Mooney, CatherineCatherineMooneyVullo, AlessandroAlessandroVulloPollastri, GianlucaGianlucaPollastri2021-07-282021-07-282006 the A2006-01-20http://hdl.handle.net/10197/12353A 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 3 new alphabets of local structural motifs. The motifs are built by applying multidimensional scaling (MDS) and clustering to pair-wise angular distances for multiple φ-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 structural motif prediction on an independent test set for di-peptides (6 classes), tripeptides (8 classes) and tetra-peptides (14 classes), respectively, 28-30% above base-line statistical predictors. To demonstrate that structural motif predictions contain relevant structural information, we build a further system, based on ensembles of two-layered bidirectional recurrent neural networks, to map structural motif predictions into 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. All the predictive systems will be provided free of charge to academic users and made publicly available at the address http://distill.ucd.ie/.enAmino acid sequencesProtein structuresPeptide predictionPeptide classificationComputational biologyTechnical Report2021-07-2704/BR/CS035305/RFP/CMS0029RP/2005/219https://creativecommons.org/licenses/by-nc-nd/3.0/ie/