The in silico human surfaceome.
The in silico human surfaceome.
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DOI：
10.1073/pnas.1808790115
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发表时间：
2018-11-13
影响因子：
11.1
通讯作者：
Wollscheid B
中科院分区：
文献类型：
--
作者：
Bausch-Fluck D;Goldmann U;Müller S;van Oostrum M;Müller M;Schubert OT;Wollscheid B
尽管表面组作为细胞微环境的信号通路具有根本的重要性，但仍然难以确定哪些蛋白形式存在于质膜中以及它们如何相互作用以实现上下文依赖的信号功能。我们应用了一种利用域特异性特征的机器学习方法来开发精确的表面组预测器SURFY，并使用它来定义2，886种蛋白质的人类计算机表面组。计算机表面组是一种公共资源，可用于过滤多组学数据以揭示细胞表型和表面组标记。通过我们的特定于域的特征机器学习方法，我们间接地表明，环境（细胞外，细胞质或囊泡）反映在蛋白质结构域的生化特性中。细胞表面蛋白具有重要的生物医学意义，DrugBank数据库中列出的66%的批准的人类药物靶向细胞表面蛋白。尽管存在这种生物医学相关性，但尚未对人类表面组进行全面评估，并且预测的5，000种人类跨膜蛋白中只有一小部分已被证明位于质膜上。为了能够分析人类表面组，我们开发了基于机器学习的表面组预测器SURFY。作为训练集，我们使用来自细胞表面蛋白质图谱（CSPA）的实验验证的高置信度细胞表面蛋白质，并在每个蛋白质的131个特征上训练随机森林分类器，特别是每个拓扑结构域。SURFY用于预测2，886种蛋白质的人表面组，准确度为93.5%，其显示出与已知细胞表面蛋白质类别（即，受体）。在储存的mRNA数据中，我们发现在癌细胞系中表达了543至1，100个表面基因组基因，并且在胚胎干细胞和衍生系中最多表达了1，700个表面基因组基因。因此，表面蛋白质组的多样性取决于细胞类型，似乎比非表面蛋白质组更动态。为了使预测的surfaceome容易访问的研究社区，我们提供直观的询问可视化工具（wlab.ethz.ch/surfaceome）。计算机表面组能够过滤多组学筛选产生的数据，并支持表面组纳米级组织的阐明。
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. 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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影响因子：
14.9
作者：
Briesemeister S;Rahnenführer J;Kohlbacher O
通讯作者：
Kohlbacher O
DOI：
10.1016/j.bbrc.2007.06.027
发表时间：
2007-08-24
影响因子：
3.1
作者：
Chou, Kuo-Chen;Shen, Hong-Bin
通讯作者：
Shen, Hong-Bin
影响因子：
5.4
作者：
Almen, Markus Sallman;Nordstrom, Karl J. V.;Schioth, Helgi B.
通讯作者：
Schioth, Helgi B.
影响因子：
3.7
作者：
Bausch-Fluck D;Hofmann A;Bock T;Frei AP;Cerciello F;Jacobs A;Moest H;Omasits U;Gundry RL;Yoon C;Schiess R;Schmidt A;Mirkowska P;Härtlová A;Van Eyk JE;Bourquin JP;Aebersold R;Boheler KR;Zandstra P;Wollscheid B
通讯作者：
Wollscheid B
影响因子：
3.8
作者：
Cerciello, Ferdinando;Choi, Meena;Wollscheid, Bernd
通讯作者：
Wollscheid, Bernd