## 5 Conclusions  
This study aimed to refine conditions to enhance protein identification in DDA mode while maintaining the method's throughput. Data analysis was performed using Mascot and PEAKS Studio 11 and provides a comparative evaluation of the efficiency of different LC-MS/MS methods and bioinformatics tools for protein identification. We found that combining higher MS acquisitions and using PEAKS software resulted in more protein identifications for human plasma, _Escherichia coli_, and CHO cell lines. For instance, despite applying a more stringent 1% FDR (false discovery rate) threshold and using a shorter gradient than previously reported studies, our analysis identified more proteins in CHO cell samples--yielding 950 proteins with Mascot and 1505 proteins with PEAKS using a 15 cm column. The use of raw files, along with the \"deep learning boost\" feature in PEAKS, increased protein group identifications by up to 21.9% and 28%, respectively. The coefficients of variation (CVs) for protein groups ranged from 3.6% to 21.4%, for peptides from 1.6% to 24.8%, and for peptide spectrum matches (PSM) from 1.2% to 21.5%. The mass spectrometry proteomics data have been deposited in the MassIVE proteomics repository under accession numbers MSV000094935, MSV000094936, and MSV000094944. These data may be useful for further basic and translational research related to the analysis of differential protein expression [55], post-translational modifications [56], and in vivo interactions between proteins and other biomolecules: RNA [57], DNA [58; 59], proteins [60; 61], and small molecules [62]. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15020666/s1](https://www.mdpi.com/article/10.3390/app15020666/s1)]([https://www.mdpi.com/article/10.3390/app15020666/s1](https://www.mdpi.com/article/10.3390/app15020666/s1)), Table S1: Basic statistical analysis of protein groups for methods A, B, C and D, Table S2: Basic statistical analysis of peptide sequences for methods A, B, C and D, Table S3: Basic statistical analysis of peptide spectrum matches (PSM) for methods A, B, C and D, Table S4: Size of output raw files from methods A, B, C and D. Table S5: Effect of deep learning boost on protein groups for methods A, B, C and D, Table S6: Peak capacity (PC) calculated from averaged FWHM for methods A, B, C and D, Table S7. Increment in % of protein identifications after changes of frequency, output file extension, gradient time and DLboost feature. Figure S1. Albumin depletion from human plasma. Figure S2: Optimization of MS/MS parameters of DDA mode for analysis of highly complex proteomics sample on example of HEK293T cell digest, Figure S3: Protein groups and Venn diagrams, Figure S4: Peptide sequences and Venn diagrams, Figure S5: Protein sequence coverage results of Mascot (left) and PEAKS (right) of proteins from CHO cells, Figure S6: Protein sequence coverage results of Mascot (left) and PEAKS (right) of proteins from CHO cells, Figure S7: Protein sequence coverage results of Mascot (left) and PEAKS (right) of proteins from CHO cells, Figure S8: Protein sequence coverage results of Mascot (left) and PEAKS (right) of proteins from CHO cells, Figure S9: Protein sequence coverage results of Mascot (left) and PEAKS (right) of proteins from CHO cells, Figure S10: Protein sequence coverage results of Mascot (left) and PEAKS (right) of proteins from CHO cells, Figure S11: Protein sequence coverage results of Mascot (left) and PEAKS (  
E. coli,_ Figure S7: Protein sequence coverage results of Mascot (left) and PEAKS (right) of proteins from albumin-depleted human plasma, Figure S8: Data QC results from CHO (a) _E. coli_ (b) and albumin-depleted human plasma (c) computed by PEAKS, Figure S9: MS/MS spectra of peptides and precursor profiles from CHO sample (PEAKS Studio 11), Figure S10: MS/MS spectra of peptides and precursor profiles from _E.