Various deep learning-assisted data analysis pipelines developed recently are able to boost the performance of mass spectrometry-based proteomics. One such pipeline is Oktoberfest that can assist the analysis of DDA, DIA, and PRM data. Oktoberfest is based on Prosit, a deep neural network that predicts multiple peptide properties. Since its release in 2019, it was used to generated >20 billion predicted spectra. Here, we summarize the available Prosit models for peptide property predictions and specifically highlight recent developments to generate a new single model for fragment ion intensity prediction covering tryptic and non-tryptic, modified and unmodified, and labeled and unlabeled peptides for different mass analyzers and fragmentation methods.
The application of deep learning in proteomics is still in its early stages. While it holds the potential to assist upstream and downstream analysis and interpretation of proteomics data even further, the lack of reproducibility and FAIRness of models are widely acknowledged concerns. To limit such issues, we have developed a number of re-usable open-source packages relevant to the development and application of deep learning models in proteomics. Beyond the development of novel peptide property predictors, we will showcase the potential of AI on use cases such as interpretable AI and drug response prediction.