Over the past decade, the human gut microbiome has been extensively studied because of its profound influence on human physiology and likely involvement in a range of chronic diseases including autoimmune, metabolic and neurodegenerative diseases as well as cancer. The large-scale application of high-throughput metagenomic analyses has highlighted differences in taxonomic composition and functional potential in the context of these diseases. However, the functional implications of changes in microbiome structure are mostly unknown. More specifically, which microbial effector molecules may be mechanistically involved in triggering and/or sustaining disease-linked pathways is unclear. Proteins constitute the key operational units for the majority of cellular functions and in the context of chronic disease, microbial proteins likely play multiple roles including in inflammation, autoimmunity and toxicity. Metaproteomics has emerged as the ideal approach to understand the effect of the microbiome-derived protein complement and has already been applied on a subset of chronic diseases. Yet, because of incomplete and/or poorly annotated databases, the vast majority of the measured MS/MS spectra remain unidentified or identified as proteins of unknown function (PUFs), which severely restricts the analysis of the datasets. Our open-science metaPUF framework aims to unravel the dark matter of proteins of unknown function within the human gut microbiome by re-analysing publicly available meta-omic datasets using recently developed tools to produce metaproteomic databases and spectral libraries integrated with a newly generated human gut microbiome genome catalogue. This novel workflow will provide a comprehensive search space to improve protein identification as well as functional annotation of PUFs with the ultimate aim to prioritize expressed disease-related proteins for further functional elucidation. Finally, the developed approaches and resources will be integrated within pre-existing and well-established open-access frameworks and will provide a sustainable and solid foundation for future functional meta-omic works.