Literature DB >> 23768230

Modeling compound-target interaction network of traditional Chinese medicines for type II diabetes mellitus: insight for polypharmacology and drug design.

Sheng Tian1, Youyong Li, Dan Li, Xiaojie Xu, Junmei Wang, Qian Zhang, Tingjun Hou.   

Abstract

In this study, in order to elucidate the action mechanism of traditional Chinese medicines (TCMs) that exhibit clinical efficacy for type II diabetes mellitus (T2DM), an integrated protocol that combines molecular docking and pharmacophore mapping was employed to find the potential inhibitors from TCM for the T2DM-related targets and establish the compound-target interaction network. First, the prediction capabilities of molecular docking and pharmacophore mapping to distinguish inhibitors from noninhibitors for the selected T2DM-related targets were evaluated. The results show that molecular docking or pharmacophore mapping can give satisfactory predictions for most targets but the validations are still quite necessary because the prediction accuracies of these two methods are variable across different targets. Then, the Bayesian classifiers by integrating the predictions from molecular docking and pharmacophore mapping were developed, and the well-validated Bayesian classifiers for 15 targets were utilized to find the potential inhibitors from TCM and establish the compound-target interaction network. The analysis of the compound-target network demonstrates that a small portion (18.6%) of the predicted inhibitors can interact with multitargets. The pharmacological activities for some potential inhibitors have been experimentally confirmed, highlighting the reliability of the Bayesian classifiers. Besides, it is interesting to find that a considerable number of the predicted multitarget inhibitors have free radical scavenging/antioxidant activities, which are closely related to T2DM. It appears that the pharmacological effect of the TCM formulas is determined not only by the compounds that interact directly with one or more T2DM-related targets, but also by the compounds with other supplementary bioactivities important for relieving T2DM, such as free radical scavenging/antioxidant effects. The mechanism uncovered by this study may offer a deep insight for understanding the theory of the classical TCM formulas for combating T2DM. Moreover, the predicted inhibitors for the T2DM-related targets may provide a good source to find new lead compounds against T2DM.

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Year:  2013        PMID: 23768230     DOI: 10.1021/ci400146u

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  10 in total

1.  Identification of compound-protein interactions through the analysis of gene ontology, KEGG enrichment for proteins and molecular fragments of compounds.

Authors:  Lei Chen; Yu-Hang Zhang; Mingyue Zheng; Tao Huang; Yu-Dong Cai
Journal:  Mol Genet Genomics       Date:  2016-08-16       Impact factor: 3.291

2.  Herb-target interaction network analysis helps to disclose molecular mechanism of traditional Chinese medicine.

Authors:  Hao Liang; Hao Ruan; Qi Ouyang; Luhua Lai
Journal:  Sci Rep       Date:  2016-11-11       Impact factor: 4.379

3.  Computational Insight into Protein Tyrosine Phosphatase 1B Inhibition: A Case Study of the Combined Ligand- and Structure-Based Approach.

Authors:  Xiangyu Zhang; Hailun Jiang; Wei Li; Jian Wang; Maosheng Cheng
Journal:  Comput Math Methods Med       Date:  2017-12-26       Impact factor: 2.238

4.  Transcriptomic and proteomic analysis of potential therapeutic target genes in the liver of metformin‑treated Sprague‑Dawley rats with type 2 diabetes mellitus.

Authors:  Yitao Chen; Yangsheng Wu; Yuanxiao Yang; Zhiwei Xu; Junfeng Tong; Zheming Li; Xiaojie Zhou; Changyu Li
Journal:  Int J Mol Med       Date:  2018-03-06       Impact factor: 4.101

5.  Network Pharmacology-Based Identification of the Mechanisms of Shen-Qi Compound Formula in Treating Diabetes Mellitus.

Authors:  Zhipeng Hu; Maoyi Yang; Liangjun Yang; Chunguang Xie; Hong Gao; Xiaoxu Fu; Hongyan Xie; Ya Liu
Journal:  Evid Based Complement Alternat Med       Date:  2020-06-04       Impact factor: 2.629

6.  Improving Small-Molecule Force Field Parameters in Ligand Binding Studies.

Authors:  Stefano Raniolo; Vittorio Limongelli
Journal:  Front Mol Biosci       Date:  2021-12-13

7.  A Drug-Target Network-Based Approach to Evaluate the Efficacy of Medicinal Plants for Type II Diabetes Mellitus.

Authors:  Jiangyong Gu; Lirong Chen; Gu Yuan; Xiaojie Xu
Journal:  Evid Based Complement Alternat Med       Date:  2013-10-10       Impact factor: 2.629

Review 8.  Network Pharmacology Studies on the Bioactive Compounds and Action Mechanisms of Natural Products for the Treatment of Diabetes Mellitus: A Review.

Authors:  Weiwei Li; Guoqi Yuan; Yuxiang Pan; Cong Wang; Haixia Chen
Journal:  Front Pharmacol       Date:  2017-02-23       Impact factor: 5.810

Review 9.  Molecular insight into the therapeutic potential of phytoconstituents targeting protein conformation and their expression.

Authors:  Vishvanath Tiwari
Journal:  Phytomedicine       Date:  2018-09-26       Impact factor: 5.340

10.  A Network Pharmacology Approach to Uncover the Molecular Mechanisms of Herbal Formula Kang-Bai-Ling for Treatment of Vitiligo.

Authors:  Manyuan Xu; Jianxin Shi; Zhongsheng Min; Hongliu Zhu; Weiguo Sun
Journal:  Evid Based Complement Alternat Med       Date:  2019-11-03       Impact factor: 2.629

  10 in total

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