Co-reporter:Jiansong Fang, Zengrui Wu, Chuipu Cai, Qi Wang, Yun Tang, and Feixiong Cheng
Journal of Chemical Information and Modeling November 27, 2017 Volume 57(Issue 11) pp:2657-2657
Publication Date(Web):September 28, 2017
DOI:10.1021/acs.jcim.7b00216
Natural products with diverse chemical scaffolds have been recognized as an invaluable source of compounds in drug discovery and development. However, systematic identification of drug targets for natural products at the human proteome level via various experimental assays is highly expensive and time-consuming. In this study, we proposed a systems pharmacology infrastructure to predict new drug targets and anticancer indications of natural products. Specifically, we reconstructed a global drug–target network with 7,314 interactions connecting 751 targets and 2,388 natural products and built predictive network models via a balanced substructure–drug–target network-based inference approach. A high area under receiver operating characteristic curve of 0.96 was yielded for predicting new targets of natural products during cross-validation. The newly predicted targets of natural products (e.g., resveratrol, genistein, and kaempferol) with high scores were validated by various literature studies. We further built the statistical network models for identification of new anticancer indications of natural products through integration of both experimentally validated and computationally predicted drug–target interactions of natural products with known cancer proteins. We showed that the significantly predicted anticancer indications of multiple natural products (e.g., naringenin, disulfiram, and metformin) with new mechanism-of-action were validated by various published experimental evidence. In summary, this study offers powerful computational systems pharmacology approaches and tools for the development of novel targeted cancer therapies by exploiting the polypharmacology of natural products.
Co-reporter:Hanwen Du, Junhao Li, Yingchun Cai, Hongxiao Zhang, Guixia Liu, Yun Tang, and Weihua Li
Journal of Chemical Information and Modeling March 27, 2017 Volume 57(Issue 3) pp:616-616
Publication Date(Web):February 21, 2017
DOI:10.1021/acs.jcim.7b00012
Human cytochrome P450 3A4 (CYP3A4) is a major drug-metabolizing enzyme responsible for the metabolism of ∼50% of clinically used drugs and is often involved in drug–drug interactions. It exhibits atypical binding and kinetic behavior toward many ligands. Binding of ligands to CYP3A4 is a complex process. Recent studies from both crystallography and biochemistry suggested the existence of a peripheral ligand-binding site at the enzyme surface. However, the stability of the ligand bound at this peripheral site and the possibility of discovering new CYP3A4 ligands based on this site remain unclear. In this study, we employed a combination of molecular docking, multiparalleled molecular dynamics (MD) simulations, virtual screening, and experimental bioassay to investigate these issues. Our results revealed that the binding mode of progesterone (PGS), a substrate of CYP3A4, in the crystal structure was not stable and underwent a significant conformational change. Through Glide docking and MD refinement, it was found that PGS was able to stably bind at the peripheral site via contacts with Phe215, Phe219, Phe220, and Asp214. On the basis of the refined peripheral site, virtual screening was then performed against the Enamine database. A total of three compounds were finally found to have inhibitory activity against CYP3A4 in both human liver microsome and recombinant human CYP3A4 enzyme assays, one of which showed potent inhibitory activity with IC50 lower than 1 μM and two of which exhibited moderate inhibitory activity with IC50 values lower than 10 μM. The findings not only presented the dynamic behavior of PGS at the peripheral site but also demonstrated the first indication of discovering CYP3A4 inhibitors based on the peripheral site.
Co-reporter:Hongbin Yang, Jie Li, Zengrui Wu, Weihua Li, Guixia Liu, and Yun Tang
Chemical Research in Toxicology June 19, 2017 Volume 30(Issue 6) pp:1355-1355
Publication Date(Web):May 9, 2017
DOI:10.1021/acs.chemrestox.7b00083
Identification of structural alerts for toxicity is useful in drug discovery and other fields such as environmental protection. With structural alerts, researchers can quickly identify potential toxic compounds and learn how to modify them. Hence, it is important to determine structural alerts from a large number of compounds quickly and accurately. There are already many methods reported for identification of structural alerts. However, how to evaluate those methods is a problem. In this paper, we tried to evaluate four of the methods for monosubstructure identification with three indices including accuracy rate, coverage rate, and information gain to compare their advantages and disadvantages. The Kazius’ Ames mutagenicity data set was used as the benchmark, and the four methods were MoSS (graph-based), SARpy (fragment-based), and two fingerprint-based methods including Bioalerts and the fingerprint (FP) method we previously used. The results showed that Bioalerts and FP could detect key substructures with high accuracy and coverage rates because they allowed unclosed rings and wildcard atom or bond types. However, they also resulted in redundancy so that their predictive performance was not as good as that of SARpy. SARpy was competitive in predictive performance in both training set and external validation set. These results might be helpful for users to select appropriate methods and further development of methods for identification of structural alerts.
Co-reporter:Hongbin Yang;Xiao Li;Yingchun Cai;Qin Wang;Weihua Li;Guixia Liu
MedChemComm (2010-Present) 2017 vol. 8(Issue 6) pp:1225-1234
Publication Date(Web):2017/06/21
DOI:10.1039/C7MD00074J
Chemical subcellular localization is closely related to drug distribution in the body and hence important in drug discovery and design. Although many in vivo and in vitro methods have been developed, in silico methods play key roles in the prediction of chemical subcellular localization due to their low costs and high performance. For that purpose, machine learning-based methods were developed here. At first, 614 unique compounds localized in the lysosome, mitochondria, nucleus and plasma membrane were collected from the literature. 80% of the compounds were used to build the models and the rest as the external validation set. Both fingerprints and molecular descriptors were used to describe the molecules, and six machine learning methods were applied to build the multi-classification models. The performance of the models was measured by 5-fold cross-validation and external validation. We further detected key substructures for each localization and analyzed potential structure–localization relationships, which could be very helpful for molecular design and modification. The key substructures can also be used as features complementary to fingerprints to improve the performance of the models.
Co-reporter:Qin Wang;Xiao Li;Hongbin Yang;Yingchun Cai;Yinyin Wang;Zhuang Wang;Weihua Li;Guixia Liu
RSC Advances (2011-Present) 2017 vol. 7(Issue 11) pp:6697-6703
Publication Date(Web):2017/01/18
DOI:10.1039/C6RA25267B
Rapidly and correctly identifying eye irritants or corrosive chemicals is an important issue in health hazard assessment. The purpose of this study is to describe the development of in silico methods for the classification of chemicals into irritants/corrosives or non-irritants/non-corrosives. A total of 5220 chemicals for a serious eye irritation (EI) dataset and 2299 chemicals as an eye corrosion (EC) dataset were collected from available databases and literature. Structure–activity relationship (SAR) models were developed to separately predict serious EI or EC via machine learning methods. According to the overall prediction accuracy, the Pub-SVM model gave the best results for both serious EI (overall classification accuracy CA = 0.946) and EC (CA = 0.959). The sensitivity and specificity of serious EI were 97.3% and 86.7% for the training set, and 96.9% and 82.7% for the external validation set, respectively. Similarly, the sensitivity and specificity of EC were 95.5% and 96.2% for the training set, and 94.9% and 96.2% for the external validation set, respectively. The high specificity and sensitivity indicated that our models were reliable and robust, which can be used to predict the potential seriousness of EI/EC of compounds. Moreover, several structural alerts for characterizing serious EI/EC were identified using the combination of information gain and substructure frequency analysis.
Co-reporter:Fuxing Li;Defang Fan;Hao Wang;Hongbin Yang;Weihua Li;Guixia Liu
Toxicology Research (2012-Present) 2017 vol. 6(Issue 6) pp:831-842
Publication Date(Web):2017/10/30
DOI:10.1039/C7TX00144D
Aquatic toxicity is an important issue in pesticide development. In this study, using nine molecular fingerprints to describe pesticides, binary and ternary classification models were constructed to predict aquatic toxicity of pesticides via six machine learning methods: Naïve Bayes (NB), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), Classification Tree (CT), Random Forest (RF) and Support Vector Machine (SVM). For the binary models, local models were obtained with 829 pesticides on rainbow trout (RT) and 151 pesticides on lepomis (LP), and global models were constructed on the basis of 1258 diverse pesticides on RT and LP and 278 on other fish species. After analyzing the local binary models, we found that fish species caused influence in terms of accuracy. Considering the data size and predictive range, the 1258 pesticides were also used to build global ternary models. The best local binary models were Maccs_ANN for RT and Maccs_SVM for LP, which exhibited accuracies of 0.90 and 0.90, respectively. For global binary models, the best model was Graph_SVM with an accuracy of 0.89. Accuracy of the best global ternary model Graph_SVM was 0.81, which was a little lower than that of the best global binary model. In addition, several substructural alerts were identified including nitrobenzene, chloroalkene and nitrile, which could significantly correlate with pesticide aquatic toxicity. This study provides a useful tool for an early evaluation of pesticide aquatic toxicity in environmental risk assessment.
