DMSS: An Attention-Based Deep Learning Model for High-Quality Mass Spectrometry Prediction

Abstract

Accurate prediction of peptide spectra is crucial for improving the efficiency and reliability of proteomic analysis, as well as for gaining insight into various biological processes. In this study, we introduce Deep MS Simulator (DMSS), a novel attention-based model tailored for forecasting theoretical spectra in mass spectrometry. DMSS has undergone rigorous validation through a series of experiments, consistently demonstrating superior performance compared to current methods in forecasting theoretical spectra. The superior ability of DMSS to distinguish extremely similar peptides highlights the potential application of incorporating our predicted intensity information into mass spectrometry search engines to enhance the accuracy of protein identification. These findings contribute to the advancement of proteomics analysis and highlight the potential of the DMSS as a valuable tool in the field.

Keywords

• mass spectrometry
• proteomics
• machine learning
• deep learning