基于完整的合成人类蛋白质组构建蛋白质组工具。
Building ProteomeTools based on a complete synthetic human proteome.
作者信息
Zolg Daniel P, Wilhelm Mathias, Schnatbaum Karsten, Zerweck Johannes, Knaute Tobias, Delanghe Bernard, Bailey Derek J, Gessulat Siegfried, Ehrlich Hans-Christian, Weininger Maximilian, Yu Peng, Schlegl Judith, Kramer Karl, Schmidt Tobias, Kusebauch Ulrike, Deutsch Eric W, Aebersold Ruedi, Moritz Robert L, Wenschuh Holger, Moehring Thomas, Aiche Stephan, Huhmer Andreas, Reimer Ulf, Kuster Bernhard
机构信息
Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany.
JPT Peptide Technologies GmbH, Berlin, Germany.
出版信息
Nat Methods. 2017 Mar;14(3):259-262. doi: 10.1038/nmeth.4153. Epub 2017 Jan 30.
Abstract
We describe ProteomeTools, a project building molecular and digital tools from the human proteome to facilitate biomedical research. Here we report the generation and multimodal liquid chromatography-tandem mass spectrometry analysis of >330,000 synthetic tryptic peptides representing essentially all canonical human gene products, and we exemplify the utility of these data in several applications. The resource (available at http://www.proteometools.org) will be extended to >1 million peptides, and all data will be shared with the community via ProteomicsDB and ProteomeXchange.
摘要
我们介绍了ProteomeTools，这是一个利用人类蛋白质组构建分子和数字工具以促进生物医学研究的项目。在此，我们报告了对代表几乎所有典型人类基因产物的超过330,000条合成胰蛋白酶肽段进行的生成及多模式液相色谱-串联质谱分析，并举例说明了这些数据在多个应用中的实用性。该资源（可在http://www.proteometools.org获取）将扩展至超过100万个肽段，所有数据将通过ProteomicsDB和ProteomeXchange与科学界共享。
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2
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3
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