In Proteins
RNA-binding proteins (RBPs) play significant roles in many biological life activities, many algorithms and tools are proposed to predict RBPs for researching biological mechanisms of RNA-protein binding sites. Deep learning algorithms based on traditional machine learning get better result for predicting RBPs. Recently, deep learning method fused with attention mechanism has attracted huge attention in many fields and gets competitive result. Thus, attention mechanism module may also improve model performance for predicting RNA-protein binding sites. In this study, we propose convolutional residual multi-head self-attention network (CRMSNet) that combines CNN, ResNet and multi-head self-attention blocks to find RBPs for RNA sequence. First, CRMSNet incorporates convolutional neural networks, recurrent neural networks and multi-head self-attention block. Second, CRMSNet can draw binding motif pictures from the convolutional layer parameters. Third, attention mechanism module combines the local and global RNA sequence information for capturing long sequence feature. CRMSNet gets competitive AUC (area under the ROC curve) result in a large-scale dataset RBP-24. And CRMSNet experiment result is also compared with other state-of-the-art methods. The source code of our proposed CRMSNet method can be found in https://github.com/biomg/CRMSNet. This article is protected by copyright. All rights reserved.
Pan Zhengsen, Zhou Shusen, Zou Hailin, Liu Chanjuan, Zang Mujun, Liu Tong, Wang Qingjun
2023-Mar-19
RNA-protein binding sites, convolutional neural networks, deep learning, multi-head self-attention, prediction, residual neural networks
