Comprehensive top-down mass spectral repository enables pan-dataset analysis and top-down spectral prediction

Abstract Mass spectral libraries have become essential resources for training deep learning (DL) models for spectral prediction and de novo sequencing in bottom-up mass spectrometry (BU-MS). Compared with BU-MS, top-down MS (TD-MS) offers unique advantages for characterizing intact proteoforms by analyzing proteoforms without enzymatic digestion. Despite these advantages, large-scale spectral libraries for TD-MS are currently lacking. Here we present TopRepo, the first comprehensive and publicly available repository of TD-MS spectra, comprising more than 12 million spectra acquired from 12 species across seven types of mass spectrometers. Using TopRepo, we constructed a large-scale top-down spectral library containing over 5 million spectra with proteoform and fragment-ion annotations. We demonstrate that TopRepo enables pan-dataset analyses of N-terminal processing, mass shifts, and other proteoform characteristics identified by TD-MS. Furthermore, we show that the TopRepo spectral library substantially improves proteoform identification through spectral library searching and supports the training of DL models for high-accuracy top-down spectral prediction. Competing Interest Statement X.L. has a research project contract with Bioinformatics Solutions Inc., a company that develops software for MS data processing. Footnotes In the revised version, a total of 544 raw files were excluded from TopRepo (Supplemental Table S2) for several reasons, including MS runs with no samples analyzed, absence of MS/MS spectra, bottom-up MS experiments, medium-resolution MS1 runs, selected ion monitoring (SIM) runs, MS imaging data, and errors encountered during data analysis. Because more raw files were excluded than in the previous version, we have revised the text accordingly and updated most of the figures in both the manuscript and the supplementary materials. We also added Dr. Philipp T. Kaulich as a co-author due to his contributions to data collection and processing.