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Identification of the Structural Features of Guanine Derivatives as MGMT Inhibitors Using 3D-QSAR Modeling Combined with Molecular Docking.
Molecules. 2016 Jun 23; 21(7)M

Abstract

DNA repair enzyme O⁶-methylguanine-DNA methyltransferase (MGMT), which plays an important role in inducing drug resistance against alkylating agents that modify the O⁶ position of guanine in DNA, is an attractive target for anti-tumor chemotherapy. A series of MGMT inhibitors have been synthesized over the past decades to improve the chemotherapeutic effects of O⁶-alkylating agents. In the present study, we performed a three-dimensional quantitative structure activity relationship (3D-QSAR) study on 97 guanine derivatives as MGMT inhibitors using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. Three different alignment methods (ligand-based, DFT optimization-based and docking-based alignment) were employed to develop reliable 3D-QSAR models. Statistical parameters derived from the models using the above three alignment methods showed that the ligand-based CoMFA (Qcv² = 0.672 and Rncv² = 0.997) and CoMSIA (Qcv² = 0.703 and Rncv² = 0.946) models were better than the other two alignment methods-based CoMFA and CoMSIA models. The two ligand-based models were further confirmed by an external test-set validation and a Y-randomization examination. The ligand-based CoMFA model (Qext² = 0.691, Rpred² = 0.738 and slope k = 0.91) was observed with acceptable external test-set validation values rather than the CoMSIA model (Qext² = 0.307, Rpred² = 0.4 and slope k = 0.719). Docking studies were carried out to predict the binding modes of the inhibitors with MGMT. The results indicated that the obtained binding interactions were consistent with the 3D contour maps. Overall, the combined results of the 3D-QSAR and the docking obtained in this study provide an insight into the understanding of the interactions between guanine derivatives and MGMT protein, which will assist in designing novel MGMT inhibitors with desired activity.

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Authors+Show Affiliations

Sun G
Beijing Key Laboratory of Environmental & Viral Oncology, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China. sunguohui@emails.bjut.edu.cn.
Fan T
Beijing Key Laboratory of Environmental & Viral Oncology, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China. fantengjiao2014@emails.bjut.edu.cn.
Zhang N
Beijing Key Laboratory of Environmental & Viral Oncology, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China. nanatonglei@bjut.edu.cn.
Ren T
Beijing Key Laboratory of Environmental & Viral Oncology, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China. renting@bjut.edu.cn.
Zhao L
Beijing Key Laboratory of Environmental & Viral Oncology, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China. zhaolijiao@bjut.edu.cn.
Zhong R
Beijing Key Laboratory of Environmental & Viral Oncology, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China. lifesci@bjut.edu.cn.

MeSH

DNA Modification MethylasesEnzyme ActivationEnzyme InhibitorsGuanineModels, MolecularMolecular ConformationMolecular Docking SimulationProtein BindingQuantitative Structure-Activity Relationship

Pub Type(s)

Journal Article

Language

eng

PubMed ID

27347909

Citation

Sun, Guohui, et al. "Identification of the Structural Features of Guanine Derivatives as MGMT Inhibitors Using 3D-QSAR Modeling Combined With Molecular Docking." Molecules (Basel, Switzerland), vol. 21, no. 7, 2016.
Sun G, Fan T, Zhang N, et al. Identification of the Structural Features of Guanine Derivatives as MGMT Inhibitors Using 3D-QSAR Modeling Combined with Molecular Docking. Molecules. 2016;21(7).
Sun, G., Fan, T., Zhang, N., Ren, T., Zhao, L., & Zhong, R. (2016). Identification of the Structural Features of Guanine Derivatives as MGMT Inhibitors Using 3D-QSAR Modeling Combined with Molecular Docking. Molecules (Basel, Switzerland), 21(7). https://doi.org/10.3390/molecules21070823
Sun G, et al. Identification of the Structural Features of Guanine Derivatives as MGMT Inhibitors Using 3D-QSAR Modeling Combined With Molecular Docking. Molecules. 2016 Jun 23;21(7) PubMed PMID: 27347909.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Identification of the Structural Features of Guanine Derivatives as MGMT Inhibitors Using 3D-QSAR Modeling Combined with Molecular Docking. AU - Sun,Guohui, AU - Fan,Tengjiao, AU - Zhang,Na, AU - Ren,Ting, AU - Zhao,Lijiao, AU - Zhong,Rugang, Y1 - 2016/06/23/ PY - 2016/05/10/received PY - 2016/06/08/revised PY - 2016/06/18/accepted PY - 2016/6/28/entrez PY - 2016/6/28/pubmed PY - 2017/4/19/medline KW - 3D-QSAR KW - CoMFA KW - CoMSIA KW - MGMT KW - docking KW - inhibitors JF - Molecules (Basel, Switzerland) JO - Molecules VL - 21 IS - 7 N2 - DNA repair enzyme O⁶-methylguanine-DNA methyltransferase (MGMT), which plays an important role in inducing drug resistance against alkylating agents that modify the O⁶ position of guanine in DNA, is an attractive target for anti-tumor chemotherapy. A series of MGMT inhibitors have been synthesized over the past decades to improve the chemotherapeutic effects of O⁶-alkylating agents. In the present study, we performed a three-dimensional quantitative structure activity relationship (3D-QSAR) study on 97 guanine derivatives as MGMT inhibitors using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. Three different alignment methods (ligand-based, DFT optimization-based and docking-based alignment) were employed to develop reliable 3D-QSAR models. Statistical parameters derived from the models using the above three alignment methods showed that the ligand-based CoMFA (Qcv² = 0.672 and Rncv² = 0.997) and CoMSIA (Qcv² = 0.703 and Rncv² = 0.946) models were better than the other two alignment methods-based CoMFA and CoMSIA models. The two ligand-based models were further confirmed by an external test-set validation and a Y-randomization examination. The ligand-based CoMFA model (Qext² = 0.691, Rpred² = 0.738 and slope k = 0.91) was observed with acceptable external test-set validation values rather than the CoMSIA model (Qext² = 0.307, Rpred² = 0.4 and slope k = 0.719). Docking studies were carried out to predict the binding modes of the inhibitors with MGMT. The results indicated that the obtained binding interactions were consistent with the 3D contour maps. Overall, the combined results of the 3D-QSAR and the docking obtained in this study provide an insight into the understanding of the interactions between guanine derivatives and MGMT protein, which will assist in designing novel MGMT inhibitors with desired activity. SN - 1420-3049 UR - https://www.unboundmedicine.com/medline/citation/27347909/Identification_of_the_Structural_Features_of_Guanine_Derivatives_as_MGMT_Inhibitors_Using_3D_QSAR_Modeling_Combined_with_Molecular_Docking_ DB - PRIME DP - Unbound Medicine ER -