[chiggins]

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]

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

[ktamiola]

Excellent reference!