Screening Inhibitors of α-Amylase in Polygala Radix Based on an Online Targeted Detection System and Molecular Docking.

IF 3 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Zenghu Su, Hongbo Xu, Shizhong Chen, Shuming Li, Jingyu Weng, Yuangui Yang
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引用次数: 0

Abstract

Introduction: Targeted screening of inhibitors of key enzymes in the progression of diabetes from natural products is one of the effective methods for the treatment of diabetes. Polygala has been proved to reduce glucose levels; however, the bioactive compounds in Polygalae Radix (PR) that have anti-diabetic properties are unknown.

Objective: The purpose of this study was to explore the material basis of the anti-diabetic effect of PR by inhibiting α-amylase through an online detection system and molecular docking.

Methods: An online analysis platform was established and optimized for the screening of potent enzyme inhibitors from complex mixtures based on ultra-performance liquid chromatography-photodiode array-quadrupole-time-of-flight-mass spectrometry-α-amylase-fluorescence detector (UHPLC-PDA-Q-TOF-MSn-α-amylase-FLD) detection system and molecular docking, which could efficiently separate extracts, quickly detect α-amylase inhibitors, and determine their structures. Molecular docking confirms the inhibition of these compounds. The molecular interaction between α-amylase and the active compound was evaluated.

Results: Among the 101 compounds identified, 28 compounds had a strong inhibitory effect on α-amylase. Molecular docking screening confirmed the inhibition of these compounds and evaluated the molecular interactions between α-amylase and 30 active compounds, which strongly supported the experimental results. Among the evaluated compounds, onjisaponin R (83) and polygalaxanthone III (11) have the strongest inhibitory activity to α-amylase (the binding energies were -9.639 and -8.972 kcal/mol, respectively) and are potential lead compounds against diabetes.

Conclusion: This study proved the feasibility of using the existing platform to screen the active ingredients in PR extract, and provided a practical method for the rapid screening of potential anti-diabetic active ingredients in traditional Chinese medicine.

基于在线靶向检测系统和分子对接筛选远志中的α-淀粉酶抑制剂
导言:从天然产品中筛选糖尿病关键酶的靶向抑制剂是治疗糖尿病的有效方法之一。远志已被证实能降低血糖水平,但远志中具有抗糖尿病特性的生物活性化合物尚不清楚:本研究的目的是通过在线检测系统和分子对接,探索远志中抑制α-淀粉酶、具有抗糖尿病作用的物质基础:建立并优化了基于超高效液相色谱-光电二极管阵列-四极杆-飞行时间质谱-α-淀粉酶-荧光检测器(UHPLC-PDA-Q-TOF-MSn-α-淀粉酶-FLD)检测系统和分子对接的在线分析平台,可从复杂混合物中高效分离提取物,快速检测α-淀粉酶抑制剂并确定其结构。分子对接证实了这些化合物的抑制作用。评估了α-淀粉酶与活性化合物之间的分子相互作用:结果:在鉴定出的 101 个化合物中,28 个化合物对 α 淀粉酶有很强的抑制作用。分子对接筛选证实了这些化合物的抑制作用,并评估了α-淀粉酶与 30 种活性化合物之间的分子相互作用,这有力地支持了实验结果。在评估的化合物中,芒柄皂苷 R(83)和远志酮 III(11)对α-淀粉酶的抑制活性最强(结合能分别为-9.639和-8.972 kcal/mol),是治疗糖尿病的潜在先导化合物:本研究证明了利用现有平台筛选 PR 提取物中有效成分的可行性,为快速筛选潜在的中药抗糖尿病有效成分提供了实用方法。
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来源期刊
Phytochemical Analysis
Phytochemical Analysis 生物-分析化学
CiteScore
6.00
自引率
6.10%
发文量
88
审稿时长
1.7 months
期刊介绍: Phytochemical Analysis is devoted to the publication of original articles concerning the development, improvement, validation and/or extension of application of analytical methodology in the plant sciences. The spectrum of coverage is broad, encompassing methods and techniques relevant to the detection (including bio-screening), extraction, separation, purification, identification and quantification of compounds in plant biochemistry, plant cellular and molecular biology, plant biotechnology, the food sciences, agriculture and horticulture. The Journal publishes papers describing significant novelty in the analysis of whole plants (including algae), plant cells, tissues and organs, plant-derived extracts and plant products (including those which have been partially or completely refined for use in the food, agrochemical, pharmaceutical and related industries). All forms of physical, chemical, biochemical, spectroscopic, radiometric, electrometric, chromatographic, metabolomic and chemometric investigations of plant products (monomeric species as well as polymeric molecules such as nucleic acids, proteins, lipids and carbohydrates) are included within the remit of the Journal. Papers dealing with novel methods relating to areas such as data handling/ data mining in plant sciences will also be welcomed.
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