Optimizing metaproteomics database construction: lessons from a study of the vaginal microbiome

Area of Science:

• Microbiology
• Proteomics
• Bioinformatics

Background:

• Metaproteomics enables functional insights into microbial communities by identifying proteins.
• Database composition critically influences peptide identification in metaproteomic analyses.
• Optimizing metaproteomic workflows is essential for increasing usable data output.

Purpose of the Study:

• To compare the performance of six different protein database construction strategies for metaproteomic analysis.
• To evaluate the impact of database size and origin (sample-matched vs. public repositories) on peptide identification.
• To determine the optimal database strategy for accurate protein identification in cervicovaginal lavage (CVL) samples.

Main Methods:

• Utilized cervicovaginal lavage (CVL) samples from a bacterial vaginosis (BV) study.
• Compared databases built with six distinct strategies, including broad vs. sample-matched and metagenomic translation vs. public repositories.
• Evaluated hybrid databases combining NCBI RefSeq proteins and sample-derived metagenomic translations.

Main Results:

• Smaller, sample-matched databases significantly outperformed larger ones due to statistical constraints.
• Large databases incorrectly attributed up to 34% of bacterial hits to absent taxa.
• Hybrid databases integrating public and sample-specific data yielded the best results, identifying ~30% more proteins than typical databases.

Conclusions:

• Database composition and size are critical factors influencing metaproteomic results.
• Hybrid, sample-matched databases offer superior performance for accurate protein identification.
• Findings guide optimal database selection for robust metaproteomic analysis and biological conclusions.