MSBooster: improving peptide identification rates using deep learning-based features.

> 商业许可源文 · EUROPE_PMC · [CC-BY](https://creativecommons.org/licenses/by/)

书目信息

引用：Yang KL, Yu F, Teo GC, Li K, Demichev V, Ralser M, Nesvizhskii AI. (2023). MSBooster: improving peptide identification rates using deep learning-based features. Nature communications. PMID 37500632 · PMC10374903 · DOI 10.1038/s41467-023-40129-9

证据类型：PRIMARY_RESEARCH

主题：proteomics

被引次数（采集时）：231

原始记录：[Europe PMC](https://europepmc.org/article/MED/37500632)

来源许可：[CC-BY](https://creativecommons.org/licenses/by/)

作者摘要（按来源许可复用）

Peptide identification in liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments relies on computational algorithms for matching acquired MS/MS spectra against sequences of candidate peptides using database search tools, such as MSFragger. Here, we present a new tool, MSBooster, for rescoring peptide-to-spectrum matches using additional features incorporating deep learning-based predictions of peptide properties, such as LC retention time, ion mobility, and MS/MS spectra. We demonstrate the utility of MSBooster, in tandem with MSFragger and Percolator, in several different workflows, including nonspecific searches (immunopeptidomics), direct identification of peptides from data independent acquisition data, single-cell proteomics, and data generated on an ion mobility separation-enabled timsTOF MS platform. MSBooster is fast, robust, and fully integrated into the widely used FragPipe computational platform.

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