Co-reporter:Ji Ye Wang;Hong Chen;Yin Yin Wang;Xiao Qin Wang;Han Ying Chen
BMC Systems Biology 2017 Volume 11( Issue 1) pp:103
Publication Date(Web):16 November 2017
DOI:10.1186/s12918-017-0486-1
Vitiligo is a long-term skin disease characterized by the loss of pigment in the skin. The current therapeutic approaches are limited. Although the anti-vitiligo mechanisms of Vernonia anthelmintica (L.) remain ambiguous, the herb has been broadly used in Uyghur hospitals to treat vitiligo. The overall objective of the present study aims to identify the potential lead compounds from Vernonia anthelmintica (L.) in the treatment of vitiligo via an oral route as well as the melanogenic mechanisms in the systematic approaches in silico of admetSAR and substructure-drug-target network-based inference (SDTNBI).The results showed that the top 5 active compounds with a relatively higher bioavailability that interacted with 23 therapeutic targets were identified in Vernonia anthelmintica (L.) using admetSAR and SDTNBI methods. Among these compounds, Isorhamnetin and Kaempferide, which are methyl-flavonoids, performed 1st and 2nd. Isorhamnetin and Kaempferide significantly increased the expression of melanin-biosynthetic genes (MC1R, MITF, TYR, TYRP1 and DCT) and the tyrosinase activity in B16F10 cells. Isorhamnetin and Kaempferide significantly increased the mRNA-expression of melanin-biosynthetic genes (MC1R, MITF, TYR, TYRP1 and DCT), the protein level of MITF and the tyrosinase activity. Based on the SDTNBI method and experimental verification, Isorhamnetin and Kaempferide effectively increased melanogenesis by targeting the MC1R-MITF signaling pathway, MAPK signaling pathway, PPAR signaling pathway (PPARA, PPARD, PPARG), arachidonic acid metabolism pathway (ALOX12, ALOX15, CBR1) and serotonergic synapses (ALOX12, ALOX15) in the treatment of vitiligo from a network perspective.We identified the melanogenic activity of the methyl-flavonoids Isorhamnetin and Kaempferide, which were successfully predicted in a network pharmacological analysis of Vernonia anthelmintica (L.) by admetSAR and SDTNBI methods.
Co-reporter:Junhao Li, Jinya Cai, Haixia Su, Hanwen Du, Juan Zhang, Shihui Ding, Guixia Liu, Yun Tang and Weihua Li
Molecular BioSystems 2016 vol. 12(Issue 3) pp:868-878
Publication Date(Web):05 Jan 2016
DOI:10.1039/C5MB00784D
Structure-based prediction of sites of metabolism (SOMs) mediated by cytochrome P450s (CYPs) is of great interest in drug discovery and development. However, protein flexibility and active site water molecules remain a challenge for accurate SOM prediction. CYP2C19 is one of the major drug-metabolizing enzymes and has attracted considerable attention because of its polymorphism and capability of metabolizing ∼7% clinically used drugs. In this study, we systematically evaluated the effects of protein flexibility and active site water molecules on SOM prediction for CYP2C19 substrates. Multiple conformational sampling techniques including GOLD flexible residues sampling, molecular dynamics (MD) and tCONCOORD side-chain sampling were adopted for assessing the influence of protein flexibility on SOM prediction. The prediction accuracy could be significantly improved when protein flexibility was considered using the tCONCOORD sampling method, which indicated that the side-chain conformation was important for accurate prediction. However, the inclusion of the crystallographic or MD-derived water molecule(s) does not necessarily improve the prediction accuracy. Finally, a combination of docking results with SMARTCyp was found to be able to increase the SOM prediction accuracy.
Co-reporter:Junhao Li, Hanwen Du, Zengrui Wu, Haixia Su, Guixia Liu, Yun Tang and Weihua Li
Molecular BioSystems 2016 vol. 12(Issue 6) pp:1913-1921
Publication Date(Web):05 Apr 2016
DOI:10.1039/C6MB00139D
Cytochrome P450 2C19 (CYP2C19) is one of 57 drug metabolizing enzymes in humans and is responsible for the metabolism of ∼7–10% of drugs in clinical use. Recently omeprazole-based analogues were reported to be the potent inhibitors of CYP2C19 and have the potential to be used as the tool compounds for studying the substrate selectivity of CYP2C19. However, the binding modes of these compounds with CYP2C19 remain to be elucidated. In this study, a combination of molecular docking, molecular dynamics (MD), and MM/GBSA calculations was employed to systematically investigate the interactions between these compounds and CYP2C19. The binding modes of these analogues were analyzed in detail. The results indicated that the inclusion of explicit active site water molecules could improve binding energy prediction when the water molecules formed a hydrogen bonding network between the ligand and protein. We also found that the effect of active site water molecules on binding free energy prediction was dependent on the ligand binding modes. Our results unravel the interactions of these omeprazole-based analogues with CYP2C19 and might be helpful for the future design of potent CYP2C19 inhibitors with improved metabolic properties.
Co-reporter:Juan Zhang, Shikai Gu, Xianqiang Sun, Weihua Li, Yun Tang and Guixia Liu
RSC Advances 2016 vol. 6(Issue 16) pp:13490-13497
Publication Date(Web):27 Jan 2016
DOI:10.1039/C5RA26102C
The glucagon-like peptide-1 receptor (GLP-1R) has captivated researchers because of its tremendous therapeutic effects for the treatment of type 2 diabetes mellitus (T2DM). However, since the full-length crystal structure of GLP-1R has not been revealed yet, the molecular binding mode and the activation mechanism remain unclear, which will be the obstacle for the discovery of novel potent GLP-1R agonists. In the present study, we constructed the model of GLP-1R in its full length and explored the binding modes between GLP-1 and GLP-1R by means of a bunch of computational methods including homology modeling, protein–protein docking, and molecular dynamics simulations. Our model is in agreement with previous experiment and the results from our MD simulations that verified the binding modes between GLP-1 and GLP-1R are reasonable. What's more, we found the absence or presence of GLP-1 significantly affected the conformation of extracellular domain (ECD) of GLP-1R. The GLP-1R in the apo form stabilized in a ‘closed’ state which is unfavorable to the binding of GLP-1, resembling as the GCGR. By contrast, in the GLP-1/GLP-1R complex, GLP-1R maintained an ‘open’ state.
Co-reporter:Jianxin Cheng, Weihua Li, Guixia Liu, Weiliang Zhu and Yun Tang
RSC Advances 2016 vol. 6(Issue 17) pp:13626-13635
Publication Date(Web):22 Jan 2016
DOI:10.1039/C5RA24911B
In drug design and discovery, ligand binding kinetics combines pharmacokinetics and pharmacodynamics and more and more attention is paid to it. The κ-opioid G-protein coupled receptor (κ-OR) has been determined to be a promising drug target for the treatment of depression- and anxiety-related diseases. Among the κ-OR selective antagonists, JDTic is a failed antidepressant with a short drug-target residence time (RT), whereas LY2456302 exhibits better effects with a longer RT than JDTic. To investigate the inhibition mechanism of the κ-OR induced by the two ligands, unbiased molecular dynamics and well-tempered metadynamics simulations were performed on JDTic–κ-OR and LY2456302–κ-OR complexes. Through detailed analyses of the simulations, a strong but single interaction mode was found to be responsible for the adverse effects and short RT of JDTic, which could be considered as an alert for other chemotypes, whereas LY2456302 was more advanced, mainly due to its multiple metastable states. Based on Eyring’s equation, the relative RT of LY2456302/JDTic, determined from the activation free energy of dissociation ΔG≠off, was efficiently calculated and was in good agreement with experimental data. Thus, these simulations might be helpful for the further design of antidepressants targeting κ-OR with reasonable RT.
Co-reporter:Chen Zhang, Yuan Zhou, Shikai Gu, Zengrui Wu, Wenjie Wu, Changming Liu, Kaidong Wang, Guixia Liu, Weihua Li, Philip W. Lee and Yun Tang
Toxicology Research 2016 vol. 5(Issue 2) pp:570-582
Publication Date(Web):14 Jan 2016
DOI:10.1039/C5TX00294J
The human ether-a-go-go related gene (hERG) plays an important role in cardiac action potential. It encodes an ion channel protein named Kv11.1, which is related to long QT syndrome and may cause avoidable sudden cardiac death. Therefore, it is important to assess the hERG channel blockage of lead compounds in an early drug discovery process. In this study, we collected a large data set containing 1163 diverse compounds with IC50 values determined by the patch clamp method on mammalian cell lines. The whole data set was divided into 80% as the training set and 20% as the test set. Then, five machine learning methods were applied to build a series of binary classification models based on 13 molecular descriptors, five fingerprints and molecular descriptors combining fingerprints at four IC50 thresholds to discriminate hERG blockers from nonblockers, respectively. Models built by molecular descriptors combining fingerprints were validated by using an external validation set containing 407 compounds collected from the hERGCentral database. The performance indicated that the model built by molecular descriptors combining fingerprints yielded the best results and each threshold had its best suitable method, which means that hERG blockage assessment might depend on threshold values. Meanwhile, kNN and SVM methods were better than the others for model building. Furthermore, six privileged substructures were identified using information gain and frequency analysis methods, which could be regarded as structural alerts of cardiac toxicity mediated by hERG channel blockage.
