Abstract：
Natural products-based screening of active ingredients and their interactions with target proteins is an important ways to discover new drugs. Assessing the binding capacity of target proteins, particularly when multiple components are involved, presents a significant challenge for sensors. As far as we know, there is currently no sensor that can accomplish high-throughput quantitative analysis of natural product-target protein binding capacity based on Raman spectroscopy. In this study, a novel sensor model has been developed for the quantitative analysis of binding capacity based on Surface-Enhanced Raman Spectroscopy (SERS) and Photocrosslinked Molecular Probe (PCMP) technology. This sensor, named SERS-PCMP, leverages the high throughput of molecular probe technology to investigate the active ingredients in natural products, along with the application of SERS labelling technology for target proteins. Thus it significantly improves the efficiency and accuracy of target protein identification. Based on the novel strategy, quantitative analysis of the binding capacity of 20 components from Shenqi Jiangtang Granules (SJG) to α-Glucosidase were completed. Ultimately, the binding capacity of these active ingredients was ranked based on the detected Raman Intensity. The compounds with higher binding capacity were Astragaloside IV (Intensity, 138.17), Ginsenoside Rh2 (Intensity, 87.46), Ginsenoside Rg3 (Intensity, 73.92) and Ginsenoside Rh1 (Intensity, 64.37), which all exceeded the binding capacity of the positive drug Acarbose (Intensity, 28.75). Furthermore, this strategy also performed a high detection sensitivity. The limit of detection for the enzyme using 0.1 mg of molecular probe magnetic nanoparticles (MP MNPs) was determined to be no less than 0.375 μg/mL. SERS-PCMP sensor integrating SERS labeling and photocrosslinked molecular probes which offers a fresh perspective for future drug discovery studies. Such as high-throughput drug screening and the exploration of small molecule-target protein interactions in vitro.
摘要：
基于天然产物的活性成分筛选及其与靶蛋白的相互作用是发现新药的重要途径。评估靶蛋白的结合能力，特别是当涉及多个组件时，对传感器提出了重大挑战。据我们所知,目前还没有基于拉曼光谱的传感器能够实现对天然产物-靶蛋白结合能力的高通量定量分析。在这项研究中,基于表面增强拉曼光谱（SERS）和光交联分子探针（PCMP）技术，开发了一种用于定量分析结合能力的新型传感器模型。这个传感器，名为SERS-PCMP，利用分子探针技术的高通量研究天然产物中的活性成分，随着SERS标记技术对靶蛋白的应用。从而显著提高了靶蛋白鉴定的效率和准确性。基于新颖的策略，对参芪降糖颗粒中20种成分与α-葡萄糖苷酶的结合能力进行了定量分析。最终,根据检测到的拉曼强度对这些活性成分的结合能力进行分级。具有较高结合能力的化合物是黄芪甲苷(强度，138.17),人参皂苷Rh2（强度，87.46),人参皂苷Rg3（强度，73.92)和人参皂苷Rh1(强度，64.37),所有这些都超过了阳性药物阿卡波糖(强度，28.75)。此外，该策略还具有很高的检测灵敏度。测定使用0.1mg分子探针磁性纳米颗粒(MPMNPs)的酶的检测限不小于0.375μg/mL。SERS-PCMP传感器集成了SERS标记和光交联分子探针，为未来的药物发现研究提供了新的视角。如高通量药物筛选和体外小分子-靶蛋白相互作用的探索。
