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During the last decade, metaproteomics has provided a better understanding and functional characterization of the microbiome. A large body of evidence now reveals interspecies, species of bacteria–host interactions, via the secreted modulatory microbial protein “metaproteome”. Although high-throughput state-of-art mass spectrometry has recently empowered metaproteomics, its profile remains unclear, and, most importantly, the exact consequences and underlying mechanism of these protein molecules on the host are insufficiently understood. Here we address the current progress in the study of the human metaproteome, suggesting possible modulation, a metaproteome dysbiotic signature, challenges, and future perspectives.

The tRNA adaptation index (tAI) is a translation efficiency metric that considers weighted values (Sij values) for codon–tRNA wobble interaction efficiencies. The initial implementation of the tAI had significant flaws. For instance, generated Sij weights were optimized based on gene expression in Saccharomyces cerevisiae, which is expected to vary among different species. Consequently, a species-specific approach (stAI) was developed to overcome those limitations. However, the stAI method employed a hill climbing algorithm to optimize the Sij weights, which is not ideal for obtaining the best set of Sij weights because it could struggle to find the global maximum given a complex search space, even after using different starting positions. In addition, it did not perform well in computing the tAI of fungal genomes in comparison with the original implementation. We developed a novel approach named genetic tAI (gtAI) implemented as a Python package (https://github.com/AliYoussef96/gtAI), which employs a genetic algorithm to obtain the best set of Sij weights and follows a new codon usage-based workflow that better computes the tAI of genomes from the three domains of life. The gtAI has significantly improved the correlation with the codon adaptation index (CAI) and the prediction of protein abundance (empirical data) compared to the stAI.

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Ancient protein analysis provides clues to human life and diseases from ancient times. Paleoproteomics has the potential to give a better understanding of the modes of fabrication of ancient materials, their composition, and pathways of degradation, as well as the development of animal fibers through domestication and breeding. Thus, this study aimed at providing guidance for choosing proteomics workflows to analyze leather samples and their capacity to distinguish between unknown archeological species. Here, we performed shotgun proteomics of archeological animal skin for the first time. The raw output data were analyzed using three different software (Proteome Discoverer, Protein Pilot, and Peptide Shaker) with their impeded algorithms. The study found that the best species identification percentage was obtained using protein piolet with protein database. Particularly prevalent and relatively high collagen expression suggests its resistance to degradation, despite the samples’ exposure to environmental and chemical alterations. The success of this case study indicates that further analyses could assist in reworking historical baseline data for putative identification of unknown archeological samples.