Deep learning-assisted single-molecule detection of protein post-translational modifications with a biological nanopore

Abstract

Protein post-translational modifications (PTMs) play a crucial role in countless biological processes, profoundly modulating protein properties on both the spatial and temporal scales. Protein PTMs have also emerged as reliable biomarkers for several diseases. However, only a handful of techniques are available to accurately measure their levels, capture their complexity at a single molecule level and characterize their multifaceted roles in health and disease. Nanopore sensing provides high sensitivity for the detection of low-abundance proteins, holding the potential to impact single-molecule proteomics and PTM detection in particular. Here, we demonstrate the ability of a biological nanopore, the pore-forming toxin aerolysin, to detect and distinguish -synuclein-derived peptides bearing single or multiple PTMs, namely phosphorylation, nitration and oxidation occurring at different positions and in various combinations. The characteristic current signatures of the -synuclein peptide and its PTM variants could be confidently identified using a deep learning model for signal processing. We further demonstrate that this framework can quantify -synuclein peptides at picomolar concentration and detect the C-terminal peptides generated by digestion of full-length -synuclein. Collectively, our work highlights the unique advantage of using nanopore as a tool for simultaneous detection of multiple PTMs and paves the way for their use in biomarker discovery and diagnostics.