ArticleOmics : a journal of integrative biology2012
Prediction of missed proteolytic cleavages for the selection of surrogate peptides for quantitative proteomics.
Article in Omics : a journal of integrative biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
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- Bottlenecks in Proteomics: An Addendum.Advances in experimental medicine and biology · 2026Review
- The State of Peptide Detectability in Computational Proteomics and Guidelines for AI Applications.Computational and structural biotechnology journal · 2026Article
- Peptide Property Prediction for Mass Spectrometry Using AI: An Introduction to State of the Art Models.Proteomics · 2025Review
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- Optimal conditions for carrying out trypsin digestions on complex proteomes: From bulk samples to single cells.Journal of proteomics · 2024Review
- 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
- AQUA Mutant Protein Quantification of Endomyocardial Biopsy-Sized Samples From a Patient With Hypertrophic Cardiomyopathy.Frontiers in cardiovascular medicine · 2022Article
- Deep Learning-Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction.Methods in molecular biology (Clifton, N.J.) · 2022Article
- The proteolytic landscape of cells exposed to non-lethal stresses is shaped by executioner caspases.Cell death discovery · 2021Article
- The Time Has Come for Quantitative Protein Mass Spectrometry Tests That Target Unmet Clinical Needs.Journal of the American Society for Mass Spectrometry · 2021Article
- Pseudotrypsin: A Little-Known Trypsin Proteoform.Molecules (Basel, Switzerland) · 2018Review
- MS Western, a Method of Multiplexed Absolute Protein Quantification is a Practical Alternative to Western Blotting.Molecular & cellular proteomics : MCP · 2018Article
- DOSCATs: Double standards for protein quantification.Scientific reports · 2017Article
- Adaptive Multiview Nonnegative Matrix Factorization Algorithm for Integration of Multimodal Biomedical Data.Cancer informatics · 2017Article
- Absolute protein quantification of the yeast chaperome under conditions of heat shock.Proteomics · 2016Article
- Dynamic phosphorylation of RelA on Ser42 and Ser45 in response to TNFα stimulation regulates DNA binding and transcription.Open biology · 2016Article
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Authors and funding
2 authors.
Funding
Abstract
Quantitative proteomics experiments are usually performed using proteolytic peptides as surrogates for their parent proteins, inferring protein amounts from peptide-level quantitation. This process is frequently dependent on complete digestion of the parent protein to its limit peptides so that their signal is truly representative. Unfortunately, proteolysis is often incomplete, and missed cleavage peptides are frequently produced that are unlikely to be optimal surrogates for quantitation, particularly for label-mediated approaches seeking to derive absolute values. We have generated a predictive computational tool that is able to predict which candidate proteolytic peptide bonds are likely to be missed by the standard enzyme trypsin. Our cross-validated prediction tool uses support vector machines and achieves high accuracy in excess of 0.94 precision (PPV), with attendant high sensitivity of 0.79, across multiple proteomes. We believe this is a useful tool for selecting candidate quantotypic peptides, seeking to minimize likely loss owing to missed cleavage, which will be a boon for quantitative proteomic pipelines as well as other areas of proteomics. Our results are discussed in the context of recent results examining the kinetics of missed cleavages in proteomic digestion protocols, and show agreement with observed experimental trends. The software has been made available at http://king.smith.man.ac.uk/mcpred .
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.