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Chemistry Nobel: Computational protein design and protein structure prediction

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Re: Chemistry Nobel: Computational protein design and protein structure prediction

#231

Post by flobosg »

dekhn wrote: post #229
timr wrote: post #217

Earlier quoted context omitted.

That's a bit like saying that the invention of the airplane proved that animals can fly, when birds are swooping around your head. I mean, sure, prior to alphafold, the notion that sequence / structure relationship was "sufficient to predict" protein structure was merely a very confident theory that was used to regularly make the most reliable kind of structure predictions via homology modeling (it was also core to R…

I think an important detail here is that Rosetta did something beyond traditional homology models- it basically shrank the size of the alignments to small (n=7 or so?) sequences and used just tiny fragments from the PDB, assembled together with other fragments. That's sort of fundamentally distinct from homology modelling which tends to focus on much larger sequences.

> and used just tiny fragments from the PDB

3-mers and 9-mers, if I recall correctly. The fragment-based approach helped immensely with cutting down the conformational search space. The secondary structure of those fragments was enough to make educated guesses of the protein backbone’s, at a time where ab initio force field predictions struggled with it.

Re: Chemistry Nobel: Computational protein design and protein structure prediction

#232

Post by pama »

eig wrote: post #117

I think I disagree with most of the comments here stating it’s premature to give the Nobel to AlphaFold. I’m in biotech academia and it has changed things already. Yes the protein folding problem isn’t “solved” but no problem in biology ever is. Comparing to previous bio/chem Nobel winners like Crispr, touch receptors, quantum dots, click chemistry, I do think AlphaFold already has reached sufficient level of impact.

Agreed. There are too many different directions of impact to point out explicitly, so I'll give a short vignette on one of the most immediate impacts, which was the use in protein crystallography. Many aspiring crystallographers correctly reorganized their careers following AlphaFold2, and everyone else started using it for molecular replacement as a way to solve the phase problem in crystallography; the models from AF2 allowed people to resolve new crystal structures from data measured years prior to the AF2 release.

Re: Chemistry Nobel: Computational protein design and protein structure prediction

#233

Post by shnock »

mk_stjames wrote: post #218
ThePhysicist wrote: post #67

Demis Hasabis has a really interesting and unusual CV for a nobel laureate [1], he started his career in AI game programming (he worked e.g. on Popoulous II, Syndicate, Theme Park for Bullfrog, and later for Lionhead Studios on Black & White) before doing a PhD in neuroscience, becoming an entrepreneur and starting DeepMind. I would say this is a refreshing and highly uncommon pick for a nobel prize, really cool to s…

I'm always interested in hearing about these people who go and get a PhD in an unrelated field to their original studies, often years after leaving university and working in an industry. Here it says Hasabis did an undergraduate degree in a computer science program, and them spent a decade working on computer games at studios, and then somehow just rocked up to a university and asked to do a PhD in neuroscience. I fe…

I think PhD's are generally different enough in Europe vs the US that this might be less surprising upon further research

Re: Chemistry Nobel: Computational protein design and protein structure prediction

#234

Post by flobosg »

pama wrote: post #232
eig wrote: post #117

I think I disagree with most of the comments here stating it’s premature to give the Nobel to AlphaFold. I’m in biotech academia and it has changed things already. Yes the protein folding problem isn’t “solved” but no problem in biology ever is. Comparing to previous bio/chem Nobel winners like Crispr, touch receptors, quantum dots, click chemistry, I do think AlphaFold already has reached sufficient level of impact.

Agreed. There are too many different directions of impact to point out explicitly, so I'll give a short vignette on one of the most immediate impacts, which was the use in protein crystallography. Many aspiring crystallographers correctly reorganized their careers following AlphaFold2, and everyone else started using it for molecular replacement as a way to solve the phase problem in crystallography; the models from…

Same with Rosetta, and even Foldit[1]! – https://www.nature.com/articles/nsmb.2119

[1]: https://en.wikipedia.org/wiki/Foldit

Re: Chemistry Nobel: Computational protein design and protein structure prediction

#235

Post by divbzero »

theGnuMe wrote: post #103
ackbar03 wrote: post #10

Earlier quoted context omitted.

I was just wondering when they were going to award the alphafold2 guys the nobel after after seeing Hinton win the physics one. 100% agree, all three of them totally deserve this one. Baker's lab is pretty much keeping Deepmind in check at this point and ensuring open source research is keeping up. Hats off

Baker has been in the protein folding game for a long time and was the leader before Alphafold came in... His generative paper came out what last year (2023)? I mean this is a fast award cycle.

David Baker’s RoseTTAFold was first released in 2021.

