In-silico exploration of Attukal Kizhangu L. compounds: Promising candidates for periodontitis treatment

IF 2.6 4区 生物学 Q2 BIOLOGY Computational Biology and Chemistry Pub Date : 2024-09-07 DOI:10.1016/j.compbiolchem.2024.108186
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Abstract

A medicinal pteridophyte known as Attukal Kizhangu L. has been used to cure patients for centuries by administering plant parts based on conventional and common practices. Regarding its biological functions, significant use and advancement have been made. Extract of Attukal Kizhangu L. is the subject of the current study, which uses network pharmacology as its foundation. Three targeted compounds such as α-Lapachone, Dihydrochalcone, and Piperine were chosen for additional research from the 17 Phytoconstituents that were filtered out by the Coupled UPLC-HRMS study since they followed to Lipinski rule and showed no toxicity. The pharmacokinetics and physicochemical properties of these targeted compounds were analyzed by using three online web servers pkCSM, Swiss ADME, and Protox-II. This is the first in silico study to document these compound's effectiveness against the standard drug DOX in treating Periodontitis. The Swiss target prediction database was used to retrieve the targets of these compounds. DisGeNET and GeneCards were used to extract the targets of periodontitis. The top five hub genes were identified by Cytoscape utilizing the protein-protein interaction of common genes, from which two hub genes and three binding proteins of collagenase enzymes were used for further studies AA2, PGE2, PI2, TNFA, and PGP. The minimal binding energy observed in molecular docking, indicative of the optimal docking score, corresponds to the highest affinity between the protein and ligand. To corroborate the findings of the docking study, molecular dynamics (MD) simulations, and MMPBSA calculations were conducted for the complexes involving AA2-α-LPHE, AA2-DHC, and AA2-PPR. This research concluded that AA2-DHC was the most stable complex among the investigated interactions, surpassing the stability of the other complexes examined in comparison with the standard drug DOX. Overall, the findings supported the promotion of widespread use of Attukal Kizhangu L. in clinics as a potential therapeutic agent or may be employed for the treatment of acute and chronic Periodontitis.

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对Attukal Kizhangu L.复合物的分子内探索:有望治疗牙周炎的候选化合物
几个世纪以来,人们一直在使用一种名为阿图卡尔-奇占古(Attukal Kizhangu L.)的药用翼手目植物,根据传统和常见的做法,通过施用植物部分来治疗病人。关于它的生物功能,已经有了重要的应用和进展。本研究以阿图卡尔-基赞古提取物为主题,以网络药理学为基础。由于α-拉帕醌、二氢查尔酮和胡椒碱符合利宾斯基规则且无毒性,因此从耦合 UPLC-HRMS 研究筛选出的 17 种植物成分中选择了三种目标化合物进行进一步研究。我们使用 pkCSM、Swiss ADME 和 Protox-II 这三个在线网络服务器分析了这些目标化合物的药代动力学和理化性质。这是首次在硅学研究中证明这些化合物在治疗牙周炎方面对标准药物 DOX 的有效性。瑞士靶点预测数据库用于检索这些化合物的靶点。DisGeNET 和 GeneCards 被用来提取牙周炎的靶点。Cytoscape利用常见基因的蛋白质-蛋白质相互作用确定了前五个中心基因,并从中选出两个中心基因和三个胶原酶结合蛋白用于进一步研究:AA2、PGE2、PI2、TNFA和PGP。分子对接中观察到的最小结合能(表明最佳对接得分)与蛋白质和配体之间的最高亲和力相对应。为了证实对接研究的结果,对涉及 AA2-α-LPHE、AA2-DHC 和 AA2-PPR 的复合物进行了分子动力学(MD)模拟和 MMPBSA 计算。研究结果表明,在所研究的相互作用中,AA2-DHC 是最稳定的复合物,与标准药物 DOX 相比,其稳定性超过了所研究的其他复合物。总之,研究结果支持在临床上广泛使用阿图卡尔-奇占古作为一种潜在的治疗剂,或可用于治疗急性和慢性牙周炎。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computational Biology and Chemistry
Computational Biology and Chemistry 生物-计算机:跨学科应用
CiteScore
6.10
自引率
3.20%
发文量
142
审稿时长
24 days
期刊介绍: Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered. Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered. Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.
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