Abstract
The human genome harbors just 20,000 genes suggesting that the variety of possible protein products per gene plays a significant role in generating functional diversity. In bottom-up proteomics peptides are mapped back to proteins and proteoforms to describe a proteome; however, accurate quantitation of proteoforms is challenging due to incomplete protein sequence coverage and mapping ambiguities. Here, we demonstrate that a new software tool called ProteinClusterQuant (PCQ) can be used to deduce the presence of proteoforms that would have otherwise been missed, as exemplified in a proteomic comparison of two fly species, Drosophilamelanogaster and D. virilis. PCQ was used to identify reduced levels of serine/threonine protein kinases PKN1 and PKN4 in CFBE41o− cells compared to HBE41o− cells and to elucidate that shorter proteoforms of full-length caspase-4 and ephrin B receptor are differentially expressed. Thus, PCQ extends current analyses in quantitative proteomics and facilitates finding differentially regulated proteins and proteoforms.
Highlights
The human genome harbors just 20,000 genes suggesting that the variety of possible protein products per gene plays a significant role in generating functional diversity
To identify differentially expressed proteoforms from quantitative proteomic datasets, all peptide-to-protein relationships derived from a bottom-up proteomics experiment are displayed in a bipartite network representation in which peptide nodes are connected to protein nodes and the edges between them indicate that the peptide sequence is part of the respective protein sequence
To test whether PCQ can be used to deduce the presence of additional, regulated proteoforms in a proteomic dataset, we set up an experiment that compared the proteome of two fruit fly species, Drosophila melanogaster and D. virilis (Fig. 1a)
Summary
The human genome harbors just 20,000 genes suggesting that the variety of possible protein products per gene plays a significant role in generating functional diversity. Peptides can be grouped and an average relative abundance calculated based on protein FASTA annotations or by maximum parsimony per gene16 This approach reduces the repeated use of measurements to quantify different proteoforms, but it does not eliminate repeated use when quantifying proteins of different genes. To identify differentially expressed proteoforms from quantitative proteomic datasets, all peptide-to-protein relationships derived from a bottom-up proteomics experiment are displayed in a bipartite network representation in which peptide nodes are connected to protein nodes and the edges between them indicate that the peptide sequence is part of the respective protein sequence. Quantitative information about both absolute and relative abundance is included in each peptide node This kind of bipartite network allows a redundancy-free representation and interpretation of proteomic datasets and has previously been used to represent parts of a proteome. A systematic analysis of peptide-to-protein networks for relative quantification of proteoforms in a two-sample comparison has not yet been realized
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