ArticleMolecular & cellular proteomics : MCP2011
CONSeQuence: prediction of reference peptides for absolute quantitative proteomics using consensus machine learning approaches.
Article in Molecular & cellular proteomics : MCP, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.
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Who cites it
56 citing papers in PubMed.
- A multiplexed targeted mass spectrometric assay for quantifying obesity- associated biomarkers in human plasma.Research square · 2026Article
- The State of Peptide Detectability in Computational Proteomics and Guidelines for AI Applications.Computational and structural biotechnology journal · 2026Article
- To Fly, or Not to Fly, That Is the Question: A Deep Learning Model for Peptide Detectability Prediction in Mass Spectrometry.Journal of proteome research · 2025Article
- Article
- Use of Nonhuman Sera as a Highly Cost-Effective Internal Standard for Quantitation of Multiple Human Proteins Using Species-Specific Tryptic Peptides: Applicability in Clinical LC-MS Analyses.Journal of proteome research · 2024Article
- Targeted Proteomics Reveals Quantitative Differences in Low-Abundance Glycosyltransferases of Patients with Congenital Disorders of Glycosylation.International journal of molecular sciences · 2024Article
- Standardization of a Mass Spectrometry-Based Workflow for Food Allergen Quantification.Methods in molecular biology (Clifton, N.J.) · 2024Article
- DbyDeep: Exploration of MS-Detectable Peptides via Deep Learning.Analytical chemistry · 2023Article
- AlacatDesigner─Computational Design of Peptide Concatamers for Protein Quantitation.Journal of proteome research · 2023Article
- Insight on physicochemical properties governing peptide MS1 response in HPLC-ESI-MS/MS: A deep learning approach.Computational and structural biotechnology journal · 2023Article
- Reducing Peptide Sequence Bias in Quantitative Mass Spectrometry Data with Machine Learning.Journal of proteome research · 2022Article
- LC-MS peak assignment based on unanimous selection by six machine learning algorithms.Scientific reports · 2021Article
- Construction of à la carte QconCAT protein standards for multiplexed quantification of user-specified target proteins.BMC biology · 2021Article
- CIDer: A Statistical Framework for Interpreting Differences in CID and HCD Fragmentation.Journal of proteome research · 2021Article
- PepMANDIS: A Peptide Selection Tool for Designing Function-Based Targeted Proteomic Assays in Complex Microbial Systems.Frontiers in chemistry · 2021Article
- Integration of Proteomics and Metabolomics Into the Design, Build, Test, Learn Cycle to Improve 3-Hydroxypropionic Acid Production inFrontiers in bioengineering and biotechnology · 2021Article
- Evaluation of chromosomal insertion loci in the Pseudomonas putida KT2440 genome for predictable biosystems design.Metabolic engineering communications · 2020Article
- BIN1 protein isoforms are differentially expressed in astrocytes, neurons, and microglia: neuronal and astrocyte BIN1 are implicated in tau pathology.Molecular neurodegeneration · 2020Article
- CIRFESS: An Interactive Resource for Querying the Set of Theoretically Detectable Peptides for Cell Surface and Extracellular Enrichment Proteomic Studies.Journal of the American Society for Mass Spectrometry · 2020Article
- The Skyline ecosystem: Informatics for quantitative mass spectrometry proteomics.Mass spectrometry reviews · 2020Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Mass spectrometric based methods for absolute quantification of proteins, such as QconCAT, rely on internal standards of stable-isotope labeled reference peptides, or "Q-peptides," to act as surrogates. Key to the success of this and related methods for absolute protein quantification (such as AQUA) is selection of the Q-peptide. Here we describe a novel method, CONSeQuence (consensus predictor for Q-peptide sequence), based on four different machine learning approaches for Q-peptide selection. CONSeQuence demonstrates improved performance over existing methods for optimal Q-peptide selection in the absence of prior experimental information, as validated using two independent test sets derived from yeast. Furthermore, we examine the physicochemical parameters associated with good peptide surrogates, and demonstrate that in addition to charge and hydrophobicity, peptide secondary structure plays a significant role in determining peptide "detectability" in liquid chromatography-electrospray ionization experiments. We relate peptide properties to protein tertiary structure, demonstrating a counterintuitive preference for buried status for frequently detected peptides. Finally, we demonstrate the improved efficacy of the general approach by applying a predictor trained on yeast data to sets of proteotypic peptides from two additional species taken from an existing peptide identification repository.
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