Proteomics

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Retention time calibration for RP-HPLC at pH 9

ABSTRACT: The change in pH of the mobile phase drastically changes the retention mechanism of peptides, rendering currently available retention time prediction models useless. The aim of this study is to create a dataset of peptide retention times under basic (pH 9) HPLC conditions, allowing for the creation of a retention time prediction model specific for these conditions. Peptides were separated using basic mobile phases and analyzed in positive ion mode, allowing identification with common shotgun proteomics pipeline.

INSTRUMENT(S):

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Cervix Epithelium

DISEASE(S): Endocervical Adenocarcinoma

SUBMITTER: Vladimir Gorshkov  

LAB HEAD: Frank Kjeldsen

PROVIDER: PXD064311 | Pride | 2026-02-13

REPOSITORIES: Pride

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Dataset's files

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20220718-1955_LUM2_1003927_FKJ_PP_22060209_HeLa_TrypsinPromega_imidazole_Fraction1.features.tsv.gz Tabular
20220718-1955_LUM2_1003927_FKJ_PP_22060209_HeLa_TrypsinPromega_imidazole_Fraction1.mzML Mzml
20220718-1955_LUM2_1003927_FKJ_PP_22060209_HeLa_TrypsinPromega_imidazole_Fraction1.mzid Mzid
20220718-1955_LUM2_1003927_FKJ_PP_22060209_HeLa_TrypsinPromega_imidazole_Fraction1.raw Raw
20220718-2224_LUM2_1003928_FKJ_PP_22060210_HeLa_TrypsinPromega_imidazole_Fraction2.features.tsv.gz Tabular
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Publications

Transfer learning in DeepLC improves LC retention time prediction across substantially different modifications and setups.

Bouwmeester Robbin R   Nameni Alireza A   Declercq Arthur A   Devreese Robbe R   Velghe Kevin K   Gorshkov Vladimir V   Penanes Pelayo A PA   Kjeldsen Frank F   Rompais Magali M   Carapito Christine C   Gabriels Ralf R   Martens Lennart L  

Nature communications 20260210

While LC retention time prediction of peptides and their modifications has proven useful, widespread adoption and optimal performance are hindered by variations in experimental parameters. These variations can render retention time prediction models inaccurate and dramatically reduce the value of predictions for identification, validation, and DIA spectral library generation. To date, mitigation of these issues has been attempted through calibration or by training bespoke models for specific exp  ...[more]

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