Allostery is considered important in regulating protein’s activity. Drug development depends on the understanding of allosteric mechanisms, especially the identification of allosteric sites, which is a prerequisite in drug discovery and design. Many computational methods have been developed for allosteric site prediction using pocket features and protein dynamics. Here, we present an ensemble learning method, consisting of eXtreme gradient boosting and graph convolutional neural network, to predict allosteric sites. Our model can learn physical properties and topology without any prior information, and shows good performance under multiple indicators. Prediction results showed that 84.9% of allosteric pockets in the test set appeared in the top 3 positions. The PASSer: Protein Allosteric Sites Server (https://passer.smu.edu), along with a command line interface (https://github.com/smutaogroup/passerCLI) provide insights for further analysis in drug discovery.

变构被认为在调节蛋白质活性方面很重要。药物开发取决于对变构机制的理解，尤其是变构位点的识别，这是药物发现和设计的先决条件。已经开发了许多计算方法来使用口袋特征和蛋白质动力学进行变构位点预测。在这里，我们提出了一种集成学习方法，由 eXtreme 梯度提升和图卷积神经网络组成，用于预测变构位点。我们的模型可以在没有任何先验知识的情况下学习物理特性和拓扑信息，并在多项指标下表现良好。预测结果显示，测试集中 84.9% 的变构口袋出现在前 3 个位置。PASSer：蛋白质变构站点服务器 (https://passer.smu.edu) 以及命令行界面 (https://github.com/smutaogroup/passerCLI) 为药物发现的进一步分析提供了见解。