The DOI matches its Crossref record; 68 of the 68 references it cites were found in a registry or an index.
The in silico human surfaceome
Open accessAbstract
Significance: Despite the fundamental importance of the surfaceome as a signaling gateway to the cellular microenvironment, it remains difficult to determine which proteoforms reside in the plasma membrane and how they interact to enable context-dependent signaling functions. We applied a machine-learning approach utilizing domain-specific features to develop the accurate surfaceome predictor SURFY and used it to define the human in silico surfaceome of 2,886 proteins. The in silico surfaceome is a public resource which can be used to filter multiomics data to uncover cellular phenotypes and surfaceome markers. By our domain-specific feature machine-learning approach, we show indirectly that the environment (extracellular, cytoplasm, or vesicle) is reflected in the biochemical properties of protein domains reaching into that environment.
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Classification and Regression by randomForest
Liaw, A. I., & Wiener, M. C. (2007) · Cited by 18,594
Krogh, A., Larsson, B., von Heijne, G., & Sonnhammer, E. L. L. (2001) · Journal of Molecular Biology · doi:10.1006/jmbi.2000.4315 · Cited by 12,654
SignalP 4.0: discriminating signal peptides from transmembrane regions
Petersen, T. N., Brunak, S., von Heijne, G., & Nielsen, H. (2011) · Nature Methods · doi:10.1038/nmeth.1701 · Cited by 8,847
Global quantification of mammalian gene expression control
Schwanhäusser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., Chen, W., & Selbach, M. (2011) · Nature · doi:10.1038/nature10098 · Cited by 6,566 · cited 3×
“Although it has been shown that this derivation is possible with a gene-specific conversion factor (64), it was also demonstrated that the mRNA–protein correlation is less accurate for cell surface than for cytoplasmic proteins (44).”
BackgroundInfluentialIdentification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites
Nielsen, H., Engelbrecht, J., Brunak, S., & von Heijne, G. (1997) · Protein Engineering Design and Selection · doi:10.1093/protein/10.1.1 · Cited by 5,356
UniProt: a hub for protein information
The UniProt Consortium (2015) · Nucleic Acids Research · doi:10.1093/nar/gku989 · Cited by 5,106
Predicting Subcellular Localization of Proteins Based on their N-terminal Amino Acid Sequence
Emanuelsson, O., Nielsen, H., Brunak, S., & von Heijne, G. (2000) · Journal of Molecular Biology · doi:10.1006/jmbi.2000.3903 · Cited by 4,263
Gillet, L. C., Navarro, P., Tate, S., Röst, H., Selevsek, N., Reiter, L., Bonner, R., & Aebersold, R. (2012) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o111.016717 · Cited by 2,877
A subcellular map of the human proteome
Thul, P. J., Åkesson, L., Wiking, M., Mahdessian, D., Geladaki, A., Ait Blal, H., Alm, T., Asplund, A., Björk, L., Breckels, L. M., et al. (2017) · Science · doi:10.1126/science.aal3321 · Cited by 2,813
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Targeted activation in localized protein environments via deep red photoredox catalysis
Tay, N. E. S., Ryu, K. A., Weber, J. L., Olow, A. K., Cabanero, D. C., Reichman, D. R., Oslund, R. C., Fadeyi, O. O., & Rovis, T. (2023) · Nature Chemistry · doi:10.1038/s41557-022-01057-1 · Cited by 166
Müller, M., Gräbnitz, F., Barandun, N., Shen, Y., Wendt, F., Steiner, S. N., Severin, Y., Vetterli, S. U., Mondal, M., Prudent, J. R., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-27280-x · Cited by 101
Self-citationLeung, K. K., Wilson, G. M., Kirkemo, L. L., Riley, N. M., Coon, J. J., & Wells, J. A. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1917947117 · Cited by 83
Basisty, N., Kale, A., Patel, S., Campisi, J., & Schilling, B. (2020) · Expert Review of Proteomics · doi:10.1080/14789450.2020.1766976 · Cited by 69
Understanding the molecular diversity of synapses
van Oostrum, M., & Schuman, E. M. (2025) · Nature Reviews Neuroscience · doi:10.1038/s41583-024-00888-w · Cited by 49
Metadata
- Authors
- Bausch-Fluck, D., Goldmann, U., Müller, S., van Oostrum, M., Müller, M., Schubert, O. T., & Wollscheid, B.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2018-10-29
- Volume
- 115
- Issue
- 46
- Pages
- E10988-E10997
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2898873576
- Cited by
- 369
- References
- 68
- FWCI
- 6.91
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Bausch-Fluck, D., Goldmann, U., Müller, S., van Oostrum, M., Müller, M., Schubert, O. T., & Wollscheid, B. (2018). The in silico human surfaceome. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1808790115
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