Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

AlphaFold: a solution to a 50-year-old grand challenge in biology

deepmind.com

Post reply on HN
Print view View original

441–450 of 683 posts

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#441

Post by xphos »

Odenwaelder wrote: post #357
xphos wrote: post #346

Like this is awesome and a huge advancement but one thing that worries me with an AI solution is that it doesn't really draw us any closer to the why. Why do proteins fold the way they do? We can predict the resulting structure which is extremely significant, we have no clue why. While we get the insight of being able to predict some structures we don't get the insight of why things are happening the way they are. In…

I have no idea what you are talking about.

AI solve the process but doesn't give a whole lot of insight into the formulas and the description what's going on. Where we as humans have reasonably found that e = mc^2. However AI would gives us e or m but backboxes us away from seeing that c aka the speed of light was involved(unless we implied that before). There might be interesting relationships that are useful that AI unintentionally masks that could be ground breaking if we could only understand process more holistically. I think a different commenter eluded in this case we think we understand protein folding well we just struggle to synthesis it in a compact mathematical way even though with AI we can simulate the process well for known examples.

The issue with AI is we don't know if our current example set includes every case what if there is a strange sequence of amino acid that causes something "weird" to happen that we have haven't seen. AI cannot predict something novel it or us haven't seen which is the issue. The process(if it exists) of how one could solve this problem might also be exportable to other fields if it was formulized with math rather than estimated with AI.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#442

Post by maxlamb »

dluan wrote: post #425

I worked in the lab that helped develop folding@home, as well as the game where the crowd was the chaotically trained machine that folded and unfolded one amino acid at a time. This feels like a pretty significant new chapter in the humanity movie. A few times, I get immense pangs of jealousy for younger people a generation or a half before me. And I'm only 30! This is one of those times.

Is the team really that young? 20 year olds?

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#443

Post by npunt »

harperlee wrote: post #12

Not knowing a lot about biotechnology, I read the article and it sounds great, but how big is this as a gamechanger? Can someone comment on how big are the implications of this in, let’s say, 5 years from now, on day to day life? Does this mean that biotech is going to explode? Or just that drugs will come to market faster, perhaps cheaper for rare diseases, but from the same industry structure as always?

In short, a core problem of biochem (the wagon) was just hitched to Moore's law (the horse). Our understanding of proteins will now grow exponentially not linearly, helping us to move up a level of abstraction to higher level biochemistry and biology problems.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#444

Post by deeviant »

jeffxtreme wrote: post #423

GDT_TS for AlphaFold is now comparable is at experimental levels; but that's based on the class of proteins for which we've been able to determine the 3D structure of the protein, for which there might be selection bias. I wonder if we can determine if this extends to proteins that aren't as keen to determining their 3D structure? For example, certain proteins are more crystallizable than others.. For these non-cryst…

> I wonder if we can determine if this extends to proteins that aren't as keen to determining their 3D structure?

This is already happened.

"An AlphaFold prediction helped to determine the structure of a bacterial protein that Lupas’s lab has been trying to crack for years. Lupas’s team had previously collected raw X-ray diffraction data, but transforming these Rorschach-like patterns into a structure requires some information about the shape of the protein. Tricks for getting this information, as well as other prediction tools, had failed. “The model from group 427 gave us our structure in half an hour, after we had spent a decade trying everything,” Lupas says."

From: https://www.nature.com/articles/d41586-020-03348-4

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#445

Post by [deleted] »

0-_-0 wrote: post #206
asdfasgasdgasdg wrote: post #183

Earlier quoted context omitted.

I have a related question about this. If experimental methods produce results around a score of 90, what is the baseline we are comparing the DeepMind results against? If the experimental error is equal to the observed DeepMind error, how can we say which one is actually more erroneous?

That's a damn good question, it looks like we don't know how much above 90 AlphaFold is.

