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bioRxiv · 10.1101/2022.04.11.487945

Reducing peptide sequence bias in quantitative mass spectrometry data with machine learning

Dincer, A. B.·Lu, Y.·Schweppe, D.·Oh, S.·Noble, W. S.
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Abstract

Quantitative mass spectrometry measurements of peptides necessarily incorporate sequence-specific biases that reflect the behavior of the peptide during enzymatic digestion, liquid chromatography, and in the mass spectrometer. These sequence-specific effects impair quantification accuracy, yielding peptide quantities that are systematically under- or over-estimated. We provide empirical evidence for the existence of such biases, and we use a deep neural network, called Pepper, to automatically identify and reduce these biases. The model generalizes to new proteins and new runs within a related set of MS/MS experiments, and the learned coefficients themselves reflect expected physicochemical properties of the corresponding peptide sequences. The resulting adjusted abundance measurements are more correlated with mRNA-based gene expression measurements than the unadjusted measurements. Pepper is suitable for data generated on a variety of mass spectrometry instruments, and can be used with labeled or label-free approaches, and with data-independent or data-dependent acquisition.

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Dincer, A. B., Lu, Y., Schweppe, D., Oh, S., Noble, W. S.. 2022-04-12. Reducing peptide sequence bias in quantitative mass spectrometry data with machine learning. https://doi.org/10.1101/2022.04.11.487945

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