Single-Molecule SERS Detection of Phosphorylation in Serine and Tyrosine Using Deep Learning-Assisted Plasmonic Nanopore

Single-molecule detection of post-translational modifications (PTMs) such as phosphorylation plays a crucial role in early diagnosis of diseases and therapeutics development. Although single-molecule surface-enhanced Raman spectroscopy (SM-SERS) detection of PTMs has been demonstrated, the data analysis and detection accurracies were hindered by interference from citrate signals and lack of reference databases. Previous reports required complete coverage of the nanoparticle surface by analyte molecules to replace citrates, hampering the detection limit. Here, we developed a high-accuracy SM-SERS approach by combining a plasmonic particle-in-pore sensor to collect SM-SERS spectra of phosphorylation at Serine and Tyrosine, k-means-based clustering for citrate signal removal, and a one-dimensional convolutional neural network (1D-CNN) for phosphorylation identification. Significantly, we collected SM-SERS data with submonolayer analyte coverage of the particle surface and discriminated the phosphorylation in Serine and Tyrosine with over 95% and 97% accuracy, respectively. Finally, the 1D-CNN features were interpreted by a one-dimensional gradient feature weight and SM-SERS peak occurrence frequencies.