The fragmentation pattern and retention time of a peptide are (currently) two of the most important features for any mass spectrometry-based proteomics method. However, the generation of comprehensive proteome-wide spectral libraries is time consuming and arguable not possible. Because of this, spectral libraries generated from prior DDA experiments are an essential step before conducting DIA or PRM experiments. Here, we show how Prosit, our deep-learning framework for predicting fragment intensities and retention times of peptides, can be used to generate proteome-wide in-silico spectral libraries with near reference data quality for virtually any (un)modified (non-)tryptic peptide irrespective of its origin. Because Prosit was trained on systematically acquired data from the ProteomeTools project, it is able to predict spectra at any commonly used collision energy. This allows users to calibrate predictions to their mass spectrometer avoiding time-consuming re-training. We show that our predictions allow the confident identification of peptides when investigating excessively large search spaces by DDA, perform on par with custom spectral libraries when analyzing DIA data and can be used to speed up assay development using MRM or PRM acquisition schemes.-Dr. Mathias Wilhelm