Towards recognition of protein function based on its structure using deep convolutional networks

This paper proposes a novel method for protein function recognition using deep learning. Recently, deep convolutional neural networks (DCNNs) demonstrated high performances in many areas of pattern recognition. Protein function is often associated with its tertiary structure denoting the active domain of a protein. This investigation develops a novel DCNN for protein functionality recognition based on its tertiary structure. Two rounds of experiments are performed. The initial experiment on tertiary protein structure alignment shows promising performances (94% accuracy rate) such that it shows the model robustness against rotations, local translations, and scales of the 3D structure. With these results, the main experiments contain five different datasets obtained by similarity measures between pairs of gene ontology terms. The experimental results for protein function recognition on selected datasets show 87.6% and 80.7% maximum and average accuracy rates respectively. The initial success of the DCNN in tertiary protein structure recognition supports further investigations with respect to tertiary protein retrieval and pattern mining on large scale problems.

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