Co-reporter:Jinya Cai, Junhao Li, Juan Zhang, Shihui Ding, Guixia Liu, Weihua Li and Yun Tang
RSC Advances 2015 vol. 5(Issue 110) pp:90871-90880
Publication Date(Web):14 Oct 2015
DOI:10.1039/C5RA19602G
Human aromatase, also known as cytochrome P450 19A1, specifically catalyzes the conversion of androgens to estrogens, and therefore represents an important drug target for the treatment of breast cancer. Recently, azole compounds previously used as agricultural fungicides and antimycotic drugs were reported to exhibit potent inhibitory activity against aromatase. However, the molecular mechanism of these azole compounds against aromatase remains unclear. In this study, a combination of molecular docking and several types of molecular dynamics (MD) simulations including conventional MD, random acceleration MD and steered MD, was employed to investigate the interactions of aromatase with letrozole and imazalil, two azole compounds with distinct inhibitory activities against aromatase. The binding modes of these two inhibitors were obtained by molecular docking and refined by MD simulation. The binding free energies were calculated based on the MD snapshots by using the MM-GBSA method and were found to be in agreement with the relative potency of the experimental binding affinities. Our results further demonstrated that these inhibitors had different favorable unbinding pathways in aromatase, and the unbinding manners differed in their favorable dissociation routes. Several residues lining the pathways were found important for the inhibitor egress. These findings would be helpful not only for understanding the inhibitory mechanism of azole compounds against aromatase, but also for designing new aromatase inhibitors.
Co-reporter:Lu Sun, Chen Zhang, Yingjie Chen, Xiao Li, Shulin Zhuang, Weihua Li, Guixia Liu, Philip W. Lee and Yun Tang
Toxicology Research 2015 vol. 4(Issue 2) pp:452-463
Publication Date(Web):06 Jan 2015
DOI:10.1039/C4TX00174E
Aquatic toxicity is an important endpoint in the evaluation of chemically adverse effects on ecosystems. In this study, in silico models were developed for the prediction of chemical aquatic toxicity in different fish species. Firstly, a large data set containing 6422 data points on aquatic toxicity with 1906 diverse chemicals was constructed. Using molecular descriptors and fingerprints to represent the molecules, local and global models were then developed with five machine learning methods based on three fish species (rainbow trout, fathead minnow and bluegill sunfish). For the local models, both binary and ternary classification models were obtained for each of the three fish species. For the global models, data of all the three fish species were used together. The predictive accuracy of both the local and global models was around 0.8 for the test sets. Moreover, data of the sheepshead minnow were used as an external validation set. For the best local model (model 2), the predictive accuracy was 0.875 for the sheepshead minnow, while for the best global model (model 14), the predictive accuracy was 0.872 for the sheepshead minnow. The FN compounds in model 2 and model 14 were 18 and 10, respectively. Hence, model 14 was the best model, and thus could predict the toxicity of other fish species’. Furthermore, information gain and ChemoTyper methods were used to identify toxic substructures, which could significantly correlate with chemical aquatic toxicity. This study provides critical tools for an early evaluation of chemical aquatic toxicity in an environmental hazard assessment.
Co-reporter:Xiao Li, Lei Chen, Feixiong Cheng, Zengrui Wu, Hanping Bian, Congying Xu, Weihua Li, Guixia Liu, Xu Shen, and Yun Tang
Journal of Chemical Information and Modeling 2014 Volume 54(Issue 4) pp:1061-1069
Publication Date(Web):April 4, 2014
DOI:10.1021/ci5000467
Chemical acute oral toxicity is an important end point in drug design and environmental risk assessment. However, it is difficult to determine by experiments, and in silico methods are hence developed as an alternative. In this study, a comprehensive data set containing 12 204 diverse compounds with median lethal dose (LD50) was compiled. These chemicals were classified into four categories, namely categories I, II, III and IV, based on the criterion of the U.S. Environmental Protection Agency (EPA). Then several multiclassification models were developed using five machine learning methods, including support vector machine (SVM), C4.5 decision tree (C4.5), random forest (RF), κ-nearest neighbor (kNN), and naïve Bayes (NB) algorithms, along with MACCS and FP4 fingerprints. One-against-one (OAO) and binary tree (BT) strategies were employed for SVM multiclassification. Performances were measured by two external validation sets containing 1678 and 375 chemicals, separately. The overall accuracy of the MACCS-SVMOAO model was 83.0% and 89.9% for external validation sets I and II, respectively, which showed reliable predictive accuracy for each class. In addition, some representative substructures responsible for acute oral toxicity were identified using information gain and substructure frequency analysis methods, which might be very helpful for further study to avoid the toxicity.
Co-reporter:Yayun Sheng, Yingjie Chen, Lei Wang, Guixia Liu, Weihua Li, Yun Tang
Journal of Molecular Graphics and Modelling 2014 Volume 54() pp:90-99
Publication Date(Web):November 2014
DOI:10.1016/j.jmgm.2014.09.005
•Structure-based method was used for SOM prediction of CYP2A6 substrates.•Effects of CYP2A6 protein flexibility on SOM prediction for its substrates were explored.•The snapshot structures from MD simulations exhibited higher prediction accuracy than the crystal structures.•Prediction accuracy for the low Km substrates is comparable to that by ligand-based methods.Structure-based prediction for the site of metabolism (SOM) of a compound metabolized by human cytochrome P450s (CYPs) is highly beneficial in drug discovery and development. However, the flexibility of the CYPs’ active site remains a huge challenge for accurate SOM prediction. Compared with other CYPs, the active site of CYP2A6 is relatively small and rigid. To address the impact of the flexibility of CYP2A6 active site residues on the SOM prediction for substrates, in this work, molecular dynamics (MD) simulations and molecular docking were used to predict the SOM of 96 CYP2A6 substrates. Substrates with known SOM were docked into the snapshot structures from MD simulations and the crystal structures of CYP2A6. Compared to the crystal structures, the protein structures obtained from MD simulations showed more accurate prediction for SOM. Our results indicated that the flexibility of the active site of CYP2A6 significantly affects the SOM prediction results. Further analysis for the 40 substrates with definite Km values showed that the prediction accuracy for the low Km substrates is comparable to that by ligand-based methods.
Co-reporter:Feixiong Cheng, Weihua Li, Zengrui Wu, Xichuan Wang, Chen Zhang, Jie Li, Guixia Liu, and Yun Tang
Journal of Chemical Information and Modeling 2013 Volume 53(Issue 4) pp:753-762
Publication Date(Web):March 25, 2013
DOI:10.1021/ci400010x
Prediction of polypharmacological profiles of drugs enables us to investigate drug side effects and further find their new indications, i.e. drug repositioning, which could reduce the costs while increase the productivity of drug discovery. Here we describe a new computational framework to predict polypharmacological profiles of drugs by the integration of chemical, side effect, and therapeutic space. On the basis of our previous developed drug side effects database, named MetaADEDB, a drug side effect similarity inference (DSESI) method was developed for drug–target interaction (DTI) prediction on a known DTI network connecting 621 approved drugs and 893 target proteins. The area under the receiver operating characteristic curve was 0.882 ± 0.011 averaged from 100 simulated tests of 10-fold cross-validation for the DSESI method, which is comparative with drug structural similarity inference and drug therapeutic similarity inference methods. Seven new predicted candidate target proteins for seven approved drugs were confirmed by published experiments, with the successful hit rate more than 15.9%. Moreover, network visualization of drug–target interactions and off-target side effect associations provide new mechanism-of-action of three approved antipsychotic drugs in a case study. The results indicated that the proposed methods could be helpful for prediction of polypharmacological profiles of drugs.
Co-reporter:Feixiong Cheng, Weihua Li, Xichuan Wang, Yadi Zhou, Zengrui Wu, Jie Shen, and Yun Tang
Journal of Chemical Information and Modeling 2013 Volume 53(Issue 4) pp:744-752
Publication Date(Web):March 22, 2013
DOI:10.1021/ci4000079
Adverse drug events (ADEs) are the harms associated with uses of given medications at normal dosages, which are crucial for a drug to be approved in clinical use or continue to stay on the market. Many ADEs are not identified in trials until the drug is approved for clinical use, which results in adverse morbidity and mortality. To date, millions of ADEs have been reported around the world. Methods to avoid or reduce ADEs are an important issue for drug discovery and development. Here, we reported a comprehensive database of adverse drug events (namely MetaADEDB), which included more than 520 000 drug–ADE associations among 3059 unique compounds (including 1330 drugs) and 13 200 ADE items by data integration and text mining. All compounds and ADEs were annotated with the most commonly used concepts defined in Medical Subject Headings (MeSH). Meanwhile, a computational method, namely the phenotypic network inference model (PNIM), was developed for prediction of potential ADEs based on the database. The area under the receive operating characteristic curve (AUC) is more than 0.9 by 10-fold cross validation, while the AUC value was 0.912 for an external validation set extracted from the US-FDA Adverse Events Reporting System, which indicated that the prediction capability of the method was reliable. MetaADEDB is accessible free of charge at http://www.lmmd.org/online_services/metaadedb/. The database and the method provide us a useful tool to search for known side effects or predict potential side effects for a given drug or compound.
