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The in silico human surfaceome.

Damaris Bausch-Fluck1,2, Ulrich Goldmann1, Sebastian Müller1

  • 1Institute of Molecular Systems Biology at the Department of Biology, ETH Zurich, 8093 Zurich, Switzerland.

Proceedings of the National Academy of Sciences of the United States of America
|October 31, 2018
PubMed
Summary
This summary is machine-generated.

Researchers developed SURFY, a machine learning tool, to predict the human surfaceome, identifying 2,886 cell-surface proteins. This comprehensive surfaceome dataset aids in understanding protein dynamics and drug development.

Keywords:
SURFYcell surface proteinmachine learningmultiomicssurfaceome

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Area of Science:

  • Biochemistry
  • Proteomics
  • Bioinformatics

Background:

  • Cell-surface proteins are crucial drug targets, with 66% of approved drugs acting on them.
  • A comprehensive understanding of the human surfaceome is lacking, with only a fraction of predicted transmembrane proteins confirmed at the plasma membrane.

Purpose of the Study:

  • To develop a predictive tool for the human surfaceome.
  • To create a comprehensive catalog of cell-surface proteins for research.

Main Methods:

  • Developed SURFY, a machine learning model utilizing a random forest classifier.
  • Trained SURFY on 131 features per protein using experimentally verified cell-surface proteins from the Cell Surface Protein Atlas (CSPA).

Main Results:

  • Predicted a human surfaceome of 2,886 proteins with 93.5% accuracy.
  • Observed dynamic expression of surfaceome genes (543-1,700 genes) across different cell types, including cancer cell lines and embryonic stem cells.
  • Demonstrated excellent overlap of predicted proteins with known cell-surface protein classes like receptors.

Conclusions:

  • The developed SURFY predictor provides a robust resource for the human surfaceome.
  • The surfaceome exhibits cell-type-dependent dynamics, differing from the nonsurface proteome.
  • The predicted surfaceome and associated visualization tools facilitate multiomics data analysis and research into cell-surface protein organization.