布鲁顿酪氨酸激酶(Bruton's tyrosine kinase,BTK)在B细胞恶性肿瘤和自身免疫性疾病等多种生物学过程中发挥重要作用,因此,抑制BTK是治疗多种疾病的一种有效策略.为了开发一种基于机器学习的虚拟筛选方法,以识别潜在的BTK抑制剂.首先,通过收集了一个由3 499个活性BTK抑制剂和7 927个非活性化合物组成的数据集,并使用3种分子描述符和6个机器学习算法构建了 18个分类模型和2种集成分类模型.模型性能评估显示,基于直径为4的扩展连接指纹(extend-ed-connectivity fingerprint with diameter 4,ECFP4)描述符和深度神经网络(deep neural network,DNN)、随机森林(random forest,RF)、支持向量机(support vector machine,SVM)算法构建的集成模型更加准确可靠.接着,利用最佳集成模型从ZINC数据库中的5 ×106个分子中筛选出93个潜在的新型的BTK抑制剂,通过分子对接进一步分析它们与BTK蛋白晶体的结合模式.最终筛选出6个具有高亲和力的BTK抑制剂,它们能与活性口袋中的关键的氨基酸残基Thr474、Glu475、Met477、Cys481和Asp539等形成稳定的氢键相互作用.预测的吸收、分布、代谢、排泄和毒性(absorption,distribution,metabolism,excretion and toxicity,ADMET)参数表明这些候选化合物具有良好的药代动力学参数和安全性.分子动力学模拟进一步说明了这些化合物能与BTK蛋白稳定结合,有望成为开发新型BTK抑制剂的先导化合物.
Screening of Novel BTK Inhibitors Based on Ensemble Machine Learning and Molecular Docking Method
BTK(Bruton's tyrosine kinase)is played a crucial role in various biological processes,including B-cell malignancies and autoimmune diseases.Thus,the inhibition of BTK represents an effective strategy for treating a broad range of diseases.The objec-tive of this study is to develop a machine learning-based virtual screening method for the identification of potential BTK inhibitors.First,a dataset consisting of 3 499 active BTK inhibitors and 7 927 inactive compounds was collected.Then,18 classification models and two integrated classification models were constructed using three molecular descriptors and six machine learning algorithms.The model performance evaluation was conducted,showing that the integrated model constructed based on ECFP(4extended-connectivity fingerprint with diameter 4)descriptors and DNN(deep neural network),RF(random forest),SVM(support vector machine)algo-rithms was more accurate and reliable Next,93 potential novel BTK inhibitors were screened from 5 million molecules in the ZINC da-tabase using the best-integrated model.Their binding modes to BTK protein crystals were further analyzed by molecular docking.Ulti-mately,six BTK inhibitors exhibiting high affinity are identified based on their potential to form stable hydrogen bond interactions with critical amino acid residues,namely Thr474,Glu475,Met477,Cys481,and Asp539 within the enzyme's active pocket.The predicted ADMET(absorption,distribution,metabolism,excretion and toxicity)parameters suggest that the identified candidate compounds possess favorable pharmacokinetic parameters and safety profiles.Molecular dynamics simulation further demonstrates that these com-pounds can stably bind to BTK protein and are expected to be lead compounds for the development of novel BTK inhibitors.
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