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Alphafold

github.com

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Re: Alphafold

#41

Post by nikhilsimha »

rllearneratwork wrote: post #27
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?

The implicit assumption you are making is that the demand increases in lock step with productivity gains. 100x faster drug discovery, 100x more drugs need to be discovered => same number of people employed.

These correlations do hold for technical fields, but logically there should be a point beyond which productivity gains outpace, demand growth / demand could even stop growing. One should either retool to solve a newer problem before this point is reached, or hope that the point is not reached in the span of their career.

Oil rig builders for example - manufacturing has been increasingly automated, but the demand for oil rig building has grown consistently. But they should probably look into solving other problems given that demand is shifting.

Re: Alphafold

#42

Post by wpasc »

Cas9 wrote: post #32

Honest question: since AlphaFold doesn't really _solve_ the protein folding problem (it's NP-complete after all), but only _approximates_ solutions very well, what are the real impacts of this? Isn't a good approximation of a protein enough to cause unexpected problems? How do we know that an approximate structure will perform the same as the correct solution?

A very-non-expert opinion, if an approach approximates it pretty well and can be improved upon, then it could end up being quite useful. Given that biology exists on a real, tangible scale then perfection in the fold prediction isn't necessary, instead just an approximation that is sufficiently good to be functionally useful.

^ That sounds like word-salad BS but I think there's some truth to it. I know protein folding has been postulated to be useful in terms of understanding basic biology, understanding disease pathology, and drug prediction. While a wide range of approximations are functionally useless, perhaps the Alphafold approach or some improved version of it surpasses the functionally useful threshold.

At least I hope so

Re: Alphafold

#43

Post by ponsko »

COGlory wrote: post #28
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 iss…

You should check out Salipro (https://www.salipro.com/) for membrane protein reconstitution.

Re: Alphafold

#44

Post by ashtonbaker »

Cas9 wrote: post #32

Honest question: since AlphaFold doesn't really _solve_ the protein folding problem (it's NP-complete after all), but only _approximates_ solutions very well, what are the real impacts of this? Isn't a good approximation of a protein enough to cause unexpected problems? How do we know that an approximate structure will perform the same as the correct solution?

Not really an answer to your question, but is the problem really NP-complete, or just combinatorially difficult? For example how is this condition of NP-completeness satisfied?

> it is a problem for which the correctness of each solution can be verified quickly [0]

[0] https://en.wikipedia.org/wiki/NP-completeness

Re: Alphafold

#45

Post by radus »

whimsicalism wrote: post #37
Cas9 wrote: post #32

Honest question: since AlphaFold doesn't really _solve_ the protein folding problem (it's NP-complete after all), but only _approximates_ solutions very well, what are the real impacts of this? Isn't a good approximation of a protein enough to cause unexpected problems? How do we know that an approximate structure will perform the same as the correct solution?

You want to find a protein that has X structure (since structure determines function to a degree). If AlphaFold is substantially more accurate at solving proteins, it can mean that drug discovery is faster, assays are faster, etc. etc. The "unexpected problems" would be caught in the assay stage.

Kind of disagree with this.. solving protein structures is not the rate limiting step in drug discovery or in biochemical assays -- not by a long shot. See this excellent comment by @dekhn on a related submission: https://news.ycombinator.com/item?id=27849046

Re: Alphafold

#46

Post by mrfusion »

Cas9 wrote: post #32

Honest question: since AlphaFold doesn't really _solve_ the protein folding problem (it's NP-complete after all), but only _approximates_ solutions very well, what are the real impacts of this? Isn't a good approximation of a protein enough to cause unexpected problems? How do we know that an approximate structure will perform the same as the correct solution?

Is it really np complete? If so we could map other np complete problems onto it and let biology solve it for us.

Re: Alphafold

#47

Post by zosima »

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…

It can be an aid in drug development, and can perhaps assist a bit in tuning small molecule drugs for more stable binding.

Though I think the major impacts will be two-fold:

(1) The field of structural biology is going to see a change, with much more data available. Some structures of difficult to crystallize proteins will be solved, which may lead to much greater biological understanding. We may enter a time, where once you have a primary sequence, you also have a likely 3d-structure, which will probably change the daily work of quite a few biologists a bit.

(2) Industrial protein design. A tool such as this can potentially have great utility in optimizing proteins as chemical catalysts for various processes in different industries. This includes expanding the conditions under which a protein is active and also making their conformation more stable and so the protein more long-lived in solution.

Re: Alphafold

#48

Post by [deleted] »

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).

[deleted]

Re: Alphafold

#49

Post by radus »

Cas9 wrote: post #32

Honest question: since AlphaFold doesn't really _solve_ the protein folding problem (it's NP-complete after all), but only _approximates_ solutions very well, what are the real impacts of this? Isn't a good approximation of a protein enough to cause unexpected problems? How do we know that an approximate structure will perform the same as the correct solution?

Yes, it is still useful. Even structures obtained through traditional means (eg. x-ray crystallography) are approximations to an extent since there are limits to the resolution that you can obtain and oftentimes regions of proteins are "disordered". Additionally, these structures are only snapshots of a protein in a particular state, which may not completely reflect the dynamics of the protein in its native environment.
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