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Motif-based phosphoproteome clustering improves modeling and interpretation

View ORCID ProfileMarc Creixell, View ORCID ProfileAaron S. Meyer
doi: https://doi.org/10.1101/2021.06.09.447799
Marc Creixell
1Department of Bioengineering, University of California, Los Angeles, United States of America
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Aaron S. Meyer
1Department of Bioengineering, University of California, Los Angeles, United States of America
2Department of Bioinformatics, University of California, Los Angeles, United States of America
3Jonsson Comprehensive Cancer Center, University of California, Los Angeles, United States of America
4Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, United States of America
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  • For correspondence: ameyer@asmlab.org
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Abstract

Cell signaling is orchestrated in part through a network of protein kinases and phosphatases. Dysregulation of kinase signaling is widespread in diseases such as cancer and is readily targetable through inhibitors of kinase enzymatic activity. Mass spectrometry-based analysis of kinase signaling can provide a global view of kinase signaling regulation but making sense of these data is complicated by its stochastic coverage of the proteome, measurement of substrates rather than kinase signaling itself, and the scale of the data collected. Here, we implement a dual data and motif clustering strategy (DDMC) that simultaneously clusters substrate peptides into similarly regulated groups based on their variation within an experiment and their sequence profile. We show that this can help to identify putative upstream kinases and supply more robust clustering. We apply this clustering to large-scale clinical proteomic profiling of lung cancer and identify conserved proteomic signatures of tumorigenicity, genetic mutations, and tumor immune infiltration. We propose that DDMC provides a general and flexible clustering strategy for the analysis of phosphoproteomic data.

One-sentence Summary DDMC is a general and flexible strategy for phosphoproteomic analysis by clustering phosphopeptides using both their phosphorylation abundance and sequence motifs.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/meyer-lab/resistance-MS

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted June 10, 2021.
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Motif-based phosphoproteome clustering improves modeling and interpretation
Marc Creixell, Aaron S. Meyer
bioRxiv 2021.06.09.447799; doi: https://doi.org/10.1101/2021.06.09.447799
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Motif-based phosphoproteome clustering improves modeling and interpretation
Marc Creixell, Aaron S. Meyer
bioRxiv 2021.06.09.447799; doi: https://doi.org/10.1101/2021.06.09.447799

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