Accurate Single-Sequence Prediction of Protein Intrinsic Disorder by an Ensemble of Deep Recurrent and Convolutional Architectures.

Recognizing the widespread existence of intrinsically disordered regions in proteins spurred the development of computational techniques for their detection. All existing techniques can be classified into methods relying on single-sequence information and those relying on evolutionary sequence profiles generated from multiple-sequence alignments. The methods based on sequence profiles are, in general, more accurate because the presence or absence of conserved amino acid residues in a protein sequence provides important information on the structural and functional roles of the residues. However, the wide applicability of profile-based techniques is limited by time-consuming calculation of sequence profiles. Here we demonstrate that the performance gap between profile-based techniques and single-sequence methods can be reduced by using an ensemble of deep recurrent and convolutional neural networks that allow whole-sequence learning. In particular, the single-sequence method (called SPOT-Disorder-Single) is more accurate than SPOT-Disorder (a profile-based method) for proteins with few homologous sequences and comparable for proteins in predicting long-disordered regions. The method performance is robust across four independent test sets with different amounts of short- and long-disordered regions. SPOT-Disorder-Single is available as a Web server and as a standalone program at http://sparks-lab.org/jack/server/SPOT-Disorder-Single .