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Background: Microbiome research is providing important new insights into the metabolic interactions of complex microbial ecosystems involved in fields as diverse as the pathogenesis of human diseases, agriculture and climate change. Poor correlations typically observed between RNA and protein expression datasets make it hard to accurately infer microbial protein synthesis from metagenomic data. Additionally, mass spectrometry-based metaproteomic analyses typically rely on focused search sequence databases based on prior knowledge for protein identification that may not represent all the proteins present in a set of samples. Metagenomic 16S rRNA sequencing only targets the bacterial component, while whole genome sequencing is at best an indirect measure of expressed proteomes. Here we describe a novel approach, MetaNovo, that combines existing open-source software tools to perform scalable de novo sequence tag matching with a novel algorithm for probabilistic optimization of the entire UniProt knowledgebase to create tailored sequence databases for target-decoy searches directly at the proteome level, enabling metaproteomic analyses without prior expectation of sample composition or metagenomic data generation and compatible with standard downstream analysis pipelines., Results: We compared MetaNovo to published results from the MetaPro-IQ pipeline on 8 human mucosal-luminal interface samples, with comparable numbers of peptide and protein identifications, many shared peptide sequences and a similar bacterial taxonomic distribution compared to that found using a matched metagenome sequence database-but simultaneously identified many more non-bacterial peptides than the previous approaches. MetaNovo was also benchmarked on samples of known microbial composition against matched metagenomic and whole genomic sequence database workflows, yielding many more MS/MS identifications for the expected taxa, with improved taxonomic representation, while also highlighting previously described genome sequencing quality concerns for one of the organisms, and identifying an experimental sample contaminant without prior expectation., Conclusions: By estimating taxonomic and peptide level information directly on microbiome samples from tandem mass spectrometry data, MetaNovo enables the simultaneous identification of peptides from all domains of life in metaproteome samples, bypassing the need for curated sequence databases to search. We show that the MetaNovo approach to mass spectrometry metaproteomics is more accurate than current gold standard approaches of tailored or matched genomic sequence database searches, can identify sample contaminants without prior expectation and yields insights into previously unidentified metaproteomic signals, building on the potential for complex mass spectrometry metaproteomic data to speak for itself., Competing Interests: The authors declare that they have no competing interests., (Copyright: © 2023 Potgieter et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Filter-aided sample preparation is a proteomic technique for the preparation and on column proteolysis of proteins. Recently an enhanced FASP protocol was developed that uses deoxycholic acid (DCA) and that reportedly enhances trypsin proteolysis, resulting in increases cytosolic and membrane protein representation. FASP and eFASP were re-evaluated by ultra-high-performance liquid chromatography coupled to a quadrupole mass filter Orbitrap analyzer (Q Exactive). Although there was no difference in trypsin activity, 14,099 and 13,414 peptides, describing 1723 and 1793 protein groups, from Escherichia coli K12 were identified using FASP and eFASP, respectively. Characterization of the physicochemical properties of identified peptides showed no significant differences other than eFASP extracting slightly more basic peptides. At the protein level, both methods extracted essentially the same number of hydrophobic transmembrane helix-containing proteins as well as proteins associated with the cytoplasm or the cytoplasmic and outer membranes. By employing state-of-the-art LC-MS/MS shot gun proteomics, our results indicate that FASP and eFASP showed no significant differences at the protein level. However, because of the slight differences in selectivity at the physicochemical level of peptides, these methods can be seen to be somewhat complementary for analyses of complex peptide mixtures.