Co-reporter:Ruliang Xie, Qianfei Zhao, Tao Zhang, Jing Fang, Xiangdong Mei, Jun Ning, Yun Tang
Bioorganic & Medicinal Chemistry 2013 Volume 21(Issue 1) pp:278-282
Publication Date(Web):1 January 2013
DOI:10.1016/j.bmc.2012.10.030
The cluster effect is an effective strategy to explore new lead compounds, and has been successfully applied in rational drug design and screening. A series of novel organophosphorous-homodimers were designed and synthesized based on the dual-site structure characteristics of acetylcholinesterase (AChE). The compounds were evaluated in vitro for their inhibitory activity to AChE extracted from Drosophila melanogaster and Musca domestic. Compound 4H showed an excellent inhibitor activity to both Drosophila melanogaster and Musca domestic with the corresponding IC50 values of 23 and 168 nM, respectively. Meanwhile, its activities against Drosophila melanogaster and Musca domestic AChE were more than 10,00,000 and 100,000-fold higher compared with the parent compound (MH), and was up to 245 and 107-fold higher than those of the positive control omethoate. The molecular docking study revealed that 4H possessed an optimal spacer length and can perfectly fit into the central pocket, active gorge, and peripheral site of DmAChE, and consequently exhibited highly improved inhibitor potency to DmAChE. The bioassay tests showed that 4 series compounds showed prominent insecticidal activities against both Lipaphser erysimi and Tetranychus cinnbarinus at a concentration of 200 mg/L. The insecticide activity of compound 4H was particularly significant that can cause 96% mortality to Tetranychus cinnbarinus after 24 h of treatment.The molecular doking between 4H and 1QO9
Co-reporter:Feixiong Cheng, Weihua Li, Yadi Zhou, Jie Li, Jie Shen, Philip W. Lee and Yun Tang
Molecular BioSystems 2013 vol. 9(Issue 6) pp:1316-1325
Publication Date(Web):04 Feb 2013
DOI:10.1039/C3MB25309K
New technologies for systems-level determinants of human exposure to drugs, industrial chemicals, pesticides, and other environmental agents provide an invaluable opportunity to extend the understanding of human health and potential environmental hazards. We report here the development of a new computational-systems toxicology framework, called predictive toxicogenomics-derived models (PTDMs). PTDMs integrate three networks of chemical–gene interactions (CGIs), chemical–disease associations (CDAs) and gene–disease associations (GDAs) to infer chemical hazard profiles, identify exposure data gaps and to incorporate genes and disease networks into chemical safety evaluations. Three comprehensive networks addressing CGI, CDA and GDA extracted from the comparative toxicogenomics database (CTD) were constructed. The areas under the receiver operating characteristics curve ranged from 0.85 to 0.97 and were yielded using our methodology using a 10-fold cross validation by a simulation carried out 100 times. As the illustrated examples show, we predicted new potential target genes and diseases for bisphenol A and aspirin. The molecular hypothesis and experimental evidence from published literature for these predictions were provided. The results demonstrated that our method has potential applications for chemical profiling in human health exposure and environmental hazard assessment.
Co-reporter:Rongwei Shi;Weihua Li;Guixia Liu
Chinese Journal of Chemistry 2013 Volume 31( Issue 9) pp:1219-1227
Publication Date(Web):
DOI:10.1002/cjoc.201300427
Abstract
Drug metabolism is an important issue in drug discovery. Understanding how a drug is metabolized in the body will provide helpful information for lead optimization. Cytochrome P450 2D6 (CYP2D6) is a key enzyme for drug metabolism and responsible for the metabolism of about one third marketed drugs. Aripiprazole is an atypical antipsychotic and metabolized by CYP2D6 to its hydroxylated form. In this study, a series of computational methods were performed to understand how CYP2D6 accomplishes the 4-hydroxylation of aripiprazole. Molecular docking and molecular dynamics simulations were first performed to prepare the initial conformations for QM/MM calculations. The results revealed two possible conformations for the drug-CYP2D6 complex. The ONIOM method for QM/MM calculations was then carried out to show detailed reaction pathways for the CYP2D6-catalyzed aripiprazole hydroxylation reaction, which demonstrated that the dominant reactive channel was electrophilic and involved an initial attack on the π-system of the dichlorophenyl group of aripiprazole to produce cation δ-complex. Furthermore, the product complex for each conformation was thermodynamically stable, which is in good agreement with previous reports.
Co-reporter:Guoping Hu ; Xi Li ; Xuan Zhang ; Yaozong Li ; Lei Ma ; Liu-Meng Yang ; Guixia Liu ; Weihua Li ; Jin Huang ; Xu Shen ; Lihong Hu ; Yong-Tang Zheng
Journal of Medicinal Chemistry 2012 Volume 55(Issue 22) pp:10108-10117
Publication Date(Web):October 9, 2012
DOI:10.1021/jm301226a
This study aims to identify inhibitors that bind at the interface of HIV-1 integrase (IN) and human LEDGF/p75, which represents a novel target for anti-HIV therapy. To date, only a few such inhibitors have been reported. Here structure-based virtual screening was performed to search for the inhibitors from an in-house library of natural products and their derivatives. Among the 38 compounds selected by our strategy, 18 hits were discovered. The two most potent inhibitors showed IC50 values at 0.32 and 0.26 μM, respectively. Three compounds were subsequently selected for anti-HIV assays, among which (E)-3-(2-chlorophenyl)-1-(2,4-dihydroxyphenyl)prop-2-en-1-one (NPD170) showed the highest antiviral activity (EC50 = 1.81 μM). The antiviral mechanism of these compounds was further explored, and the results validated that the compounds interrupted the binding of transfected IN to endogenous LEDGF/p75. These findings could be helpful for anti-HIV drug discovery.
Co-reporter:Weihua Li, Jing Fu, Feixiong Cheng, Mingyue Zheng, Jian Zhang, Guixia Liu, and Yun Tang
Journal of Chemical Information and Modeling 2012 Volume 52(Issue 11) pp:3043-3052
Publication Date(Web):October 28, 2012
DOI:10.1021/ci300459k
Farnesoid X receptor (FXR, NR1H4) is a member of a nuclear receptor superfamily, which plays important roles in bile acid homeostasis, lipoprotein and glucose metabolism, and hepatic regeneration. GW4064 is a potent and selective FXR agonist and has become a tool compound to probe the physiological functions of FXR. Until now, the mechanism of GW4064 entering and leaving the FXR pocket is still poorly understood. Here, we report a computational study of GW4064 unbinding pathways from FXR by using several molecular dynamics (MD) simulation techniques. Based on the crystal structure of FXR in complex with GW4064, conventional MD was first used to refine the binding and check the stability of GW4064 in the FXR pocket. Random acceleration MD simulations were then performed to explore the possible unbinding pathways of GW4064 from FXR. Four main pathway clusters were found, among which three subpathways, namely Paths 2A, 2B, and 1B, were observed most frequently. Multiple steered MD simulations were further employed to estimate the maximum rupture force and the sum of the forces and to characterize the intermediate states of the ligand unbinding process. By comparing the average force profiles and structural changes, Paths 2A and 2B were identified to be the most favorable unbinding pathways. The former is located between the H1–H2 loop and the H5–H6 loop, and the latter is located in the cleft formed by the H5–H6 loop, H6, and H7. Moreover, the residues lining the pathways were analyzed for their roles in ligand unbinding. Based on our results, the possible structural modification strategies on GW4064 were also proposed.
Co-reporter:Congying Xu, Feixiong Cheng, Lei Chen, Zheng Du, Weihua Li, Guixia Liu, Philip W. Lee, and Yun Tang
Journal of Chemical Information and Modeling 2012 Volume 52(Issue 11) pp:2840-2847
Publication Date(Web):October 2, 2012
DOI:10.1021/ci300400a
Mutagenicity is one of the most important end points of toxicity. Due to high cost and laboriousness in experimental tests, it is necessary to develop robust in silico methods to predict chemical mutagenicity. In this paper, a comprehensive database containing 7617 diverse compounds, including 4252 mutagens and 3365 nonmutagens, was constructed. On the basis of this data set, high predictive models were then built using five machine learning methods, namely support vector machine (SVM), C4.5 decision tree (C4.5 DT), artificial neural network (ANN), k-nearest neighbors (kNN), and naïve Bayes (NB), along with five fingerprints, namely CDK fingerprint (FP), Estate fingerprint (Estate), MACCS keys (MACCS), PubChem fingerprint (PubChem), and Substructure fingerprint (SubFP). Performances were measured by cross validation and an external test set containing 831 diverse chemicals. Information gain and substructure analysis were used to interpret the models. The accuracies of fivefold cross validation were from 0.808 to 0.841 for top five models. The range of accuracy for the external validation set was from 0.904 to 0.980, which outperformed that of Toxtree. Three models (PubChem-kNN, MACCS-kNN, and PubChem-SVM) showed high and reliable predictive accuracy for the mutagens and nonmutagens and, hence, could be used in prediction of chemical Ames mutagenicity.
Co-reporter:Feixiong Cheng, Yutaka Ikenaga, Yadi Zhou, Yue Yu, Weihua Li, Jie Shen, Zheng Du, Lei Chen, Congying Xu, Guixia Liu, Philip W. Lee, and Yun Tang
Journal of Chemical Information and Modeling 2012 Volume 52(Issue 3) pp:655-669
Publication Date(Web):February 14, 2012
DOI:10.1021/ci200622d
Biodegradation is the principal environmental dissipation process. Due to a lack of comprehensive experimental data, high study cost and time-consuming, in silico approaches for assessing the biodegradable profiles of chemicals are encouraged and is an active current research topic. Here we developed in silico methods to estimate chemical biodegradability in the environment. At first 1440 diverse compounds tested under the Japanese Ministry of International Trade and Industry (MITI) protocol were used. Four different methods, namely support vector machine, k-nearest neighbor, naïve Bayes, and C4.5 decision tree, were used to build the combinatorial classification probability models of ready versus not ready biodegradability using physicochemical descriptors and fingerprints separately. The overall predictive accuracies of the best models were more than 80% for the external test set of 164 diverse compounds. Some privileged substructures were further identified for ready or not ready biodegradable chemicals by combining information gain and substructure fragment analysis. Moreover, 27 new predicted chemicals were selected for experimental assay through the Japanese MITI test protocols, which validated that all 27 compounds were predicted correctly. The predictive accuracies of our models outperform the commonly used software of the EPI Suite. Our study provided critical tools for early assessment of biodegradability of new organic chemicals in environmental hazard assessment.
