphafold github page (12 vCPU, 1 GPU, 85 GB) costs you between $1 and $4/hour.
  - visarga 5y ago
    > Why did they open source it? Wouldn’t this model be very valuable to the pharma industry? This is a question we should remember when we feel like condemning big corporations for monopolizing AI. HuggingFace lists 12,257 models in its zoo, many coming from FAANG. You can start one in 3 lines of Python, or fine-tune it with a little more effort.
  - 5y ago
swazzy 5y ago
https://github.com/deepmind/alphafold https://github.com/deepmind/alphafold
- kevincox 5y ago
  Apache 2.0 https://github.com/deepmind/alphafold/blob/main/LICENSE https://github.com/deepmind/alphafold/blob/main/LICENSE
Game_Ender 5y ago
To me the most interesting part of the article is the cometary on where basic research is going to happen in the future. The fear is that if it only happens in large companies, then the unbiased pool of experts society relies on will be smaller and less informed. Along with the issue of nobody being around for the slog of defining a field, setting up databases, competitions and standards. These are what allow well funded corporate labs to apply their skills and compute and blow a problem out of the water. The problem is, would they do the work to define an unknown problem in the first place?
- mrfusion 5y ago
  > unbiased pool of experts Sounds like an oxymoron these days.
  - suetoniusp 5y ago
    Always has been
- jonas21 5y ago
  > DeepMind claimed that they used “128 TPUv3 cores or roughly equivalent to ~100-200 GPUs”. Although this amount of compute seems beyond the wildest dreams of most academic researchers... So, we're talking like what? Maybe $100K to $300K of hardware? Wet biology labs often have multiple pieces of $100K+ equipment at their disposal. Why shouldn't computational labs too?
  - Synaesthesia 5y ago
    Yeah but it's also putting it together and properly utlizing it, which takes specialist knowledge.
    - tiborsaas 5y ago
      This was never really a bottleneck for science. One needs to realize first that something can be done, then it will be done. The cost of computing will also go down in the future and for government funds this sounds like a drop in the sea when they are building multi billion dollar particle accelerators.
spywaregorilla 5y ago
What are the big implications of being good at predicting protein structures?
- ansible 5y ago
  The goal all along has been to design proteins with a specific structure. This can be applied to just about any area of biology. You could design novel antigens to combat disease, and then easily mass-produce them. Or just inject the RNA to have the body produce them. But the applications are boundless, from genetically modifying crops, to anti-aging, and more. It is also one of the key pathways to molecular nanotechnology, where instead of building arbitrary structures out of amino acids, we increase the range of arbitrary molecules we can design, build, and produce in quantity.
  - spywaregorilla 5y ago
    Is it the structure that's important? Or is the structure just a way to combine certain amino acids in a stable manner and its the combination of acids that we care about? Or is structure just a way of saying a specific permutation of amino acids?
    - ansible 5y ago
      The structure is the whole point. As I understand it, you can link together nearly arbitrary sequences of amino acids. But a random string of AAs will just result in a jumbled protein that doesn't do anything useful. Specific structures are useful in all manner of ways, from cleaving a DNA molecule at a specific point, enzymes for breaking apart molecules, etc. Very, very useful.
      - vokep 5y ago
        >Very, very useful. Just to frame it a particular way, biological systems are basically solved nanotechnology, extremely good, self-sustaining, resilient little machines that have spent a long time optimizing to be better and better. But all the designs are preset, if we can crack the code and design our own little machines, then amazing things like more plastic-like cellulose could be made, all sorts of problems are suddenly far easier to solve. But also a lot of new problems emerge that weren't even imaginable before, since the code being cracked is a big chunk of the code of life itself. So, yknow, playing God and all, so there probably will be some negative consequences of this too.
woliveirajr 5y ago
Article at Nature: https://www.nature.com/articles/s41586-021-03819-2 https://www.nature.com/articles/s41586-021-03819-2
dekhn 5y ago
So, unsurprisngly, it appears that applying a transformer to multiple sequence alignments extracts somewhat more spatial information about proteins than we had been able to previously squeeze out. It's pretty clear at this point that the work led to a large improvement in psp scores, but there's literally nothing else groundbreaking about it; I don't mean that in a bad way, except to criticize all the breathless press about applications and pharma.
- mda 5y ago
  Well it did gave geoundbreking results, it is weird to see people dismisses it as "Not groundbrraking enough".
  - dekhn 5y ago
    it was a nice improvement. that's fine. But it's ultimately just statistical modelling based on deep evolutionary information. It only works on homology modelling, it doesn't actually solve the larger protein structure prediction problem. Therefore it's not groundbreaking but a significant improvement.
    - IshKebab 5y ago
      It's perfectly reasonable to describe a very large improvement as groundbreaking.
    - couteiral 5y ago
      I respectfully disagree. AlphaFold 2 demonstrated almost perfect performance for a multitude of proteins for which no meaningful templates were available -- hence, it was not doing homology modelling as it is generally understood, but ab initio protein structure prediction. What I would support is that AlphaFold 2 does not solve the protein folding problem: how, as opposed to what to, a protein folds.
      - dekhn 5y ago
        how could they do ab initio? They depend on multiple sequence alignments. If I'm mistaken about this then I'll happily take back what I said, but there's no way that AF2 could work wihtout MSAs, therefore, it is not ab initio. Ah, OK checked the paper again. They're working on the "template" category which means there is structure-sequence information... maybe CASP organizers consider this ab initio ? The paper never mentions anything about ab initio predicitons. Is that what you're saying, that template methods are ab initio?
TaupeRanger 5y ago
Here we go again with the hyperbole...very tiring.
phkahler 5y ago
How does work like this get funded? It's awesome, but it seems so far removed from... let's say "profit". And there are several teams competing in these things. Are there places that really fund advanced work like this, or is it mostly graduate student underpaid labor?
- dekhn 5y ago
  Most of the US researchers who do CASP are funded by NIH or NSF. Some are funded by private foundations, or are independently wealthy. Typically, as a "principal investigator" (postdoc, professor, scientist at a national lab) you write a proposal saying "here's my preivous work, here's the next obvious step, plz give monies so I can feed the dean's fund and pay for my grad students to manage my modest closet cluster". A group of your competitors then trashes your proposal in a group and if you've properly massaged the right backs, you get a pittance, which permits you to struggle to keep up with all your promises.
  - infogulch 5y ago
    Yikes that sounds bleak.
    - cing 5y ago
      Yet, there were still 136 human teams who competed in CASP14 (https://predictioncenter.org/casp14/docs.cgi?view=groupsbyname https://predictioncenter.org/casp14/docs.cgi?view=groupsbyna...), including DeepMind. Even if a significant fraction of these projects were done piggy-backing another grant, this work does receive research funding.
      - dekhn 5y ago
        Be fair. Many of those rows contain duplicate names (identical teams), so the count is much smaller.
- seventytwo 5y ago
  Government funding (eg. DARPA) or from large corporations that have skunkworks teams (Google, IBM, Microsoft, etc.)
wly_cdgr 5y ago
Cool. How many years closer has this brought us to a $1 pill that extends life span by 1 year? Cos let's face it that's the only prize that really matters in this whole field
throwamon 5y ago
So... could anyone with experience in the area give an estimate of how much the likelihood of an unstoppable, untraceable "DIY" bioweapon appearing in the next decade has increased thanks to this?
- jcranmer 5y ago
  So I don't have experience in the area, but I'd give it about 0%. To paraphrase Derek Lowe a lot (see, e.g., https://blogs.sciencemag.org/pipeline/archives/2021/03/19/ai-and-drug-discovery-attacking-the-right-problems https://blogs.sciencemag.org/pipeline/archives/2021/03/19/ai...), there are several hard problems in biology, and the kind of progress embodied in AlphaFold isn't progress towards the rate-limiting problems. And many of the things that make drugs hard to develop are going to carry over into making bioweapons hard to develop.
- matt2000 5y ago
  I'm not an expert, but in a recent article about the new mRNA synthesis techniques they were asked the same question. The answer was there's already lots of potential bioweapons and many simpler techniques for producing them, so these new technologies don't change the danger level much.
- fastball 5y ago
  You can already make a pretty terrifying bioweapon with GoF research / CRISPR / etc. Better ability to design your own proteins doesn't move the needle much, and is still much harder than the other methods.
- omgwtfbbq 5y ago
  I believe it's already straightforward for a decent bio lab at a large University to synthesize dangerous viruses. So it would only be a matter of time until they could selectively add or subtract from the genetic code to create exceptionally dangerous mutations. Don't need any protein folding to do that. https://www.theguardian.com/science/2014/jun/11/crazy-dangerous-creation-deadly-airborne-flu-virus https://www.theguardian.com/science/2014/jun/11/crazy-danger...
p1131 5y ago
I wonder if this is the result of us having significantly better understanding of our biology or the major advancements in AI and computer performance. Or both?
shaggie76 5y ago
I'm curious how this will effect the Folding@Home project; will it open up possibilities or will this type of approach be ill-suited to long-distance distributed volunteer effort because of the memory footprint (and bandwidth?) needed?

