AlzhCPI: A knowledge base for predicting chemical-protein interactions towards Alzheimer’s disease

PLOS ONE, Dec 2019

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.

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AlzhCPI: A knowledge base for predicting chemical-protein interactions towards Alzheimer’s disease

May AlzhCPI: A knowledge base for predicting chemical-protein interactions towards Alzheimer's disease Jiansong Fang 0 1 2 Ling Wang 2 Yecheng Li 2 Wenwen Lian 2 Xiaocong Pang 2 Hong Wang 1 2 Dongsheng Yuan 1 2 Qi Wang 0 1 2 Ai-Lin Liu 2 Guan-Hua Du 2 0 Department of Encephalopathy, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine , Guangzhou , China , 3 Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Pre-Incubator for Innovative Drugs & Medicine, School of Bioscience and Bioengineering, South China University of Technology , Guangzhou , China , 4 Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing , PR China 1 Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine , Guangzhou , China 2 Editor: Jinn-Moon Yang, National Chiao Tung University College of Biological Science and Technology , TAIWAN 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 structureactivity relationship (mt-QSAR) method. In this investigation, we aimed to apply the mtQSAR 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. - Data Availability Statement: All relevant data are within the paper and its Supporting Information files. Medicine Collaborative Innovation Center (No.A1AFD01514A05). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Introduction Alzheimer's disease (AD) is the most common neurodegenerative disease in elderly people, which is accompanied by the progressive impairment of memory and cognitive function [ 1 ]. The pathological hallmarks of AD are mainly characterized by extracellular senile plaques (SPs) and intracellular neurofibrillary tangles (NFTs), as well as selective cholinergic neuronal loss [ 2 ]. Current drugs for AD treatment that target cholinergic and glutamatergic neurotransmission, such as donepezil and memantine, show limited benefits to most AD patients [ 3, 4 ]. Therefore, there is an urgent need to develop an effective treatment that could not only improve symptoms but also modify the disease process. The aetiology of AD is multifactorial. Considering the complexity of AD, the classic ªone drug, one targetº solution is not effective enough [ 5 ]. Indeed, many research projects in the field have been focused on developing multi-target/multifunctional therapies to modify the disease process [6±9]. Experimental identification of hits that interact with multiple proteins is costly, time consuming, and labour intensive. In silico target prediction is a fast and cheap alternative to experimental target identification approaches, which could accelerate the discovery of ªmulti-target-directed ligands (MTDLs)º against AD. The central issue of target prediction is to identify the chemical-protein interactions (CPI) between chemicals and proteins. Two main computational methods are used to predict the CPI for a given ligand, which were summarized by a recent review [ 10 ]. The methods are the ligand-based target prediction (LBTP) approach [ 11, 12 ] and the structure-based target prediction (SBTP) approach [ 13, 14 ]. As an LPTP approach, the multi-target quantitative structureactivity relationship (mt-QSAR) method is highly predictive and convenient and can simultaneously predict activities against different targets by using large and heterogeneous chemical datasets [ 15 ]. Cheng et al. built 200 mt-QSAR models for 100 GPCRs and 100 kinases using the support vector machine (SVM) algori (...truncated)


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Jiansong Fang, Ling Wang, Yecheng Li, Wenwen Lian, Xiaocong Pang, Hong Wang, Dongsheng Yuan, Qi Wang, Ai-Lin Liu, Guan-Hua Du. AlzhCPI: A knowledge base for predicting chemical-protein interactions towards Alzheimer’s disease, PLOS ONE, 2017, Volume 12, Issue 5, DOI: 10.1371/journal.pone.0178347