Empirical Statistical Model To Estimate the Accuracy of Peptide Identifications Made by MS/MS and Database Search

Keller, Andrew; Nesvizhskii, Alexey I.; Kolker, Eugene; Aebersold, Ruedi

doi:10.1021/ac025747h

Cited by 4,562 publications

(4,353 citation statements)

References 30 publications

Supporting

Mentioning

4,324

Contrasting

Unclassified

Order By: Relevance

“…Peptide identifications were accepted if they could be established at greater than 95.0% probability using the Peptide Prophet algorithm. (42) Protein identifications were accepted if they had greater than 95.0% probability and at least two identified peptides, again assigned using the Protein Prophet algorithm. (43) N-terminal sequencing OPN at a 1 mM concentration was incubated for 15 minutes at 378C with 2 mM PHEX and then electrophoresed on a 10% SDS-PAGE gel and transferred to a PVDF (polyvinylidene fluoride) membrane (Millipore, Bedford, MA, USA) in glycine-containing transfer buffer.…”

Section: Phex-protein Digestionsmentioning

confidence: 99%

Proteolytic processing of osteopontin by PHEX and accumulation of osteopontin fragments in Hyp mouse bone, the murine model of X-linked hypophosphatemia

Barros

Hoac

Neves

et al. 2012

Journal of Bone and Mineral Research

125

111

View full text Add to dashboard Cite

X-linked hypophosphatemia (XLH/HYP)-with renal phosphate wasting, hypophosphatemia, osteomalacia, and tooth abscesses-is caused by mutations in the zinc-metallopeptidase PHEX gene (phosphate-regulating gene with homologies to endopeptidase on the X chromosome). PHEX is highly expressed by mineralized tissue cells. Inactivating mutations in PHEX lead to distal renal effects (implying accumulation of a secreted, circulating phosphaturic factor) and accumulation in bone and teeth of mineralization-inhibiting, acidic serine-and aspartate-rich motif (ASARM)-containing peptides, which are proteolytically derived from the mineral-binding matrix proteins of the SIBLING family (small, integrin-binding ligand N-linked glycoproteins). Although the latter observation suggests a local, direct matrix effect for PHEX, its physiologically relevant substrate protein(s) have not been identified. Here, we investigated two SIBLING proteins containing the ASARM motif-osteopontin (OPN) and bone sialoprotein (BSP)-as potential substrates for PHEX. Using cleavage assays, gel electrophoresis, and mass spectrometry, we report that OPN is a full-length protein substrate for PHEX. Degradation of OPN was essentially complete, including hydrolysis of the ASARM motif, resulting in only very small residual fragments. Western blotting of Hyp (the murine homolog of human XLH) mouse bone extracts having no PHEX activity clearly showed accumulation of an $35 kDa OPN fragment that was not present in wild-type mouse bone. Immunohistochemistry and immunogold labeling (electron microscopy) for OPN in Hyp bone likewise showed an accumulation of OPN and/or its fragments compared with normal wild-type bone. Incubation of Hyp mouse bone extracts with PHEX resulted in the complete degradation of these fragments. In conclusion, these results identify full-length OPN and its fragments as novel, physiologically relevant substrates for PHEX, suggesting that accumulation of mineralization-inhibiting OPN fragments may contribute to the mineralization defect seen in the osteomalacic bone characteristic of XLH/HYP. ß

show abstract

Section: Phex-protein Digestionsmentioning

confidence: 99%

Proteolytic processing of osteopontin by PHEX and accumulation of osteopontin fragments in Hyp mouse bone, the murine model of X-linked hypophosphatemia

Barros

Hoac

Neves

et al. 2012

Journal of Bone and Mineral Research

125

111

View full text Add to dashboard Cite

show abstract

“…Machine learning techniques are widely used to build re-ranking models [9-14]. These methods require high-quality MS/MS spectra as training data to generate an accurate classification or regression model.However, it is very difficult to obtain a discriminative model that is universally applicable to different platforms and experimental conditions.…”

