MassIVE MSV000091580

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An electroaffinity labeling platform for chemoproteomic-based target identification

Description

Target identification involves deconvoluting the protein target of a pharmacologically active small molecule ligand, which is critical for early drug discovery yet technically challenging. Photoaffinity labeling strategies have become the benchmark for small molecule target deconvolution, but covalent protein capture requires the use of high energy UV light, which can complicate downstream target identification. Thus, there is a strong demand for alternative technologies that allow for controlled activation of chemical probes to covalently label their protein target. Here, we introduce an electroaffinity labeling platform that leverages the use of a small, redox-active diazetidinone functional group to enable chemoproteomic-based target identification of pharmacophores within live cell environments. The underlying discovery to enable this platform is that the diazetidinone can be electrochemically oxidized to reveal reactive intermediate useful for covalent modification of proteins. This work, for the first time, demonstrates the electrochemical platform to be a functional tool for drug-target identification. [doi:10.25345/C5833N80M] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: affinity labeling ; chemoproteomics

Contact

Principal Investigators:
(in alphabetical order)
Olugbeminiyi Fadeyi, InduPro Therapeutics, USA
Phil Baran, The Scripps Research Institute, USA
Rob Oslund, InduPro Therapeutics, USA
Submitting User: ryukeu
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Originally identified proteins that were automatically remapped by MassIVE to proteins in the SwissProt human reference database.

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Number of distinct peptide sequences (including modified variants or peptidoforms) reported across all analyses (original submission and reanalyses) associated with this dataset.

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Number of distinct proteins found to be differentially abundant in at least one comparison across all analyses (original submission and reanalyses) associated with this dataset.

A protein is differentially abundant if its change in abundance across conditions is found to be statistically significant with an adjusted p-value <= 0.05 and lists no issues associated with statistical tests for differential abundance.

Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

"N/A" means no results of this type were submitted.
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