Co-reporter:Guoping Hu, Guanglin Kuang, Wen Xiao, Weihua Li, Guixia Liu, and Yun Tang
Journal of Chemical Information and Modeling 2012 Volume 52(Issue 5) pp:1103-1113
Publication Date(Web):May 2, 2012
DOI:10.1021/ci300030u
Virtual screening (VS) can be accomplished in either ligand- or structure-based methods. In recent times, an increasing number of 2D fingerprint and 3D shape similarity methods have been used in ligand-based VS. To evaluate the performance of these ligand-based methods, retrospective VS was performed on a tailored directory of useful decoys (DUD). The VS performances of 14 2D fingerprints and four 3D shape similarity methods were compared. The results revealed that 2D fingerprints ECFP_2 and FCFP_4 yielded better performance than the 3D Phase Shape methods. These ligand-based methods were also compared with structure-based methods, such as Glide docking and Prime molecular mechanics generalized Born surface area rescoring, which demonstrated that both 2D fingerprint and 3D shape similarity methods could yield higher enrichment during early retrieval of active compounds. The results demonstrated the superiority of ligand-based methods over the docking-based screening in terms of both speed and hit enrichment. Therefore, considering ligand-based methods first in any VS workflow would be a wise option.
Co-reporter:Feixiong Cheng, Weihua Li, Yadi Zhou, Jie Shen, Zengrui Wu, Guixia Liu, Philip W. Lee, and Yun Tang
Journal of Chemical Information and Modeling 2012 Volume 52(Issue 11) pp:3099-3105
Publication Date(Web):October 23, 2012
DOI:10.1021/ci300367a
Absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties play key roles in the discovery/development of drugs, pesticides, food additives, consumer products, and industrial chemicals. This information is especially useful when to conduct environmental and human hazard assessment. The most critical rate limiting step in the chemical safety assessment workflow is the availability of high quality data. This paper describes an ADMET structure–activity relationship database, abbreviated as admetSAR. It is an open source, text and structure searchable, and continually updated database that collects, curates, and manages available ADMET-associated properties data from the published literature. In admetSAR, over 210 000 ADMET annotated data points for more than 96 000 unique compounds with 45 kinds of ADMET-associated properties, proteins, species, or organisms have been carefully curated from a large number of diverse literatures. The database provides a user-friendly interface to query a specific chemical profile, using either CAS registry number, common name, or structure similarity. In addition, the database includes 22 qualitative classification and 5 quantitative regression models with highly predictive accuracy, allowing to estimate ecological/mammalian ADMET properties for novel chemicals. AdmetSAR is accessible free of charge at http://www.admetexp.org.
Co-reporter:Siyuan Li, Chunying Guo, Xianqiang Sun, Yaozong Li, Hongli Zhao, Dongmei Zhan, Minbo Lan, Yun Tang
European Journal of Medicinal Chemistry 2012 Volume 49() pp:271-278
Publication Date(Web):March 2012
DOI:10.1016/j.ejmech.2012.01.021
4-anilinoquinazoline and 4-anilinoquinoline scaffolds bearing a 2,2,6,6-tetramethylpiperidine-N-oxyl(TEMPO) have been synthesized and evaluated for their ability to inhibit EGFR tyrosine kinase and A431 cell lines. Compared to their corresponding parent compounds, all of the new compounds bearing a TEMPO showed more efficient inhibition for EGFR and A431 cells. Furthermore, we have proved that these molecules bearing a TEMPO can exactly get into A431 cells exerting inhibitory effect that may be used for EPR detecting. In our docking model, quinazolines bearing a TEMPO on either 6- or 3-positions took different linking modes according to EGFR crystal structure. In contrast to their parent compounds, these new TEMPO-derived analogues possessed compatible inhibitory effect that might be useful as potential EGFR inhibitors and as EPR bio-probes.Highlights► 4-anilinoquinazoline and 4-anilinoquinoline bearing a TEMPO were synthesized. ► These compounds showed inhibitory activity on EGFR kinase and A431 cells. ► These compounds might be useful as EPR bio-probes. ► Docking studies were carried out.
Co-reporter:Feixiong Cheng, Yadi Zhou, Jie Li, Weihua Li, Guixia Liu and Yun Tang
Molecular BioSystems 2012 vol. 8(Issue 9) pp:2373-2384
Publication Date(Web):24 May 2012
DOI:10.1039/C2MB25110H
Elucidation of chemical–protein interactions (CPI) is the basis of target identification and drug discovery. It is time-consuming and costly to determine CPI experimentally, and computational methods will facilitate the determination of CPI. In this study, two methods, multitarget quantitative structure–activity relationship (mt-QSAR) and computational chemogenomics, were developed for CPI prediction. Two comprehensive data sets were collected from the ChEMBL database for method assessment. One data set consisted of 81689 CPI pairs among 50924 compounds and 136 G-protein coupled receptors (GPCRs), while the other one contained 43965 CPI pairs among 23376 compounds and 176 kinases. The range of the area under the receiver operating characteristic curve (AUC) for the test sets was 0.95 to 1.0 and 0.82 to 1.0 for 100 GPCR mt-QSAR models and 100 kinase mt-QSAR models, respectively. The AUC of 5-fold cross validation were about 0.92 for both 176 kinases and 136 GPCRs using the chemogenomic method. However, the performance of the chemogenomic method was worse than that of mt-QSAR for the external validation set. Further analysis revealed that there was a high false positive rate for the external validation set when using the chemogenomic method. In addition, we developed a web server named CPI-Predictor, http://www.lmmd.org/online_services/cpi_predictor/, which is available for free. The methods and tool have potential applications in network pharmacology and drug repositioning.
Co-reporter:Guoping Hu;Xi Li;Yaozong Li;Xianqiang Sun;Guixia Liu;Weihua Li;Jin Huang;Xu Shen
Chinese Journal of Chemistry 2012 Volume 30( Issue 12) pp:
Publication Date(Web):
DOI:10.1002/cjoc.201290035
Co-reporter:Xiangui Huang;Hongwei Shi;Jiangmeng Ren;Guixia Liu;Bubing Zeng
Chinese Journal of Chemistry 2012 Volume 30( Issue 6) pp:1305-1309
Publication Date(Web):
DOI:10.1002/cjoc.201200298
Abstract
This paper describes a rapid and practical synthetic route involving six-step reactions towards the diastereoselectively synthesis of (±)-endo-Epibatidine, starting from 6-chloro-3-pyridinecarboxaldehye. The effective Henry reaction gave precursor (E)-6-(6-chloropyridin-3-yl)-5-nitrohex-5-en-2-one (3a) which could be used in the next step. Various benzoic acid derivatives were used to optimize intramolecular Michael addition of ketone to pyridinylnitroolefins to provide the key intermediate 3-(6-chloropyridin-3-yl)-4-nitrocyclohexanone ((±)-7a) with high yield.
Co-reporter:Guoping Hu;Xi Li;Yaozong Li;Xianqiang Sun;Guixia Liu;Weihua Li;Jin Huang;Xu Shen
Chinese Journal of Chemistry 2012 Volume 30( Issue 12) pp:2752-2758
Publication Date(Web):
DOI:10.1002/cjoc.201200897
Abstract
HIV-1 integrase (IN)-mediated integration of viral DNA into the host chromosome is an essential step in the virus life cycle. Human lens epithelium-derived growth factor (LEDGF/p75) has been found to function as a cellular cofactor in this process. The LEDGF/p75-IN interaction hence represents an attractive target for anti-HIV therapy. In this study, natural products were virtually screened against the LEDGF/p75 binding pocket of HIV-1 IN. 24 compounds were selected and obtained from the National Compound Resource Center of China. AlphaScreen assays characterized 8 of these 24 natural products as potent LEDGF/p75-IN interaction inhibitors. The active compounds whose IC50 values ranged from 0.56 to 14.55 µmol/L could be used as lead compounds for further investigation. This work confirmed that natural products are valuable resources for antiviral drug discovery.
Co-reporter:Siyuan Li, Xianqiang Sun, Hongli Zhao, Yun Tang, Minbo Lan
Bioorganic & Medicinal Chemistry Letters 2012 Volume 22(Issue 12) pp:4004-4009
Publication Date(Web):15 June 2012
DOI:10.1016/j.bmcl.2012.04.092
By using of structure-based virtual screening, 13 novel epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors were discovered from 197,116 compounds in the SPECS database here. Among them, 8 compounds significantly inhibited EGFR kinase activity with IC50 values lower than 10 μM. 3-{[1-(3-Chloro-4-fluorophenyl)-3,5-dioxo-4-pyrazolidinylidene]methyl}phenyl 2-thiophenecarboxylate (13), particularly, was the most potent inhibitor possessing the IC50 value of 3.5 μM. The docking studies also provide some useful information that the docking models of the 13 compounds are beneficial to find a new path for designing novel EGFR inhibitors.By using of structure-based virtual screening, 13 novel epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors were hit from SPECS database.