[deleted]

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#446

Post by heycosmo »

flobosg wrote: post #428
heycosmo wrote: post #412

Fascinating! AlphaFold (and other competitors) seem to use MSA (Multiple Sequence Aligment) and this (brilliant) idea of co-evolving residues to build an initial graph of sections of protein chain that are likely proximal. This seems like a useful trick for predicting existing biological structures (i.e. ones that evolved) from genomic data. I wonder (as very much a non-biologist), do MSA-based approaches also help u…

> do MSA-based approaches also help understand "first-principles" folding physics any better? Not really. MSA-based approaches, as most structure prediction methods, have as a goal to find the lowest energy conformation of the protein chain, disregarding folding kinetics and basically all dynamic aspects of protein structure. > If I write a random genetic sequence (think drug discovery) that has many aligned sequence…

> I don't think I fully understood this, but I'll give it a shot anyway. If your artificial sequence aligns with others, there's a chance that it will fold like them, depending on the quality and accuracy of the multiple sequence alignment. Since multiple sequence alignments are built under the assumption of homology (all sequences have a common ancestor), it's a matter of how far from the "sequence sampling space" your sequence is located compared to the others.

I understand that similar sequences may fold similarly (although as length increases, I highly doubt it, but IDK). I'm talking about aligned sub-sequences within one chain and their ultimate distance from each other in the final structure. Co-evolution suggests that aligned sub-sequences are also proximal. But manufactured chains did not evolve, therefore the assumption is no longer useful.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#447

Post by contravariant »

treis wrote: post #143
mabbo wrote: post #30

Sometimes announcements like this are a bit over-the-top. But what really, to me, cements the 'big-deal' of this is the "Median Free-Modelling Accuracy" graph half way down the page. Scores of 30-45 for 15 years. Now scores of 87-92. This isn't a minor improvement, it's a leap forward.

That is an impressive improvement, but I think you've missed the most important point: >a score of around 90 GDT is informally considered to be competitive with results obtained from experimental methods So DeepMind is to the point where it's a question of whether their generated model or the experimentally determined structure is closest to the actual physical structure.

Of course this may no longer be the case for methods solely trained to optimize that particular metric.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#448

Post by AlexCoventry »

derefr wrote: post #89
fabian2k wrote: post #59

Earlier quoted context omitted.

"A few" does appear quite dismissive of the enormous amounts of effort in structural biology so far. There are more than 170,000 structures in the PDB right now. To determine potential targets for drugs we have to understand what the proteins do. Having the structure is not really enough for that, it doesn't tell you the purpose of the protein (though it certainly can give you some hints). In most cases the proteins…

170k is "a few" compared to 180 million (i.e. the size of the PDB as soon as someone runs AlphaFold over everything in the UniProt.) > In most cases the proteins were determined to be interesting by other experiments, and then people decided to try and solve their structure. Yes, that's what we're doing right now , because structure is not a useful predictor, because we don't have structure available in advance of st…

> as soon as someone runs AlphaFold over everything in the UniProt

It'll take a while before those results can be trusted, though, right? There's probably a selection bias in the training data for proteins which are easy to crystallize, so many proteins probably aren't well represented by the training examples.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#450

Post by deeviant »

xphos wrote: post #346

Like this is awesome and a huge advancement but one thing that worries me with an AI solution is that it doesn't really draw us any closer to the why. Why do proteins fold the way they do? We can predict the resulting structure which is extremely significant, we have no clue why. While we get the insight of being able to predict some structures we don't get the insight of why things are happening the way they are. In…

> While we get the insight of being able to predict some structures we don't get the insight of why things are happening the way they are.

This isn't something specific to AI, but science itself. We know the value of C, but now why the value is C, sure we can point to something like the Lorentz transformation, but we can't and probably won't even be able to explain why it has these particular constants, we just know that we can measure them and they are this.

Science isn't in the business of answering why. A successful scientific theory does two things, A) Makes useful predictions, B) Is correct in its predictions. It'd be wrong to call a NN a scientific theory, but it certainly does make predictions and as these results show, it is correct in its predictions.

Sometime soon, humanity is going to have to come to terms that we will soon (or perhaps already have) enter an age where mankind is not the only source of new knowledge. AI-derived knowledge will only increase as the future unfolds and the analysis of such knowledge will likely become it's own branch of study itself.

Post reply on HN