Abstract

Protein–DNA and protein–RNA interactions are involved in many biological processes and regulate many cellular functions. Moreover, they are related to many human diseases. To understand the molecular mechanism of protein–DNA binding and protein–RNA binding, it is important to identify which residues in the protein sequence bind to DNA and RNA. At present, there are few methods for specifically identifying the binding sites of disease-related protein–DNA and protein–RNA. In this study, so we combined four machine learning algorithms into an ensemble classifier (EPDRNA) to predict DNA and RNA binding sites in disease-related proteins. The dataset used in model was collated from UniProt and PDB database, and PSSM, physicochemical properties and amino acid type were used as features. The EPDRNA adopted soft voting and achieved the best AUC value of 0.73 at the DNA binding sites, and the best AUC value of 0.71 at the RNA binding sites in 10-fold cross validation in the training sets. In order to further verify the performance of the model, we assessed EPDRNA for the prediction of DNA-binding sites and the prediction of RNA-binding sites on the independent test dataset. The EPDRNA achieved 85% recall rate and 25% precision on the protein–DNA interaction independent test set, and achieved 82% recall rate and 27% precision on the protein–RNA interaction independent test set. The online EPDRNA webserver is freely available at http://www.s-bioinformatics.cn/epdrna.

中文翻译：

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EPDRNA：识别疾病相关蛋白质中 DNA-RNA 结合位点的模型

摘要

蛋白质-DNA 和蛋白质-RNA 相互作用参与许多生物过程并调节许多细胞功能。而且，它们与许多人类疾病有关。为了了解蛋白质-DNA 结合和蛋白质-RNA 结合的分子机制，确定蛋白质序列中的哪些残基与 DNA 和 RNA 结合非常重要。目前，特异性鉴定疾病相关蛋白-DNA和蛋白-RNA结合位点的方法很少。在这项研究中，我们将四种机器学习算法组合成一个集成分类器 (EPDRNA)，以预测疾病相关蛋白质中的 DNA 和 RNA 结合位点。模型使用的数据集来自UniProt和PDB数据库，并以PSSM、理化性质和氨基酸类型作为特征。EPDRNA采用软投票，在训练集的10倍交叉验证中，DNA结合位点的最佳AUC值为0.73，RNA结合位点的最佳AUC值为0.71。为了进一步验证模型的性能，我们在独立测试数据集上评估了EPDRNA对DNA结合位点的预测和RNA结合位点的预测。EPDRNA在蛋白质-DNA相互作用独立测试集上实现了85%的召回率和25%的精确度，在蛋白质-RNA相互作用独立测试集上实现了82%的召回率和27%的精确度。在线 EPDRNA 网络服务器可在 http://www.s-bioinformatics.cn/epdrna 免费获取。