In silico studies of diarylpyridine derivatives as novel HIV-1 NNRTIs using docking-based 3D-QSAR, molecular dynamics, and pharmacophore modeling approaches

A series of novel diarylpyridine derivatives has recently been identified as HIV-1 non-nucleoside reverse transcriptase inhibitors (NNRTIs), and most of them exhibited potent activities against HIV-1 strains, with EC 50 values in the low nanomolar range. However, the three-dimensional quantitative s...

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Veröffentlicht in:RSC advances 2018-12, Vol.8 (71), p.4529-4543
Hauptverfasser: Wan, Youlan, Tian, Yafeng, Wang, Wenjie, Gu, Shuangxi, Ju, Xiulian, Liu, Genyan
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container_end_page 4543
container_issue 71
container_start_page 4529
container_title RSC advances
container_volume 8
creator Wan, Youlan
Tian, Yafeng
Wang, Wenjie
Gu, Shuangxi
Ju, Xiulian
Liu, Genyan
description A series of novel diarylpyridine derivatives has recently been identified as HIV-1 non-nucleoside reverse transcriptase inhibitors (NNRTIs), and most of them exhibited potent activities against HIV-1 strains, with EC 50 values in the low nanomolar range. However, the three-dimensional quantitative structure-activity relationships (3D-QSARs) and pharmacophore characteristics of these compounds remain to be studied. In the present study, forty-two diarylpyridine derivatives were firstly docked into HIV-1 reverse transcriptase, and molecular dynamics (10 ns) simulations were further performed to validate the reliability of the docking results, which indicated that residues Lys101, Tyr181, Tyr188, Phe227, and Trp229 might play important roles in binding with these diarylpyridines. The "U"-shaped docking conformations of all compounds were then used to construct 3D-QSAR and pharmacophore models. The satisfactory statistical parameters of CoMFA ( q loo 2 = 0.665, r ncv 2 = 0.989, r pred 2 = 0.962, etc. ) and CoMSIA ( q loo 2 = 0.727, r ncv 2 = 0.988, r pred 2 = 0.912, etc. ) models demonstrated that both constructed models had excellent predictability, and their contour maps gave insights into the structural requirements of the diarylpyridines for the anti-HIV-1 activity. A docking-conformation-based pharmacophore model, containing three hydrophobic centers, three hydrogen-bond acceptors, and three hydrogen-bond donors, was also established. The observations in this study might provide important information for the rational design and development of novel HIV-1 NNRTIs. Computational modeling approaches were successfully applied to a series of diarylpyridine derivatives as novel HIV-1 NNRTIs.
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However, the three-dimensional quantitative structure-activity relationships (3D-QSARs) and pharmacophore characteristics of these compounds remain to be studied. In the present study, forty-two diarylpyridine derivatives were firstly docked into HIV-1 reverse transcriptase, and molecular dynamics (10 ns) simulations were further performed to validate the reliability of the docking results, which indicated that residues Lys101, Tyr181, Tyr188, Phe227, and Trp229 might play important roles in binding with these diarylpyridines. The "U"-shaped docking conformations of all compounds were then used to construct 3D-QSAR and pharmacophore models. The satisfactory statistical parameters of CoMFA ( q loo 2 = 0.665, r ncv 2 = 0.989, r pred 2 = 0.962, etc. ) and CoMSIA ( q loo 2 = 0.727, r ncv 2 = 0.988, r pred 2 = 0.912, etc. ) models demonstrated that both constructed models had excellent predictability, and their contour maps gave insights into the structural requirements of the diarylpyridines for the anti-HIV-1 activity. A docking-conformation-based pharmacophore model, containing three hydrophobic centers, three hydrogen-bond acceptors, and three hydrogen-bond donors, was also established. The observations in this study might provide important information for the rational design and development of novel HIV-1 NNRTIs. 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subjects Chemistry
Computer simulation
Construction
Derivatives
Molecular docking
Molecular dynamics
Pharmacology
Three dimensional models
title In silico studies of diarylpyridine derivatives as novel HIV-1 NNRTIs using docking-based 3D-QSAR, molecular dynamics, and pharmacophore modeling approaches
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