DNA-binding protein (DBP) and RNA-binding protein (RBP) are playing crucial roles in gene expression. Accurate identification of them is of great significance, and accurately computational predictors are highly required. In previous studies, DBP recognition and RBP recognition were treated as two separate tasks. Because the functional and structural similarities between DBPs and RBPs are high, the DBP predictors tend to predict RBPs as DBPs, while the RBP predictors tend to predict the DBPs as the RBPs, leading to high cross-prediction rate and low prediction precision. Here we introduced a multi-label learning model based on the motif-based convolutional neural network, and a sequence-based computational method called iDRBP_MMC was proposed to solve the cross-prediction problem so as to improve the predictive performance of DBPs and RBPs. The results on four test datasets showed that it outperformed other state-of-the-art DBP predictors and RBP predictors. When applied to analyze the tomato genome, the results reveal the ability of iDRBP_MMC for large-scale data analysis. Moreover, iDRBP_MMC can identify the proteins binding to both DNA and RNA, which is beyond the scope of existing DBP predictors or RBP predictors. The web-server of iDRBP_MMC is freely available at http://bliulab.net/iDRBP_MMC.

DNA结合蛋白（DBP）和RNA结合蛋白（RBP）在基因表达中起着至关重要的作用。准确地识别它们具有重要意义，因此非常需要精确的计算预测变量。在以前的研究中，DBP识别和RBP识别被视为两个单独的任务。由于DBP和RBP之间的功能和结构相似性很高，因此DBP预测变量倾向于将RBP预测为DBP，而RBP预测变量倾向于将DBP预测为RBP，从而导致较高的交叉预测率和较低的预测精度。在这里，我们介绍了一种基于基序的卷积神经网络的多标签学习模型，并提出了一种基于序列的计算方法iDRBP_MMC，以解决交叉预测问题，从而提高DBP和RBP的预测性能。在四个测试数据集上的结果表明，它优于其他最新的DBP预测器和RBP预测器。当用于分析番茄基因组时，结果揭示了iDRBP_MMC进行大规模数据分析的能力。此外，iDRBP_MMC可以识别与DNA和RNA结合的蛋白质，这超出了现有DBP预测因子或RBP预测因子的范围。iDRBP_MMC的Web服务器可从http://bliulab.net/iDRBP_MMC免费获得。