[1]: https://www.science.org/doi/10.1126/science.abj8754

[2]: https://cen.acs.org/analytical-chemistry/structural-biology/...

Re: Chemistry Nobel: Computational protein design and protein structure prediction

#236

Post by tsimionescu »

cool-RR wrote: post #95

I wonder why various outlets, including DeepMind's blog, say that John Jumper is a "Senior Research Scientist". That's L5 which sounds like quite a low rank for a Nobel prize winner. I checked his LinkedIn and he's a director, which is around L8. I thought that maybe he was L5 during the publishing of the results, but no, he was either L6 or L7.

L5 doesn't mean anything to anyone outside of whatever organization you're talking about (Google?). A Senior Research Scientist means "a person who is a scientist, works in research, and is very experienced in that role". Even if this is not the title he holds in his organization, it is an objective title that applies to him.

Re: Chemistry Nobel: Computational protein design and protein structure prediction

#237

Post by km155 »

nextworddev wrote: post #135
singularity2001 wrote: post #128

Earlier quoted context omitted.

whose father was one of the leading local industrialists (installing the first electric lighting for the 1885 Munich Oktoberfest)

It’s fascinating how the affluent backgrounds of many famous scientists and entrepreneurs are downplayed. Eg Warren Buffet, Jeff Bezos, etc

hypothesis : it's not per se affluence. it's the culture of the family and social circle. A dollop of $ to have some free time and maybe buy some books would help and might be necessary.

imagine a family where youngster is encouraged to work on intellectual problems. where you aren't made fun of for touching nerdy things. or for doing puzzles. where the social circle endorses learning. these things more important than $ in a first world economy. (if third world, yes give me some money please for a book or even just food. and hopefully with time, an internet connected device then the cream will rise they can just watch feynman on YouTube...)

that said, it's "better" than it used to be. hundreds of years ago most interesting science, etc. was done by the royal class. not because they are smarter (I assume). But they had free time. And, social encouragement perhaps too.

bill gates and zuck dropped out of Harvard right? it's not per se Harvard, at least not the graduating bit? being surrounded by other smart people is helpful -- and or people who encourage intellectual endeavors.

Re: Chemistry Nobel: Computational protein design and protein structure prediction

#238

Post by PaulDavisThe1st »

huijzer wrote: post #226
PaulDavisThe1st wrote: post #170

Earlier quoted context omitted.

10k random Americans, sure. 10k random Americans with backgrounds in software and a business idea? Not so clear. You also seem very certain that Amazon's scale is a good thing, overall, which I remain unconvinced of.

> You also seem very certain that Amazon's scale is a good thing, overall, which I remain unconvinced of. What do you find unconvincing about roughly 30 billion in net income and free cashflow in 2023?

That's one metric, that only reflects Amazon's function as an income generator.

I view businesses through other metrics as well, including their impact on society in a variety of different ways. From some of those perspectives, it is not clear to me that Amazon (where I was the 2nd employee) is a net benefit.

Re: Chemistry Nobel: Computational protein design and protein structure prediction

#239

Post by nanoxide »

ThePhysicist wrote: post #67

Demis Hasabis has a really interesting and unusual CV for a nobel laureate [1], he started his career in AI game programming (he worked e.g. on Popoulous II, Syndicate, Theme Park for Bullfrog, and later for Lionhead Studios on Black & White) before doing a PhD in neuroscience, becoming an entrepreneur and starting DeepMind. I would say this is a refreshing and highly uncommon pick for a nobel prize, really cool to s…

Yep. I distinctly remember reading an interview in the German GameStar magazine in '99 or something with him where he talks about his early work with Bullfrog. Over the years I read his name from time to time as he moved towards research. Pretty amazing career.

Re: Chemistry Nobel: Computational protein design and protein structure prediction

#240

Post by roughly »

eig wrote: post #117

I think I disagree with most of the comments here stating it’s premature to give the Nobel to AlphaFold. I’m in biotech academia and it has changed things already. Yes the protein folding problem isn’t “solved” but no problem in biology ever is. Comparing to previous bio/chem Nobel winners like Crispr, touch receptors, quantum dots, click chemistry, I do think AlphaFold already has reached sufficient level of impact.

It also proved that deep learning models are a valid approach to bioinformatics - for all its flaws and shortcomings, AlphaFold solves arbitrary protein structure in minutes on commodity hardware, whereas previous approaches were, well, this: https://en.wikipedia.org/wiki/Folding@home

A gap between biological research and biological engineering is that, for bioengineering, the size of the potential solution space and the time and resources required to narrow it down are fundamental drivers of the cost of creating products - it turns out that getting a shitty answer quickly and cheaply is worth more than getting the right answer slowly.

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