The in silico human surfaceome
dc.contributor.author
Bausch-Fluck, Damaris
dc.contributor.author
Goldmann, Ulrich
dc.contributor.author
Müller, Sebastian
dc.contributor.author
van Oostrum, Marc
dc.contributor.author
Müller, Maik
dc.contributor.author
Schubert, Olga T.
dc.contributor.author
Wollscheid, Bernd
dc.date.accessioned
2019-02-15T07:46:29Z
dc.date.available
2019-02-15T07:46:29Z
dc.date.issued
2018-11-13
dc.identifier.issn
0027-8424
dc.identifier.issn
1091-6490
dc.identifier.other
10.1073/pnas.1808790115
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/325378
dc.identifier.doi
10.3929/ethz-b-000305906
dc.description.abstract
Cell-surface proteins are of great biomedical importance, as demonstrated by the fact that 66% of approved human drugs listed in the DrugBank database target a cell-surface protein. Despite this biomedical relevance, there has been no comprehensive assessment of the human surfaceome, and only a fraction of the predicted 5,000 human transmembrane proteins have been shown to be located at the plasma membrane. To enable analysis of the human surfaceome, we developed the surfaceome predictor SURFY, based on machine learning. As a training set, we used experimentally verified high-confidence cell-surface proteins from the Cell Surface Protein Atlas (CSPA) and trained a random forest classifier on 131 features per protein and, specifically, per topological domain. SURFY was used to predict a human surfaceome of 2,886 proteins with an accuracy of 93.5%, which shows excellent overlap with known cell-surface protein classes (i.e., receptors). In deposited mRNA data, we found that between 543 and 1,100 surfaceome genes were expressed in cancer cell lines and maximally 1,700 surfaceome genes were expressed in embryonic stem cells and derivative lines. Thus, the surfaceome diversity depends on cell type and appears to be more dynamic than the nonsurface proteome. To make the predicted surfaceome readily accessible to the research community, we provide visualization tools for intuitive interrogation (wlab.ethz.ch/surfaceome). The in silico surfaceome enables the filtering of data generated by multiomics screens and supports the elucidation of the surfaceome nanoscale organization.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
National Academy of Sciences
en_US
dc.rights.uri
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject
Surfaceome
en_US
dc.subject
SURFY
en_US
dc.subject
Machine learning
en_US
dc.subject
Cell surface protein
en_US
dc.subject
Multiomics
en_US
dc.title
The in silico human surfaceome
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
dc.date.published
2018-10-29
ethz.journal.title
Proceedings of the National Academy of Sciences of the United States of America
ethz.journal.volume
115
en_US
ethz.journal.issue
46
en_US
ethz.journal.abbreviated
Proc Natl Acad Sci U S A
ethz.pages.start
E10988
en_US
ethz.pages.end
E10997
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.grant
Direct identification of lateral protein interactions in the plasma membrane of living cells and tissues
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Washington, DC
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02070 - Dep. Gesundheitswiss. und Technologie / Dep. of Health Sciences and Technology::02072 - Proteomics Plattform D-HEST
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02070 - Dep. Gesundheitswiss. und Technologie / Dep. of Health Sciences and Technology::02072 - Proteomics Plattform D-HEST
ethz.grant.agreementno
160259
ethz.grant.fundername
SNF
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
Projekte Lebenswissenschaften
ethz.relation.isPartOf
10.3929/ethz-b-000306057
ethz.date.deposited
2018-11-24T15:46:40Z
ethz.source
WOS
ethz.source
BATCH
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2019-02-15T07:47:03Z
ethz.rosetta.lastUpdated
2022-03-28T22:17:28Z
ethz.rosetta.versionExported
true
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/305906
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/322772
ethz.COinS
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