241 points by couteiral 5y ago

> Like most bioinformatics programs, AlphaFold 2 comes equipped with a “preprocessing pipeline”, which is the discipline’s lingo for “a Bash script that calls some other codes”. Having Bioinformatics people requiring to stray a long way from their core competency to learn a scripting language from the 80's to write glue code seems... suboptimal. How many hours of expert time has been wasted figuring out how to split a string in bash? Can us software people build a better tool to eliminate the need for this?

johnnycerberus 5y ago
I think the tools already exist just that people are conservative with their choices.
- ethbr0 5y ago
  (Obligatory xkcd; you know the one)
  - FartyMcFarter 5y ago
    Not really?
    - londons_explore 5y ago
      https://xkcd.com/927/ https://xkcd.com/927/
- pbronez 5y ago
  MLops is a pretty hot area right now. The industry is trying to figure out how to engineer these things in a robust way. It’s not ubiquitous at all yet. There are lots of tools that wrap K8s and help you train models, but trying in DataOps in a robust way…. I haven’t seen the definitive answer yet. Seriously please tell me if you are founding this company so I can invest.
cinntaile 5y ago
Pretty much anyone that works with data has to clean their data, learning to use the tools to do that is important. Whether that's bash, perl, R, python, ... doesn't really matter that much. If they already know bash then bash is a good tool since they can now focus on their data instead of wasting time on learning a new tool to do the same thing.
culopatin 5y ago
It’s sad. Bio people really need friends in software to boost their research. Unfortunately they do a codeacademy python course for 5 min and try to get their projects going. Sometimes they succeed, sometimes they fail. But they don’t really have much time to dedicate to properly learn software dev and it’s not what they are into anyway, it’s a necessity. I think we could create something like a GitHub of bio projects that need help and people assist with hopes of getting their names in a paper
ackbar03 5y ago
I think DeepMind went one step further and solved the entire problem for them. They don't even have to touch their keyboards anymore
stingraycharles 5y ago
Sure there are better alternatives, but the advantage of bash / shell scripting is that it’s very easy to glue a whole collection of tools together, and that expertise in this transfers well between domains. They probably could have achieved the same by invoking things in Python, but it would have been slower and not achieved a lot, other than “not using shell scripts”. And once you go down the path of optimizing this enough, you’ll end up reinventing shell scripts altogether.
- sanxiyn 5y ago
  Well, AlphaFold 2 generates MSA by invoking things in Python: https://github.com/deepmind/alphafold/blob/main/alphafold/data/tools/jackhmmer.py https://github.com/deepmind/alphafold/blob/main/alphafold/da.... So the article is actually mistaken on this point.
knuthsat 5y ago
From looking at the code, Bash looks pretty clean. I also use Bash and AWK for preprocessing a lot.
- laichzeit0 5y ago
  I used to be that guy as well, till a college convinced me that anything I can do in Bash, Awk I could probably do easier in Perl. Then everyone sort of drifted to Python. I get that if you never used Perl it’s pointless to learn it if you’re already in the Python stack, but… damn Perl’s regular expressions and how it’s so baked into the syntax of the language makes using regex in Python seem like going back to the Stone Age.
volta83 5y ago
Bash is easier to explain and use than, eg, explaining ppl how to use Python subprocess Module Launch different apps, capture their output, etc. I find astonishing how bad Python is as a bash replacement. I often rather write an argument parser in bash than use Python if I have to invoke a bunch of commands.
- hortense 5y ago
  Python is bad, but bash is worse as soon as you need any kind of logic. > explaining ppl how to use Python subprocess Module Launch different apps, capture their output There's no shame in using `os.system`.
  - maliker 5y ago
    Well, technically subprocess.check_call if you need to capture output.
couteiral 5y ago
There is a growing trend to include Docker (or Singularity, which is more compatible with HPC architectures common in bioinformatics) images alongside codes. In particular, AlphaFold 2 does provide a Dockerfile, and they even include a Python "launcher script" hiding all the details of running the code. Sadly, this is very uncommon in the community. In a bioinformatics meeting, the sentence "I spent X days setting up Y software" will not raise many eyebrows
- maliker 5y ago
  I work in power systems, and the situation is similar. Maybe worse because paper authors often come up with new computational techniques but don’t implement them in code (much less code with a dockerfile). I look with much jealousy over at the computer science field where papers often include code, multiple versions under version control, automated tests, setup/docker scripts, and demonstration workflows and interfaces.
dfas231 5y ago
* Can us software people build a better tool to eliminate the need for this? Most probably, not. Bash is currently the sweet spot. It is actually the best tool for this job. Any other option comes with increased complexity, will make the whole software less stable.
cabalamat 5y ago
> How many hours of expert time has been wasted figuring out how to split a string in bash? Probably a good many more than would be needed to learn how to split a string in Python.
6gvONxR4sf7o 5y ago
Among the bioinformatics folks I know, bash is already a core competency. If you’re using your average biologist as your mental model, you’re thinking of the wrong people.
jweather 5y ago
I don't see this as a bad thing. Shell scripts are a great way to prototype text processing pipelines using multiple smaller components.

