Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Chen, Meimei, Yang, Xuemei, Lai, Xinmei, Kang, Jie, Gan, Huijuan, Gao, Yuxing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
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
_version_ 1782429464765923328
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
work_keys_str_mv AT chenmeimei structuralinvestigationforoptimizationofanthranilicacidderivativesaspartialfxragonistsbyinsilicoapproaches
AT yangxuemei structuralinvestigationforoptimizationofanthranilicacidderivativesaspartialfxragonistsbyinsilicoapproaches
AT laixinmei structuralinvestigationforoptimizationofanthranilicacidderivativesaspartialfxragonistsbyinsilicoapproaches
AT kangjie structuralinvestigationforoptimizationofanthranilicacidderivativesaspartialfxragonistsbyinsilicoapproaches
AT ganhuijuan structuralinvestigationforoptimizationofanthranilicacidderivativesaspartialfxragonistsbyinsilicoapproaches
AT gaoyuxing structuralinvestigationforoptimizationofanthranilicacidderivativesaspartialfxragonistsbyinsilicoapproaches