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Alphafold

github.com

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Re: Alphafold

#21

Post by dekhn »

gopalv wrote: post #19
dekhn wrote: post #15

Fantastic, they released the dataset and code to train the model. Science will be able to proceed. edit: not the code to train the model, just the code to run inference. The underlying sequence datasets include PDB strucrures and sequences, and how those map to large collections of sequences with no known structure (no surprise). Each of those datasets represents decades of thousands of scientists work, along with pr…

> The total download size is around 428 GB and the total size when unzipped is 2.2 TB. Please make sure you have a large enough hard drive space, bandwidth and time to download. > This was tested on Google Cloud with a machine using the nvidia-gpu-cloud-image with 12 vCPUs, 85 GB of RAM, a 100 GB boot disk, the databases on an additional 3 TB disk, and an A100 GPU. This is amazingly detailed for a researcher who want…

Yes, all science should be communicated in the form of an academic paper wiht a supporting git repo and quickly downloadable dataset and a fast path to reproducing the work. That would be a huge change from the establishment.

It's quite unclear what value this will have to pharma; personally I doubt this has any direct applications (and I'm one of the few people in the world that can say that with deep authority).

Re: Alphafold

#22

Post by stupidcar »

The model parameters are only available for non-commercial use. That's a shame, as I presume there might be a lot of medical startups that would benefit from having this kind protein-folding tech available.

Re: Alphafold

#23

Post by timr »

nextos wrote: post #16

Alphafold 2 is very very cool, but we need a little dose of reality. It's still a bit away from really solving protein folding as it was marketed. For example, multi-complex proteins are not well predicted yet and these are really important in many biological processes and drug design: https://occamstypewriter.org/scurry/2020/12/02/no-deepmind-h... A disturbing thing is that the architecture is much less novel than I…

> A disturbing thing is that the architecture is much less novel than I originally thought it would be, so this shows perhaps one of the major difficulties was having the resources to try different things on a massive set of multiple alignments. This is something an industrial lab like DeepMind excels at. Whereas universities tend to suck at anything that requires a directed effort of more than a handful of people.

Yeah, the HN commentary on Alphafold has a high heat-to-light ratio. I'm eager to read the paper because the previous description of the method sounded remarkably similar to methods that have been around for ages, plus a few twists.

The devil is going to be in the details on this one.

Re: Alphafold

#24

Post by COGlory »

I am a structural biologist. This is one of the handful of topics that overlaps with my field here. I'm very excited to play with this, although it might eventually put me out of a job.

Re: Alphafold

#25

Post by aantix »

dekhn wrote: post #21
gopalv wrote: post #19

Earlier quoted context omitted.

> The total download size is around 428 GB and the total size when unzipped is 2.2 TB. Please make sure you have a large enough hard drive space, bandwidth and time to download. > This was tested on Google Cloud with a machine using the nvidia-gpu-cloud-image with 12 vCPUs, 85 GB of RAM, a 100 GB boot disk, the databases on an additional 3 TB disk, and an A100 GPU. This is amazingly detailed for a researcher who want…

Yes, all science should be communicated in the form of an academic paper wiht a supporting git repo and quickly downloadable dataset and a fast path to reproducing the work. That would be a huge change from the establishment. It's quite unclear what value this will have to pharma; personally I doubt this has any direct applications (and I'm one of the few people in the world that can say that with deep authority).

Who benefits from this work?

Re: Alphafold

#26

Post by timr »

gonehome wrote: post #17

Does anyone on HN work in bio or drug discovery? Could you give an overview of how people can leverage this (or how you might?). From reading around about it, it sounds like there's often a need to find a certain type of molecule to activate/inhibit another based on shape and the ability to programmatically solve for this makes the searching way easier. Is this too oversimplified/wrong? How will this be used in pract…

> Could you give an overview of how people can leverage this (or how you might?).

Short answer: nobody knows. Traditionally, protein folding is a solution in search of a problem, but that's largely because the predictions were...unusably bad. This was always more of a super-difficult validation problem for the force fields and simulation methods, which could then be used for other problems of greater value (such as rational protein design, or simulation of the motion of proteins with known structures).

These predictions are better, but still pretty far from the level of precision that you'd want for any kind of rational drug design, where the exact locations of protein side-chains (for example) matter a lot. You'll note that AlphaFold returns structures that are "relaxed" using one of the oldest simulation systems for proteins: AMBER. So it's not exactly a clean-room solution to the problem, and you can't assume that the details (which matter to drug design) are going to be any better than for the older methods.

But that said, if you have a method that can reliably give you a blurry view of the overall shape of a protein, even that could be useful for things like target discovery or inference of biological networks. But this is still a lot closer to pure research than "revolutionizing drug discovery", as is frequently batted around on reddit, HN and the press.

Re: Alphafold

#27

Post by rllearneratwork »

COGlory wrote: post #24

I am a structural biologist. This is one of the handful of topics that overlaps with my field here. I'm very excited to play with this, although it might eventually put me out of a job.

why would it put you out of job? Wouldn't it just become one of the tools you use?

Re: Alphafold

#28

Post by COGlory »

gonehome wrote: post #17

Does anyone on HN work in bio or drug discovery? Could you give an overview of how people can leverage this (or how you might?). From reading around about it, it sounds like there's often a need to find a certain type of molecule to activate/inhibit another based on shape and the ability to programmatically solve for this makes the searching way easier. Is this too oversimplified/wrong? How will this be used in pract…

You can't do intelligent drug design if you don't know what the target protein looks like. We've gotten great at solving protein structures with things like crystallography and cryo-EM microscopy. Unfortunately, many interesting drug targets reside in the membrane of a cell, which means you can't easily work with them in a lab because they aren't soluble in anything but a plasma membrane. For instance, this is an issue with the 5HT2A protein, a g coupled protein receptor that is implicated in many serotonin related pathways.

Being able to predict what it would look like would be a huge deal because then you can go about intelligently designing drugs for it.

Re: Alphafold

#30

Post by dekhn »

gonehome wrote: post #17

Does anyone on HN work in bio or drug discovery? Could you give an overview of how people can leverage this (or how you might?). From reading around about it, it sounds like there's often a need to find a certain type of molecule to activate/inhibit another based on shape and the ability to programmatically solve for this makes the searching way easier. Is this too oversimplified/wrong? How will this be used in pract…

I've worked in bio and drug discovery for some 25 years. That includes building classifiers using gradient descent in the 90s (when algorithms, computers and data were all much worse). I ported DOCK to Linux in ~96 or 97. Since then I built an academic and then industrial career with some emphasis on using computing to solve problems in drug discovery, but I don't play that role any more.

It doesn't look like the models produced by this would immediately turn the challenging problem of finding, approving, and marketing successful pharmaceuticals (IE, it doesn't eliminate any real bottleneck).

There was a long-term dream of structure-based drug discovery based on docking, but IMO, it has never really proved itself (most of the examples of success are cherry picked from a much larger pile of massive failures).

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