Reducing Peptide Sequence Bias in Quantitative Mass Spectrometry Data with Machine Learning.

ABSTRACT: Quantitative mass spectrometry measurements of peptides necessarily incorporate sequence-specific biases that reflect the behavior of the peptide during enzymatic digestion and liquid chromatography and in a mass spectrometer. These sequence-specific effects impair quantification accuracy, yielding peptide quantities that are systematically under- or overestimated. 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 tandem mass spectrometry experiments, and the learned coefficients themselves reflect expected physicochemical properties of the corresponding peptide sequences. The resulting adjusted abundanc

SUBMITTER: Dincer AB 

PROVIDER: S-EPMC9531543 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature