ESPDHot: An Effective Machine Learning-Based Approach for Predicting Protein–DNA Interaction Hotspots

Protein–DNA interactions are pivotal to various
cellular
processes. Precise identification of the hotspot residues for protein–DNA
interactions holds great significance for revealing the intricate
mechanisms in protein–DNA recognition and for providing essential
guidance for protein engineering. Aiming at protein–DNA interaction
hotspots, this work introduces an effective prediction method, ESPDHot
based on a stacked ensemble machine learning framework. Here, the
interface residue whose mutation leads to a binding free energy change
(ΔΔG) exceeding 2 kcal/mol is defined
as a hotspot. To tackle the imbalanced data set issue, the adaptive
synthetic sampling (ADASYN), an oversampling technique, is adopted
to synthetically generate new minority samples, thereby rectifying
data imbalance. As for molecular characteristics, besides traditional
features, we introduce three new characteristic types including residue
interface preference proposed by us, residue fluctuation dynamics
characteristics, and coevolutionary features. Combining the Boruta
method with our previously developed Random Grouping strategy, we
obtained an optimal set of features. Finally, a stacking classifier
is constructed to output prediction results, which integrates three
classical predictors, Support Vector Machine (SVM), XGBoost, and Artificial
Neural Network (ANN) as the first layer, and Logistic Regression (LR)
algorithm as the second one. Notably, ESPDHot outperforms the current
state-of-the-art predictors, achieving superior performance on the
independent test data set, with F1, MCC, and AUC reaching 0.571, 0.516,
and 0.870, respectively.

蛋白质-DNA相互作用（Protein–DNA interaction）对诸多细胞过程至关重要。精准识别蛋白质-DNA相互作用的热点残基（hotspot residues），对于阐明蛋白质-DNA识别的复杂机制、为蛋白质工程研究提供重要指导均具有深远意义。针对蛋白质-DNA相互作用热点残基这一研究对象，本研究提出了一种基于堆叠集成机器学习框架的有效预测方法ESPDHot。本研究将突变后可导致结合自由能变化量（ΔΔG）超过2 kcal/mol的界面残基（interface residue）定义为热点残基。为解决数据集不平衡问题，本研究采用自适应合成采样（ADASYN）这一过采样技术，通过合成生成新的少数类样本以校正数据分布不平衡问题。在分子特征维度，除传统特征外，本研究还引入了三种全新的特征类型：本团队提出的残基界面偏好性、残基波动动力学特征以及共进化特征。结合Boruta方法（Boruta method）与本团队此前开发的随机分组策略，我们筛选得到了最优特征子集。最终，本研究构建了一个堆叠分类器以输出预测结果：该分类器以支持向量机（SVM）、极端梯度提升树（XGBoost）和人工神经网络（ANN）作为第一层分类器，以逻辑回归（LR）算法作为第二层分类器。值得注意的是，ESPDHot的性能优于当前主流的先进预测器，在独立测试数据集上取得了更优异的表现，其F1值、马修斯相关系数（MCC）和受试者工作特征曲线下面积（AUC）分别达到0.571、0.516和0.870。