Co-reporter:Guoping Hu;Xi Li;Xianqiang Sun;Weiqiang Lu;Guixia Liu
Journal of Molecular Modeling 2012 Volume 18( Issue 12) pp:4995-5003
Publication Date(Web):2012 December
DOI:10.1007/s00894-012-1494-0
Integration of viral-DNA into host chromosome mediated by the viral protein HIV-1 integrase (IN) is an essential step in the HIV-1 life cycle. In this process, human protein Lens epithelium-derived growth factor (LEDGF/p75) is discovered to function as a cellular co-factor for integration. LEDGF/p75-HIV-1 IN interaction represents an attractive target for anti-HIV therapy. In this study, approved drugs were investigated for the finding of potential inhibitors on this target. Via molecular docking against the LEDGF/p75-binding pocket of HIV-1 IN, 26 old drugs were selected from the DrugBank and purchased for bioassays. Among them, eight, namely Atorvastatin, Bumetanide, Candesartan, Carbidopa, Diclofenac, Diflunisal, Eprosartan, and Sulindac, were identified as potential inhibitors of LEDGF/p75- HIV-1 IN interaction, whose IC50 values ranged from 6.5 μM to 36.8 μM. In addition, Atorvastatin was previously reported to block HIV-1 replication and may have an important implication for the treatment of AIDS. Our results suggested a mechanism of action for the anti-HIV effects of Atorvastatin. This work provides a new example of inhibitors targeting protein-protein interaction and confirmed that old drugs were valuable sources for antiviral drug discovery.
Co-reporter:ZheJun Xu;FeiXiong Cheng;Jie Li;YaDi Zhou;Ni Su;WeiHua Li
Science China Chemistry 2012 Volume 55( Issue 11) pp:2407-2418
Publication Date(Web):2012 November
DOI:10.1007/s11426-012-4606-x
Adenosine receptors are promising therapeutic targets in drug discovery. In this study, three-dimensional pharmacophore models of human adenosine receptor A1 and A3 antagonists were developed based on 26 and 23 diverse compounds, respectively. The best A1 pharmacophore model (A1_Hopy1) consists of four features: one hydrogen bond donor, one hydrophobic point and two ring aromatics, while the best A3 pharmacophore model (A3_Hopy1) also has four features: one hydrogen bond acceptor, one hydrophobic point and two ring aromatics. The correlation coefficients were 0.840 for A1 test set with 146 diverse compounds and 0.827 for A3 test set with 238 diverse compounds. In the simulated virtual screening experiments, high enrichment factors of 6.51 and 6.90 were obtained for A1_Hopy1 and A3_Hopy1 models, respectively. Moreover, two models also showed high subtype-selectivity in the simulated virtual screening experiments. These results could be helpful for the discovery of novel potent and selective A1 and A3 antagonists.
Co-reporter:Jing Zhao;Yan-Yan Chu;Ai-Tao Li;Xin Ju;Xu-Dong Kong;Jiang Pan;Jian-He Xu
Advanced Synthesis & Catalysis 2011 Volume 353( Issue 9) pp:1510-1518
Publication Date(Web):
DOI:10.1002/adsc.201100031
Abstract
A novel epoxide hydrolase (BMEH) with unusual (R)-enantioselectivity and very high activity was cloned from Bacillus megaterium ECU1001. Highest enantioselectivities (E>200) were achieved in the bioresolution of ortho-substituted phenyl glycidyl ethers and para-nitrostyrene oxide. Worthy of note is that the substrate structure remarkably affected the enantioselectivities of the enzyme, as a reversed (S)-enantiopreference was unexpectedly observed for the ortho-nitrophenyl glycidyl ether. As a proof-of-concept, five enantiopure epoxides (>99% ee) were obtained in high yields, and a gram-scale preparation of (S)-ortho-methylphenyl glycidyl ether was then successfully performed within a few hours, indicating that BMEH is an attractive biocatalyst for the efficient preparation of optically active epoxides.
Co-reporter:Cui Li, Xiao-Peng He, Yin-Jie Zhang, Zhen Li, Li-Xin Gao, Xiao-Xin Shi, Juan Xie, Jia Li, Guo-Rong Chen, Yun Tang
European Journal of Medicinal Chemistry 2011 Volume 46(Issue 9) pp:4212-4218
Publication Date(Web):September 2011
DOI:10.1016/j.ejmech.2011.06.025
With an aim of developing novel protein tyrosine phosphatase (PTP) 1B inhibitors based on sugar scaffolds, a focused library of benzyl 6-triazolo(hydroxy)benzoic glucosides was efficiently constructed via the modular and selective Cu(I)-catalyzed azide-alkyne 1,3-dipolar cycloaddtion (click chemistry). These glycoconjugates bearing alkyl chain length-varied bridges between the sugar and (hydroxy)-benzoic moieties were identified as new PTP1B inhibitors with selectivity over T-Cell PTP (TCPTP), SH2-Containing PTP-1 (SHP-1), SHP-2 and Leukocyte Antigen-Related Tyrosine Phosphatase (LAR). Molecular docking study sequentially elaborated the plausible binding modes of the structurally diverse sugar-based inhibitors with PTP1B.Novel PTP1B inhibitors based on a sugar scaffold were efficiently constructed via click chemistry. Molecular docking study sequentially elaborated their plausible binding modes with PTP1B.Highlights► Novel PTP1B inhibitors based on a glucosyl scaffold were developed. ► Click chemistry was employed to modularly construct the focused library. ► The inhibitors were identified with high selectivity on PTP1B over others. ► Plausible PTP1B inhibitor interactions were suggested by molecular docking.
Co-reporter:Feixiong Cheng, Yue Yu, Yadi Zhou, Zhonghua Shen, Wen Xiao, Guixia Liu, Weihua Li, Philip W. Lee, and Yun Tang
Journal of Chemical Information and Modeling 2011 Volume 51(Issue 10) pp:2482-2495
Publication Date(Web):August 29, 2011
DOI:10.1021/ci200317s
Cytochrome P450 inhibitory promiscuity of a drug has potential effects on the occurrence of clinical drug–drug interactions. Understanding how a molecular property is related to the P450 inhibitory promiscuity could help to avoid such adverse effects. In this study, an entropy-based index was defined to quantify the P450 inhibitory promiscuity of a compound based on a comprehensive data set, containing more than 11,500 drug-like compounds with inhibition against five major P450 isoforms, 1A2, 2C9, 2C19, 2D6, and 3A4. The results indicated that the P450 inhibitory promiscuity of a compound would have a moderate correlation with molecular aromaticity, a minor correlation with molecular lipophilicity, and no relations with molecular complexity, hydrogen bonding ability, and TopoPSA. We also applied an index to quantify the susceptibilities of different P450 isoforms to inhibition based on the same data set. The results showed that there was a surprising level of P450 inhibitory promiscuity even for substrate specific P450, susceptibility to inhibition follows the rank-order: 1A2 > 2C19 > 3A4 > 2C9 > 2D6. There was essentially no correlation between P450 inhibitory potency and specificity and minor negative trade-offs between P450 inhibitory promiscuity and catalytic promiscuity. In addition, classification models were built to predict the P450 inhibitory promiscuity of new chemicals using support vector machine algorithm with different fingerprints. The area under the receiver operating characteristic curve of the best model was about 0.9, evaluated by 5-fold cross-validation. These findings would be helpful for understanding the mechanism of P450 inhibitory promiscuity and improving the P450 inhibitory selectivity of new chemicals in drug discovery.
Co-reporter:Feixiong Cheng, Yue Yu, Jie Shen, Lei Yang, Weihua Li, Guixia Liu, Philip W. Lee, and Yun Tang
Journal of Chemical Information and Modeling 2011 Volume 51(Issue 5) pp:996-1011
Publication Date(Web):April 14, 2011
DOI:10.1021/ci200028n
Adverse side effects of drug–drug interactions induced by human cytochrome P450 (CYP) inhibition is an important consideration, especially, during the research phase of drug discovery. It is highly desirable to develop computational models that can predict the inhibitive effect of a compound against a specific CYP isoform. In this study, inhibitor predicting models were developed for five major CYP isoforms, namely 1A2, 2C9, 2C19, 2D6, and 3A4, using a combined classifier algorithm on a large data set containing more than 24,700 unique compounds, extracted from PubChem. The combined classifiers algorithm is an ensemble of different independent machine learning classifiers including support vector machine, C4.5 decision tree, k-nearest neighbor, and naïve Bayes, fused by a back-propagation artificial neural network (BP-ANN). All developed models were validated by 5-fold cross-validation and a diverse validation set composed of about 9000 diverse unique compounds. The range of the area under the receiver operating characteristic curve (AUC) for the validation sets was 0.764 to 0.815 for CYP1A2, 0.837 to 0.861 for CYP2C9, 0.793 to 0.842 for CYP2C19, 0.839 to 0.886 for CYP2D6, and 0.754 to 0.790 for CYP3A4, respectively, using the new developed combined classifiers. The overall performance of the combined classifiers fused by BP-ANN was superior to that of three classic fusion techniques (Mean, Maximum, and Multiply). The chemical spaces of data sets were explored by multidimensional scaling plots, and the use of applicability domain improved the prediction accuracies of models. In addition, some representative substructure fragments differentiating CYP inhibitors and noninhibitors were characterized by the substructure fragment analysis. These classification models are applicable for virtual screening of the five major CYP isoforms inhibitors or can be used as simple filters of potential chemicals in drug discovery.
Co-reporter:Yanyan Chu, Xianjun Chen, Yi Yang, Yun Tang
Bioorganic & Medicinal Chemistry Letters 2011 Volume 21(Issue 4) pp:1118-1121
Publication Date(Web):15 February 2011
DOI:10.1016/j.bmcl.2010.12.129
Ero1p, using molecular oxygen as its preferred terminal electron acceptor, promotes disulfide bond formation by interaction with protein disulfide isomerase. Dysfunction of Ero1p leads to strong activation of the unfolded protein response and marked loss of cell viability. However, modest attenuation of Ero1p improves the fitness of yeast challenged with high levels of protein misfolding in their endoplasmic reticulum stress. Partial inhibition of Ero1p is hence of great significance. In the present paper, a docking-based virtual screening method was performed to identify inhibitors of Ero1p and 12 hits were successfully obtained from 81 purchased compounds with micromolar inhibition against Ero1p. Particularly, six of the hits demonstrated remarkable potency with IC50 <30 μM and held the prospect of becoming lead compounds. Then the interaction modes were analyzed for further lead optimization.
