###### Abstract  
RNA-binding proteins (RBPs) play an important role in the synthesis and degradation of ribonucleucleic acid (RNA) molecules. The rapid and accurate identification of RBPs is essential for understanding the mechanisms of cell activity. Since identifying RBPs experimentally is expensive and time-consuming, computational methods have been explored to predict RBPs directly from protein sequences. In this paper, we developed an RBP prediction method named CnnRBP based on a convolution neural network. CnnRBP derived a sparse high-dimensional di- and tripeptide frequency feature vector from a protein sequence and then reduced this vector to a low-dimensional one using the Light Gradient Boosting Machine (LightGBM) algorithm. Then, the low-dimensional vectors derived from both RNA-binding proteins and non-RNA-binding proteins were fed to a multi-layer one-dimensional convolution network. Meanwhile, the SMOTE algorithm was used to alleviate the class imbalance in the training data. Extensive experiments showed that the proposed method can extract discriminative features to identify RBPs effectively. With 10-fold cross-validation on the training datasets, CnnRBP achieved AUC values of 99.98%, 99.69% and 96.72% for humans, _E. coli_ and Salmonella, respectively. On the three independent datasets, CnnRBP achieved AUC values of 0.91, 0.96 and 0.91, outperforming the recent tripeptide-based method (i.e., TriPerSVM) by 8%, 4% and 5%, respectively. Compared with the state-of-the-art CNN-based predictor (i.e., iDRBP_MMC), CnnRBP achieved MCC values of 0.67, 0.68 and 0.73 with significant improvements by 6%, 6% and 15%, respectively. In addition, the cross-species testing shows that CnnRBP has a robust generalization performance for cross-species RBP prediction between close species. R 20  
complexes [3]. In the absence of RBPs, most RNAs cannot normally regulate and metabolize, except for a few RNAs that can act alone in the form of nuclease [4]. Moreover, RBPs may have one or more target RNAs, and their expression defects can cause a variety of diseases, such as neurodegenerative diseases, cancer and metabolic disorders [5; 6]. The efficient identification of RBPs is important to medical research. Traditional experimental methods are time-consuming and expensive because they identify RBPs via RNA interactome capture (RIC), which relies on in vivo UV cross-linking and oligo(dT) capture. Moreover, these methods may fail in some bacterial species that lack polyadenylation. The above reasons limit the application of traditional experimental methods to high-throughput data. The computational methods are time efficient, oligo(dT)-independent and can make up for the shortcomings of traditional experimental methods. Therefore, the computational RBP prediction methods have received more and more attention from researchers. The existing methods mainly describe proteins using features derived from protein primary sequences and then train classification models based on these features. The most commonly involved features include evolutionary conservation information, physicochemical properties, predicted structure information, position-specific scoring matrixes, physicochemical properties, and so on. And the most commonly used models include support vector machines, convolutional neural networks, and so on. However, recent studies on RBPs have made progress. [PERSON] et al. [7] found that some short motifs (e.g., tripeptide or dipeptide) show specific conservation in the internal disordered regions (IDRs) of RBPs. Although existing methods have made significant progress on RBP prediction, there is still a lot of room for improvement. Motivated by the work of [PERSON] et al. [7], we propose a deep model named CnnRBP based on frequencies of the short motif of amino acid residues in this paper. The proposed method described proteins with short peptide motifs. A convolutional neural network was trained based on features derived from these short peptide motifs.