Rapid Targeted Screening and Identification of Active Ingredients in Herbal Extracts through Ligand-Detected NMR and Database Matching
收藏中国科学院兰州化学物理研究所科学数据中心2026-01-14 更新2026-01-17 收录
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Herbal extracts are rich sources of active compounds that can be used for drug screening due to their diverse and unique chemical structures. However, traditional methods for screening these compounds are notably laborious and time-consuming. In this manuscript, we introduce a new high-throughput approach that combines nuclear magnetic resonance (NMR) spectroscopy with a tailored database and algorithm to rapidly identify bioactive components in herbal extracts. This method distinguishes characteristic signals and structural motifs of active constituents in the raw extracts through a relaxation-weighted technique, particularly utilizing the perfect echo Carr-Purcell-Meiboom-Gill (peCPMG) sequence, com-plemented by precise 2D spectroscopic strategies. The cornerstone of our approach is a customized database designed to filter potential compounds based on defined parameters, such as the presence of CHn segments and unique chemical shifts, thereby expediting the identification of promising compounds. This innovative technique was applied to identifying sub-stances interacting with choline kinase α (ChoKα1), resulting in the discovery of four new inhibitors. Our findings demon-strate a powerful tool for unraveling the complex chemical landscape of herbal extracts, considerably facilitating the search for new pharmaceutical candidates. This approach offers an efficient alternative to traditional methods in the quest for drug discovery from natural sources.
植物提取物富含活性化合物,凭借其多样且独特的化学结构,可用于药物筛选。然而,传统的这类化合物筛选方法往往耗时费力。本文提出一种全新的高通量筛选方法:将核磁共振波谱法(nuclear magnetic resonance spectroscopy, NMR)与定制化数据库及算法相结合,可快速识别植物提取物中的生物活性成分。该方法通过弛豫加权技术,尤其利用完美回波Carr-Purcell-Meiboom-Gill(peCPMG)序列,并辅以精准的二维光谱策略,区分原始提取物中活性成分的特征信号与结构基序。本方法的核心是定制化数据库,可基于预设参数(如CHₙ片段的存在及独特化学位移)筛选潜在化合物,从而加速有潜力化合物的识别进程。将该创新技术应用于识别与胆碱激酶α(ChoKα1)相互作用的物质,最终发现了四种新型抑制剂。本研究结果证实,该工具可有效解析植物提取物的复杂化学全貌,极大推动新型药物候选物的筛选工作。在从天然资源中探寻药物的研究中,该方法为传统手段提供了高效替代方案。
提供机构:
中国科学院兰州化学物理研究所科学数据中心
创建时间:
2026-01-14



