Database Selection Shapes Protein Identification and Quantification in Proteomics

Objective: This study evaluates the effect of protein database composition, size, and subcellular specificity on protein identification, peptide coverage, and quantitative accuracy in MS-based proteomic analyses.

Methods: Whole proteome analyses were performed using HeLa cells. Label-free quantification (LFQ) analyses of secretome samples were conducted using the breast cell lines MCF-10A and MDA-MB-231 to assess database-related effects on quantification. Nuclear proteome analyses were carried out using raw MS data generated in previous studies. Reviewed, unreviewed, and subcellular proteome–specific databases were systematically analysed and compared to evaluate the effect of datasets on LC-MS/MS results.

Results: Using unreviewed protein entries did not result in a meaningful increase in protein or peptide identifications. Results showed that using gene name provided more accurate results comparing with accession number. In addition, subcellular proteome–specific databases improved peptide-to-protein assignment consistency and reduced ambiguity. Even modest differences in peptide counts propagated into downstream quantitative analyses, affecting abundance ratios.

Conclusion: Results indicate that database choice plays a crucial role in proteomic data clarity, consistency, and quantitative reliability, and that the use of curated, reviewed, and target-specific subcellular databases enhances analytical confidence, emphasizing the importance of careful database selection in MS-based proteomic studies.