The in silico human surfaceome
OPEN ACCESS
Loading...
Author / Producer
Date
2018-11-13
Publication Type
Journal Article
ETH Bibliography
yes
Citations
Altmetric
OPEN ACCESS
Data
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.
Permanent link
Publication status
published
External links
Editor
Book title
Volume
115 (46)
Pages / Article No.
Publisher
National Academy of Sciences
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Surfaceome; SURFY; Machine learning; Cell surface protein; Multiomics
Organisational unit
02072 - Proteomics Plattform D-HEST
Notes
Funding
160259 - Direct identification of lateral protein interactions in the plasma membrane of living cells and tissues (SNF)
Related publications and datasets
Is part of: 10.3929/ethz-b-000306057