MaXLinker: Proteome-wide Cross-link Identifications with High Specificity and Sensitivity.

Autor:	Yugandhar K; Department of Computational Biology, Cornell University, Ithaca, New York,14853; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853., Wang TY; Department of Computational Biology, Cornell University, Ithaca, New York,14853; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853., Leung AK; Department of Computational Biology, Cornell University, Ithaca, New York,14853; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853., Lanz MC; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853; Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York 14853., Motorykin I; Mass Spectrometry and Proteomics Facility, Institute of Biotechnology, Cornell University, Ithaca, New York,14853., Liang J; Department of Computational Biology, Cornell University, Ithaca, New York,14853; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853., Shayhidin EE; Department of Computational Biology, Cornell University, Ithaca, New York,14853; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853., Smolka MB; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853; Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York 14853., Zhang S; Mass Spectrometry and Proteomics Facility, Institute of Biotechnology, Cornell University, Ithaca, New York,14853., Yu H; Department of Computational Biology, Cornell University, Ithaca, New York,14853; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853. Electronic address: haiyuan.yu@cornell.edu.
Jazyk:	angličtina
Zdroj:	Molecular & cellular proteomics : MCP [Mol Cell Proteomics] 2020 Mar; Vol. 19 (3), pp. 554-568. Date of Electronic Publication: 2019 Dec 15.
DOI:	10.1074/mcp.TIR119.001847
Abstrakt:	Protein-protein interactions play a vital role in nearly all cellular functions. Hence, understanding their interaction patterns and three-dimensional structural conformations can provide crucial insights about various biological processes and underlying molecular mechanisms for many disease phenotypes. Cross-linking mass spectrometry (XL-MS) has the unique capability to detect protein-protein interactions at a large scale along with spatial constraints between interaction partners. The inception of MS-cleavable cross-linkers enabled the MS2-MS3 XL-MS acquisition strategy that provides cross-link information from both MS2 and MS3 level. However, the current cross-link search algorithm available for MS2-MS3 strategy follows a "MS2-centric" approach and suffers from a high rate of mis-identified cross-links. We demonstrate the problem using two new quality assessment metrics ["fraction of mis-identifications" (FMI) and "fraction of interprotein cross-links from known interactions" (FKI)]. We then address this problem, by designing a novel "MS3-centric" approach for cross-link identification and implementing it as a search engine named MaXLinker. MaXLinker outperforms the currently popular search engine with a lower mis-identification rate, and higher sensitivity and specificity. Moreover, we performed human proteome-wide cross-linking mass spectrometry using K562 cells. Employing MaXLinker, we identified a comprehensive set of 9319 unique cross-links at 1% false discovery rate, comprising 8051 intraprotein and 1268 interprotein cross-links. Finally, we experimentally validated the quality of a large number of novel interactions identified in our study, providing a conclusive evidence for MaXLinker's robust performance. Competing Interests: * The authors declare that they have no conflicts of interest with the contents of this article. (© 2020 Yugandhar et al.)
Databáze:	MEDLINE
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