Mass spectrometry plays a crucial role in biomedicine by detecting isotopes, contributing significantly to research, diagnostics, and therapy development. This study introduces IsoFusion, a deep learning model designed to address isotope detection in raw mass spectra. Rather than directly applying convolutional layers to all signal and noise peaks, IsoFusion employs a trial-and-error strategy. First, it investigates all potential charge states (trials) and collects signal peaks around expected m/z values for each trial. Then, convolutional layers extract features from each trial, which are fused to identify the correct one. Finally, the reparameterization trick predicts isotope features based on this correct trial. A key advantage of IsoFusion is shared model parameters across all trials, enhancing feature learning for less common charge states using data from prevalent ones. Our results show a significant accuracy improvement for charge state 5, reaching 99.42%, compared to DeepIso’s 43.36%. Moreover, IsoFusion achieves a 97.33% detection accuracy for isotopes, with 2.4% of detected isotopes previously unidentified by four commonly used methods.

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.