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The in silico human surfaceome

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 frac...

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Autores principales: Bausch-Fluck, Damaris, Goldmann, Ulrich, Müller, Sebastian, van Oostrum, Marc, Müller, Maik, Schubert, Olga T., Wollscheid, Bernd
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6243280/
https://www.ncbi.nlm.nih.gov/pubmed/30373828
http://dx.doi.org/10.1073/pnas.1808790115
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author Bausch-Fluck, Damaris
Goldmann, Ulrich
Müller, Sebastian
van Oostrum, Marc
Müller, Maik
Schubert, Olga T.
Wollscheid, Bernd
author_facet Bausch-Fluck, Damaris
Goldmann, Ulrich
Müller, Sebastian
van Oostrum, Marc
Müller, Maik
Schubert, Olga T.
Wollscheid, Bernd
author_sort Bausch-Fluck, Damaris
collection PubMed
description 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.
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spelling pubmed-62432802018-11-27 The in silico human surfaceome Bausch-Fluck, Damaris Goldmann, Ulrich Müller, Sebastian van Oostrum, Marc Müller, Maik Schubert, Olga T. Wollscheid, Bernd Proc Natl Acad Sci U S A PNAS Plus 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. National Academy of Sciences 2018-11-13 2018-10-29 /pmc/articles/PMC6243280/ /pubmed/30373828 http://dx.doi.org/10.1073/pnas.1808790115 Text en Copyright © 2018 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/ This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle PNAS Plus
Bausch-Fluck, Damaris
Goldmann, Ulrich
Müller, Sebastian
van Oostrum, Marc
Müller, Maik
Schubert, Olga T.
Wollscheid, Bernd
The in silico human surfaceome
title The in silico human surfaceome
title_full The in silico human surfaceome
title_fullStr The in silico human surfaceome
title_full_unstemmed The in silico human surfaceome
title_short The in silico human surfaceome
title_sort in silico human surfaceome
topic PNAS Plus
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6243280/
https://www.ncbi.nlm.nih.gov/pubmed/30373828
http://dx.doi.org/10.1073/pnas.1808790115
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