An informatic framework for decoding protein complexes by top-down mass spectrometry

Owen S. Skinner, Pierre C. Havugimana, Nicole A. Haverland, Luca Fornelli, Bryan P. Early, Joseph B. Greer, Ryan T. Fellers, Kenneth R. Durbin, Luis H F Do Vale, Rafael D. Melani, Henrique S. Seckler, Micah T. Nelp, Mikhail E. Belov, Stevan R. Horning, Alexander A. Makarov, Richard D. LeDuc, Vahe Bandarian, Philip D. Compton, Neil L. Kelleher

Research output: Contribution to journalArticle

37 Scopus citations

Abstract

Efforts to map the human protein interactome have resulted in information about thousands of multi-protein assemblies housed in public repositories, but the molecular characterization and stoichiometry of their protein subunits remains largely unknown. Here, we report a computational search strategy that supports hierarchical top-down analysis for precise identification and scoring of multi-proteoform complexes by native mass spectrometry.

Original languageEnglish (US)
Pages (from-to)237-240
Number of pages4
JournalNature Methods
Volume13
Issue number3
DOIs
StatePublished - Feb 25 2016

ASJC Scopus subject areas

  • Biotechnology
  • Molecular Biology
  • Biochemistry
  • Cell Biology

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    Skinner, O. S., Havugimana, P. C., Haverland, N. A., Fornelli, L., Early, B. P., Greer, J. B., Fellers, R. T., Durbin, K. R., Do Vale, L. H. F., Melani, R. D., Seckler, H. S., Nelp, M. T., Belov, M. E., Horning, S. R., Makarov, A. A., LeDuc, R. D., Bandarian, V., Compton, P. D., & Kelleher, N. L. (2016). An informatic framework for decoding protein complexes by top-down mass spectrometry. Nature Methods, 13(3), 237-240. https://doi.org/10.1038/nmeth.3731