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Removal of NHS-labelling by-products in proteomic samples

ABSTRACT: N-Hydroxysuccinimide chemistry is used extensively across proteomics sample preparation. One of the most popular applications is its use in isobaric reagent based quantitation; specifically both the iTRAQ and TMT approaches which rely on complete labelling of primary amines. However, serines, tyrosines and threonines can also react during TMT labelling procedures. These products are typically classed as over-labelled and are disregarded for quantification. In this study we developed a robust method to remove such over-labelled peptides to improve identification rates and quantitation precision and tested the new method or compatibility with phoshpo-peptides.

INSTRUMENT(S): Orbitrap Ascend

ORGANISM(S): Homo Sapiens (human) Bos Taurus (bovine)

TISSUE(S): Permanent Cell Line Cell

SUBMITTER: Yana Demyanenko

LAB HEAD: Shabaz Mohammed

PROVIDER: PXD056723 | Pride | 2025-03-14

REPOSITORIES: Pride

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Dataset's files

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			Action	DRS
	240710_MQ_enriched_combined_mqpar.zip	Other
	240710_Overlabel_check.zip	Other
	240710_Underlabel_check.zip	Other
	240916_BSACas_mqpar_combined.zip	Other
	ASND01_YD1_YD1_240711_expi_127_tmtpro_tris_OT_14.raw	Raw

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Removal of NHS-labelling by-products in proteomic samples

Project description:N-Hydroxysuccinimide (NHS) chemistry is used extensively across proteomics sample preparation. One of the most popular applications is its use in isobaric reagent based quantitation; specifically both the iTRAQ and TMT approaches. The labels are introduced onto primary Serines, tyrosines, and threonines can be labelled during TMT labelling reactions in addition to the desired amides. These peptides are typically classed as over-labelled and are disregarded for quantification. Their presence also unnecessarily increases sample complexity, which reduces identification rates. In this study, we developed a robust method to remove over-labelled peptides through systematic screening of nucleophilic aminolysis reagents and reaction conditions. The new method reduces the proportion of over-labelled peptides in the sample to less than 1% without affecting the labelling rate or introducing other modifications, leading to superior identification rates and quantitation precision.

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