Dataset Information

MealTime-MS: A Machine Learning-Guided Real-Time Mass Spectrometry Analysis for Proteomics

ABSTRACT: Mass spectrometry-based proteomics technologies are the prime methods for the high-throughput identification of proteins expressed in complex biological samples. Nevertheless, mass spectrometry’s technical limitations still hinder its ability to identify low abundance proteins in complex samples. Characterizing such proteins is essential to provide a comprehensive understanding of the biological processes taking place in a sample. Still today, a large part of the mass spectrometry-based proteomics performed use a data-dependent approach that favors the acquisition of mass spectra and detection of proteins of higher abundance. Combined to the fact that the computational identification of proteins from mass spectrometry data is typically performed after mass spectrometry data acquisition, large numbers of mass spectra are redundantly collected from the same abundant proteins and little to no mass spectra are acquired for proteins of lower abundance. To address this problem, we propose a novel supervised learning algorithm that identifies proteins in real-time as mass spectrometry data is acquired and prevents the further data acquisition related to confidently identified proteins to improve the identification sensitivity of low abundance proteins. We show in real-time simulations of a previously performed mass spectrometry analysis of a HEK293 cell lysates that our approach can identify 92.1% of the proteins using 66.2% of the MS2 spectra acquired in the experiment. We also demonstrate that our approach is fast enough for real-time mass spectrometry analysis, is flexible and that it outperforms previously proposed methods. Our method efficient usage of mass spectrometry resources will provide a more comprehensive characterization of proteomes in complex samples.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Embryonic Cell, Cell Culture

SUBMITTER: Mathieu Lavallée-Adam

LAB HEAD: Mathieu Lavallée-Adam

PROVIDER: PXD017673 | Pride | 2020-10-27

REPOSITORIES: Pride

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Publications

MealTime-MS: A Machine Learning-Guided Real-Time Mass Spectrometry Analysis for Protein Identification and Efficient Dynamic Exclusion.

Pelletier Alexander R AR Chung Yun-En YE Ning Zhibin Z Wong Nora N Figeys Daniel D Lavallée-Adam Mathieu M

Journal of the American Society for Mass Spectrometry 20200617 7

Mass spectrometry-based proteomics technologies are prime methods for the high-throughput identification of proteins in complex biological samples. Nevertheless, there are still technical limitations that hinder the ability of mass spectrometry to identify low abundance proteins in complex samples. Characterizing such proteins is essential to provide a comprehensive understanding of the biological processes taking place in cells and tissues. Still today, most mass spectrometry-based proteomics a ...[more]

PMID: 32510216

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Project description:Abstract Rationale The goal of this work was to identify phosphorylation sites within the amino acid sequence of human ADAP1. Using traditional mass spectrometry-based techniques we were unable to produce interpretable spectra demonstrating modification by phosphorylation. This prompted us to employ a strategy in which phosphorylated peptides were first predicted using peptide mapping followed by targeted MS/MS acquisition. Methods ADAP1 was immunoprecipitated from extracts of HEK293 cells stably-transfected with ADAP1 cDNA. Immunoprecipitated ADAP1 was digested with proteolytic enzymes and analyzed by LC-MS in MS1 mode by high-resolution quadrupole time-of-flight mass spectrometry (QTOF-MS). Peptide molecular features were extracted using an untargeted data mining algorithm. Extracted peptide neutral masses were matched against the ADAP1 amino acid sequence with phosphorylation included as a predicted modification. Peptides with predicted phosphorylation sites were analyzed by targeted LC-MS2. Acquired MS2 spectra were then analyzed using database search engines to confirm phosphorylation. Spectra of phosphorylated peptides were validated by manual interpretation. Further confirmation was performed by manipulating phospho-peptide abundance using calf intestinal phosphatase (CIP) and the phorbol ester, phorbol 12-myristate 13-acetate (PMA). Results Of five predicted phosphopeptides, one, comprised of the sequence AVDRPMLPQEYAVEAHFK, was confirmed to be phosphorylated on a Tyrosine at position 364. Pre-treatment of cells with PMA prior to immunoprecipitation increased the ratio of phosphorylated to unphosphorylated peptide as determined by area counts of extracted ion chromatograms (EIC). Addition of CIP to immunoprecipitation reactions eliminated the phosphorylated form. Conclusions A novel phosphorylation site was identified at Tyrosine 364. Phosphorylation at this site is increased by treatment with PMA. PMA promotes membrane translocation and activation of protein kinase C (PKC), indicating that Tyrosine 364 is phosphorylated by a PKC-dependent mechanism.

Dataset Information

MealTime-MS: A Machine Learning-Guided Real-Time Mass Spectrometry Analysis for Proteomics

Publications

MealTime-MS: A Machine Learning-Guided Real-Time Mass Spectrometry Analysis for Protein Identification and Efficient Dynamic Exclusion.

Similar Datasets

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