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As an outsider to both biology and AI, I feel like there's a more key problem than structure estimation from pattern-matching (over-fitting?) known data: Learni

As an outsider to both biology and AI, I feel like there's a more key problem than structure estimation from pattern-matching (over-fitting?) known data: Learning how the molecular dynamics work. I'm fascinated with how the folding might happen. The intermediate steps; how the backbone and sidechains react to change in temperatures; how much motion is going on in the sidechains in response to constant interaction with water molecules etc. How well does a classical rigidity/elasticity etc model apply to covalently-bonded molecules. How much flexing do various parts of the protein to real-time. How to make a model that ab-initio folds (or unfolds) without being tuned to experimental data. How much of the quantum interactions can we dodge due to working at the atomic scale vice electron-scale. How accurately can we model using dipoles, hydrophobic interactions, simplified hydrogen bond models etc. How much of the picture are we missing by only looking at the static aspect of the folded protein - ie experimentally or AI-determined static atom coordinates as the model.

siver_john 4y ago
As an insider to both fields (and specifically their juncture to molecular dynamics). What you are discussing is of course being studied but the problem is that it is a lot more computationally expensive. We do have simulations of simple (read very small) proteins folding and unfolding but for larger ones the computational time to watch them fold can be gigantic, if not impossible due to the fact that proteins often fold as they are being made. Which means including a much larger process into a folding one which just further stresses computational resources. This computational problem is so enormous that a company at the cutting edge of research D.E. Shaw built a specialized computer solely for simulating proteins. Also most of the software used for this until recently had abandoned multi-GPU paralellism because it didn't scale well. The pandemic caused the need to simulate the virus on the entirety of Summit and introduced some work back into that route but it is still specialized (and wouldn't help for systems below a certain size anyways). Also my previous points have been for atomic models (e.g. we treat everything more like newtonian particles and ignore quantum effects) some things definitely need more resolution and at that level you are lucky to see protein fluctuations let alone folding.
- dekhn 4y ago
  Did simulating covid on summit actually help anything?
  - siver_john 4y ago
    My memory is a little rusty, but I believe yes. If I remember correctly simulation of the virus helped medically in a few ways. Specifically I think it gave insight into the mRNA vaccines and what sequences to use to make them effective (by basically making a slightly worse spike protein). I am sure it helped in drug discovery as I know our lab used simulations to suggest some potential drug pockets. There were some really good talks about it at NVIDIA's GTC last year or so (maybe more at this recent GTC but I had too much going on personally to watch the VODs).
- flobosg 4y ago
  > This computational problem is so enormous that a company at the cutting edge of research D.E. Shaw built a specialized computer solely for simulating proteins. See https://en.wikipedia.org/wiki/Anton_(computer) https://en.wikipedia.org/wiki/Anton_(computer)
dekhn 4y ago
These questions are better addressed by folding@home and we're still very much computationally limited in our ability to answer these questions. Do you want to answer these questions for the satisifcation of understanding the underlying physical rules that drive folding? Why? It's unclear that knowing those things would actually make a large impact in any industrially/medically useful contexts. It uses huge amounts of CPU to sample these functions accurately enough to replace actual physical experiments on protein motion. Just doesn't seem like an effective investment of brain or computer time. (I say this as somebody whose entire career was predicated on using MD to answer these questions; see https://www.nature.com/articles/nchem.1821 https://www.nature.com/articles/nchem.1821 for our attempt in that space)
- mechagodzilla 4y ago
  The folding@home approach is very limited to short individual simulation times (a millisecond total, maybe, but 100 disconnected nanoseconds at a time), so it then relies on various 'enhanced sampling' techniques to try to put your thumb on the scale to bias things into exploring interesting dynamics. It seems like it is probably more effective the more you already know about a given protein target. Meta's approach (which seems like AF2, but faster/worse?) seems to have a similar problem, in that it's even less trustworthy when you apply it to a new target you have relatively little concrete information about.
- COGlory 4y ago
  There are huge swaths of proteins AlphaFold can't predict. Also a number of protein folding diseases.
xiphias2 4y ago
That's what the Alphafold team is working on for some time now. The only difference is that it will be relevant for drug research, so I don't expect Alphabet to give it away for free as well.
COGlory 4y ago
A number of people, myself included, have railed against the terminology that frequently surrounds these otherwise great achievements, because it does a disservice, precisely on this topic. AlphaFold (I haven't looked at Meta AI yet, beyond a cursory glance), is solving the state of the folded protein, based primarily off the state of other similar folded proteins. There's some, but extremely limited, modeling of unknown states. The major breakthrough for AlphaFold appears to be that it is substantially better at detecting meaningful signal in homology-based sequence alignments. That means, it can figure out the parts of a protein that are similar to other known folded proteins, with much greater success than previous models (2x-3x better iIrc). As far as modeling proteins or portions of proteins for which there are no known homologs, the model becomes significantly weaker (although not nonexistant). In my opinion, and also the opinion of a number of my colleagues, advertising AlphaFold as having solved the "protein folding problem", and other similar language (used by both the media and the AlphaFold press releases) is completely disingenuous. We are effectively no closer to understanding how proteins fold, nor are we closer to being able to predict how a protein folds without any a priori information. Furthermore, homology modeling, while quite successful so far for a lot of proteins, breaks down on edge cases, higher-order structures, and unknown folds. It seems unlikely that it will ever completely solve the problem, and therefore, an entirely different approach will be needed to modeling protein folding that encompasses the entire field, edge cases and all (a quite lofty goal that may also never be reached). All this is to say: AlphaFold is a great tool that I use frequently and am grateful it exists, but it hasn't solved protein folding, and the thing is has solved is intrinsically limited, and we should probably use different language to describe what it's done. Either way, I'm glad to see progress being made here, and eagerly await finding out how proteins actually fold.
- deltree7 4y ago
  How do we know that you aren't missing a piece of the puzzle that others are/may see? It's a genuine question. A lot of experts have a lot of blind spots especially around prediction, rate of growth, potential, vision. History is littered with people who are really good at a field but fail to see 2/3/5/10 years ahead.
  - 4y ago

siver_john 4y ago

As an insider to both fields (and specifically their juncture to molecular dynamics). What you are discussing is of course being studied but the problem is that it is a lot more computationally expensive. We do have simulations of simple (read very small) proteins folding and unfolding but for larger ones the computational time to watch them fold can be gigantic, if not impossible due to the fact that proteins often fold as they are being made. Which means including a much larger process into a folding one which just further stresses computational resources. This computational problem is so enormous that a company at the cutting edge of research D.E. Shaw built a specialized computer solely for simulating proteins. Also most of the software used for this until recently had abandoned multi-GPU paralellism because it didn't scale well. The pandemic caused the need to simulate the virus on the entirety of Summit and introduced some work back into that route but it is still specialized (and wouldn't help for systems below a certain size anyways). Also my previous points have been for atomic models (e.g. we treat everything more like newtonian particles and ignore quantum effects) some things definitely need more resolution and at that level you are lucky to see protein fluctuations let alone folding.

dekhn 4y ago
Did simulating covid on summit actually help anything?
- siver_john 4y ago
  My memory is a little rusty, but I believe yes. If I remember correctly simulation of the virus helped medically in a few ways. Specifically I think it gave insight into the mRNA vaccines and what sequences to use to make them effective (by basically making a slightly worse spike protein). I am sure it helped in drug discovery as I know our lab used simulations to suggest some potential drug pockets. There were some really good talks about it at NVIDIA's GTC last year or so (maybe more at this recent GTC but I had too much going on personally to watch the VODs).
flobosg 4y ago
> This computational problem is so enormous that a company at the cutting edge of research D.E. Shaw built a specialized computer solely for simulating proteins. See https://en.wikipedia.org/wiki/Anton_(computer) https://en.wikipedia.org/wiki/Anton_(computer)

dekhn 4y ago

These questions are better addressed by folding@home and we're still very much computationally limited in our ability to answer these questions. Do you want to answer these questions for the satisifcation of understanding the underlying physical rules that drive folding? Why? It's unclear that knowing those things would actually make a large impact in any industrially/medically useful contexts. It uses huge amounts of CPU to sample these functions accurately enough to replace actual physical experiments on protein motion. Just doesn't seem like an effective investment of brain or computer time. (I say this as somebody whose entire career was predicated on using MD to answer these questions; see https://www.nature.com/articles/nchem.1821 https://www.nature.com/articles/nchem.1821 for our attempt in that space)

mechagodzilla 4y ago
The folding@home approach is very limited to short individual simulation times (a millisecond total, maybe, but 100 disconnected nanoseconds at a time), so it then relies on various 'enhanced sampling' techniques to try to put your thumb on the scale to bias things into exploring interesting dynamics. It seems like it is probably more effective the more you already know about a given protein target. Meta's approach (which seems like AF2, but faster/worse?) seems to have a similar problem, in that it's even less trustworthy when you apply it to a new target you have relatively little concrete information about.
COGlory 4y ago
There are huge swaths of proteins AlphaFold can't predict. Also a number of protein folding diseases.

xiphias2 4y ago

That's what the Alphafold team is working on for some time now. The only difference is that it will be relevant for drug research, so I don't expect Alphabet to give it away for free as well.

COGlory 4y ago

A number of people, myself included, have railed against the terminology that frequently surrounds these otherwise great achievements, because it does a disservice, precisely on this topic. AlphaFold (I haven't looked at Meta AI yet, beyond a cursory glance), is solving the state of the folded protein, based primarily off the state of other similar folded proteins. There's some, but extremely limited, modeling of unknown states. The major breakthrough for AlphaFold appears to be that it is substantially better at detecting meaningful signal in homology-based sequence alignments. That means, it can figure out the parts of a protein that are similar to other known folded proteins, with much greater success than previous models (2x-3x better iIrc). As far as modeling proteins or portions of proteins for which there are no known homologs, the model becomes significantly weaker (although not nonexistant). In my opinion, and also the opinion of a number of my colleagues, advertising AlphaFold as having solved the "protein folding problem", and other similar language (used by both the media and the AlphaFold press releases) is completely disingenuous. We are effectively no closer to understanding how proteins fold, nor are we closer to being able to predict how a protein folds without any a priori information. Furthermore, homology modeling, while quite successful so far for a lot of proteins, breaks down on edge cases, higher-order structures, and unknown folds. It seems unlikely that it will ever completely solve the problem, and therefore, an entirely different approach will be needed to modeling protein folding that encompasses the entire field, edge cases and all (a quite lofty goal that may also never be reached). All this is to say: AlphaFold is a great tool that I use frequently and am grateful it exists, but it hasn't solved protein folding, and the thing is has solved is intrinsically limited, and we should probably use different language to describe what it's done. Either way, I'm glad to see progress being made here, and eagerly await finding out how proteins actually fold.

deltree7 4y ago
How do we know that you aren't missing a piece of the puzzle that others are/may see? It's a genuine question. A lot of experts have a lot of blind spots especially around prediction, rate of growth, potential, vision. History is littered with people who are really good at a field but fail to see 2/3/5/10 years ahead.
- 4y ago