Comparative performance of Scribe and database search engines in metaproteomic profiling of a ground-truth microbiome dataset

Mass spectrometry-based metaproteomics, the identification and quantification of thousands of proteins expressed by complex microbial communities, has become pivotal for unraveling functional interactions within microbiomes. However, metaproteomics data analysis encounters many challenges, including the search of tandem mass spectra against a protein sequence database using proteomics database search algorithms. We used a ground-truth dataset to assess a spectral library searching method against established database searching approaches. Mass spectrometry data collected by data-dependent acquisition (DDA-MS) was analyzed using database searching approaches (MaxQuant and FragPipe), as well as using Scribe with Prosit predicted spectral libraries. We used FASTA databases that included protein sequences from microbial species present in the ground-truth dataset along with background protein sequences, to estimate error rates and assess the effects on detection, peptide-spectral match quality, and quantification. Using the Scribe search engine resulted in more proteins detected at a 1 % false discovery rate (FDR) compared to MaxQuant or FragPipe, while FragPipe detected more peptides verified by PepQuery. Scribe was able to detect more low-abundance proteins in the microbiome dataset and was more accurate in quantifying the microbial community composition. This research provides insights and guidance for metaproteomics researchers aiming to optimize results in their analysis of DDA-MS data. Significance of the study: Metaproteomics requires a balance between high numbers of peptide and protein identification and confidence in the accuracy of the identifications made. We demonstrate the utility of the Scribe search engine for metaproteomics applications, as it was found to detect low-abundance proteins with accurate quantitation than other DDA-MS search engines. This tool has great utility for both novel metaproteomics studies as well as hypothesis-generating experiments using previously acquired open source proteomics raw data.