Evaluation of machine learning models for proteoform retention and migration time prediction in top-down mass spectrometry

Abstract

Reversed-phase liquid chromatography (RPLC) and capillary zone electrophoresis (CZE) are two popular proteoform separation methods in mass spectrometry (MS)-based top-down proteomics. The prediction of proteoform retention time in RPLC and migration time in CZE provides additional information that can increase the accuracy of proteoform identification and quantification. Whereas existing methods for retention and migration time prediction are mainly focused on peptides in bottom-up MS, there is still a lack of methods for the problem in top-down MS. We systematically evaluated 6 models for proteoform retention and/or migration time prediction in top-down MS and showed that the Prosit model achieved a high accuracy (R2 > 0.91) for proteoform retention time prediction and that the Prosit model and a fully connected neural network model obtained a high accuracy (R2 > 0.94) for proteoform migration time prediction.