The invention relates to a single-molecule
peptide recognition and
enzyme activity detection method, in particular to a beta-
secretase activity detection method based on fluorescent
scintillation fingerprints and
deep learning recognition. The method comprises the following steps: marking a spontaneous
scintillation type fluorescent dye on a specific
peptide substrate, and fixing the specific
peptide substrate on the surface of a functionalized slide by utilizing
click chemistry to realize stable anchoring of a single
peptide molecule; then, a
total internal reflection fluorescence microscope is adopted to collect a
time sequence fluorescence track, and single molecule
scintillation fingerprints before and after
enzyme digestion are obtained. And track data is classified and analyzed by combining a
deep learning model, so that whether the peptide molecules are subjected to
enzyme digestion or not can be accurately judged, and quantitative detection of the enzyme activity is further realized. Compared with a traditional method, the method has the
advantage that the detection sensitivity, the specificity and the adaptability to complex samples are remarkably improved. The beta-
secretase is used as a model, the
verification accuracy rate exceeds 88%, a new technical path is provided for enzyme activity analysis and early diagnosis of related diseases, and the method has a wide application prospect.