  We have developed a Generative Recurrent Neural Networks (GRNN) that learns
the probability of the next residue torsions $X_{i+1}=\
[\phi_{i+1},\psi_{i+1},\omega _{i+1}, \chi_{i+1}]$ from the previous residue in
the sequence $X_i$ to generate new IDP conformations. In addition, we couple
the GRNN with a Bayesian model, X-EISD, in a reinforcement learning step that
biases the probability distributions of torsions to take advantage of
experimental data types such as J-couplingss, NOEs and PREs. We show that
updating the generative model parameters according to the reward feedback on
the basis of the agreement between structures and data improves upon existing
approaches that simply reweight static structural pools for disordered
proteins. Instead the GRNN "DynamICE" model learns to physically change the
conformations of the underlying pool to those that better agree with
experiment.