Co-reporter:Qianfei Zhao, Ruliang Xie, Tao Zhang, Jing Fang, Xiangdong Mei, Jun Ning, Yun Tang
Bioorganic & Medicinal Chemistry Letters 2011 Volume 21(Issue 21) pp:6404-6408
Publication Date(Web):1 November 2011
DOI:10.1016/j.bmcl.2011.08.098
Homo- and hetero-dimers of inactive organophosphorous group(s) dramatically enhanced the acetylcholinesterase (AChE; EC 3.1.1.7) inhibiting potency, with the highest potency observed at a tether length of 6 methylene groups (6d) for the homodimers, and 7 methylene groups (8e) for the heterodimers. The docking model of Drosophila melanogaster AChE suggested that 6d and 8e bound at the catalytic and peripheral sites of AChE, in which two organophosphorous groups of 6d individually oriented towards TRP83 of catalytic sites and TRP321 of peripheral sites, and phthalicimide group of 8e was appropriately arranged for a π–π interaction with the phenyl ring of TYR330, furthermore, the organophosphorous group introduced hydrophobic interaction with TRP83. The compounds prepared in this work demonstrated high insecticidal activity to Lipaphis erysimi and Tetranychus cinnbarinus at the concentration 300 mg/L.Hypothetical binding mode between 6d (carbon in green, oxygen in red, phosphorus in purple and sulfur in yellow) and the DmAChE active site gorge (only a few amino acid residues are shown for clarity).Hypothetical binding mode between 8e (carbon in green, oxygen in red, phosphorus in purple and sulfur in yellow) and the DmAChE active site gorge (only a few amino acid residues are shown for clarity).
Co-reporter:Jianxin Cheng;Guixia Liu;Jing Zhang;Zhejun Xu
Journal of Molecular Modeling 2011 Volume 17( Issue 3) pp:477-493
Publication Date(Web):2011 March
DOI:10.1007/s00894-010-0745-1
To probe the selective mechanism of agonists binding to three opioid receptor subtypes, ligand-based and receptor-based methods were implemented together and subtype characteristics of opioid agonists were clearly described. Three pharmacophore models of opioid agonists were generated by the Catalyst/HypoGen program. The best pharmacophore models for μ, δ and κ agonists contained four, five and five features, respectively. Meanwhile, the three-dimensional structures of three receptor subtypes were modeled on the basis of the crystal structure of β2-adrenergic receptor, and molecular docking was conducted further. According to these pharmacophore models and docking results, the similarities and differences among agonists of three subtypes were identified. μ or δ agonists, for example, could form one hydrogen bond separately with Tyr129 and Tyr150 at TMIII, whereas κ ones formed a π-π interaction in that place. These findings may be crucial for the development of novel selective analgesic drugs.
Co-reporter:Chunhua Lu;Fangfang Jin;Cui Li;Weihua Li;Guixia Liu
Journal of Molecular Modeling 2011 Volume 17( Issue 10) pp:2513-2523
Publication Date(Web):2011 October
DOI:10.1007/s00894-010-0936-9
5-hydroxytryptamine-2c (5-HT2c) receptor antagonists have clinical utility in the management of nervous system. In this work, ligand-based and receptor-based methods were used to investigate the binding mode of h5-HT2c receptor antagonists. First, the pharmacophore modeling of the h5-HT2c receptor antagonists was carried out by CATALYST. Then, the h5-HT2c antagonists were docked to the h5-HT2c receptor model. Subsequently, the comprehensive analysis of the pharmacophore and docking results revealed the structure-activity relationship of 5-HT2c receptor antagonists and the key residues involved in the interactions. For example, three hydrophobic points in the ligands corresponded to the region surrounded by Val135, Val208, Phe214, Ala222, Phe327, Phe328 and Val354 of the h5-HT2c receptor. The carbonyl group of compound 1 formed a hydrogen bond with Asn331. The nitrogen atom in the piperidine of compound 1 corresponding to the positive ionizable position of the best pharmacophore formed the electrostatic interactions with the carbonyl of Asp134, Asn331 and Val354, and with the hydroxyl group of Ser334. In addition, a predictive CoMFA model was developed based on the 24 compounds that were used as the training set in the pharmacophore modeling. Our results were not only useful to explore the detailed mechanism of the interactions between the h5-HT2c receptor and antagonists, but also provided suggestions in the discovery of novel 5-HT2c receptor antagonists.
Co-reporter:You Xu, Zhonghua Shen, Jie Shen, Guixia Liu, Weihua Li, Yun Tang
Journal of Molecular Graphics and Modelling 2011 30() pp: 1-9
Publication Date(Web):September 2011
DOI:10.1016/j.jmgm.2011.05.002
Co-reporter:Xianqiang Sun, Yaozong Li, Weihua Li, Zhejun Xu, Yun Tang
Journal of Molecular Graphics and Modelling 2011 Volume 29(Issue 5) pp:693-701
Publication Date(Web):February 2011
DOI:10.1016/j.jmgm.2010.12.001
Type 2 histamine receptor (H2R) is widely distributed in the body. Its main function is modulating the secretion of gastric acid. Most gastric acid-related diseases are closely associated with it. In this study, a combination of pharmacophore modeling, homology modeling, molecular docking and molecular dynamics methods were performed on human H2R and its agonists to investigate interaction details between them. At first, a pharmacophore model of H2R agonists was developed, which was then validated by QSAR and database searching. Afterwards, a model of the H2R was built utilizing homology modeling method. Then, a reference agonist was docked into the receptor model by induced fit docking. The ‘induced’ model can dramatically improve the recovery ratio from 46.8% to 69.5% among top 10% of the ranked database in the simulated virtual screening. The pharmocophore model and the receptor model matched very well each other, which provided valuable information for future studies. Asp98, Asp186 and Tyr190 played key roles in the binding of H2R agonists, and direct interactions were observed between the three residues and agonists. Residue Tyr250 could also form a hydrogen bond with H2R agonists. These findings would be very useful for the discovery of novel and potent H2R agonists.Graphical abstractResearch highlights▶ A H2 receptor agonist pharmacophore model was built. ▶ A homology model was built for H2 receptor. ▶ The models were validated by diverse methods and they were consistent with each other. ▶ The ‘induced’ model can dramatically improve the enrichment factor.
Co-reporter:Jie Shen ; Chengfang Tan ; Yanyan Zhang ; Xi Li ; Weihua Li ; Jin Huang ; Xu Shen
Journal of Medicinal Chemistry 2010 Volume 53(Issue 14) pp:5361-5365
Publication Date(Web):June 16, 2010
DOI:10.1021/jm100369g
With virtual screening based on a structure optimized through molecular dynamics (MD) and bioassays, 18 potent ligands of estrogen receptor (ER) β were discovered from 70 purchased compounds here. Among them, dual profile was observed in two ligands (1a and 1b), as agonists for ERβ and antagonists for ERα, and they might serve as lead compounds for selective ER modulators. The results also suggest that structures optimized through MD are applicable to lead discovery.
Co-reporter:Feixiong Cheng, Zhejun Xu, Guixia Liu, Yun Tang
European Journal of Medicinal Chemistry 2010 Volume 45(Issue 8) pp:3459-3471
Publication Date(Web):August 2010
DOI:10.1016/j.ejmech.2010.04.039
Ligand-based and receptor-based methods were used to investigate the binding modes of human adenosine A2B antagonists. At first, pharmacophore models were developed based on 140 diverse A2B antagonists from literature. Meanwhile, the structural model of A2B receptor was built up based on the crystal structure of human A2A receptor and validated by Induced Fit docking, Glide-XP and Glide-SP docking. Two models matched each other very well and some important implications were hence obtained. The residues of Phe173 and Glu174 in the second extracellular loop and Asn254 were crucial to the antagonists binding to form π–π stacking and hydrogen-bonding interactions. These findings would be very helpful for the discovery of novel and potent A2B antagonists.A consensus model of human A2B antagonists was developed. These results provided helpful information for the discovery of novel and potent A2B antagonists.
Co-reporter:Yaozong Li, Jie Shen, Xianqiang Sun, Weihua Li, Guixia Liu and Yun Tang
Journal of Chemical Information and Modeling 2010 Volume 50(Issue 6) pp:1134-1146
Publication Date(Web):May 19, 2010
DOI:10.1021/ci9004157
Ribonucleic acid (RNA) molecules play central roles in a variety of biological processes and, hence, are attractive targets for therapeutic intervention. In recent years, molecular docking techniques have become one of the most popular and successful approaches in drug discovery; however, almost all docking programs are protein based. The adaptability of popular docking programs in RNA world has not been systematically evaluated. This paper describes the comprehensive evaluation of two widely used protein-based docking programs—GOLD and Glide—for their docking and virtual screening accuracies against RNA targets. Using multiple docking strategies, both GOLD 4.0 and Glide 5.0 successfully reproduced most binding modes of the 60 tested RNA complexes. Applying different docking/scoring combinations, significant enrichments from the simulated virtual and fragment screening experiments were achieved against tRNA decoding A site of 16S rRNA (rRNA A-site). Our study demonstrated that current protein-based docking programs can fulfill general docking tasks against RNA, and these programs are very helpful in RNA-based drug discovery and design.
