AlzhCPI: A knowledge base for predicting chemical-protein interactions towards Alzheimer's disease.
AlzhCPI: A knowledge base for predicting chemical-protein interactions towards Alzheimer's disease.
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AlzhCPI：预测阿尔茨海默病的化学-蛋白质相互作用的知识库
DOI：
10.1371/journal.pone.0178347
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Du GH
中科院分区：
文献类型：
--
作者：
Fang J;Wang L;Li Y;Lian W;Pang X;Wang H;Yuan D;Wang Q;Liu AL;Du GH
阿尔茨海默病（AD）是一种复杂的进行性神经退行性疾病。为了对抗阿尔茨海默病，科学家们正在寻找多靶点定向配体（mtdl）来延缓疾病进展。化学-蛋白质相互作用（CPI）的计算机预测可以加速靶点识别和药物发现。在此之前，我们开发了100个二元分类器，使用多目标定量构效关系（mt-QSAR）方法预测25个关键目标的AD CPI。在本研究中，我们旨在应用mt-QSAR方法来扩大模型库，以预测CPI对AD的影响。基于朴素贝叶斯（NB）和递归分割（RP）算法，进一步构建了104个二分类器来预测26个临床前AD靶点的CPI。采用内部5倍交叉验证和外部测试集验证分别对训练集和测试集的性能进行评估。受试者工作特征曲线（ROC）下面积为0.629 ~ 1.0，平均值为0.903。此外，我们开发了一个名为AlzhCPI的web服务器，集成了大约204个二元分类器的综合信息，在网络药理学和药物重新定位方面具有潜在的应用前景。AlzhCPI网站为http://rcidm.org/AlzhCPI/index.html。为说明AlzhCPI的适用性，本研究采用开发的系统药理学方法对石菖蒲抗AD进行了系统药理学研究，从整体上加深对石菖蒲作用机制的认识。
Alzheimer's disease (AD) is a complicated progressive neurodegeneration disorder. To confront AD, scientists are searching for multi-target-directed ligands (MTDLs) to delay disease progression. The in silico prediction of chemical-protein interactions (CPI) can accelerate target identification and drug discovery. Previously, we developed 100 binary classifiers to predict the CPI for 25 key targets against AD using the multi-target quantitative structure-activity relationship (mt-QSAR) method. In this investigation, we aimed to apply the mt-QSAR method to enlarge the model library to predict CPI towards AD. Another 104 binary classifiers were further constructed to predict the CPI for 26 preclinical AD targets based on the naive Bayesian (NB) and recursive partitioning (RP) algorithms. The internal 5-fold cross-validation and external test set validation were applied to evaluate the performance of the training sets and test set, respectively. The area under the receiver operating characteristic curve (ROC) for the test sets ranged from 0.629 to 1.0, with an average of 0.903. In addition, we developed a web server named AlzhCPI to integrate the comprehensive information of approximately 204 binary classifiers, which has potential applications in network pharmacology and drug repositioning. AlzhCPI is available online at http://rcidm.org/AlzhCPI/index.html. To illustrate the applicability of AlzhCPI, the developed system was employed for the systems pharmacology-based investigation of shichangpu against AD to enhance the understanding of the mechanisms of action of shichangpu from a holistic perspective.