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A Protein Standard That Emulates Homology for the Characterization of Protein Inference Algorithms
KTH, Centres, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0002-5401-5553
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
EBI, EMBL, Wellcome Trust Genome Campus, Cambridge CB10 1SD, England..ORCID iD: 0000-0001-6579-6941
Pacific Northwest Natl Lab, Biol Sci Div, Richland, WA 99352 USA..ORCID iD: 0000-0002-8351-1994
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2018 (English)In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 17, no 5, p. 1879-1886Article in journal (Refereed) Published
Abstract [en]

A natural way to benchmark the performance of an analytical experimental setup is to use samples of known measured analytes are peptides and not the actual proteins one of the inherent problems of interpreting data is that the composition and see to what degree one can correctly infer the content of such a sample from the data. For shotgun proteomics, themselves. As some proteins share proteolytic peptides, there might be more than one possible causative set of proteins resulting in a given set of peptides and there is a need for mechanisms that infer proteins from lists of detected peptides. A weakness of commercially available samples of known content is that they consist of proteins that are deliberately selected for producing tryptic peptides that are unique to a single protein. Unfortunately, such samples do not expose any complications in protein inference. Hence, for a realistic benchmark of protein inference procedures, there is a need for samples of known content where the present proteins share peptides with known absent proteins. Here, we present such a standard, that is based on E. coli expressed human protein fragments. To illustrate the application of this standard, we benchmark a set of different protein inference procedures on the data. We observe that inference procedures excluding shared peptides provide more accurate estimates of errors compared to methods that include information from shared peptides, while still giving a reasonable performance in terms of the number of identified proteins. We also demonstrate that using a sample of known protein content without proteins with shared tryptic peptides can give a false sense of accuracy for many protein inference methods.

Place, publisher, year, edition, pages
AMER CHEMICAL SOC , 2018. Vol. 17, no 5, p. 1879-1886
Keywords [en]
mass spectrometry, proteomics, protein inference, sample of known content, protein standard, proteofom, peptide, homology, benchmark
National Category
Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:kth:diva-228270DOI: 10.1021/acs.jproteome.7b00899ISI: 000431726700013PubMedID: 29631402Scopus ID: 2-s2.0-85046629294OAI: oai:DiVA.org:kth-228270DiVA, id: diva2:1209208
Note

QC 20180522

Available from: 2018-05-22 Created: 2018-05-22 Last updated: 2018-05-22Bibliographically approved

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The, MatthewForsström, BjörnKäll, Lukas

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The, MatthewEdfors, FredrikPerez-Riverol, YassetPayne, Samuel H.Palmblad, MagnusForsström, BjörnKäll, Lukas
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Science for Life Laboratory, SciLifeLabSystems BiologyAlbanova VinnExcellence Center for Protein Technology, ProNovaSeRC - Swedish e-Science Research Centre
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