Co-reporter:Jie Shen, Feixiong Cheng, You Xu, Weihua Li and Yun Tang
Journal of Chemical Information and Modeling 2010 Volume 50(Issue 6) pp:1034-1041
Publication Date(Web):May 18, 2010
DOI:10.1021/ci100104j
Over the past decade, absorption, distribution, metabolism, and excretion (ADME) property evaluation has become one of the most important issues in the process of drug discovery and development. Since in vivo and in vitro evaluations are costly and laborious, in silico techniques had been widely used to estimate ADME properties of chemical compounds. Traditional prediction methods usually try to build a functional relationship between a set of molecular descriptors and a given ADME property. Although traditional methods have been successfully used in many cases, the accuracy and efficiency of molecular descriptors must be concerned. Herein, we report a new classification method based on substructure pattern recognition, in which each molecule is represented as a substructure pattern fingerprint based on a predefined substructure dictionary, and then a support vector machine (SVM) algorithm is applied to build the prediction model. Therefore, a direct connection between substructures and molecular properties is built. The most important substructure patterns can be identified via the information gain analysis, which could help to interpret the models from a medicinal chemistry perspective. Afterward, this method was verified with two data sets, one for blood-brain barrier (BBB) penetration and the other for human intestinal absorption (HIA). The results demonstrated that the overall predictive accuracies of the best HIA model for the training and test sets were 98.5 and 98.8%, and the overall predictive accuracies of the best BBB model for the training and test sets were 98.8 and 98.4%, which confirmed the reliability of our method. In the additional validations, the predictive accuracies were 94 and 69.5% for the HIA and the BBB models, respectively. Moreover, some of the representative key substructure patterns which significantly correlated with the HIA and BBB penetration properties were also presented.
Co-reporter:Zhejun Xu;Feixiong Cheng;Chenxiao Da;Guixia Liu
Journal of Molecular Modeling 2010 Volume 16( Issue 12) pp:1867-1876
Publication Date(Web):2010 December
DOI:10.1007/s00894-010-0690-z
Three-dimensional pharmacophore models of human adenosine receptor A2A antagonists were developed based on 23 diverse compounds selected from a large number of A2A antagonists. The best pharmacophore model, Hypo1, contained five features: one hydrogen bond donor , three hydrophobic points and one ring aromatic. Its correlation coefficient, root mean square deviation, and cost difference values were 0.955, 0.921 and 84.4, respectively, suggested that the Hypo1 model was reasonable and reliable. This model was validated by three methods: a test set of 106 diverse compounds, a simulated virtual screening, and superimposition with the crystal structure of A2A receptor. The results showed that Hypo1 was not only in agreement with the A2A crystal structure and literature reports, but also well identified active A2A antagonists from the virtual database. This methodology provides helpful information and a robust tool for the discovery of potent A2A antagonists.
Co-reporter:Weihua Li, Hirotaka Ode, Tyuji Hoshino, Hong Liu, Yun Tang and Hualiang Jiang
Journal of Chemical Theory and Computation 2009 Volume 5(Issue 5) pp:1411-1420
Publication Date(Web):March 30, 2009
DOI:10.1021/ct900018t
Human cytochrome P450 2A6 is the major enzyme to catalyze coumarin 7-hydroxylation, and this enzyme also plays an important role in the metabolism of nicotine and other tobacco-specific compounds. Recent experimental data showed that the N297S and A481T mutants of P450 2A6 decreased the catalytic activity toward coumarin by about 4-fold and 10-fold, respectively. These two mutants also had about 30-fold decrease in binding affinity for coumarin when compared to its wild type. At present, however, how the mutations affect the enzymatic activity and/or the substrate binding remains unclear. In this study, a combination of molecular docking and molecular dynamics (MD) simulation was employed to investigate the above question. Our results demonstrated that the N297S mutation altered the hydrogen-bonding network mediated by a water molecule between the B′−C loop and the I helix and thus a shift of the B′ helix/B′−C loop region, whereas the A481T mutation triggered the conformational changes of its adjacent residues including Phe209 and Phe280 via an indirect manner to affect the substrate binding. However, the mutations did not significantly alter the substrate binding orientation because the only polar residue 297 in the active site provided the hydrogen-bonding donor to guide the binding of coumarin. Both mutations perturbed the shape of “Phe-cluster” in the active site and thus weakened the interactions with coumarin. The calculated binding free energies were in agreement with the relative potency of the experimental binding affinities.
Co-reporter:Jie Shen, Weihua Li, Guixia Liu, Yun Tang and Hualiang Jiang
The Journal of Physical Chemistry B 2009 Volume 113(Issue 30) pp:10436-10444
Publication Date(Web):July 7, 2009
DOI:10.1021/jp903785h
Estrogen receptors (ER) belong to the nuclear receptor superfamily, and two subtypes, ERα and ERβ, have been identified to date. The differentiated functions and receptor expressions of ERα and ERβ made it attracted to discover subtype-specified ligands with high selectivity. However, these two subtypes are highly homologous and only two residues differ in the ligand binding pocket. Therefore, the mechanism of ligand selectivity has become an important issue in searching selective ligands of ER subtypes. In this study, steered molecular dynamics simulations were carried out to investigate the unbinding pathways of two selective ERβ ligands from the binding pocket of both ERα and ERβ, which demonstrated that the pathway between the H11 helix and the H7∼H8 loop was the most probable for ligand escaping. Then potentials of mean force for ligands unbinding along this pathway were calculated in order to gain insights into the molecular basis for energetics of ligand unbinding and find clues of ligand selectivity. The results indicated that His524/475 in ERα/ERβ acted as a “gatekeeper” during the ligand unbinding. Especially, the H7∼H8 loop of ERβ acted as a polar “transmitter” that controlled the ligand unbinding from the binding site and contributed to the ligand selectivity. Finally, the mechanism of ligand selectivity of ER subtypes was discussed from a kinetic perspective and suggestions for improving the ligand selectivity of ERβ were also presented. These findings could be helpful for rational design of highly selective ERβ ligands.
Co-reporter:Ya-Ju Zhou, Li-Ping Zhu, Yun Tang, De-Yong Ye
European Journal of Medicinal Chemistry 2007 Volume 42(Issue 7) pp:977-984
Publication Date(Web):July 2007
DOI:10.1016/j.ejmech.2006.12.029
Structure-based 3D-QSAR studies were performed on a series of novel heteroarylpiperazine derivatives as 5-HT3 receptor antagonists with comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. The compounds were initially docked into the binding pocket of the homology model of 5-HT3 receptor using GOLD program. The docked conformations with the highest score were then extracted and used to build the 3D-QSAR models, with cross-validated rcv2 values 0.716 and 0.762 for CoMFA and CoMSIA, respectively. The CoMFA and CoMSIA contour plots were also fitted into the 3D structural model of the receptor to identify the key interactions between them, which might be helpful for designing new potent 5-HT3 receptor antagonists.3D-QSAR studies were performed on 28 heteroarylpiperazine derivatives as 5-HT3 receptor ligands by CoMFA and CoMSIA methods to discover new potent and selective 5-HT3R antagonists.
Co-reporter:Xiao Li, Weihua Li, Guixia Liu, Xu Shen, Yun Tang
Archives of Gerontology and Geriatrics (November–December 2015) Volume 61(Issue 3) pp:510-516
Publication Date(Web):November–December 2015
DOI:10.1016/j.archger.2015.08.004
Co-reporter:Chen Zhang, Feixiong Cheng, Lu Sun, Shulin Zhuang, Weihua Li, Guixia Liu, Philip W. Lee, Yun Tang
Chemosphere (March 2015) Volume 122() pp:280-287
Publication Date(Web):1 March 2015
DOI:10.1016/j.chemosphere.2014.12.001
•Robust classification models were developed by machine learning methods.•Different avian toxicity data points were discussed by category approaches.•Privileged substructures were identified via the information gain analysis.Avian species are sensitive to pesticides and industrial chemicals, and hence used as model species in evaluation of chemical toxicity. In present study, we assessed the toxicity of more than 663 diverse chemicals on 17 avian species. All the chemicals were classified into three categories, i.e. highly toxic, slightly toxic and non-toxic, based on the toxicity classification criteria of the United States Environmental Protection Agency (EPA). To evaluate these chemicals, the toxicity prediction models were built using chemical category approaches with molecular descriptors and five commonly used fingerprints, in which five machine learning methods were performed on two standard test species: aquatic bird mallard duck and terrestrial bird northern bobwhite quail. The support vector machine (SVM) method with Pubchem fingerprint performed best as revealed by 5-fold cross-validation and the external validation set on Japanese quail. No species difference existed in our database despite several chemicals with different toxicity on some avian species. The best model had an overall accuracy at 0.851 for the prediction of toxicity on avian species, which outperformed the work of Mazzatorta et al. Furthermore, several representative substructures for characterizing avian toxicity were identified via information gain (IG) method. This study would provide a new tool for chemical safety assessment.Download high-res image (149KB)Download full-size image
Co-reporter:Xiao Li, Weihua Li, Guixia Liu, Xu Shen, Yun Tang
Archives of Gerontology and Geriatrics (July–August 2016) Volume 65() pp:260
Publication Date(Web):July–August 2016
DOI:10.1016/j.archger.2016.04.007
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