Taxonomic-Level Protein Quantification in Metaproteomics Using a Biomass-Constrained Expectation–Maximization Approach

Microbiome communities are found across diverse environments and play critical roles in both ecosystem function and human health. Mass-spectrometry-based metaproteomics provides a powerful means for directly identifying and quantifying microbial proteins. However, its application is hindered by the shared peptide problem, where peptides map to multiple proteins across taxa, complicating taxon–protein quantification. To address this challenge, we extend a previously published modified expectation–maximization algorithm that incorporates taxonomic biomass constraints into the Microorganism Classification and Identification (MiCId) workflow. This enhanced expectation–maximization algorithm is used to quantify taxon–protein pairs derived from clusters of identified taxon–protein pairs, thereby enabling more accurate quantification and representation of taxonomic-level proteomes. The performance of the approach is evaluated using synthetic datasets consisting of simple mixtures with known relative species abundances, a more complex 24-species synthetic dataset, and a clinical human stool microbiome dataset. It is shown that, in simple synthetic datasets, fold changes computed for species–protein pairs closely match the expected values and are consistent with those obtained from MaxQuant. Using the 24-species synthetic dataset, we show that the algorithm accurately redistributes peptide extracted ion count among taxon–protein pairs that share peptides. Finally, analyzing the clinical stool microbiome dataset, we demonstrate that MiCId’s results are accurate and consistent with previously reported findings. These results demonstrate the robustness of MiCId’s algorithm for quantifying taxon–protein pairs in complex microbial communities. By resolving the shared peptide problem, the method enables accurate representation of taxonomic-level proteomes, thereby advancing the application of metaproteomics in microbiome research.