Accurate Prediction of y Ions in Beam-Type Collision-Induced Dissociation Using Deep Learning.

ABSTRACT: Peptide fragmentation spectra contain critical information for the identification of peptides by mass spectrometry. In this study, we developed an algorithm that more accurately predicts the high-intensity peaks among the peptide spectra. The training data are composed of 180,833 peptides from the National Institute of Standards and Technology and Proteomics Identification database, which were fragmented by either quadrupole time-of-flight or triple-quadrupole collision-induced dissociation methods. Exploratory analysis of the peptide fragmentation pattern was focused on the highest intensity peaks that showed proline, peptide length, and a sliding window of four amino acid combination that can be exploited as key features. The amino acid sequence of each peptide and each of the key featur

SUBMITTER: Shin H 

PROVIDER: S-EPMC9178553 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature