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Predicting protein structure based on a given DNA/RNA sequence has been a field of study that has existed for quite a while now. There have been two primary met

Predicting protein structure based on a given DNA/RNA sequence has been a field of study that has existed for quite a while now. There have been two primary methodologies that have been explored, one of which has been to try and simulate the actual physical dynamics of a given system at a molecular/atomic level. At places like D.E. Shaw or with Folding@Home, you'll see approaches like these being taken and with relative success. Though generally with purely physics based solutions, you quickly run into exponentially growing simulation time scales as well as a lack of accuracy due to a currently insufficient understanding of molecular mechanics. The other approach has been to take the problem and look at it purely as a translation problem, ignoring simulation of intermediary steps, to go directly from sequence to folded protein target. With the advent of deep learning and a massive repository of data from an existing Protein Data Bank (PDB) et al., this approach has become increasingly popular, and for protein structure competitions like CASP, has quickly become state of the art within the field. DeepMind's recent breakthrough with AlphaFold in the paper above is just another solid step in the right direction.

dekhn 7y ago
PDB is not a massive repository. It's a very biased, tiny dataset (~20K structures) and an enormous amount of data cleaning has to be done to do anything related to big-data machine learning on it. What's far more important is evolutionary data- for example, making alignments of many similar proteins, and computing correlated variations across them. Those variations are often the best structural clues-- better and cheaper to obtain than protein structures. I wouldn't really call DM's work a "breakthrough", other groups were exploring similar ideas. DM executed well (they're a games company and understand the rules of the competition) and had a huge amount of compute resources which handles a lot of the challenges of optimizing a process like this.
- lawrenceyan 7y ago
  My summary is pretty generalized, aimed more towards a layman audience, and so I definitely am missing pieces. Co-evolutionary couplings between different protein sequences provide a very rich source of information, and are definitely very important! For those of you that are curious as to what these couplings represent, the basic idea, is that intuitively you can sort of see how given proteins are a product of evolution, that they're might be a large amount of conserved structure between one protein to another. Co-evolutionary coupling is just an attempt at quantifying this relationship in a rigorous statistical manner.
  - dekhn 7y ago
    I mainly don't want ML folks to suddenly think protein folding is easy because the PDB is a good training set. It's not.
    - deleted 7y ago
      [deleted]
    - lawrenceyan 7y ago
      Why frame things in such a pessimistic manner? It seems like it would only be a net benefit to have more people become interested in protein folding as a field of study. Is there really a need for this type of gate keeping here?
      - dekhn 7y ago
        yes, personally I think there is. I've spent a tremendous amount of my career watching computer scientists misunderstand how to work on protein folding and waste a lot of people's time. Because the concept of protein folding is so unbelievably complex, most CS and ML folks get the basic talk: "nearly all proteins fold reversibly to a global minimum energy structure which is completely defined by the sequence of the protein", which isn't remotely true (basically a weak form of Anfinsen's dogma and Levinthal's paradox). It's easy to explain, and CS and ML people get excited and go off to work on the problem. This led to a lot of publications that focused on rapidly finding heuristics that could sample enough space to find an approximation of the lowest energy structure. these methods typically failed to make good predictions although eventually methods like Rosetta did start making good predictions around 15-20 years ago (amusingly, the author of Rosetta told me: "the larger the PDB (training data set) gets, the worse the predictions we make". But people who spend a long time getting a biological education know why this is true: most proteins don't fold to their energetic minimum, they fold to a collection of kinetically accessible states, rarely finding their true minimum (some small proteins do fold quickly, and we typically can predict their structure). And, many of the physical approximations that are used lead to inaccuracies (for example, some variables are constrained to specific values to save time, but making good predictions requires them to be unconstrained). Some of my work made significant contributions to changing these beliefs, and I've very thankful for the CS and ML folks who contributed to that, but all of them spent a lot of time learning about proteins before they were useful contributors. Myself I've had to "unlearn" a lot of the early things that were explained to me when I was a layperson, because when you're first learning something, if somebody gives you a simplified view, it can be really hard to move on to the more subtle and nuanced details in the field (for example, many people learn Mendelian genetics and then spend years struggling to understand why most traits don't follow mendelian statistics). My goal here is to prevent wasted time on behalf of the experienced contributors in the field. I do appreciate good attempts at explaining the field to laypeople, but want to set the expectations on contributing accurately.

dekhn 7y ago

PDB is not a massive repository. It's a very biased, tiny dataset (~20K structures) and an enormous amount of data cleaning has to be done to do anything related to big-data machine learning on it. What's far more important is evolutionary data- for example, making alignments of many similar proteins, and computing correlated variations across them. Those variations are often the best structural clues-- better and cheaper to obtain than protein structures. I wouldn't really call DM's work a "breakthrough", other groups were exploring similar ideas. DM executed well (they're a games company and understand the rules of the competition) and had a huge amount of compute resources which handles a lot of the challenges of optimizing a process like this.

lawrenceyan 7y ago
My summary is pretty generalized, aimed more towards a layman audience, and so I definitely am missing pieces. Co-evolutionary couplings between different protein sequences provide a very rich source of information, and are definitely very important! For those of you that are curious as to what these couplings represent, the basic idea, is that intuitively you can sort of see how given proteins are a product of evolution, that they're might be a large amount of conserved structure between one protein to another. Co-evolutionary coupling is just an attempt at quantifying this relationship in a rigorous statistical manner.
- dekhn 7y ago
  I mainly don't want ML folks to suddenly think protein folding is easy because the PDB is a good training set. It's not.
  - deleted 7y ago
    [deleted]
  - lawrenceyan 7y ago
    Why frame things in such a pessimistic manner? It seems like it would only be a net benefit to have more people become interested in protein folding as a field of study. Is there really a need for this type of gate keeping here?
    - dekhn 7y ago
      yes, personally I think there is. I've spent a tremendous amount of my career watching computer scientists misunderstand how to work on protein folding and waste a lot of people's time. Because the concept of protein folding is so unbelievably complex, most CS and ML folks get the basic talk: "nearly all proteins fold reversibly to a global minimum energy structure which is completely defined by the sequence of the protein", which isn't remotely true (basically a weak form of Anfinsen's dogma and Levinthal's paradox). It's easy to explain, and CS and ML people get excited and go off to work on the problem. This led to a lot of publications that focused on rapidly finding heuristics that could sample enough space to find an approximation of the lowest energy structure. these methods typically failed to make good predictions although eventually methods like Rosetta did start making good predictions around 15-20 years ago (amusingly, the author of Rosetta told me: "the larger the PDB (training data set) gets, the worse the predictions we make". But people who spend a long time getting a biological education know why this is true: most proteins don't fold to their energetic minimum, they fold to a collection of kinetically accessible states, rarely finding their true minimum (some small proteins do fold quickly, and we typically can predict their structure). And, many of the physical approximations that are used lead to inaccuracies (for example, some variables are constrained to specific values to save time, but making good predictions requires them to be unconstrained). Some of my work made significant contributions to changing these beliefs, and I've very thankful for the CS and ML folks who contributed to that, but all of them spent a lot of time learning about proteins before they were useful contributors. Myself I've had to "unlearn" a lot of the early things that were explained to me when I was a layperson, because when you're first learning something, if somebody gives you a simplified view, it can be really hard to move on to the more subtle and nuanced details in the field (for example, many people learn Mendelian genetics and then spend years struggling to understand why most traits don't follow mendelian statistics). My goal here is to prevent wasted time on behalf of the experienced contributors in the field. I do appreciate good attempts at explaining the field to laypeople, but want to set the expectations on contributing accurately.