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bioRxiv · 10.1101/2022.10.19.512904

MSBooster: Improving Peptide Identification Rates using Deep Learning-Based Features

Yang, K. L.·Yu, F.·Teo, G. C.·Demichev, V.·Ralser, M.·Nesvizhskii, A. I.
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Abstract

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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Yang, K. L., Yu, F., Teo, G. C., Demichev, V., Ralser, M., Nesvizhskii, A. I.. 2022-10-21. MSBooster: Improving Peptide Identification Rates using Deep Learning-Based Features. https://doi.org/10.1101/2022.10.19.512904

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