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Alphafold

github.com

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Re: Alphafold

#81

Post by timr »

dm319 wrote: post #77
timr wrote: post #23

Earlier quoted context omitted.

> 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. Y…

The key difference seems to be using the multiple alignments and assumption about evolutionary conservation? Useful for genes conserved, but less useful for de-novo proteins (like COVID and cancer) I guess?

Dunno yet. MSAs were always a key input to Rosetta (previous best method). How they were used was very different.

Fundamentally, everything in this space (= non-physical methods) is about inferring structure from things that are closely related. And you can't solve the problem at all for non-trivial proteins using physics, so here we are.

Re: Alphafold

#82

Post by [deleted] »

devindotcom wrote: post #76

Also announced today was RoseTTAFold from UW's Baker Lab, which claims nearly the same accuracy at much higher efficiencies. There's a public server and paper in Science. More info here and here: https://www.bakerlab.org/index.php/2021/07/15/accurate-prote... https://techcrunch.com/2021/07/15/researchers-match-deepmind...

[deleted]

Re: Alphafold

#83

Post by gnufx »

dekhn wrote: post #35
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…

many of these resources are available, it's mostly that academic scientists don't have the time, money, or expertise to manage large datasets. However, the community has maintained high quality MSA database for decades and that's exactly the work that DM drafted off.

> academic scientists don't have the time, money, or expertise to manage large datasets

I may be cynical about general expertise, as a support person, but large datasets have long been stock in trade of areas I'm more or less familiar with, whether "large" is TBs or PBs like CERN experiments. (When I were a lad, it was what you could push past the tape interface in a few days -- data big in cubic feet...)

Re: Alphafold

#84

Post by TaupeRanger »

timr wrote: post #23
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. Y…

That's the case with basically everything DeepMind does. They have a very good PR department which hypes up everything they do while conveniently ignoring that basically nothing of any practical consequence has come of their endeavors. But I do think it's important that these companies exist now so we can see what not to try going forward.

Re: Alphafold

#86

Post by gnufx »

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).

Surely not all science. Just as well Dirac wasn't required to communicate that way the equation that fundamentally underlies the phenomenon discussed, and you couldn't put the unique facility my thesis work pioneered into git! I do highly approve of publishing software and data where possible, of course, since before Free Software needed to be coined, and it's much easier now.

Re: Alphafold

#87

Post by astro-codes »

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).

Why wouldn't this have much value to pharma? Is it because its application is actually really limited in scope?

Re: Alphafold

#88

Post by HPsquared »

Azrael3000 wrote: post #63
MrsPeaches wrote: post #59

Earlier quoted context omitted.

> high heat-to-light ratio Sorry for the ignorance but what does this mean?

Incandescent light bulbs are generally very inefficient in producing light, compared to LED for example. They produce a lot of heat and not much light for which they are made. So in this context I suppose that gp implies that these threads don't provide much meaningful discussion but rather lots of hand waving.

Light is also often used in metaphors relating to knowledge, wisdom etc.

Re: Alphafold

#89

Post by timr »

TaupeRanger wrote: post #84
timr wrote: post #23

Earlier quoted context omitted.

> 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. Y…

That's the case with basically everything DeepMind does. They have a very good PR department which hypes up everything they do while conveniently ignoring that basically nothing of any practical consequence has come of their endeavors. But I do think it's important that these companies exist now so we can see what not to try going forward.

Well, the CASP14 results do speak for themselves. Protein structure prediction is not necessarily of great meaning to drug discovery or biology, but they pretty much blew everyone else out of the water in a fair contest. For that reason, they deserve praise.

It's a little like making a robot that is very, very good at something pointless (say, using a yo-yo). Who knows where it might lead, but if they make the best damned yo-yo bot in the world, they deserve whatever praise they get from the yo-yo community.

Re: Alphafold

#90

Post by AlexCoventry »

MrsPeaches wrote: post #59
timr wrote: post #23

Earlier quoted context omitted.

> 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. Y…

> high heat-to-light ratio Sorry for the ignorance but what does this mean?

Emotion-to-understanding ratio
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