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通过基于配体和结构的计算方法鉴定 3-((1-(苄基(2-羟基-2-苯乙基)氨基)-1-氧代-3-苯基丙-2-基)氨甲酰基)吡嗪-2-羧酸作为非核苷类逆转录酶抑制剂的潜在抑制剂。

Identification of 3-((1-(Benzyl(2-hydroxy-2-phenylethyl)amino)-1-oxo-3-phenylpropan-2-yl)carbamoyl)pyrazine-2-carboxylic Acid as a Potential Inhibitor of Non-Nucleosidase Reverse Transcriptase Inhibitors through InSilico Ligand- and Structure-Based Approaches.

机构信息

School of Pharmacy and Research, Sanskriti University, 28 K. M. Stone, Mathura Delhi Highway, Chhata, Mathura 281401, Uttar Pradesh, India.

Department of Clinical Pharmacy, College of Pharmacy, King Khalid University, P.O. Box 61413, Abha 62529, Saudi Arabia.

出版信息

Molecules. 2021 Aug 30;26(17):5262. doi: 10.3390/molecules26175262.

DOI:10.3390/molecules26175262
PMID:34500699
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8433663/
Abstract

Non-nucleosidase reverse transcriptase inhibitors (NNRTIs) are highly promising agents for use in highly effective antiretroviral therapy. We implemented a rational approach for the identification of promising NNRTIs based on the validated ligand- and structure-based approaches. In view of our state-of-the-art techniques in drug design and discovery utilizing multiple modeling approaches, we report here, for the first time, quantitative pharmacophore modeling (HypoGen), docking, and in-house database screening approaches in the identification of potential NNRTIs. The validated pharmacophore model with three hydrophobic groups, one aromatic ring group, and a hydrogen-bond acceptor explains the interactions at the active site by the inhibitors. The model was implemented in pharmacophore-based virtual screening (in-house and commercially available databases) and molecular docking for prioritizing the potential compounds as NNRTI. The identified leads are in good corroboration with binding affinities and interactions as compared to standard ligands. The model can be utilized for designing and identifying the potential leads in the area of NNRTIs.

摘要

非核苷类逆转录酶抑制剂(NNRTIs)是极具应用前景的高效抗逆转录病毒治疗药物。我们基于已验证的配体和基于结构的方法,采用合理的方法来鉴定有前途的 NNRTIs。鉴于我们在利用多种建模方法进行药物设计和发现方面的最新技术,我们首次在这里报告了定量药效基团建模(HypoGen)、对接和内部数据库筛选方法在鉴定潜在 NNRTIs 中的应用。经过验证的药效基团模型具有三个疏水区、一个芳环组和一个氢键受体,可解释抑制剂在活性部位的相互作用。该模型已应用于基于药效基团的虚拟筛选(内部和商业数据库)和分子对接,以优先考虑作为 NNRTI 的潜在化合物。与标准配体相比,确定的先导化合物与结合亲和力和相互作用相符。该模型可用于 NNRTIs 领域的潜在先导化合物的设计和鉴定。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee83/8433663/f19d78f08888/molecules-26-05262-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee83/8433663/43cdd116b245/molecules-26-05262-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee83/8433663/4a45baaf0948/molecules-26-05262-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee83/8433663/fca517dd0765/molecules-26-05262-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee83/8433663/124ba4ac97bf/molecules-26-05262-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee83/8433663/abb6785c6d7b/molecules-26-05262-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee83/8433663/6c183f2dfe90/molecules-26-05262-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee83/8433663/a38425c90535/molecules-26-05262-g007a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee83/8433663/a7e0709670ca/molecules-26-05262-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee83/8433663/f19d78f08888/molecules-26-05262-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee83/8433663/43cdd116b245/molecules-26-05262-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee83/8433663/4a45baaf0948/molecules-26-05262-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee83/8433663/fca517dd0765/molecules-26-05262-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee83/8433663/124ba4ac97bf/molecules-26-05262-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee83/8433663/abb6785c6d7b/molecules-26-05262-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee83/8433663/6c183f2dfe90/molecules-26-05262-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee83/8433663/a38425c90535/molecules-26-05262-g007a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee83/8433663/a7e0709670ca/molecules-26-05262-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee83/8433663/f19d78f08888/molecules-26-05262-g009.jpg

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