Deep Learning Enables Automatic Correction of Experimental HDX-MS Data with Applications in Protein Modeling.

ABSTRACT: Observed mass shifts associated with deuterium incorporation in hydrogen-deuterium exchange mass spectrometry (HDX-MS) frequently deviate from the initial signals due to back and forward exchange. In typical HDX-MS experiments, the impact of these disparities on data interpretation is generally low because relative and not absolute mass changes are investigated. However, for more advanced data processing including optimization, experimental error correction is imperative for accurate results. Here the potential for automatic HDX-MS data correction using models generated by deep neural networks is demonstrated. A multilayer perceptron (MLP) is used to learn a mapping between uncorrected HDX-MS data and data with mass shifts corrected for back and forward exchange. The model is rigorously te

SUBMITTER: Salmas RE 

PROVIDER: S-EPMC10853964 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature