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Covariation of Peptide Abundances Accurately Reflects Protein Concentration Differences
KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH, Centres, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0001-5689-9797
2017 (English)In: Molecular & Cellular Proteomics, ISSN 1535-9476, E-ISSN 1535-9484, Vol. 16, no 5, 936-948 p.Article in journal (Refereed) Published
Abstract [en]

Most implementations of mass spectrometry-based proteomics involve enzymatic digestion of proteins, expanding the analysis to multiple proteolytic peptides for each protein. Currently, there is no consensus of how to summarize peptides' abundances to protein concentrations, and such efforts are complicated by the fact that error control normally is applied to the identification process, and do not directly control errors linking peptide abundance measures to protein concentration. Peptides resulting from suboptimal digestion or being partially modified are not representative of the protein concentration. Without a mechanism to remove such unrepresentative peptides, their abundance adversely impacts the estimation of their protein's concentration. Here, we present a relative quantification approach, Diffacto, that applies factor analysis to extract the covariation of peptides' abundances. The method enables a weighted geometrical average summarization and automatic elimination of incoherent peptides. We demonstrate, based on a set of controlled label-free experiments using standard mixtures of proteins, that the covariation structure extracted by the factor analysis accurately reflects protein concentrations. In the 1% peptide-spectrum match-level FDR data set, as many as 11% of the peptides have abundance differences incoherent with the other peptides attributed to the same protein. If not controlled, such contradicting peptide abundance have a severe impact on protein quantifications. When adding the quantities of each protein's three most abundant peptides, we note as many as 14% of the proteins being estimated as having a negative correlation with their actual concentration differences between samples. Diffacto reduced the amount of such obviously incorrectly quantified proteins to 1.6%. Furthermore, by analyzing clinical data sets from two breast cancer studies, our method revealed the persistent proteomic signatures linked to three subtypes of breast cancer. We conclude that Diffacto can facilitate the interpretation and enhance the utility of most types of proteomics data.

Place, publisher, year, edition, pages
American Society for Biochemistry and Molecular Biology, 2017. Vol. 16, no 5, 936-948 p.
National Category
Biochemistry and Molecular Biology Bioinformatics (Computational Biology) Biophysics
Identifiers
URN: urn:nbn:se:kth:diva-207901DOI: 10.1074/mcp.O117.067728ISI: 000400759600017PubMedID: 28302922Scopus ID: 2-s2.0-85018359335OAI: oai:DiVA.org:kth-207901DiVA: diva2:1103658
Note

QC 20170530

Available from: 2017-05-30 Created: 2017-05-30 Last updated: 2017-05-30Bibliographically approved

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Käll, Lukas

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