Resolving Single-Peptide Phosphorylation Dynamics in Plasmonic Nanopores using Physics-Informed Bi-Path Model

TL;DR

This paper presents a physics-informed deep learning approach that enhances the detection of single-peptide phosphorylation events using SM-SERS in plasmonic nanopores, overcoming stochastic noise and background interference.

Contribution

It introduces a novel deep learning framework combining multiple-instance learning and temporal encoding to decode complex SM-SERS signals for PTM identification.

Findings

Robustly distinguishes single peptide phosphorylation despite noise.

Segments spectral trajectories to improve weakly supervised training.

Enables high-fidelity detection of single-molecule phosphorylation events.

Abstract

Protein phosphorylation provides a dynamic readout of cellular signaling yet remains difficult to detect at low abundance and stoichiometry. Single-molecule surface-enhanced Raman spectroscopy (SM-SERS) using particle-in-pore plasmonic nanopores offers label-free molecular detection with submolecular sensitivity. However, reliable identification of subtle post-translational modifications (PTMs) is hindered by the stochastic nature of SM-SERS signals, partial excitation of peptide residues within the plasmonic hotspot, and background interference. Here, we introduce a physics-informed deep learning framework to decode complex SM-SERS dynamics and identify single-peptide PTMs. The model integrates multiple-instance learning with a temporal encoder combining temporal convolutional networks and bidirectional gated recurrent units to capture both local spectral variability and long-range blinking dynamics. To address diffusion-driven spectral heterogeneity, long spectral trajectories are segmented using Pearson-correlation, enabling weakly supervised training under label ambiguity. This framework robustly distinguishes single peptide phosphorylation despite strong background interference and stochastic signal fluctuations. By coupling nanoplasmonic confinement with spatiotemporal deep learning, our approach enables high-fidelity detection of single-molecule phosphorylation events and advances ultrasensitive phosphoproteomic analysis.