Computational prediction of proteotypic peptides for quantitative proteomics

Mallick, Parag; Schirle, Markus; Chen, Sharon S.; Flory, Mark R.; Lee, Hookeun; Martin, Daniel; Ranish, Jeffrey A.; Raught, Brian; Schmitt, Robert; Werner, Thilo; Küster, Bernhard; Aebersold, Ruedi

doi:10.1038/nbt1275

Cited by 636 publications

(684 citation statements)

References 29 publications

Supporting

Mentioning

673

Contrasting

Unclassified

Order By: Relevance

“…Informatic strategies for the prediction of the most likely peptides (proteotypic peptides) that will be observed for proteins of interest are emerging [14,15,23,54,55]. Physiocochemical properties of the peptide's aminoacid content should provide predictors to enable selection of which synthetic peptides to generate for absolute protein quantification [16,22,24,25,[54][55][56]. Unfortunately, general proteomic strategies assume that any protein's peptide fragments are observed with equal likelihood, however only a few proteotypic peptides are repeatedly and consistently observed in any given experiment [55].…”

Section: Informaticsmentioning

confidence: 99%

“…As state above, the MIDAS and TIQAM workflows were developed for targeted detection of proteins [21,44], their respective PTM [8,41] and for the rapid development of MRM assays [1,23,53]. Informatic strategies for the prediction of the most likely peptides (proteotypic peptides) that will be observed for proteins of interest are emerging [14,15,23,54,55]. Physiocochemical properties of the peptide's aminoacid content should provide predictors to enable selection of which synthetic peptides to generate for absolute protein quantification [16,22,24,25,[54][55][56].…”

Section: Informaticsmentioning

confidence: 99%

“…Physiocochemical properties of the peptide's aminoacid content should provide predictors to enable selection of which synthetic peptides to generate for absolute protein quantification [16,22,24,25,[54][55][56]. Unfortunately, general proteomic strategies assume that any protein's peptide fragments are observed with equal likelihood, however only a few proteotypic peptides are repeatedly and consistently observed in any given experiment [55]. To address this issue, Kline, KG et al, have developed research grade software, named P3 (Proteotypic Peptide Predictor), that is freely available to predict proteotypic peptides [22].…”

Section: Informaticsmentioning

confidence: 99%

See 2 more Smart Citations

Current affairs in quantitative targeted proteomics: multiple reaction monitoring-mass spectrometry

Yocum¹,

Chinnaiyan²

2009

Briefings in Functional Genomics and Proteomics

108

View full text Add to dashboard Cite

Quantitative targeted proteomics has recently taken front stage in the proteomics community. Centered on multiple reaction monitoring^mass spectrometry (MRM^MS) methodologies, quantitative targeted proteomics is being used in the verification of global proteomics data, the discovery of lower abundance proteins, protein post-translational modifications, discrimination of select highly homologous protein isoforms and as the final step in biomarker discovery. An older methodology utilized with small molecule analysis, the proteomics community is making great technological strides to develop MRM^MS as the next method to address previously challenging issues in global proteomics experimentation, namely dynamic range, identification of post-translational modifications, sensitivity and selectivity of measurement which will undoubtedly further biomedical knowledge.This brief review will provide a general introduction of MRM^MS and highlight its novel application for targeted quantitative proteomic experimentations.

show abstract

Section: Informaticsmentioning

confidence: 99%

Section: Informaticsmentioning

confidence: 99%

Section: Informaticsmentioning

confidence: 99%

See 1 more Smart Citation

Current affairs in quantitative targeted proteomics: multiple reaction monitoring-mass spectrometry

Yocum¹,

Chinnaiyan²

2009

Briefings in Functional Genomics and Proteomics

108

View full text Add to dashboard Cite

show abstract

“…We also plan to extend the peptide index to allow storage of additional pieces of precomputed information, such as predicted peak intensities, predicted peptide retention time, or predicted proteotypic peptides. 24 Finally, we have plans for algorithmic improvements, including increasing the variety of features used by Percolator, improving the accuracy of our q value estimates using existing methods, 25 and implementing algorithms for protein-level identification.…”

Section: Discussionmentioning

confidence: 99%

Rapid and Accurate Peptide Identification from Tandem Mass Spectra

et al. 2008

View full text Add to dashboard Cite

Mass spectrometry, the core technology in the field of proteomics, promises to enable scientists to identify and quantify the entire complement of proteins in a complex biological sample. Currently, the primary bottleneck in this type of experiment is computational. Existing algorithms for interpreting mass spectra are slow and fail to identify a large proportion of the given spectra. We describe a database search program called Crux that reimplements and extends the widely used database search program SEQUEST. For speed, Crux uses a peptide indexing scheme to rapidly retrieve candidate peptides for a given spectrum. For each peptide in the target database, Crux generates shuffled decoy peptides on the fly, providing a good null model and, hence, accurate false discovery rate estimates. Crux also implements two recently described postprocessing methods: a p value calculation based upon fitting a Weibull distribution to the observed scores, and a semisupervised method that learns to discriminate between target and decoy matches. Both methods significantly improve the overall rate of peptide identification. Crux is implemented in C and is distributed with source code freely to noncommercial users.

show abstract

“…However, the actual value is hard to interpret, as it depends upon a number of poorly understood factors, including instrument types, energetics of the process, and physico-chemical properties of the peptide itself. Consequently, it is often the relative-abundance of a peptide, measured as the ratio of intensities of a peptide across samples, that is investigated [4,8]. By the same token, intensity values of different peptides are usually not comparable.…”

Section: Introductionmentioning

confidence: 99%

Shared Peptides in Mass Spectrometry Based Protein Quantification

Dost

Bandeira

et al. 2009

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. In analyzing the proteome using mass spectrometry, the mass values help identify the molecules, and the intensities help quantify them, relative to their abundance in other samples. Peptides that are shared across different protein sequences are typically discarded as being uninformative w.r.t each of the parent proteins. In this paper, we investigate the use of shared peptides which are ubiquitous (∼ 50% of peptides) in mass spectrometric data-sets. In many cases, shared peptides can help compute the relative amounts of different proteins that share the same peptide. Also, proteins with no unique peptide in the sample can still be analyzed for relative abundance. Our paper is the first attempt to use shared peptides in protein quantification, and makes use of combinatorial optimization to reduce the error in relative abundance measurements. We describe the topological and numerical properties required for robust estimates, and use them to improve our estimates for ill-conditioned systems. Extensive simulations validate our approach even in the presence of experimental error. We apply our method to a model of Arabidopsis root knot nematode infection, and elucidate the differential role of many protein family members in mediating host response to the pathogen.

show abstract

Computational prediction of proteotypic peptides for quantitative proteomics

Cited by 636 publications

References 29 publications

Current affairs in quantitative targeted proteomics: multiple reaction monitoring-mass spectrometry

Current affairs in quantitative targeted proteomics: multiple reaction monitoring-mass spectrometry

Rapid and Accurate Peptide Identification from Tandem Mass Spectra

Shared Peptides in Mass Spectrometry Based Protein Quantification

Contact Info

Product

Resources

About