Cucurbit[7]uril Macrocyclic Sensors for Optical Fingerprinting: Predicting Protein Structural Changes to Identifying Disease-Specific Amyloid Assemblies

化学 生物分子 荧光团 分子识别 折叠(DSP实现) 荧光 指纹(计算) 蛋白质折叠 生物物理学 淀粉样蛋白(真菌学) 计算生物学 分子 生物化学 生物 工程类 有机化学 无机化学 物理 电气工程 量子力学 计算机科学 计算机安全
作者
Nilanjana Das Saha,Soumen Pradhan,Ranjan Sasmal,Aritra Sarkar,Christian M. Berač,Jonas C. Kölsch,Meenakshi Pahwa,Sushanta Show,Yves Rozenholc,Zeki Topçu,Vivien Alessandrini,Jean Gadrey,Vassilis Tsatsaris,Nathalie Gagey‐Eilstein,Sarit S. Agasti
出处
期刊:Journal of the American Chemical Society [American Chemical Society]
卷期号:144 (31): 14363-14379 被引量:14
标识
DOI:10.1021/jacs.2c05969
摘要

In a three-dimensional (3D) representation, each protein molecule displays a specific pattern of chemical and topological features, which are altered during its misfolding and aggregation pathway. Generating a recognizable fingerprint from such features could provide an enticing approach not only to identify these biomolecules but also to gain clues regarding their folding state and the occurrence of pathologically lethal misfolded aggregates. We report here a universal strategy to generate a fluorescent fingerprint from biomolecules by employing the pan-selective molecular recognition feature of a cucurbit[7]uril (CB[7]) macrocyclic receptor. We implemented a direct sensing strategy by covalently tethering CB[7] with a library of fluorescent reporters. When CB[7] recognizes the chemical and geometrical features of a biomolecule, it brings the tethered fluorophore into the vicinity, concomitantly reporting the nature of its binding microenvironment through a change in their optical signature. The photophysical properties of the fluorophores allow a multitude of probing modes, while their structural features provide additional binding diversity, generating a distinct fluorescence fingerprint from the biomolecule. We first used this strategy to rapidly discriminate a diverse range of protein analytes. The macrocyclic sensor was then applied to probe conformational changes in the protein structure and identify the formation of oligomeric and fibrillar species from misfolded proteins. Notably, the sensor system allowed us to differentiate between different self-assembled forms of the disease-specific amyloid-β (Aβ) aggregates and segregated them from other generic amyloid structures with a 100% identification accuracy. Ultimately, this sensor system predicted clinically relevant changes by fingerprinting serum samples from a cohort of pregnant women.
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