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Predicting protein properties such as solvent accessibility and secondarystructure from its primary amino acid sequence is an important task inbioinformatics. Recently, a few deep learning models have surpassed thetraditional window based multilayer perceptron. Taking inspiration from theimage classification domain we propose a deep convolutional neural networkarchitecture, MUST-CNN, to predict protein properties. This architecture uses anovel multilayer shift-and-stitch (MUST) technique to generate fully denseper-position predictions on protein sequences. Our model is significantlysimpler than the state-of-the-art, yet achieves better results. By combiningMUST and the efficient convolution operation, we can consider far moreparameters while retaining very fast prediction speeds. We beat thestate-of-the-art performance on two large protein property prediction datasets.

MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-based Protein Structure Prediction