Mass fingerprinting of complex mixtures: protein inference from high-resolution peptide masses and predicted retention times
2013 (English)In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 12, no 12, 5730-5741 p.Article in journal (Refereed) Published
In typical shotgun experiments, the mass spectrometer records the masses of a large set of ionized analytes but fragments only a fraction of them. In the subsequent analyses, normally only the fragmented ions are used to compile a set of peptide identifications, while the unfragmented ones are disregarded. In this work, we show how the unfragmented ions, here denoted MS1-features, can be used to increase the confidence of the proteins identified in shotgun experiments. Specifically, we propose the usage of in silico mass tags, where the observed MS1-features are matched against de novo predicted masses and retention times for all peptides derived from a sequence database. We present a statistical model to assign protein-level probabilities based on the MS1-features and combine this data with the fragmentation spectra. Our approach was evaluated for two triplicate data sets from yeast and human, respectively, leading to up to 7% more protein identifications at a fixed protein-level false discovery rate of 1%. The additional protein identifications were validated both in the context of the mass spectrometry data and by examining their estimated transcript levels generated using RNA-Seq. The proposed method is reproducible, straightforward to apply, and can even be used to reanalyze and increase the yield of existing data sets.
Place, publisher, year, edition, pages
American Chemical Society (ACS), 2013. Vol. 12, no 12, 5730-5741 p.
mass spectrometry, shotgun proteomics, mass fingerprinting
Biochemistry and Molecular Biology
IdentifiersURN: urn:nbn:se:su:diva-94614DOI: 10.1021/pr400705qISI: 000328231300033OAI: oai:DiVA.org:su-94614DiVA: diva2:654554
Swedish Research Council; U.S. National Science Foundation (MRI) 0923536; American Recovery and Reinvestment Act (ARRA) funds R01 HG005805; National Institute of General Medical Sciences/Center for Systems Biology 2P50 GM076547; Luxembourg Centre for Systems Biomedicine; University of Luxembourg 2013-10-072013-10-072014-01-13Bibliographically approved