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/"oth…
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 m…
Re: Structural Propensity Database of Proteins
#11The 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.