A significant step towards establishing the structure and function of a protein is the prediction
of the local conformation of the polypeptide chain. In this article, we present systems
for the prediction of three new alphabets of local structural motifs. The motifs are built by
applying multidimensional scaling (MDS) and clustering to pair-wise angular distances for
multiple ⊘-ψ angle values collected from high-resolution protein structures. The predictive
systems, based on ensembles of bidirectional recurrent neural network architectures, and
trained on a large non-redundant set of protein structures, achieve 72%, 66%, and 60%
correct motif prediction on an independent test set for di-peptides (six classes), tri-peptides
(eight classes) and tetra-peptides (14 classes), respectively, 28–30% above baseline statistical
predictors. We then build a further system, based on ensembles of two-layered bidirectional
recurrent neural networks, to map structural motif predictions into a traditional 3-class
(helix, strand, coil) secondary structure. This system achieves 79.5% correct prediction using
the "hard" CASP 3-class assignment, and 81.4% with a more lenient assignment, outperforming
a sophisticated state-of-the-art predictor (Porter) trained in the same experimental
conditions. The structural motif predictor is publicly available at: http://distill.ucd.ie/porter+/.
