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Structural Investigation for Optimization of Anthranilic Acid Derivatives as Partial FXR Agonists by in Silico Approaches
In this paper, a three level in silico approach was applied to investigate some important structural and physicochemical aspects of a series of anthranilic acid derivatives (AAD) newly identified as potent partial farnesoid X receptor (FXR) agonists. Initially, both two and three-dimensional quantit...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4848992/ https://www.ncbi.nlm.nih.gov/pubmed/27070594 http://dx.doi.org/10.3390/ijms17040536 |
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author | Chen, Meimei Yang, Xuemei Lai, Xinmei Kang, Jie Gan, Huijuan Gao, Yuxing |
author_facet | Chen, Meimei Yang, Xuemei Lai, Xinmei Kang, Jie Gan, Huijuan Gao, Yuxing |
author_sort | Chen, Meimei |
collection | PubMed |
description | In this paper, a three level in silico approach was applied to investigate some important structural and physicochemical aspects of a series of anthranilic acid derivatives (AAD) newly identified as potent partial farnesoid X receptor (FXR) agonists. Initially, both two and three-dimensional quantitative structure activity relationship (2D- and 3D-QSAR) studies were performed based on such AAD by a stepwise technology combined with multiple linear regression and comparative molecular field analysis. The obtained 2D-QSAR model gave a high predictive ability (R(2)(train) = 0.935, R(2)(test) = 0.902, Q(2)(LOO) = 0.899). It also uncovered that number of rotatable single bonds (b_rotN), relative negative partial charges (RPC(−)), oprea's lead-like (opr_leadlike), subdivided van der Waal’s surface area (SlogP_VSA2) and accessible surface area (ASA) were important features in defining activity. Additionally, the derived3D-QSAR model presented a higher predictive ability (R(2)(train) = 0.944, R(2)(test) = 0.892, Q(2)(LOO) = 0.802). Meanwhile, the derived contour maps from the 3D-QSAR model revealed the significant structural features (steric and electronic effects) required for improving FXR agonist activity. Finally, nine newly designed AAD with higher predicted EC(50) values than the known template compound were docked into the FXR active site. The excellent molecular binding patterns of these molecules also suggested that they can be robust and potent partial FXR agonists in agreement with the QSAR results. Overall, these derived models may help to identify and design novel AAD with better FXR agonist activity. |
format | Online Article Text |
id | pubmed-4848992 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-48489922016-05-04 Structural Investigation for Optimization of Anthranilic Acid Derivatives as Partial FXR Agonists by in Silico Approaches Chen, Meimei Yang, Xuemei Lai, Xinmei Kang, Jie Gan, Huijuan Gao, Yuxing Int J Mol Sci Article In this paper, a three level in silico approach was applied to investigate some important structural and physicochemical aspects of a series of anthranilic acid derivatives (AAD) newly identified as potent partial farnesoid X receptor (FXR) agonists. Initially, both two and three-dimensional quantitative structure activity relationship (2D- and 3D-QSAR) studies were performed based on such AAD by a stepwise technology combined with multiple linear regression and comparative molecular field analysis. The obtained 2D-QSAR model gave a high predictive ability (R(2)(train) = 0.935, R(2)(test) = 0.902, Q(2)(LOO) = 0.899). It also uncovered that number of rotatable single bonds (b_rotN), relative negative partial charges (RPC(−)), oprea's lead-like (opr_leadlike), subdivided van der Waal’s surface area (SlogP_VSA2) and accessible surface area (ASA) were important features in defining activity. Additionally, the derived3D-QSAR model presented a higher predictive ability (R(2)(train) = 0.944, R(2)(test) = 0.892, Q(2)(LOO) = 0.802). Meanwhile, the derived contour maps from the 3D-QSAR model revealed the significant structural features (steric and electronic effects) required for improving FXR agonist activity. Finally, nine newly designed AAD with higher predicted EC(50) values than the known template compound were docked into the FXR active site. The excellent molecular binding patterns of these molecules also suggested that they can be robust and potent partial FXR agonists in agreement with the QSAR results. Overall, these derived models may help to identify and design novel AAD with better FXR agonist activity. MDPI 2016-04-08 /pmc/articles/PMC4848992/ /pubmed/27070594 http://dx.doi.org/10.3390/ijms17040536 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 by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chen, Meimei Yang, Xuemei Lai, Xinmei Kang, Jie Gan, Huijuan Gao, Yuxing Structural Investigation for Optimization of Anthranilic Acid Derivatives as Partial FXR Agonists by in Silico Approaches |
title | Structural Investigation for Optimization of Anthranilic Acid Derivatives as Partial FXR Agonists by in Silico Approaches |
title_full | Structural Investigation for Optimization of Anthranilic Acid Derivatives as Partial FXR Agonists by in Silico Approaches |
title_fullStr | Structural Investigation for Optimization of Anthranilic Acid Derivatives as Partial FXR Agonists by in Silico Approaches |
title_full_unstemmed | Structural Investigation for Optimization of Anthranilic Acid Derivatives as Partial FXR Agonists by in Silico Approaches |
title_short | Structural Investigation for Optimization of Anthranilic Acid Derivatives as Partial FXR Agonists by in Silico Approaches |
title_sort | structural investigation for optimization of anthranilic acid derivatives as partial fxr agonists by in silico approaches |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4848992/ https://www.ncbi.nlm.nih.gov/pubmed/27070594 http://dx.doi.org/10.3390/ijms17040536 |
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