Wang Wei, Zhang Yu, Liu Dong, Zhang HongJun, Wang XianFang, Zhou Yun
College of Computer and Information Engineering, Henan Normal University, Xinxiang, China.
Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province, College of Computer and Information Engineering, Henan Normal University, Xinxiang, China.
Front Bioeng Biotechnol. 2022 Apr 20;10:822392. doi: 10.3389/fbioe.2022.822392. eCollection 2022.
Identification of protein-ligand binding sites plays a critical role in drug discovery. However, there is still a lack of targeted drug prediction for DNA-binding proteins. This study aims at the binding sites of DNA-binding proteins and drugs, by mining the residue interaction network features, which can describe the local and global structure of amino acids, combined with sequence feature. The predictor of DNA-binding protein-drug-binding sites is built by employing the Extreme Gradient Boosting (XGBoost) model with random under-sampling. We found that the residue interaction network features can better characterize DNA-binding proteins, and the binding sites with high betweenness value and high closeness value are more likely to interact with drugs. The model shows that the residue interaction network features can be used as an important quantitative indicator of drug-binding sites, and this method achieves high predictive performance for the binding sites of DNA-binding protein-drug. This study will help in drug discovery research for DNA-binding proteins.
蛋白质-配体结合位点的识别在药物发现中起着关键作用。然而，对于DNA结合蛋白仍缺乏靶向药物预测。本研究旨在通过挖掘残基相互作用网络特征（其可描述氨基酸的局部和全局结构）并结合序列特征，来研究DNA结合蛋白与药物的结合位点。通过采用带有随机欠采样的极端梯度提升（XGBoost）模型，构建了DNA结合蛋白-药物结合位点预测器。我们发现残基相互作用网络特征能够更好地表征DNA结合蛋白，且具有高介数中心性值和高紧密中心性值的结合位点更有可能与药物相互作用。该模型表明，残基相互作用网络特征可用作药物结合位点的重要定量指标，且该方法对DNA结合蛋白-药物的结合位点具有较高的预测性能。本研究将有助于DNA结合蛋白的药物发现研究。