Section: Discussionmentioning

confidence: 99%

Score regularization for peptide identification

Zhao

2011

BMC Bioinformatics

View full text Add to dashboard Cite

BackgroundPeptide identification from tandem mass spectrometry (MS/MS) data is one of the most important problems in computational proteomics. This technique relies heavily on the accurate assessment of the quality of peptide-spectrum matches (PSMs). However, current MS technology and PSM scoring algorithm are far from perfect, leading to the generation of incorrect peptide-spectrum pairs. Thus, it is critical to develop new post-processing techniques that can distinguish true identifications from false identifications effectively.ResultsIn this paper, we present a consistency-based PSM re-ranking method to improve the initial identification results. This method uses one additional assumption that two peptides belonging to the same protein should be correlated to each other. We formulate an optimization problem that embraces two objectives through regularization: the smoothing consistency among scores of correlated peptides and the fitting consistency between new scores and initial scores. This optimization problem can be solved analytically. The experimental study on several real MS/MS data sets shows that this re-ranking method improves the identification performance.ConclusionsThe score regularization method can be used as a general post-processing step for improving peptide identifications. Source codes and data sets are available at: http://bioinformatics.ust.hk/SRPI.rar.

show abstract

“…The peptide identifications were accepted if they could be established at greater than 99.0% probability as assigned by the Peptide Prophet algorithm [49]. The protein identifications were accepted if they could be established at greater than 95% probability as assigned by the Protein Prophet algorithm [50]; were based on at least 2 identified peptides; and were detected in at least two out of three replicates (both biological and technical).…”

Section: Methodsmentioning

confidence: 99%

Comparative proteomics of twoMycoplasma hyopneumoniaestrains andMycoplasma flocculareidentified potential porcine enzootic pneumonia determinants

et al. 2018

View full text Add to dashboard Cite

Mycoplasma hyopneumoniae and Mycoplasma flocculare are genetically similar bacteria, which coinhabit the porcine respiratory tract. These mycoplasmas share most of the known virulence factors, but, while M. hyopneumoniae causes porcine enzootic pneumonia (PEP), M. flocculare is a commensal species. To identify potential PEP determinants and provide novel insights on mycoplasma-host interactions, the whole cell proteomes of two M. hyopneumoniae strains, one pathogenic (7448) and other non-pathogenic (J), and M. flocculare were compared. A cell fractioning approach combined with mass spectrometry (LC-MS/MS) proteomics was used to analyze cytoplasmic and surface-enriched protein fractions. Average detection of ~ 50% of the predicted proteomes of M. hyopneumoniae 7448 and J, and M. flocculare was achieved. Many of the identified proteins were differentially represented in M. hyopneumoniae 7448 in comparison to M. hyopneumoniae J and M. flocculare, including potential PEP determinants, such as adhesins, proteases, and redox-balancing proteins, among others. The LC-MS/MS data also provided experimental validation for several genes previously regarded as hypothetical for all analyzed mycoplasmas, including some coding for proteins bearing virulence-related functional domains. The comprehensive proteome profiling of two M. hyopneumoniae strains and M. flocculare provided tens of novel candidates to PEP determinants or virulence factors, beyond those classically described.

show abstract

Empirical Statistical Model To Estimate the Accuracy of Peptide Identifications Made by MS/MS and Database Search

Cited by 4,562 publications

References 30 publications

Proteolytic processing of osteopontin by PHEX and accumulation of osteopontin fragments in Hyp mouse bone, the murine model of X-linked hypophosphatemia

Proteolytic processing of osteopontin by PHEX and accumulation of osteopontin fragments in Hyp mouse bone, the murine model of X-linked hypophosphatemia

Score regularization for peptide identification

Comparative proteomics of twoMycoplasma hyopneumoniaestrains andMycoplasma flocculareidentified potential porcine enzootic pneumonia determinants

Contact Info

Product

Resources

About