## 4 Discussion
### Analysis of Protein Groups, Peptide Sequences, and PSM Variations  
In proteomic terminology, protein groups are defined as groups of similar proteins that cannot be distinguished based on their peptide content [38; 39]. These groups typically consist of isoforms of a specific protein or homologous proteins from different gene loci [40]. For example, heterogeneous nuclear ribonucleoproteins A3 grouped by Mascot and PEAKS are marked in blue font in Supplementary Materials Figure S3a. To ascertain the peptide sequence, identification instruments analyze the fragmentation spectra produced by the mass spectrometer. Search engines rely on a target database of theoretical fragmentation for this identification process. Using a cleavage rule similar to that of the protease employed in the experiment (e.g., trypsin), the protein sequences are digested in silico into peptides. From the anticipated backbone bond breaks of these in silico peptides, a list of expected fragment masses is generated. The search engine calculates a match score for each theoretical fragmentation spectrum within a specified peptide mass tolerance for a measured fragmentation spectrum [41]. Peptide-spectrum matching (PSM) involves assigning fragment ion spectra to corresponding peptide sequences using various methods. De novo algorithms directly determine the peptide sequence from the spectrum, while spectral library searching and database search algorithms are the most commonly used approaches. The PSM with the highest score is considered a potential candidate for the peptide's identity. Most workflows implement a target-decoy strategy to control the false discovery rate (FDR), as the highest-scoring PSM may still be a false positive [14]. This strategy involves searching fragmentation spectra against a decoy database, which is designed to generate false-positive PSMs in addition to the target database. One common method for creating decoy sequences involves reversing the target sequences, yielding peptides that do not appear in the original sequence. The Venn diagrams in Supplementary Materials Figure S3b depict the distribution of all proteins identified by [PERSON] and PEAKS in the CHO sample across the four LC-MS/MS methods (A, B, C, and D). For example, [PERSON] identified 950 total proteins, with 339 proteins common to all methods. Method D yielded the highest number of uniquely identified proteins (132 proteins) compared to methods A, B, and C. A similar distribution was observed for samples derived from _E. coli_ and albumin-depleted human plasma (Supplementary Materials Figure S3c,d). For the _E. coli_ sample, 374 proteins were common among all methods, with a total of 750 proteins identified and 69 unique proteins from method D. In the human plasma sample, 85 proteins were common (from a total of 212), with method D yielding the highest unique identifications (24 proteins). The lower number of protein identifications in albumin-depleted human plasma is attributed to the complexity of this proteome [42; 43], as albumin typically comprises over half of the total protein content, with just 12 components making up over 95% of the protein [44; 45]. For PEAKS, the number of common and unique proteins identified was higher than that for Mascot. A comparison of Mascot and PEAKS revealed a higher number of unique protein identifications in PEAKS across all samples. For the CHO sample, PEAKS identified 509 unique proteins (41.6%) compared to 120 unique proteins (9.8%) in Mascot, with 596 proteins (48.7%) common between both tools. In the _E. coli_ sample, PEAKS identified 146 unique proteins (19.3%) versus 62 (8.2%) in Mascot, with 548 proteins (72.5%) common to both. Similarly, in the albumin-depleted plasma sample, PEAKS yielded 252 unique proteins (62.2%) compared to 31 (7.7%) in Mascot, with 122 proteins (30.1%) identified by both tools. A similar trend was observed for peptide sequence comparisons (Supplementary Materials Figure S4a-c).