SP3‐FAIMS‐Enabled High‐Throughput Quantitative Profiling of the Cysteinome. Issue 7 (27th July 2022) - Desai, Heta S.
Abstract: Cysteine‐directed chemoproteomic profiling methods yield high‐throughput inventories of redox‐sensitive and ligandable cysteine residues and therefore are enabling techniques for functional biology and drug discovery. However, the cumbersome nature of many sample preparation workflows, the requirements for large amounts of input material, and the modest yields of labeled peptides are limitations that hinder most chemoproteomics studies. Here, we report an optimized chemoproteomic sample‐preparation workflow that combines enhanced peptide labeling with single‐pot, solid‐phase‐enhanced sample preparation (SP3) to improve the recovery of biotinylated peptides, even from small samples. We further tailor our SP3 method to specifically probe the redox proteome, which showcases the utility of the SP3 platform in multistep sample‐preparation workflows. By implementing a customized workflow in the FragPipe computational pipeline, we achieve accurate MS1‐based quantification, including for peptides containing multiple cysteine residues. Collectively these innovations enable enhanced high‐throughput quantitative analysis of the cysteinome. This article includes detailed protocols for cysteine labeling with isotopically labeled iodoacetamide alkyne probes, biotinylation with CuAAC, sample cleanup with SP3, enrichment of cysteines with NeutrAvidin agarose beads, LC‐FAIMS‐MS/MS analysis, and FragPipe‐IonQuant analysis. © 2022 Wiley Periodicals LLC. Basic Protocol 1 : Labeling of cysteines in human proteome and SP3‐based sample cleanup Alternate Protocol 1 : Labeling of cysteines in human proteome, SP3‐based sample cleanup, and enrichment of cysteines for isoTOP‐ABPP analysis Alternate Protocol 2 : Labeling of cysteines in human proteome and SP3‐based sample cleanup for redox proteome analysis Basic Protocol 2 : Peptide‐level cysteine enrichment Basic Protocol 3 : LC‐FAIMS‐MS/MS analysis Basic Protocol 4 : FragPipe data analysis