Quarrel 5y ago

Awesome. I wrote a thesis on protein structure prediction in 1995. We weren't very good at it then. Amazing to see this.

VSerge 5y ago
I remember that the scientific game "Fold It" was also quite exciting when it came out 12-13 years ago or so, since populations of players could get results beyond what either specialists or computer systems could achieve. I guess one could argue that an AI such as this could be compared to a large automated population of trained players trying to solve a 3D puzzle.
kevstev 5y ago
I did some undergraduate research on this around 1999. At the time we were trying to prove that we could throw more firepower at the problem by building a beowulf cluster to solve the problem. After a bit of tweaking, we were able to get more performance than a single machine, but soon seti@home was released and to me at least the writing was on the wall that we were not taking the most optimal approach. In hindsight though, we were so far off from both an algorithmic perspective and a hardware perspective to actually achieving meaningful results. I am glad, 20 years later it seems real progress is being made. I haven't really followed the folding@home project in many many years, but its not clear to me much came out of it that was all that useful, at least not in practical terms.
- ac29 5y ago
  Not sure about folding@home, but the lab that runs rosetta@home released a paper earlier this month claiming they have a new algorithm with comparable results to AlphaFold2: https://science.sciencemag.org/content/early/2021/07/19/science.abj8754 https://science.sciencemag.org/content/early/2021/07/19/scie... I don't believe this new approach runs on their distributed compute network, but its cool to see some good competition.

bamboozled 5y ago

Thank you Google...thank you!

maliker 5y ago
Why did they open source it? Wouldn’t this model be very valuable to the pharma industry?
- gostsamo 5y ago
  It is written in the article. A few reasons could be pressure from the publishing magazine, emerging open source implementations of the same idea, and the fact that this is still far from easy to comercialize.
  - londons_explore 5y ago
    Don't forget internal pressure. A lot of people will say "Unless you opensource my work and that of my colleagues, I quit". When faced with all your best people threatening to quit, you might just opensource that work. It turns out you still have an advantage by being ~1 year ahead on applying it to anything, and having all the people who know how it works on your staff.
  - credit_guy 5y ago
    Another reason could be that whoever wants to run it will very likely run it in the cloud, and there's a chance they'd run it in the Google Cloud. A machine similar to the one they mention on the alphafold github page (12 vCPU, 1 GPU, 85 GB) costs you between $1 and $4/hour.
- visarga 5y ago
  > Why did they open source it? Wouldn’t this model be very valuable to the pharma industry? This is a question we should remember when we feel like condemning big corporations for monopolizing AI. HuggingFace lists 12,257 models in its zoo, many coming from FAANG. You can start one in 3 lines of Python, or fine-tune it with a little more effort.
- 5y ago

swazzy 5y ago

https://github.com/deepmind/alphafold https://github.com/deepmind/alphafold

kevincox 5y ago
Apache 2.0 https://github.com/deepmind/alphafold/blob/main/LICENSE https://github.com/deepmind/alphafold/blob/main/LICENSE

