ArticleFrontiers in bioengineering and biotechnology2020
High-Throughput Large-Scale Targeted Proteomics Assays for Quantifying Pathway Proteins in
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Prediction of non-intuitive metabolic targets with bayesian metabolic control analysis to improve 3-hydroxypropionic acid production inFrontiers in bioengineering and biotechnology · 2026Article
- PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements.Nature communications · 2023Article
- Statistical and Computational Methods for Proteogenomic Data Analysis.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Monitoring and modelling the dynamics of the cellular glycolysis pathway: A review and future perspectives.Molecular metabolism · 2022Review
- Quantitative proteomics characterization of cancer biomarkers and treatment.Molecular therapy oncolytics · 2021Review
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Authors and funding
22 authors.
Funding
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Abstract
Targeted proteomics is a mass spectrometry-based protein quantification technique with high sensitivity, accuracy, and reproducibility. As a key component in the multi-omics toolbox of systems biology, targeted liquid chromatography-selected reaction monitoring (LC-SRM) measurements are critical for enzyme and pathway identification and design in metabolic engineering. To fulfill the increasing need for analyzing large sample sets with faster turnaround time in systems biology, high-throughput LC-SRM is greatly needed. Even though nanoflow LC-SRM has better sensitivity, it lacks the speed offered by microflow LC-SRM. Recent advancements in mass spectrometry instrumentation significantly enhance the scan speed and sensitivity of LC-SRM, thereby creating opportunities for applying the high speed of microflow LC-SRM without losing peptide multiplexing power or sacrificing sensitivity. Here, we studied the performance of microflow LC-SRM relative to nanoflow LC-SRM by monitoring 339 peptides representing 132 enzymes in
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