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Nice work lowering the entry barrier for machine learning in this space, which appears to be the aim of your company, but it's a bit of a tease to claim your da

by cing 9y ago

Nice work lowering the entry barrier for machine learning in this space, which appears to be the aim of your company, but it's a bit of a tease to claim your data representation is great for supporting ML and stop short of doing any of that in the manuscript. I take it that's the next step? On that note, is there any reason why the propensity classes alpha/beta/coil are still so widely used? Especially coil/turn/"other". It seems to me that these are ancient artifacts of structural biology that could definitely restrict human understanding of protein dynamics. Perhaps there is nuance in the chemical shift data that may help the design of better structural classes using ML.

ktamiola 9y ago
Cing! Thank you for very flattering comment. Obviously, this is database only paper. Please bare in mind, the vast majority, or perhaps even >95% of protein structure prediction methods deal with canonical secondary structure classes. We want to provide a coherent data set as a benchmark + source of information. We have in "stock" a network (obviously another paper) that will aim at propensity prediction, still in trivial alpha/coil/beta phase space.
ktamiola 9y ago
BTW, greetings from GROMACS group in Groningen :) I happend to do my PhD in NMR and Molecular Dynamics. Coming back to your comments about the canonical secondary structures; I couldn't agree more with you. The problem is quite simple, how are we going to convince the >90% of structural biochemistry society to simply accept the fact proteins are bloody dynamic and X-ray / eye candy structures may have quite little to do with the "real" picture at room temperature?
- kayhi 9y ago
  Crystallographic structures are very useful in determining the functions of proteins. Structural biochemists are very aware that proteins are dynamic and believe the concept is covered in most introducing courses.
chiggins 9y ago
All models are wrong but some are useful. My PhD was in empirical protein dynamics (solution NMR, CD, DSF, etc..) and the long and short of it is that disordered states are particularly difficult to distinguish from one another. When you consider that an even partially disordered ensemble has essentially an infinite number of nearly degenerate conformations inter-converting on timescales ranging from picoseconds to milliseconds, lumping them into coil/turn/other turns out to be just the law of large numbers in action (reversion to the mean, etc.). That and the biophysical properties conferred by partially disordered proteins makes them a motherfucker to work with outside of some archetypal domains. I liked to explain it like this. Imagine you have a piece of string three feet long. Along the length of that string you have ~1 inch segments consisting of velcro (both kinds), zippers, magnets, balloons, strawberry jello, and marshmallows--all randomly distributed along the length. Now try to fold all of that up so the jello, velcro, and balloons are on the inside. That's a simple model of a protein. Now make it start opening and closing. Now put 5 of them next to each other.
- cing 9y ago
  The traditional structural classifications just have very low information content in the context of protein dynamics. Coil especially. You've given the example of a disordered region interconverting on different timescales, but these timescales can, purportedly, be predicted from chemical shift data, etc. [1], so why not call it "fast coil" or "slow coil"? It's not only about timescales either, because you may need to do extra experiments for that data. It's about finding the highest information content descriptors for an amino acid and using machine learning to do it. Your descriptors (jello, velcro, balloons) are actually much better at conveying dynamics than the static descriptors used in structural biology. [1] http://pubs.acs.org/doi/abs/10.1021/ct501085y http://pubs.acs.org/doi/abs/10.1021/ct501085y
  - ktamiola 9y ago
    Excellent reference!

by cing 9y ago

ktamiola 9y ago

Cing! Thank you for very flattering comment. Obviously, this is database only paper. Please bare in mind, the vast majority, or perhaps even >95% of protein structure prediction methods deal with canonical secondary structure classes. We want to provide a coherent data set as a benchmark + source of information. We have in "stock" a network (obviously another paper) that will aim at propensity prediction, still in trivial alpha/coil/beta phase space.

ktamiola 9y ago

BTW, greetings from GROMACS group in Groningen :) I happend to do my PhD in NMR and Molecular Dynamics. Coming back to your comments about the canonical secondary structures; I couldn't agree more with you. The problem is quite simple, how are we going to convince the >90% of structural biochemistry society to simply accept the fact proteins are bloody dynamic and X-ray / eye candy structures may have quite little to do with the "real" picture at room temperature?

kayhi 9y ago
Crystallographic structures are very useful in determining the functions of proteins. Structural biochemists are very aware that proteins are dynamic and believe the concept is covered in most introducing courses.

chiggins 9y ago

All models are wrong but some are useful. My PhD was in empirical protein dynamics (solution NMR, CD, DSF, etc..) and the long and short of it is that disordered states are particularly difficult to distinguish from one another. When you consider that an even partially disordered ensemble has essentially an infinite number of nearly degenerate conformations inter-converting on timescales ranging from picoseconds to milliseconds, lumping them into coil/turn/other turns out to be just the law of large numbers in action (reversion to the mean, etc.). That and the biophysical properties conferred by partially disordered proteins makes them a motherfucker to work with outside of some archetypal domains. I liked to explain it like this. Imagine you have a piece of string three feet long. Along the length of that string you have ~1 inch segments consisting of velcro (both kinds), zippers, magnets, balloons, strawberry jello, and marshmallows--all randomly distributed along the length. Now try to fold all of that up so the jello, velcro, and balloons are on the inside. That's a simple model of a protein. Now make it start opening and closing. Now put 5 of them next to each other.

cing 9y ago
The traditional structural classifications just have very low information content in the context of protein dynamics. Coil especially. You've given the example of a disordered region interconverting on different timescales, but these timescales can, purportedly, be predicted from chemical shift data, etc. [1], so why not call it "fast coil" or "slow coil"? It's not only about timescales either, because you may need to do extra experiments for that data. It's about finding the highest information content descriptors for an amino acid and using machine learning to do it. Your descriptors (jello, velcro, balloons) are actually much better at conveying dynamics than the static descriptors used in structural biology. [1] http://pubs.acs.org/doi/abs/10.1021/ct501085y http://pubs.acs.org/doi/abs/10.1021/ct501085y
- ktamiola 9y ago
  Excellent reference!