Theoretical Fundamentals of Computational Proteomics and Deep Learning- Based Identification of Chimeric Mass Spectrometry Data

Sammanfattning: A complicating factor for peptide identification by MS/MS experiments is the presence of “chimeric” spectra where at least two precursor ions with similar retention time and mass co- elute in the mass spectrometer. This results in a spectrum that is a superposition of the spectra of the individual peptides. These chimeric spectra make peptide identification more difficult, so chimeric detection tools are needed to improve peptide identification rates. GLEAMS is a learned embedding algorithm for efficient joint analysis of millions of mass spectra. In this work, we first simulate chimeric spectra. Then we present a deep neural network- based classifier that learns to distinguish between chimeras and pure spectra. The result shows that GLEAMS captures a spectrum’s chimericness, which can lead to a higher protein identification rate in samples or support biomarker development processes and the like.