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University of Wisconsin–Madison

Systematic Functional Annotation of Orphan Proteins by High Throughput Mass Spectrometry Profiling

Major advances over the past two decades have identified new genes and new disease variants, but understanding the functions of the proteins these genes encode lags behind. Incomplete understanding of the human proteome—the full set of proteins present in cells—limits our understanding of disease and hinders bench-to-bedside translation of the human genome sequence into new therapies. This bottleneck is particularly constrictive for mitochondria, the energy-producing structures within cells whose dysfunction contributes to about 150 human disorders.

To address this problem, we recently devised an innovative mass spectrometry-based protein annotation strategy based on the idea that the disruption of a protein-encoding gene would elicit a defined proteomic and metabolomic response that could be compared to similar responses caused by the disruption of functionally related genes. We applied this process to ~3,500 yeast cultures from 174 single gene deletions, yielding predictions of the functions of numerous uncharacterized mitochondrial proteins.

“Systematic Functional Annotation of Orphan Proteins by High-Throughput Mass Spectrometry Profiling” will extend this strategy to 250 CRISPR/Cas9-modified human cell lines, each lacking a single gene encoding a mitochondrial protein. This project will examine the proteomic/metabolomic profiles of these cell lines to generate hypotheses about the functions of proteins they lack, and will then put the results to rigorous molecular and biochemical tests.

Beyond rapidly accelerating our understanding of mitochondrial biology and pathophysiology, the process will advance a powerful new technology for the annotation of human gene function, the discovery of therapeutic targets, and the identification of causative lesions in human diseases.

Co-Principal Investigators

  • David J. Pagliarini
    Investigator and Director of Metabolism
    Morgridge Institute for Research
    Associate Professor, Department of Biochemistry
  • Joshua J. Coon
    Departments of Biomolecular Chemistry and Chemistry


  • Mark Craven
    Departments of Biostatistics & Medical Informatics and Computer Sciences