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End-to-end differentiable learning of protein structure

biorxiv.org

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Re: End-to-end differentiable learning of protein structure

#11

Post by matt4077 »

nabla9 wrote: post #5

It would be cool if machine learning researchers would start participating CASP and CAPRI. If you crack Go, you get fame, but if you crack protein prediction, you get Nobel price and completely revolutionize biochemistry and medicine. http://predictioncenter.org/ http://www.ebi.ac.uk/msd-srv/capri/ edit: Why there is no XPRICE for protein folding?

I used to work on protein structure about ten years ago.

Back then, the mood kind of changed from “solve this and you have a Nobel waiting”: the general opinion was that progress was both significant and piecemeal, making it unlikely a Nobel will be awarded because “cracking it”would end up being to hard to assign to any three people.

Re: End-to-end differentiable learning of protein structure

#12

Post by RivieraKid »

cing wrote: post #6
nabla9 wrote: post #5

It would be cool if machine learning researchers would start participating CASP and CAPRI. If you crack Go, you get fame, but if you crack protein prediction, you get Nobel price and completely revolutionize biochemistry and medicine. http://predictioncenter.org/ http://www.ebi.ac.uk/msd-srv/capri/ edit: Why there is no XPRICE for protein folding?

DeepMind and others are trying. "Hassabis said the company is now planning to apply an algorithm based on AlphaGo Zero to other domains with real-world applications, starting with protein folding." [1] https://www.bloomberg.com/news/articles/2017-10-18/deepmind-...

That doesn't make any sense unless I'm missing something, A0 is suited for a completely different problem than protein folding...

Re: End-to-end differentiable learning of protein structure

#13

Post by RivieraKid »

nabla9 wrote: post #5

It would be cool if machine learning researchers would start participating CASP and CAPRI. If you crack Go, you get fame, but if you crack protein prediction, you get Nobel price and completely revolutionize biochemistry and medicine. http://predictioncenter.org/ http://www.ebi.ac.uk/msd-srv/capri/ edit: Why there is no XPRICE for protein folding?

I would guess that there have been many attempts to use ML for protein folding. It's one of the most obvious ways to approach the problem.

Re: End-to-end differentiable learning of protein structure

#14

Post by deepnotderp »

cing wrote: post #6
nabla9 wrote: post #5

It would be cool if machine learning researchers would start participating CASP and CAPRI. If you crack Go, you get fame, but if you crack protein prediction, you get Nobel price and completely revolutionize biochemistry and medicine. http://predictioncenter.org/ http://www.ebi.ac.uk/msd-srv/capri/ edit: Why there is no XPRICE for protein folding?

DeepMind and others are trying. "Hassabis said the company is now planning to apply an algorithm based on AlphaGo Zero to other domains with real-world applications, starting with protein folding." [1] https://www.bloomberg.com/news/articles/2017-10-18/deepmind-...

FWIW, AlphaGo like algorithms have already been applied to this domain, see AlphaChem

Re: End-to-end differentiable learning of protein structure

#15

Post by [deleted] »

matt4077 wrote: post #10
superfx wrote: post #8

Earlier quoted context omitted.

I do think however that protein folding is very much understudied in the ML community, relative to say the big three of vision, NLP, and speech. The lack of standardized data sets and benchmarks, not to mention the need for domain knowledge, have made it difficult to get into the field

CASP is a pretty nice dataset, so is all of the PDB.

[deleted]

Re: End-to-end differentiable learning of protein structure

#17

Post by superfx »

RivieraKid wrote: post #16

What are the real-world applications of protein folding (preferably, some specific example)? I always hear that it's really important for drug design and biotechnology but have a hard time imagining something concrete.

Re drug discovery, often times in “rational” drug design, medicinal chemists try to make small molecules that bind snuggly into a binding pocket on the protein. Having the structure of the protein aids greatly in that process.

Re: End-to-end differentiable learning of protein structure

#18

Post by xiao_haozi »

superfx wrote: post #17
RivieraKid wrote: post #16

What are the real-world applications of protein folding (preferably, some specific example)? I always hear that it's really important for drug design and biotechnology but have a hard time imagining something concrete.

Re drug discovery, often times in “rational” drug design, medicinal chemists try to make small molecules that bind snuggly into a binding pocket on the protein. Having the structure of the protein aids greatly in that process.

Yes! And I'd also add that there are others that come into play...

* Elucidating function by identifying similarity to other known structures

* Finding novel signaling mechanisms (see work on PHinder)

* Modeling co-receptor/ligand dynamics

* Identifying function of orphan receptors

* Working with ancestral genes by identifying descendant structure

* Classifying and clustering proteins based on solved structure

* Learning new biochemical mechanisms through active vs inactive state structures

...

Re: End-to-end differentiable learning of protein structure

#19

Post by cing »

matt4077 wrote: post #10
superfx wrote: post #8

Earlier quoted context omitted.

I do think however that protein folding is very much understudied in the ML community, relative to say the big three of vision, NLP, and speech. The lack of standardized data sets and benchmarks, not to mention the need for domain knowledge, have made it difficult to get into the field

CASP is a pretty nice dataset, so is all of the PDB.

The PDB represents the best we have, but I wouldn't call it a great dataset for learning. The 150,000 known structures are a drop in the ocean when it comes to the space of possible sequences/structures.

Re: End-to-end differentiable learning of protein structure

#20

Post by sjg007 »

nabla9 wrote: post #5

It would be cool if machine learning researchers would start participating CASP and CAPRI. If you crack Go, you get fame, but if you crack protein prediction, you get Nobel price and completely revolutionize biochemistry and medicine. http://predictioncenter.org/ http://www.ebi.ac.uk/msd-srv/capri/ edit: Why there is no XPRICE for protein folding?

It's happening.
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