biostudies-literatureUnknownLevin NEuropean Research CouncilNCI NIH HHSNIGMS NIH HHSBiotechnology and Biological Sciences Research CouncilWe built and characterised a mass spectrometer capable of performing CID (both beam type and resonant type), UVPD, EID and ECD in an automated fashion during an LCMS type experiment. We exploited this ability to generate large datasets through multienzyme deep proteomics experiments for characterisation of these activation techniques. As a further step, motivated by the complexity generated by these dissociation techniques, we developed a single Prosit deep learning model for fragment ion intensity prediction covering all of these techniques. The generated model has been made publicly available and has been utilised in FragPipe within its MSBooster module. Rescoring allowed both data-dependent acquisition (DDA) and data-independent acquisition (DIA) to achieve on average more than 10% incrbioRxiv : the preprint server for biology2025.05.28.656555https://www.ebi.ac.uk/biostudies/studies/S-EPMC12154582biostudies-literatureIntegrating Alternative Fragmentation Techniques into Standard LC-MS Workflows Using a Single Deep Learning Model Enhances Proteome Coverage.PMC12154582BB/T016272/1R01 GM094231101077037U24 CA210967Lapin JDemyanenko YLevin NMohammed SSidda JSaylan CCNesvizhskii AIWilhelm MYang KLfalseIntegrating Alternative Fragmentation Techniques into Standard LC-MS Workflows Using a Single Deep Learning Model Enhances Proteome Coverage.We built and characterised a mass spectrometer capable of performing CID (both beam type and resonant type), UVPD, EID and ECD in an automated fashion during an LCMS type experiment. We exploited this ability to generate large datasets through multienzyme deep proteomics experiments for characterisation of these activation techniques. As a further step, motivated by the complexity generated by these dissociation techniques, we developed a single Prosit deep learning model for fragment ion intensity prediction covering all of these techniques. The generated model has been made publicly available and has been utilised in FragPipe within its MSBooster module. Rescoring allowed both data-dependent acquisition (DDA) and data-independent acquisition (DIA) to achieve on average more than 10% incr2025-01-01T00:00:00Z2025 Jun2026-05-31T03:13:21.456Z2026-05-31T03:09:51.737ZS-EPMC121545824050153610.1101/2025.05.28.656555