School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China.
Suzhou Key Laboratory of Virtual Reality Intelligent Interaction and Application Technology, Suzhou University of Science and Technology, Suzhou 215009, China.
Biomed Res Int. 2020 Jul 27;2020:6984045. doi: 10.1155/2020/6984045. eCollection 2020.
The knowledge of DNA-binding proteins would help to understand the functions of proteins better in cellular biological processes. Research on the prediction of DNA-binding proteins can promote the research of drug proteins and computer acidified drugs. In recent years, methods based on machine learning are usually used to predict proteins. Although great predicted performance can be achieved via current methods, researchers still need to invest more research in terms of the improvement of predicted performance. In this study, the prediction of DNA-binding proteins is studied from the perspective of evolutionary information and the support vector machine method. One machine learning model for predicting DNA-binding proteins based on evolutionary features by using Chou's 5-step rule is put forward. The results show that great predicted performance is obtained on benchmark dataset PDB1075 and independent dataset PDB186, achieving the accuracy of 86.05% and 75.30%, respectively. Thus, the method proposed is comparable to a certain degree, and it may work even better than other methods to some extent.
DNA 结合蛋白的知识有助于更好地理解蛋白质在细胞生物过程中的功能。对 DNA 结合蛋白的预测研究可以促进药物蛋白和计算机酸化药物的研究。近年来，基于机器学习的方法通常用于预测蛋白质。尽管目前的方法可以达到很好的预测性能，但研究人员仍需要在提高预测性能方面投入更多的研究。在这项研究中，从进化信息和支持向量机方法的角度研究了 DNA 结合蛋白的预测。提出了一种基于进化特征和 Chou 的 5 步规则的 DNA 结合蛋白预测的机器学习模型。结果表明，在基准数据集 PDB1075 和独立数据集 PDB186 上均获得了很好的预测性能，准确率分别达到 86.05%和 75.30%。因此，所提出的方法具有一定的可比性，在某些方面可能比其他方法效果更好。
