Engineering the microenvironment of Cu-MOF nanozyme via modulating ligand hydrophobicity for array-based profiling of...

Natural products and their synthetic analogs have historically been fundamental to drug discovery, making their identification crucial for pharmaceutical research. This work presents a nanozyme-based sensor array for precise discrimination and quantitative determination of seven phenolic acids in plant-derived natural products. By modulating ligand hydrophobicity, we engineered three POD-mimicking Cu-MOF nanozymes (Cu-MI, Cu-EI, and Cu-PI), with their catalytic activity exhibiting increased catalytic activity proportional to the ligand alkyl chain length. Based on this, a four-channel sensor array was constructed using the distinct activities of Cu-MI and Cu-PI. In this array, the target phenolic acids selectively inhibit the nanozyme-catalyzed oxidation of 3,3',5,5'-tetramethylbenzidine (TMB), generating unique fingerprint-like responses for each analyte. The array achieved 100 % accurate classification of all seven phenolic acids using linear discriminant analysis (LDA) and hierarchical cluster analysis (HCA). Furthermore, the array allowed for the sensitive quantification of phenolic acids, offering a wide linear range (5-50 μM, R² > 0.99) and low detection limits (0.56-4.1 μM). The practical utility was rigorously validated through interference tests and the analysis of Lonicera japonica extracts. The assay accurately identified chlorogenic acid as the predominant phenolic acid in Lonicera japonica, determined its content, and demonstrated excellent accuracy with spike recoveries of 94.5-103.2 %. This study establishes a robust and reliable strategy for the quality assessment of natural products.