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基于药物信息学的方法揭示植物化合物对口腔病原体的群体感应淬灭活性

Pharmacoinformatics-Based Approach for Uncovering the Quorum-Quenching Activity of Phytocompounds against the Oral Pathogen, .

机构信息

Department of Biotechnology, Kalasalingam Academy of Research and Education, Krishnankoil 626126, India.

Department of Hydrobiology, Faculty of Biology, University of Warsaw, 02-089 Warsaw, Poland.

出版信息

Molecules. 2023 Jul 19;28(14):5514. doi: 10.3390/molecules28145514.

Abstract

, a gram-positive oral pathogen, is the primary causative agent of dental caries. Biofilm formation, a critical characteristic of , is regulated by quorum sensing (QS). This study aimed to utilize pharmacoinformatics techniques to screen and identify effective phytochemicals that can target specific proteins involved in the quorum sensing pathway of . A computational approach involving homology modeling, model validation, molecular docking, and molecular dynamics (MD) simulation was employed. The 3D structures of the quorum sensing target proteins, namely SecA, SMU1784c, OppC, YidC2, CiaR, SpaR, and LepC, were modeled using SWISS-MODEL and validated using a Ramachandran plot. Metabolites from (Neem), (Noni), and (Miswak) were docked against these proteins using AutoDockTools. MD simulations were conducted to assess stable interactions between the highest-scoring ligands and the target proteins. Additionally, the ADMET properties of the ligands were evaluated using SwissADME and pkCSM tools. The results demonstrated that campesterol, meliantrol, stigmasterol, isofucosterol, and ursolic acid exhibited the strongest binding affinity for CiaR, LepC, OppC, SpaR, and Yidc2, respectively. Furthermore, citrostadienol showed the highest binding affinity for both SMU1784c and SecA. Notably, specific amino acid residues, including ASP86, ARG182, ILE179, GLU143, ASP237, PRO101, and VAL84 from CiaR, LepC, OppC, SecA, SMU1784c, SpaR, and YidC2, respectively, exhibited significant interactions with their respective ligands. While the docking study indicated favorable binding energies, the MD simulations and ADMET studies underscored the substantial binding affinity and stability of the ligands with the target proteins. However, further in vitro studies are necessary to validate the efficacy of these top hits against .

摘要

牙龈卟啉单胞菌是一种革兰氏阳性口腔病原体,是龋齿的主要致病因子。生物膜形成是牙龈卟啉单胞菌的一个关键特征,由群体感应(QS)调节。本研究旨在利用计算药物化学技术筛选和鉴定能够针对与群体感应途径相关的特定蛋白质的有效植物化学物质。采用同源建模、模型验证、分子对接和分子动力学(MD)模拟的计算方法。使用 SWISS-MODEL 对群体感应靶蛋白(SecA、SMU1784c、OppC、YidC2、CiaR、SpaR 和 LepC)的 3D 结构进行建模,并使用 Ramachandran 图进行验证。使用 AutoDockTools 将来自 Neem、Noni 和 Miswak 的代谢物对接至这些蛋白质。进行 MD 模拟以评估最高得分配体与靶蛋白之间的稳定相互作用。此外,使用 SwissADME 和 pkCSM 工具评估配体的 ADMET 性质。结果表明,菜油甾醇、美莲醇、豆甾醇、异福可甾醇和熊果酸对 CiaR、LepC、OppC、SpaR 和 Yidc2 的结合亲和力最强。此外,橙花叔醇对 SMU1784c 和 SecA 表现出最高的结合亲和力。值得注意的是,CiaR、LepC、OppC、SecA、SMU1784c、SpaR 和 YidC2 中的特定氨基酸残基,包括 ASP86、ARG182、ILE179、GLU143、ASP237、PRO101 和 VAL84,分别与各自的配体表现出显著的相互作用。虽然对接研究表明具有有利的结合能,但 MD 模拟和 ADMET 研究强调了配体与靶蛋白的结合亲和力和稳定性。然而,需要进一步的体外研究来验证这些顶级命中物对 的功效。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f5a/10383507/eaace41ee65f/molecules-28-05514-g001.jpg

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