Game_Ender 5y ago

To me the most interesting part of the article is the cometary on where basic research is going to happen in the future. The fear is that if it only happens in large companies, then the unbiased pool of experts society relies on will be smaller and less informed. Along with the issue of nobody being around for the slog of defining a field, setting up databases, competitions and standards. These are what allow well funded corporate labs to apply their skills and compute and blow a problem out of the water. The problem is, would they do the work to define an unknown problem in the first place?

mrfusion 5y ago
> unbiased pool of experts Sounds like an oxymoron these days.
- suetoniusp 5y ago
  Always has been
jonas21 5y ago
> DeepMind claimed that they used “128 TPUv3 cores or roughly equivalent to ~100-200 GPUs”. Although this amount of compute seems beyond the wildest dreams of most academic researchers... So, we're talking like what? Maybe $100K to $300K of hardware? Wet biology labs often have multiple pieces of $100K+ equipment at their disposal. Why shouldn't computational labs too?
- Synaesthesia 5y ago
  Yeah but it's also putting it together and properly utlizing it, which takes specialist knowledge.
  - tiborsaas 5y ago
    This was never really a bottleneck for science. One needs to realize first that something can be done, then it will be done. The cost of computing will also go down in the future and for government funds this sounds like a drop in the sea when they are building multi billion dollar particle accelerators.

spywaregorilla 5y ago

What are the big implications of being good at predicting protein structures?

ansible 5y ago
The goal all along has been to design proteins with a specific structure. This can be applied to just about any area of biology. You could design novel antigens to combat disease, and then easily mass-produce them. Or just inject the RNA to have the body produce them. But the applications are boundless, from genetically modifying crops, to anti-aging, and more. It is also one of the key pathways to molecular nanotechnology, where instead of building arbitrary structures out of amino acids, we increase the range of arbitrary molecules we can design, build, and produce in quantity.
- spywaregorilla 5y ago
  Is it the structure that's important? Or is the structure just a way to combine certain amino acids in a stable manner and its the combination of acids that we care about? Or is structure just a way of saying a specific permutation of amino acids?
  - ansible 5y ago
    The structure is the whole point. As I understand it, you can link together nearly arbitrary sequences of amino acids. But a random string of AAs will just result in a jumbled protein that doesn't do anything useful. Specific structures are useful in all manner of ways, from cleaving a DNA molecule at a specific point, enzymes for breaking apart molecules, etc. Very, very useful.
    - vokep 5y ago
      >Very, very useful. Just to frame it a particular way, biological systems are basically solved nanotechnology, extremely good, self-sustaining, resilient little machines that have spent a long time optimizing to be better and better. But all the designs are preset, if we can crack the code and design our own little machines, then amazing things like more plastic-like cellulose could be made, all sorts of problems are suddenly far easier to solve. But also a lot of new problems emerge that weren't even imaginable before, since the code being cracked is a big chunk of the code of life itself. So, yknow, playing God and all, so there probably will be some negative consequences of this too.

woliveirajr 5y ago

Article at Nature: https://www.nature.com/articles/s41586-021-03819-2 https://www.nature.com/articles/s41586-021-03819-2

dekhn 5y ago

So, unsurprisngly, it appears that applying a transformer to multiple sequence alignments extracts somewhat more spatial information about proteins than we had been able to previously squeeze out. It's pretty clear at this point that the work led to a large improvement in psp scores, but there's literally nothing else groundbreaking about it; I don't mean that in a bad way, except to criticize all the breathless press about applications and pharma.

