Prediction of Drift Time in Ion Mobility-Mass Spectrometry Based on Peptide Molecular Weight

Abstract

A computational model is introduced for predicting peptide drift time in ion mobility-mass spectrometry (IMMS). Each peptide was represented using a numeric descriptor: molecular weight. A simple linear regression predictor was constructed for peptides drift time prediction. Three datasets with different charge state assignments were used for the model training and testing. The dataset one contains 212 singly charged peptides, dataset two has 306 doubly charged peptides, and dataset three contains 77 triply charged peptides. Our proposed method achieved a prediction accuracy of 86.3%, 72.6%, and 59.7% for the dataset one, two and three, respectively. Peptide drift time prediction in IMMS will improve the confidence of peptide identifications by limiting the peptide search space during MS/MS database searching and therefore, reducing false discovery rate (FDR) of protein identification.