## 2 Materials and Methods
### Effect of Deep Learning Boost  
PEAKS Studio 11 offers a deep learning-boosted identification workflow option for DDA analysis, which enhances the identification of PSMs in the database search process and increases the identification rate by over 10% [30; 31]. Both mgf and raw files (.d extension) with and without this option were analyzed (Figure 3). Using raw files instead of mgf files increased protein group identifications (green versus blue in Figure 3). For instance, a maximum increase of 21.9% was observed in the CHO sample with method A, without the deep learning boost (light green versus light blue). For other methods (B, C, and D) and albumin-depleted plasma samples, the average increase was around 10%. In the _E. coli_ sample, using raw files resulted in a protein identification increase of less than 6% (Supplementary Materials Table S5). Figure 2: Comparison of the number of protein group identifications (**a**), peptide sequences (**b**), and PSM (**c**) for methods A, B, C, and D of complex proteomes from three sources (CHO, _E. coli_ and albumin-depleted plasma). The red color corresponds to Mascot and the blue color to PEAKS with deep learning boost enabled. Filled circles represent mean values and error bars are standard deviations of data from three biological replicates. Diagrams were created using OriginPro 2024b software (version 10.1.5.132). A more substantial improvement was observed after enabling the deep learning boost (darker shades of blue and green in Figure 3). For the _E. coli_ sample, the boost resulted in an increase of 20-26%, and for the CHO sample, the increase ranged from 18-28%. A more modest improvement of 1-13% was noted for albumin-depleted plasma samples. Peak Capacity and Comparison of Identification Efficiencies Following Adjustments to Various Parameters  
The chromatographic performance of an LC workflow is assessed by its peak capacity (PC), which represents the maximum number of peaks that can be separated based on the identified peak width. PC is calculated using the equation: PC = t\({}_{\text{R}}\)/FWHM, where the FWHM is averaged full width at half maximum and t\({}_{\text{R}}\) is linear gradient time (Supplementary Materials Table S6). FWHM values were located in the Quality Control tab of the PEAKS tool (Supplementary Materials Figure S8) after processing of raw files (.d extension files). These data were used for PC estimation and were similar across all sample types, including CHO, _E. coli_, and human plasma (Figure 4a). Supplementary Materials Figures S9-S11 present data examples of MS/MS spectra of peptides and precursor profiles from CHO, _E. coli_, and albumin-depleted plasma. The contribution of each parameter--acquisition frequency tuning, gradient time extension, output file format selection (.mgf or.d), and activation of the DLboost feature in PEAKS--was evaluated regarding the number of identified protein groups (Figure 4b). Increasing acquisition frequency generally led to more identifications, though the impact varied by sample type (Supplementary Materials Table S7). For the CHO sample, protein identifications increased by over 10%, while the _E. coli_ and albumin-depleted plasma samples showed gains of 3.6% and 2.6%, respectively. Using raw files (.d format) contributed an additional 12% in CHO, 3% in _E. coli_, and 8.6% in plasma. Higher contributions to protein identification were observed with extended LC gradients (21.6% for CHO, 18.2% for _E. coli_, and 10.3% for plasma) and with the activation of PEAKS' deep learning feature (22.9% for CHO, 23.2% for _E. coli_, and 9.2% for plasma). Figure 3: Effect of deep learning boost on the number of protein group identifications for methods A, B, C, and D of complex proteomes from CHO, _E. coli_, and albumin-depleted plasma. Red columns indicate Mascot data, while columns with different shades of blue (.mgf files) and green (.d files) correspond to PEAKS data. A deep learning boost option is enabled for columns with more intense shades of green and blue.