Dataset Information

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 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 α-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 α-synuclein peptide and its PTM variants could be confidently identified by using a deep learning model for signal processing. We further demonstrate that this framework can quantify α-synuclein peptides at picomolar concentrations and detect the C-terminal peptides generated by digestion of full-length α-synuclein. Collectively, our work highlights the advantage of using nanopores as a tool for simultaneous detection of multiple PTMs and facilitates their use in biomarker discovery and diagnostics.

SUBMITTER: Cao C

PROVIDER: S-EPMC10795472 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Publications

Deep Learning-Assisted Single-Molecule Detection of Protein Post-translational Modifications with a Biological Nanopore.

Cao Chan C Magalhães Pedro P Krapp Lucien F LF Bada Juarez Juan F JF Mayer Simon Finn SF Rukes Verena V Chiki Anass A Lashuel Hilal A HA Dal Peraro Matteo M

ACS nano 20231219 2

Protein post-translational modifications (PTMs) play a crucial role in countless biological processes, profoundly modulating protein properties on both 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 sensit ...[more]

PMID: 38112538

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Project description:Post-translational modifications (PTMs) are one of the most important regulatory mechanisms in cells, and they play key roles in cell signaling both in health and disease. PTM catalyzing enzymes have become significant drug targets, and therefore, tremendous interest has been focused on the development of broad-scale assays to monitor several different PTMs with a single detection platform. Most of the current methodologies suffer from low throughput or rely on antibody recognition, increasing the assay costs, and decreasing the multifunctionality of the assay. Thus, we have developed a sensitive time-resolved Förster resonance energy transfer (TR-FRET) detection method for PTMs of cysteine residues using a single-peptide approach performed in a 384-well format. In the developed assay, the enzyme-specific biotinylated substrate peptide is post-translationally modified at the cysteine residue, preventing the subsequent thiol coupling with a reactive AlexaFluor 680 acceptor dye. In the absence of enzymatic activity, increase in the TR-FRET signal between the biotin-bound Eu(III)-labeled streptavidin donor and the cysteine-coupled AlexaFluor 680 acceptor dye is observed. We demonstrate the detection concept with cysteine modifying S-nitrosylation and ADP-ribosylation reactions using a chemical nitric oxide donor S-nitrosoglutathione and enzymatic ADP-ribosyltransferase PtxS1-subunit of pertussis toxin, respectively. As a proof of concept, three peptide substrates derived from the small GTPase K-Ras and the inhibitory ?-subunit of the heterotrimeric G-protein G?i showed expected functionality in both chemical and enzymatic assays. Measurements yielded signal-to-background ratios of 28.7, 33.0, and 8.7 between the modified and the nonmodified substrates for the three peptides in the S-nitrosylation assay, 5.8 in the NAD+ hydrolysis assay, and 6.8 in the enzymatic ADP-ribosyltransferase inhibitor dose-response assay. The developed antibody-free assay for cysteine-modifying enzymes provides a detection platform with low nanomolar peptide substrate consumption, and the assay is potentially applicable to investigate various cysteine-modifying enzymes in a high throughput compatible format.

Dataset Information

Deep Learning-Assisted Single-Molecule Detection of Protein Post-translational Modifications with a Biological Nanopore.

Publications

Deep Learning-Assisted Single-Molecule Detection of Protein Post-translational Modifications with a Biological Nanopore.

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