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Chemistry Nobel: Computational protein design and protein structure prediction

nobelprize.org

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

#241

Post by patall »

theGnuMe wrote: post #209
patall wrote: post #114

Earlier quoted context omitted.

The prize winners are ultimately selected by a group of mid-age to old professors. And to tell the truth (I work at a research institute in Stockholm), some of the old folks seem to have huge FOMO. They know that they cannot keep up themselves, they have no idea (and no way of finding out) who is actually good and who is just pretending, which leads to recruitment of an 'interesting' bunch of young group leaders. Som…

I dunno, you should have AI FOMO. Or at least start focusing on computational thinking.

I do not criticize them for having FOMO. But I have my doubts when it is the 60-year-olds that are the most enthusiastic about something new (as long as it is not a new ABBA album), given the number of grifters out there. And there would have been many others that also deserve a Nobel, those three could easily have waited another 20 years. If it really was those that had the highest impact the last year who won the prize, it (or rather "Medicine") should have gone to GLP-1/Semaglutide research.

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

#242

Post by flobosg »

roughly wrote: post #240
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…

AlphaFold and Folding@home attempt to solve related, but essentially different, problems. As I already mentioned here, protein structure prediction is not fully equivalent to protein folding.

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

#243

Post by wholinator2 »

londons_explore wrote: post #194
cmavvv wrote: post #136

Earlier quoted context omitted.

Given that drugs take around 10 years to get to market, and that some time is needed for industrial adoption as well, it's not very reasonable to expect clinically approved drugs before a few years.

> around 10 years to get to market This is really sad. A new recipe for feeding honeybees to make tastier honey could get to market in perhaps a month or two. All the chemical reactions happening in the bees gut and all the chemicals in the resulting honey are unknown, yet within a matter of weeks its being eaten. Yet if we find a new way of combining chemicals to cure cancer, it takes a decade before most can benefi…

I think the idea is that we're, as a species, much more comfortable with the idea that 15 years down the line that 50% of treated colonies collapse in a way directly attributable to the treatment than we are with the idea that 15 years down the line 50% of treated humans die in a way directly attributable to the treatment.

Now if the human alternative to treatment is to die anyway than i think that balance shifts. I do think we should be somewhat liberal with experimental treatments for patients in dire need, but you have to also understand that experimental treatments can just be really expensive which limits either the people who can afford it, or if it's given for free, the amount the researcher can make/perform/provide.

10 years is a very long time. I've had close family members die of cancer and any opportunity for treatment (read: hope) is good in my opinion. But i wouldn't say there's no reason that it takes so long

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

#244

Post by timr »

flobosg wrote: post #231
dekhn wrote: post #229

Earlier quoted context omitted.

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.

Yes, Rosetta did monte carlo substitution of 9-mers, followed by a refinement phase with 3-mers. Plus a bunch of other stuff to generate more specific backbone "moves" in weird circumstances.

In order to create those fragment libraries, there was a step involving generation of multiple-sequence alignments, pruning the alignments, etc. Rosetta used sequence homology to generate structure. This wasn't a wild, untested theory.

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

#246

Post by flobosg »

timr wrote: post #244
flobosg wrote: post #231

Earlier quoted context omitted.

> 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.

Yes, Rosetta did monte carlo substitution of 9-mers, followed by a refinement phase with 3-mers. Plus a bunch of other stuff to generate more specific backbone "moves" in weird circumstances. In order to create those fragment libraries , there was a step involving generation of multiple-sequence alignments, pruning the alignments, etc. Rosetta used sequence homology to generate structure. This wasn't a wild, untested…

> Rosetta used sequence homology

Rosetta used remote sequence homology to generate the MSAs and find template fragments, which at the time was innovative. A similar strategy is employed for AlphaFold’s MSAs containing the evolutionary couplings.

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

#247

Post by refulgentis »

aithrowawaycomm wrote: post #146
InkCanon wrote: post #107

Earlier quoted context omitted.

I think looking back five years from now, this will be viewed as another Kissinger/Obama but wrt STEM. Given far too prematurely under pressure to keep up with the Joneses/chase the hype.

I am not so confident or dismissive: the real problem is that testing millions of predictions (or any fairly bold scientific development like AlphaFols) takes time, and that time simply has not elapsed. Some of the criticisms I identified might be low-hanging fruit that in 5 years will be seen as minor corrections - but we're still discovering the things that need to be corrected. It is concerning that the prize anno…

Chew on this a little, I stripped out as much as possible, but I imagine it still will feel reflexively easy to dismiss. Partially because its hard to hear criticism, at least for me. Partially because a lot was stripped out: a lot has gone sideways to get us to this point, so this may sound minor.

The fact you have to reach for "I [wonder if the votes were based on] Google / DeepMind press releases [taken] at face value." should be a red blaring alarm.

It creates a new premise[1] that enables continued permission to seek confirmation bias.

I was once told you should check your premises when facing an unexpected conclusion, and to do that before creating new ones. I strive to.

[1] All Nobel Prize voters choose their support based on reading a press release at face value

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

#249

Post by biofox »

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 have known a several people who made the jump from Computer Science to Biology at graduate school. Usually, it's either via genomics or neuroscience (as in Hassabis' case), where there is a large need for people who can do data crunching or computational modelling.

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

#250

Post by dekhn »

timr wrote: post #244
flobosg wrote: post #231

Earlier quoted context omitted.

> 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.

Yes, Rosetta did monte carlo substitution of 9-mers, followed by a refinement phase with 3-mers. Plus a bunch of other stuff to generate more specific backbone "moves" in weird circumstances. In order to create those fragment libraries , there was a step involving generation of multiple-sequence alignments, pruning the alignments, etc. Rosetta used sequence homology to generate structure. This wasn't a wild, untested…

I don't know that I agree that fragment libraries use sequence homology. From my understanding of it, homology implies an actual evolutionary relationship. Wheras fragment libraries instead are agnostic and instead seem to be based on the idea that short fragments of non-related proteins can match up in sequence and structure space. Nobody looks at 3-mers and 9-mers in homology modelling; it's typically well over 25 amino acids long, and there is usually a plausible whole-domain (in the SCOP terminology).

But, the protein field has always played loose with the term "homology".

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