Proteomics

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Characterization of peptide-protein relationships in protein ambiguity groups via bipartite graphs - data set D3 (without and with isoforms)


ABSTRACT: Motivation: In bottom-up mass spectrometry proteins are enzymatically digested before measurement. The relationship between proteins and peptides can be represented by bipartite graphs that can be split into connected components. This representation is useful to aid protein inference and quantification, which is complex due to the occurrence of shared peptides. We conducted a comprehensive analysis of these bipartite graphs using peptides from an in silico digestion of protein databases as well as quantified peptides. Results: The graphs based on quantified peptides are smaller and have less complex structures compared to the database level. However, the proportion of protein nodes without unique peptides and the proportion of graphs that contain these proteins increase. Large differences between the different underlying organisms on database as well as quantitative level could be observed. Insights of this analysis may be useful for the development of protein inference and quantification algorithms. Link to preprint: https://www.biorxiv.org/content/10.1101/2021.07.28.454128v1?ct=

INSTRUMENT(S): LTQ Orbitrap

ORGANISM(S): Homo Sapiens (human) Escherichia Coli

TISSUE(S): Cell Culture

SUBMITTER: Karin Schork  

LAB HEAD: Martin Eisenacher

PROVIDER: PXD030603 | Pride | 2022-11-11

REPOSITORIES: Pride

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Publications

Characterization of peptide-protein relationships in protein ambiguity groups via bipartite graphs.

Schork Karin K   Turewicz Michael M   Uszkoreit Julian J   Rahnenführer Jörg J   Eisenacher Martin M  

PloS one 20221021 10


In bottom-up proteomics, proteins are enzymatically digested into peptides before measurement with mass spectrometry. The relationship between proteins and their corresponding peptides can be represented by bipartite graphs. We conduct a comprehensive analysis of bipartite graphs using quantified peptides from measured data sets as well as theoretical peptides from an in silico digestion of the corresponding complete taxonomic protein sequence databases. The aim of this study is to characterize  ...[more]

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