Competitive ultrafiltration: A ligand-displacement strategy for rapid discovery of high-quality neuraminidase inhibitors from natural product mixtures.

IF 6.1 1区 化学 Q1 CHEMISTRY, ANALYTICAL
Menghan Chen, Cheng Tian, Tongtong Jian, Yongli Wei, Yizhou Xin
{"title":"Competitive ultrafiltration: A ligand-displacement strategy for rapid discovery of high-quality neuraminidase inhibitors from natural product mixtures.","authors":"Menghan Chen, Cheng Tian, Tongtong Jian, Yongli Wei, Yizhou Xin","doi":"10.1016/j.talanta.2025.128948","DOIUrl":null,"url":null,"abstract":"<p><p>The rapid discovery of high-quality natural products (NPs) that combine potent biological activity with structural diversity remains a major challenge. Traditional bioactivity-guided isolation is time-consuming and labor-intensive, and although the development of multi-omics technologies has accelerated NP digging, these approaches primarily emphasize structural novelty rather than biological activity. To overcome this limitation, we developed a novel model - competitive ultrafiltration (CUF), based on the principle of target-site occupancy competition. By exploiting the displacement effect of high-affinity inhibitors on low-affinity ligands, CUF enables the selective enrichment of potent active compounds with diverse scaffolds from complex mixtures. Using the screening of neuraminidase (NA) inhibitors as a study, we validated the feasibility of CUF through model construction, limit of detection analysis, and mixed-library verification. Subsequently, CUF was applied to Lonicera japonica extract, resulting in the identification of two NA inhibitors. Both of the chlorogenic acid and luteoloside exhibited significant inhibitory activity, confirming the practical applicability of this method. This study demonstrates that CUF provides a promising tool for lead compound discovery in drug development.</p>","PeriodicalId":435,"journal":{"name":"Talanta","volume":"298 Pt B","pages":"128948"},"PeriodicalIF":6.1000,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Talanta","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1016/j.talanta.2025.128948","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
引用次数: 0

Abstract

The rapid discovery of high-quality natural products (NPs) that combine potent biological activity with structural diversity remains a major challenge. Traditional bioactivity-guided isolation is time-consuming and labor-intensive, and although the development of multi-omics technologies has accelerated NP digging, these approaches primarily emphasize structural novelty rather than biological activity. To overcome this limitation, we developed a novel model - competitive ultrafiltration (CUF), based on the principle of target-site occupancy competition. By exploiting the displacement effect of high-affinity inhibitors on low-affinity ligands, CUF enables the selective enrichment of potent active compounds with diverse scaffolds from complex mixtures. Using the screening of neuraminidase (NA) inhibitors as a study, we validated the feasibility of CUF through model construction, limit of detection analysis, and mixed-library verification. Subsequently, CUF was applied to Lonicera japonica extract, resulting in the identification of two NA inhibitors. Both of the chlorogenic acid and luteoloside exhibited significant inhibitory activity, confirming the practical applicability of this method. This study demonstrates that CUF provides a promising tool for lead compound discovery in drug development.

竞争性超滤:从天然产物混合物中快速发现高质量神经氨酸酶抑制剂的配体置换策略。
快速发现结合有效生物活性和结构多样性的高质量天然产物(NPs)仍然是一个重大挑战。传统的以生物活性为导向的分离既耗时又费力,尽管多组学技术的发展加速了NP挖掘,但这些方法主要强调结构新颖性,而不是生物活性。为了克服这一限制,我们开发了一种基于目标位置占用竞争原理的新型模型-竞争超滤(CUF)。通过利用高亲和抑制剂对低亲和配体的置换效应,CUF能够从复杂混合物中选择性富集不同支架的有效活性化合物。以筛选神经氨酸酶(NA)抑制剂为研究对象,通过模型构建、检测限分析和混合文库验证验证了CUF的可行性。随后,将CUF应用于忍冬提取物,鉴定出两种NA抑制剂。绿原酸和木犀草苷均表现出明显的抑制活性,证实了该方法的实用性。该研究表明,CUF为药物开发中的先导化合物发现提供了一个很有前途的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Talanta
Talanta 化学-分析化学
CiteScore
12.30
自引率
4.90%
发文量
861
审稿时长
29 days
期刊介绍: Talanta provides a forum for the publication of original research papers, short communications, and critical reviews in all branches of pure and applied analytical chemistry. Papers are evaluated based on established guidelines, including the fundamental nature of the study, scientific novelty, substantial improvement or advantage over existing technology or methods, and demonstrated analytical applicability. Original research papers on fundamental studies, and on novel sensor and instrumentation developments, are encouraged. Novel or improved applications in areas such as clinical and biological chemistry, environmental analysis, geochemistry, materials science and engineering, and analytical platforms for omics development are welcome. Analytical performance of methods should be determined, including interference and matrix effects, and methods should be validated by comparison with a standard method, or analysis of a certified reference material. Simple spiking recoveries may not be sufficient. The developed method should especially comprise information on selectivity, sensitivity, detection limits, accuracy, and reliability. However, applying official validation or robustness studies to a routine method or technique does not necessarily constitute novelty. Proper statistical treatment of the data should be provided. Relevant literature should be cited, including related publications by the authors, and authors should discuss how their proposed methodology compares with previously reported methods.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信