Nobel prize in chemistry - something we all contributed to through crunching DC

petrusbroder

Elite Member
Nov 28, 2004
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The Nobel Prize in Chemistry 2024: They cracked the code for proteins’ amazing structures:​

The Royal Swedish Academy of Sciences has decided to award the Nobel Prize in Chemistry 2024 with one half to
David Baker, University of Washington, Seattle, WA, USA
“for computational protein design”
and the other half jointly to
Demis Hassabis, Google DeepMind, London, UK and
John M. Jumper, Google DeepMind, London, UK
“for protein structure prediction”.

David Baker has succeeded with the almost impossible feat of building entirely new kinds of proteins. Demis Hassabis and John Jumper have developed an AI model to solve a 50-year-old problem: predicting proteins’ complex structures. These discoveries hold enormous potential.

Source: https://www.kva.se/en/news/the-nobel-prize-in-chemistry-2024/
 

drnickriviera

Platinum Member
Jan 30, 2001
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Had to look this up to see what project it is. Rosetta. Now they just need to get more task so he can get another nobel. Any way to tell if it was directly related to DC crunching? looks like baker labs is more than Rosetta?
 
Reactions: Assimilator1
Dec 10, 2005
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Had to look this up to see what project it is. Rosetta. Now they just need to get more task so he can get another nobel. Any way to tell if it was directly related to DC crunching? looks like baker labs is more than Rosetta?
Baker has done a lot over the years: de novo structure prediction, experimental confirmation, molecular dynamics simulations, structure calculations from experimental data, and more. I used to see his stuff a lot when I worked in protein structure stuff.
 

StefanR5R

Elite Member
Dec 10, 2016
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Any way to tell if it was directly related to DC crunching? looks like baker labs is more than Rosetta?
David Baker's and Baker Lab's work is broader than Computational Protein Design indeed. Consequently, the projects which went through the Rosetta@Home platform are broader too. Yet looking at it the other way around, the computational part is just this — a part — of what it takes to design proteins.

The announcement of Royal Swedish Academy of Sciences which is linked to in post #1 has got two PDFs attached: "Popular Science Background" and "Scientific Background". The latter contains a bunch of references to papers. *Maybe* there is one or another paper included which acknowledges a contribution by the Rosetta@Home project...?
 

pututu

Member
Jul 1, 2017
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Vijay Pande (FAH founder) didn't get nominated for Nobel Prize. I'm guessing protein folding may just be a tool in helping protein design. Anyway congratz to David Baker.
 

Skillz

Golden Member
Feb 14, 2014
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They do mention Rosetta. Taken from one of the .PDF's

How did Baker and his coworkers solve the problem? The key to success here was their initialdevelopment a few years earlier (1999) of the Rosetta computer program.18 It assembles shortstructural fragments from unrelated protein structures with similar local sequences in theProtein Data Bank and simultaneously optimizes sequence and structure with respect to thetarget backbone conformation. Monte Carlo optimization was used in the calculations with anenergy function that treated van der Waals interactions (6-12 Lennard-Jones potential),hydrogen bonding and solvation effects; sidechain orientations were sampled from a largelibrary of rotamers. The program generates many putative solutions and ranks them in terms of energies.

Most important, Rosetta was designed to be a general program both for protein structureprediction and design, and it has continuously been developed since its inception, with a large cadre of users and co-developers. The key idea to build proteins from short fragments can betraced back to the work of Jones and Thirup, who showed that in the context of automatedprotein model building into crystallographic electron density maps, assembling proteins fromknown substructures is an effective strategy.19Baker and colleagues went on to show that a wide range of protein structures could be designedusing the Rosetta software. While protein design was initially just focused on designingstructures, more recent work has aimed to also design advanced protein functions. This still posesmajor challenges in terms of understanding protein dynamics, structural transitions, allostery,catalytic effects, and so forth, and is thus an area of active research
 
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crashtech

Lifer
Jan 4, 2013
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I don't think there's anything that precludes a BOINC project from running AI applications.
 

Skillz

Golden Member
Feb 14, 2014
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Related to this thread and protein folding. Not sure why they didn't mention folding@home. Now I question whether it is worth it to run fah or rosetta if ai does it so much better


If I understand that correctly then all that AI does is allows us to know how a protein is supposed to fold (its structure) and what it does. What it doesn't show, or at least it didn't say, is why and how a protein miss-folds. Which, as far as I know, is what causes a bunch of problems when they fold in a way they're not supposed to. That, I believe, is what most of the DC projects are doing such as F@H and Rosetta.
 

mmonnin03

Senior member
Nov 7, 2006
275
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Yeah I saw the video the other day from Veritasium where it mentions Alphafold predicted the correct shape of known proteins. FAH has always been about studying incorrectly folded proteins. I wouldn't be surprised if Alphafold could be tweaked to simulate known proteins in other incorrect but stable folds.
 

StefanR5R

Elite Member
Dec 10, 2016
6,341
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The fahcore_XY and rosetta applications examine molecules (and interactions between molecules) based on first principles. (Or mostly based on first principles; their models surely make use of various approximations and other shortcuts where deemed acceptable by their developers.)

Alphafold on the other hand, as I understand it, is a classic black box which was trained based on various "known" (as well as we can know them) molecules, and based on this training inferred the structure of other molecules. Within that black box (some successor of what is long known as neural network), the relationships and parameter values gained from training are a wild mixture of manifestations of said first principles and — maybe more so — of manifestations of merely "perceived" principles, or rather, likelihoods.

IIRC the F@H project highlighted that their approach allows them to look at molecules not only at specific quasi-static states, but to simulate the actual dynamics of a molecule snapping from one state to another. And the R@H project is always highlighting that their approach enables them to invent new molecules with specific properties (e.g. with specific interactions with existing molecules). That is, F@H and R@H are looking at problems quite beyond the identification of molecule structure.

I am sure that the F@H or/and R@H scientists are using "AI" (that is, stochastic models based on empiric data) in addition to their classic numeric simulation models. I thought one or the other project made a statement in that regard back around the times when Alphafold made headlines.
 
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