The raw data were searched against an in silico predicted spectral library using DIA-NN (version 1.8.1, https://github.com/vdemichev/DiaNN). The in silico predicted spectral library was generated from the human protein sequence database (UniProt id UP000005640, reviewed, canonical, 20,591 entries, March 7, 2023, download). The spectral library was generated using the following parameters: digestion enzyme, trypsin; missed cleavage, 1; peptide length, 7–45; precursor charge, 2–4; precursor m/z, 495–745; fragment ion m/z, 200–1800. Additionally, “FASTA digest for library-free search/library generation,” “Deep learning-based spectra, RTs, and IM prediction,” “n-term M excision,” and “C carbamidomethylation” were enabled. For the DIA-NN search, the following parameters were applied: mass accuracy, 10 ppm; MS1 accuracy, 10 ppm; protein inference based on genes; utilization of neural network classifiers in single-pass mode; quantification strategy using robust LC (high precision); cross-run normalization set to “RT-dependent.” Additionally, “unrelated runs,” “use isotopologues,” “heuristic protein inference,” and “no shared spectra” were enabled. The protein identification threshold was <1% for both peptide and protein false discovery rates.