School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China.
School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China.
Brief Bioinform. 2022 Jul 18;23(4). doi: 10.1093/bib/bbac236.
Protein-DNA and protein-RNA interactions are involved in many biological activities. In the post-genome era, accurate identification of DNA- and RNA-binding residues in protein sequences is of great significance for studying protein functions and promoting new drug design and development. Therefore, some sequence-based computational methods have been proposed for identifying DNA- and RNA-binding residues. However, they failed to fully utilize the functional properties of residues, leading to limited prediction performance. In this paper, a sequence-based method iDRNA-ITF was proposed to incorporate the functional properties in residue representation by using an induction and transfer framework. The properties of nucleic acid-binding residues were induced by the nucleic acid-binding residue feature extraction network, and then transferred into the feature integration modules of the DNA-binding residue prediction network and the RNA-binding residue prediction network for the final prediction. Experimental results on four test sets demonstrate that iDRNA-ITF achieves the state-of-the-art performance, outperforming the other existing sequence-based methods. The webserver of iDRNA-ITF is freely available at http://bliulab.net/iDRNA-ITF.
蛋白质-DNA 和蛋白质-RNA 相互作用涉及许多生物活性。在后基因组时代，准确识别蛋白质序列中的 DNA 和 RNA 结合残基对于研究蛋白质功能和促进新药设计和开发具有重要意义。因此，已经提出了一些基于序列的计算方法来识别 DNA 和 RNA 结合残基。然而，它们未能充分利用残基的功能特性，导致预测性能有限。本文提出了一种基于序列的方法 iDRNA-ITF，通过使用归纳和转移框架，将功能特性纳入残基表示中。核酸结合残基的特性通过核酸结合残基特征提取网络来诱导，然后转移到 DNA 结合残基预测网络和 RNA 结合残基预测网络的特征集成模块中进行最终预测。在四个测试集上的实验结果表明，iDRNA-ITF 达到了最先进的性能，优于其他现有的基于序列的方法。iDRNA-ITF 的网络服务器可免费访问：http://bliulab.net/iDRNA-ITF。