mda 5y ago
Well it did gave geoundbreking results, it is weird to see people dismisses it as "Not groundbrraking enough".
- dekhn 5y ago
  it was a nice improvement. that's fine. But it's ultimately just statistical modelling based on deep evolutionary information. It only works on homology modelling, it doesn't actually solve the larger protein structure prediction problem. Therefore it's not groundbreaking but a significant improvement.
  - IshKebab 5y ago
    It's perfectly reasonable to describe a very large improvement as groundbreaking.
  - couteiral 5y ago
    I respectfully disagree. AlphaFold 2 demonstrated almost perfect performance for a multitude of proteins for which no meaningful templates were available -- hence, it was not doing homology modelling as it is generally understood, but ab initio protein structure prediction. What I would support is that AlphaFold 2 does not solve the protein folding problem: how, as opposed to what to, a protein folds.
    - dekhn 5y ago
      how could they do ab initio? They depend on multiple sequence alignments. If I'm mistaken about this then I'll happily take back what I said, but there's no way that AF2 could work wihtout MSAs, therefore, it is not ab initio. Ah, OK checked the paper again. They're working on the "template" category which means there is structure-sequence information... maybe CASP organizers consider this ab initio ? The paper never mentions anything about ab initio predicitons. Is that what you're saying, that template methods are ab initio?

TaupeRanger 5y ago

Here we go again with the hyperbole...very tiring.

phkahler 5y ago

How does work like this get funded? It's awesome, but it seems so far removed from... let's say "profit". And there are several teams competing in these things. Are there places that really fund advanced work like this, or is it mostly graduate student underpaid labor?

dekhn 5y ago
Most of the US researchers who do CASP are funded by NIH or NSF. Some are funded by private foundations, or are independently wealthy. Typically, as a "principal investigator" (postdoc, professor, scientist at a national lab) you write a proposal saying "here's my preivous work, here's the next obvious step, plz give monies so I can feed the dean's fund and pay for my grad students to manage my modest closet cluster". A group of your competitors then trashes your proposal in a group and if you've properly massaged the right backs, you get a pittance, which permits you to struggle to keep up with all your promises.
- infogulch 5y ago
  Yikes that sounds bleak.
  - cing 5y ago
    Yet, there were still 136 human teams who competed in CASP14 (https://predictioncenter.org/casp14/docs.cgi?view=groupsbyname https://predictioncenter.org/casp14/docs.cgi?view=groupsbyna...), including DeepMind. Even if a significant fraction of these projects were done piggy-backing another grant, this work does receive research funding.
    - dekhn 5y ago
      Be fair. Many of those rows contain duplicate names (identical teams), so the count is much smaller.
seventytwo 5y ago
Government funding (eg. DARPA) or from large corporations that have skunkworks teams (Google, IBM, Microsoft, etc.)

wly_cdgr 5y ago

Cool. How many years closer has this brought us to a $1 pill that extends life span by 1 year? Cos let's face it that's the only prize that really matters in this whole field

throwamon 5y ago

So... could anyone with experience in the area give an estimate of how much the likelihood of an unstoppable, untraceable "DIY" bioweapon appearing in the next decade has increased thanks to this?

jcranmer 5y ago
So I don't have experience in the area, but I'd give it about 0%. To paraphrase Derek Lowe a lot (see, e.g., https://blogs.sciencemag.org/pipeline/archives/2021/03/19/ai-and-drug-discovery-attacking-the-right-problems https://blogs.sciencemag.org/pipeline/archives/2021/03/19/ai...), there are several hard problems in biology, and the kind of progress embodied in AlphaFold isn't progress towards the rate-limiting problems. And many of the things that make drugs hard to develop are going to carry over into making bioweapons hard to develop.
matt2000 5y ago
I'm not an expert, but in a recent article about the new mRNA synthesis techniques they were asked the same question. The answer was there's already lots of potential bioweapons and many simpler techniques for producing them, so these new technologies don't change the danger level much.
fastball 5y ago
You can already make a pretty terrifying bioweapon with GoF research / CRISPR / etc. Better ability to design your own proteins doesn't move the needle much, and is still much harder than the other methods.
omgwtfbbq 5y ago
I believe it's already straightforward for a decent bio lab at a large University to synthesize dangerous viruses. So it would only be a matter of time until they could selectively add or subtract from the genetic code to create exceptionally dangerous mutations. Don't need any protein folding to do that. https://www.theguardian.com/science/2014/jun/11/crazy-dangerous-creation-deadly-airborne-flu-virus https://www.theguardian.com/science/2014/jun/11/crazy-danger...

p1131 5y ago

I wonder if this is the result of us having significantly better understanding of our biology or the major advancements in AI and computer performance. Or both?

shaggie76 5y ago

I'm curious how this will effect the Folding@Home project; will it open up possibilities or will this type of approach be ill-suited to long-distance distributed volunteer effort because of the memory footprint (and bandwidth?) needed?