And yes, the product right now is Keras and Tensorflow database integration + the interactive database interface. The tools around are currently under stringent testing.
Thank you for very kind comment. We are now finishing a predictor, which utilizes protein propensity data for mass-scale disorder and order predictions.
The training times obviously vary on the network architecture, software and hardware. I can safely say you can process 7200+ protein sequence with average sequence length of 120 amino acids in 2h on 2 x NVIDIA Titan XP
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?
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.