Computational prediction of proteotypic peptides for quantitative proteomics

Abstract

Mass spectrometry-based quantitative proteomics has become an important component of biological and clinical research. Although such analyses typically assume that a protein's peptide fragments are observed with equal likelihood, only a few so-called 'proteotypic' peptides are repeatedly and consistently identified for any given protein present in a mixture. Using >600,000 peptide identifications generated by four proteomic platforms, we empirically identified >16,000 proteotypic peptides for 4,030 distinct yeast proteins. Characteristic physicochemical properties of these peptides were used to develop a computational tool that can predict proteotypic peptides for any protein from any organism, for a given platform, with >85% cumulative accuracy. Possible applications of proteotypic peptides include validation of protein identifications, absolute quantification of proteins, annotation of coding sequences in genomes, and characterization of the physical principles governing key elements of mass spectrometric workflows (e.g., digestion, chromatography, ionization and fragmentation).

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

“…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].…”

“…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].…”

“…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].…”

Rapid and Accurate Peptide Identification from Tandem Mass Spectra

“…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.…”

Shared Peptides in Mass Spectrometry Based Protein Quantification

“…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.…”