Abstract: The computational methods for predicting protein phosphorylation sites are usually used in the preliminary screening stage of the site identification. To further improve the prediction accuracy, a deep learning model called MAPhos is proposed. First, each residue is represented by the summation of the amino acid vector and the position vector. Second, a bidirectional GRU network is utilized to generate the hidden states of residues. Third, the multi-head attention mechanism is used for generating the context vector. Finally, the context vector and the sequence vectors are concatenated, and the concatenation vector is fed into a fully connected neural network for predicting the site. Experimental results on real-world datasets demonstrate that the MAPhos model can outperform the models based on feature extraction and the models based on convolutional neural network over several measures, and the new model has better interpretability than the convolutional neural network models.

Key words: deep learning, bioinformatics, phosphorylation sites identification, multi-head attention mechanism, residues representation