Computational prediction of proteotypic peptides for quantitative proteomics

By Parag Mallick, M. Schirle, Sharon Chen, M. Flory, Hookeun Lee, Daniel B. Martin, J. Ranish, B. Raught, R. Schmitt, Thilo Werner and 2 more. Nature Biotechnology, 2007

706 citations recorded by the source corpus.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/f553e0200691837fd2101d8f5ee1ef08cc60388d

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Citations in the corpus

Sorting Signals, N-Terminal Modifications and Abundance of the Chloroplast Proteome (2008, methodology): “Such propensity is dependent on the proteomics ‘pipeline’ (protein separation and MS platform) [42,43].”
Applying mass spectrometry-based proteomics to genetics, genomics and network biology (2009, methodology): “Next, proteotypic peptides for the chosen target proteins can be selected using either software tools to predict their sequence”
Selected reaction monitoring for quantitative proteomics: a tutorial (2008, background): “Such peptides have been termed as proteotypic peptides (PTPs) (Mallick et al, 2007).”
The Skyline Ecosystem: Informatics for Quantitative Mass Spectrometry Proteomics (2017, methodology): “In addition to empirical determination, predictive algorithms provide an alternative or complementary method to select the target peptides most likely to be high-responding for a set of proteins (Mallick et al. 2007; Fusaro et al. 2009; Eyers et al. 2011; Muntel et al. 2015).”
Analysis and validation of proteomic data generated by tandem mass spectrometry (2007, background): “The power can be improved by incorporating more information into the scoring function: for example, predicted detectability of peptide”
Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics (2009, methodology): “For each protein three to five PTPs were selected based on previous evidence (http:// www.peptideatlas.org) (Deutsch et al., 2008) or by bioinformatic prediction using PeptideSieve (Mallick et al., 2007).”
Less label, more free: Approaches in label‐free quantitative mass spectrometry (2011, methodology): “This technology is based on the findings of Mallick and colleagues in a study involving the prediction of proteotypic peptides [37].”
Protein Identification False Discovery Rates for Very Large Proteomics Data Sets Generated by Tandem Mass Spectrometry* (2009, background): “those derived from low abundance proteins, are more difficult to detect, and a large number of fragment ion spectra have to be acquired to increase the likelihood of their detection (2, 13, 14).”
Quantitative, Multiplexed Assays for Low Abundance Proteins in Plasma by Targeted Mass Spectrometry and Stable Isotope Dilution*S (2007, background): “responding peptides for each protein in complex matrices, remains to be established (38).”
Parallel Reaction Monitoring for High Resolution and High Mass Accuracy Quantitative, Targeted Proteomics* (2012, background): “lection of proteotypic peptides (31), peptide transitions, and”
The PeptideAtlas project (2005, methodology): “Each protein is digested in silico and both the PeptideSieve [23] and DetectabilityPredictor [24] software tools are used to predict which peptides might be most suitable for targeting.”