A deep learning method for protein disorder prediction. AUCpreD distinguishes itself from the others in that it applies a deep probabilistic graphical model DeepCNF to model complex sequence–structure relationship and directly optimizes the area under the ROC curve (AUC) measure to deal with the imbalanced distribution of disordered and ordered residues. DeepCNF allows us to model complex sequence–disorder relationship by a deep hierarchical architecture, and exploit interdependency between adjacent order/disorder states. Experimental results show that AUCpreD performs much better than the state-of-the-art methods of the same category in terms of AUC, AUCpr and Mcc. On long disordered regions and terminal/internal regions, AUCpreD also performs the best. Even without using sequence profile, AUCpreD still compares favorably to or outperforms the methods that use sequence profile or even protein templates.