Nucleic acid-binding protein(NABP)includes DNA-binding protein(DBP)and RNA-binding protein(RBP),and accurate identification of NABP helps to understand the mechanism of protein action.To solve cross-prediction problem in NABP prediction,a novel deep learning model called DeepDPRP is developed for predicting DBP and RBP at the same time,and the multi-label learning method is to train the model.DeepDPRP extracts global protein sequence features from posi-tion-specific scoring matrices by bidirectional long and short term memory,followed by convolution-al neural network to capture more sophisticated features,and a structural motif-based convolutional module is combined to efficiently utilise the discovered structural features of proteins.Experimental results on two independent test datasets show that DeepDPRP significantly outperforms existing NABP predictors with higher performance and lower cross-prediction.Extensive ablation experi-ments demonstrate the effectiveness of the proposed model.
nucleic acid-binding protein predictionconvolutional neural networkbidirectional long and short term memorymulti-label learning
NETL
NSTL
万方数据
