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- Feizi, Amir, 1980, et al.
(författare)
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HCSD: The human cancer secretome database
- 2015
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Ingår i: Database : the journal of biological databases and curation. - : Oxford University Press (OUP). - 1758-0463. ; 2015
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Tidskriftsartikel (refereegranskat)abstract
- The human cancer secretome database (HCSD) is a comprehensive database for human cancer secretome data. The cancer secretome describes proteins secreted by cancer cells and structuring information about the cancer secretome will enable further analysis of how this is related with tumor biology. The secreted proteins from cancer cells are believed to play a deterministic role in cancer progression and therefore may be the key to find novel therapeutic targets and biomarkers for many cancers. Consequently, huge data on cancer secretome have been generated in recent years and the lack of a coherent database is limiting the ability to query the increasing community knowledge. We therefore developed the Human Cancer Secretome Database (HCSD) to fulfil this gap. HCSD contains >80 000 measurements for about 7000 nonredundant human proteins collected from up to 35 high-throughput studies on 17 cancer types. It has a simple and user friendly query system for basic and advanced search based on gene name, cancer type and data type as the three main query options. The results are visualized in an explicit and interactive manner. An example of a result page includes annotations, cross references, cancer secretome data and secretory features for each identified protein.
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| 2. |
- Bludau, Isabell, et al.
(författare)
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Complex-centric proteome profiling by SEC-SWATH-MS for the parallel detection of hundreds of protein complexes
- 2020
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Ingår i: Nature Protocols. - : Springer Science and Business Media LLC. - 1754-2189 .- 1750-2799. ; 15:8, s. 2341-2386
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Tidskriftsartikel (refereegranskat)abstract
- Most catalytic, structural and regulatory functions of the cell are carried out by functional modules, typically complexes containing or consisting of proteins. The composition and abundance of these complexes and the quantitative distribution of specific proteins across different modules are therefore of major significance in basic and translational biology. However, detection and quantification of protein complexes on a proteome-wide scale is technically challenging. We have recently extended the targeted proteomics rationale to the level of native protein complex analysis (complex-centric proteome profiling). The complex-centric workflow described herein consists of size exclusion chromatography (SEC) to fractionate native protein complexes, data-independent acquisition mass spectrometry to precisely quantify the proteins in each SEC fraction based on a set of proteotypic peptides and targeted, complex-centric analysis where prior information from generic protein interaction maps is used to detect and quantify protein complexes with high selectivity and statistical error control via the computational framework CCprofiler (https://github.com/CCprofiler/CCprofiler). Complex-centric proteome profiling captures most proteins in complex-assembled state and reveals their organization into hundreds of complexes and complex variants observable in a given cellular state. The protocol is applicable to cultured cells and can potentially also be adapted to primary tissue and does not require any genetic engineering of the respective sample sources. At present, it requires ~8 d of wet-laboratory work, 15 d of mass spectrometry measurement time and 7 d of computational analysis.
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