title: A peptide-retrieval strategy enables significant improvement of quantitative performance without compromising confidence of identification.
abstract: Reliable quantification of low-abundance proteins in complex proteomes is challenging largely owing to the limited number of spectra/peptides identified. In this study we developed a straightforward method to improve the quantitative accuracy and precision of proteins by strategically retrieving the less confident peptides that were previously filtered out using the standard target-decoy search strategy. The filtered-out MS/MS spectra matched to confidently-identified proteins were recovered, and the peptide-spectrum-match FDR were re-calculated and controlled at a confident level of FDR≤1%, while protein FDR maintained at ~1%. We evaluated the performance of this strategy in both spectral count- and ion current-based methods. >60% increase of total quantified spectra/peptides was respectively achieved for analyzing a spike-in sample set and a public dataset from CPTAC. Incorporating the peptide retrieval strategy significantly improved the quantitative accuracy and precision, especially for low-abundance proteins (e.g. one-hit proteins). Moreover, the capacity of confidently discovering significantly-altered proteins was also enhanced substantially, as demonstrated with two spike-in datasets. In summary, improved quantitative performance was achieved by this peptide recovery strategy without compromising confidence of protein identification, which can be readily implemented in a broad range of quantitative proteomics techniques including label-free or labeling approaches.
  
  
  
  We hypothesize that more quantifiable spectra and peptides in a protein, even including less confident peptides, could help reduce variations and improve protein quantification. Hence the peptide retrieval strategy was developed and evaluated in two spike-in sample sets with different LC-MS/MS variations using both MS1- and MS2-based quantitative approach. The list of confidently identified proteins using the standard target-decoy search strategy was fixed and more spectra/peptides with less confidence matched to confident proteins were retrieved. However, the total peptide-spectrum-match false discovery rate (PSM FDR) after retrieval analysis was still controlled at a confident level of FDR≤1%. As expected, the penalty for occasionally incorporating incorrect peptide identifications is negligible by comparison with the improvements in quantitative performance. More quantifiable peptides, lower missing value rate, better quantitative accuracy and precision were significantly achieved for the same protein identifications by this simple strategy. This strategy is theoretically applicable for any quantitative approaches in proteomics and thereby provides more quantitative information, especially on low-abundance proteins.
authors.affiliation: Department of Pharmaceutical Sciences, State University of New York at Buffalo, 285 Kapoor Hall, Buffalo, NY 14260, United States; New York State Center of Excellence in Bioinformatics and Life Sciences, 701 Ellicott Street, Buffalo, NY 14203, United States. Electronic address: ctu3@buffalo.edu.
authors.forename: Chengjian
authors.initials: C
authors.lastname: Tu
authors.affiliation: Department of Pharmaceutical Sciences, State University of New York at Buffalo, 285 Kapoor Hall, Buffalo, NY 14260, United States; New York State Center of Excellence in Bioinformatics and Life Sciences, 701 Ellicott Street, Buffalo, NY 14203, United States.
authors.forename: Shichen
authors.initials: S
authors.lastname: Shen
authors.affiliation: Center for Quantitative Sciences, Vanderbilt University School of Medicine, 2220 Pierce Avenue, Nashville, TN 37232, United States.
authors.forename: Quanhu
authors.initials: Q
authors.lastname: Sheng
authors.affiliation: Center for Quantitative Sciences, Vanderbilt University School of Medicine, 2220 Pierce Avenue, Nashville, TN 37232, United States.
authors.forename: Yu
authors.initials: Y
authors.lastname: Shyr
authors.affiliation: Department of Pharmaceutical Sciences, State University of New York at Buffalo, 285 Kapoor Hall, Buffalo, NY 14260, United States; New York State Center of Excellence in Bioinformatics and Life Sciences, 701 Ellicott Street, Buffalo, NY 14203, United States. Electronic address: junqu@buffalo.edu.
authors.forename: Jun
authors.initials: J
authors.lastname: Qu
mesh_terms: D002853:Chromatography, Liquid; D019295:Computational Biology / Q000379:methods*; D010455:Peptides / Q000032:analysis; D020543:Proteome / Q000032:analysis*; D040901:Proteomics / Q000379:methods*; D015203:Reproducibility of Results; D012107:Research Design / Q000592:standards*; D053719:Tandem Mass Spectrometry / Q000379:methods
chemical_list: D010455:Peptides; D020543:Proteome
keywords: MS1-based quantification; Peptide-retrieval strategy; Spectral counting