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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

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.
- cing 7y ago
  So what you're saying is: https://xkcd.com/1831/ https://xkcd.com/1831/, except that CS/ML practitioners have a negative impact by trying to contribute without understanding the nuance. I think the next logical question is: how many years of education should you have in order to contribute? 10 years? We'll all be killed by a virus by then :)
  - dekhn 7y ago
    Hah, I forgot that one. Part of the negative impact is the time spent explaining stuff (like Brooks mythical man month). Another negative impact is that ML folks have gotten really good at hype- paper with slick web page, press release, etc, but the results don't stand up to the claims. I generally recommend PhD-level study in biology (that's 7 years on top of undergrad) but I think a really smart person could learn most of what's required in 2-3 years if they are in a good lab. No, we will not all be killed by a virus in the next 10 years; that's just media alarmism. Remember, even if coronavirus becomes a worldwide pandemic, some fraction of people will survive who will be genetically immune. We're much more at risk of wars, climate change, and driving cars.
    - mynegation 7y ago
      So, is there a (collection of) book that would save everyone’s time? Asking for a friend.
      - dekhn 7y ago
        Personally, I prefer the classic textbook approach, so I recommend Principles of Biochemistry (Lehninger), Biological Sequence Analysis (Durbin, Eddy, Krogh, Mitchison) which is sadly pretty dated now, a general Biology (Campbell), and finally if you really want to dive down the rabbit hole of a complex biological problem with huge health applications, Biology of Cancer (Weinberg). I've had this argument with folks before and some people seem fine learning in other ways, but I really prefer the textbook approach, especially textbooks which are basically just summaries of the current understanding of the field, with direct links to the detailed review articles.

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.

cing 7y ago
So what you're saying is: https://xkcd.com/1831/ https://xkcd.com/1831/, except that CS/ML practitioners have a negative impact by trying to contribute without understanding the nuance. I think the next logical question is: how many years of education should you have in order to contribute? 10 years? We'll all be killed by a virus by then :)
- dekhn 7y ago
  Hah, I forgot that one. Part of the negative impact is the time spent explaining stuff (like Brooks mythical man month). Another negative impact is that ML folks have gotten really good at hype- paper with slick web page, press release, etc, but the results don't stand up to the claims. I generally recommend PhD-level study in biology (that's 7 years on top of undergrad) but I think a really smart person could learn most of what's required in 2-3 years if they are in a good lab. No, we will not all be killed by a virus in the next 10 years; that's just media alarmism. Remember, even if coronavirus becomes a worldwide pandemic, some fraction of people will survive who will be genetically immune. We're much more at risk of wars, climate change, and driving cars.
  - mynegation 7y ago
    So, is there a (collection of) book that would save everyone’s time? Asking for a friend.
    - dekhn 7y ago
      Personally, I prefer the classic textbook approach, so I recommend Principles of Biochemistry (Lehninger), Biological Sequence Analysis (Durbin, Eddy, Krogh, Mitchison) which is sadly pretty dated now, a general Biology (Campbell), and finally if you really want to dive down the rabbit hole of a complex biological problem with huge health applications, Biology of Cancer (Weinberg). I've had this argument with folks before and some people seem fine learning in other ways, but I really prefer the textbook approach, especially textbooks which are basically just summaries of the current understanding of the field, with direct links to the detailed review articles.