The in silico human surfaceome

被引:224
作者
Bausch-Fluck, Damaris [1 ,2 ]
Goldmann, Ulrich [1 ,3 ]
Mueller, Sebastian [1 ,4 ]
van Oostrum, Marc [1 ,2 ]
Mueller, Maik [1 ,2 ]
Schubert, Olga T. [1 ,5 ]
Wollscheid, Bernd [1 ,2 ]
机构
[1] Swiss Fed Inst Technol, Dept Biol, Inst Mol Syst Biol, CH-8093 Zurich, Switzerland
[2] Swiss Fed Inst Technol, Dept Hlth Sci & Technol, Biomed Prote Platform, CH-8093 Zurich, Switzerland
[3] Austrian Acad Sci, CeMM Res Ctr Mol Med, A-1090 Vienna, Austria
[4] Biognosys, CH-8952 Schlieren, Switzerland
[5] Univ Calif Los Angeles, Dept Human Genet, Los Angeles, CA 90095 USA
基金
瑞士国家科学基金会;
关键词
surfaceome; SURFY; machine learning; cell surface protein; multiomics; PREDICTING SUBCELLULAR-LOCALIZATION; TRANSMEMBRANE PROTEIN TOPOLOGY; MEMBRANE-PROTEINS; ANALYSIS REVEALS; SIGNAL PEPTIDES; STEM-CELLS; WEB SERVER; IDENTIFICATION; GLYCOSYLATION; REGULATORS;
D O I
10.1073/pnas.1808790115
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页码:E10988 / E10997
页数:10
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