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Identification of the Structural Features of Guanine Derivatives as MGMT Inhibitors Using 3D-QSAR Modeling Combined with Molecular Docking

DNA repair enzyme O(6)-methylguanine-DNA methyltransferase (MGMT), which plays an important role in inducing drug resistance against alkylating agents that modify the O(6) position of guanine in DNA, is an attractive target for anti-tumor chemotherapy. A series of MGMT inhibitors have been synthesiz...

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Autores principales: Sun, Guohui, Fan, Tengjiao, Zhang, Na, Ren, Ting, Zhao, Lijiao, Zhong, Rugang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6273773/
https://www.ncbi.nlm.nih.gov/pubmed/27347909
http://dx.doi.org/10.3390/molecules21070823
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author Sun, Guohui
Fan, Tengjiao
Zhang, Na
Ren, Ting
Zhao, Lijiao
Zhong, Rugang
author_facet Sun, Guohui
Fan, Tengjiao
Zhang, Na
Ren, Ting
Zhao, Lijiao
Zhong, Rugang
author_sort Sun, Guohui
collection PubMed
description DNA repair enzyme O(6)-methylguanine-DNA methyltransferase (MGMT), which plays an important role in inducing drug resistance against alkylating agents that modify the O(6) 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 O(6)-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 (Q(cv)(2) = 0.672 and R(ncv)(2) = 0.997) and CoMSIA (Q(cv)(2) = 0.703 and R(ncv)(2) = 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 (Q(ext)(2) = 0.691, R(pred)(2) = 0.738 and slope k = 0.91) was observed with acceptable external test-set validation values rather than the CoMSIA model (Q(ext)(2) = 0.307, R(pred)(2) = 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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spelling pubmed-62737732018-12-28 Identification of the Structural Features of Guanine Derivatives as MGMT Inhibitors Using 3D-QSAR Modeling Combined with Molecular Docking Sun, Guohui Fan, Tengjiao Zhang, Na Ren, Ting Zhao, Lijiao Zhong, Rugang Molecules Article DNA repair enzyme O(6)-methylguanine-DNA methyltransferase (MGMT), which plays an important role in inducing drug resistance against alkylating agents that modify the O(6) 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 O(6)-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 (Q(cv)(2) = 0.672 and R(ncv)(2) = 0.997) and CoMSIA (Q(cv)(2) = 0.703 and R(ncv)(2) = 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 (Q(ext)(2) = 0.691, R(pred)(2) = 0.738 and slope k = 0.91) was observed with acceptable external test-set validation values rather than the CoMSIA model (Q(ext)(2) = 0.307, R(pred)(2) = 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. MDPI 2016-06-23 /pmc/articles/PMC6273773/ /pubmed/27347909 http://dx.doi.org/10.3390/molecules21070823 Text en © 2016 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sun, Guohui
Fan, Tengjiao
Zhang, Na
Ren, Ting
Zhao, Lijiao
Zhong, Rugang
Identification of the Structural Features of Guanine Derivatives as MGMT Inhibitors Using 3D-QSAR Modeling Combined with Molecular Docking
title Identification of the Structural Features of Guanine Derivatives as MGMT Inhibitors Using 3D-QSAR Modeling Combined with Molecular Docking
title_full Identification of the Structural Features of Guanine Derivatives as MGMT Inhibitors Using 3D-QSAR Modeling Combined with Molecular Docking
title_fullStr Identification of the Structural Features of Guanine Derivatives as MGMT Inhibitors Using 3D-QSAR Modeling Combined with Molecular Docking
title_full_unstemmed Identification of the Structural Features of Guanine Derivatives as MGMT Inhibitors Using 3D-QSAR Modeling Combined with Molecular Docking
title_short Identification of the Structural Features of Guanine Derivatives as MGMT Inhibitors Using 3D-QSAR Modeling Combined with Molecular Docking
title_sort identification of the structural features of guanine derivatives as mgmt inhibitors using 3d-qsar modeling combined with molecular docking
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6273773/
https://www.ncbi.nlm.nih.gov/pubmed/27347909
http://dx.doi.org/10.3390/molecules21070823
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