Mass spectrometry-based approaches have been successfully applied for identifying ancient proteins in bones and other tissues. On the contrary, there are relatively few examples of the successful recovery and identification of archeological protein residues from ceramic artifacts; this is because ceramics contain much lower levels of proteins which are extensively degraded by diagenetic effects. In this paper, we report the results of the characterization of proteins extracted from pottery of the Maltese site of Baħrija, the guide-site for the Baħrija period (half of 9th–second half of eighth century BCE), recently identified as the final part of the Borġ in-Nadur culture. Proteomic data here reported confirm that one of the major issue of these kind of studies is represented by contamination of animal and human agents that may complicate endogenous protein identification and authentication. The samples tested included a small group of ceramic forms, namely three tableware and six coarse ware thought to have been used in food preparation and/or storage. In this context, the limited availability of paleobotanical and archeozoological analyses may be compensated by the outcomes of the first proteomics profiling which, even if obtained on a limited selection of vessels, revealed the centrality of wheat in the diet of the ancient community of Baħrija. The data have been deposited to the ProteomeXchange with identifier < PXD022848 > .

Chickpea (Cicer arietinum L.) seed proteins show a lot of functional properties leading this legume to be an interesting component for the development of protein-enriched foods. However, both the in-depth proteomic investigation and structural characterization of chickpea seed proteins are still lacking. In this paper a detailed characterization of chickpea seed protein fraction by means of SDS-PAGE, in-gel protein digestion, high-resolution mass spectrometry, and database searching is reported. Through this approach, twenty SDS gel bands were cut and analyzed. While the majority of the bands and the identified peptides were related to vicilin and legumin storage proteins, metabolic functional proteins were also detected. Legumins, as expected, were revealed at 45–65 kDa, as whole subunits with the α- and β-chains linked together by a disulphide bond, but also at lower mass ranges (α- and β-chains migrating alone). Similarly, but not expected, the vicilins were also spread along the mass region between 65 and 23 kDa, with some of them being identified in several bands. An MS structural characterization allowed to determine that, although chickpea vicilins were always described as proteins lacking cysteine residues, they contain this amino acid residue. Moreover, similar to legumins, these storage proteins are firstly synthesized as pre-propolypeptides (Mr 50–80 kDa) that may undergo proteolytic steps that not only cut the signal peptides but also produce different subunits with lower molecular masses. Overall, about 360 different proteins specific of the Cicer arietinum L. species were identified and characterized, a result that, up to the current date, represents the most detailed description of the seed proteome of this legume.

During the last decade, paleoproteomics allowed us to open a direct window into the biological past, improving our understanding of the phylogenetic relationships of extant and extinct species, past human diseases, and reconstruction of the human diet. In particular, meta-proteomic studies, mainly carried out on ancient human dental calculus, provided insights into past oral microbial communities and ancient diets. On the contrary, very few investigations regard the analysis of ancient gut microbiota, which may enable a greater understanding of how microorganisms and their hosts have co-evolved and spread under the influence of changing diet practices and habitat. In this respect, this paper reports the results of the first-ever meta-proteomic analysis carried out on a gut tissue sample some 40,000 years old. Proteins were extracted by applying EVA (ethylene–vinyl acetate) films to the surface of the gut sample of a woolly mammoth (Mammuthus primigenus), discovered in 1972 close to the Shandrin River (Yakutia, Russia), and then investigated via a shotgun MS-based approach. Proteomic and peptidomic analysis allowed in-depth exploration of its meta-proteome composition. The results were validated through the level of deamidation and other diagenetic chemical modifications of the sample peptides, which were used to discriminate the “original” endogenous peptides from contaminant ones. Overall, the results of the meta-proteomic analysis here reported agreeing with the previous paleobotanical studies and with the reconstructed habitat of the Shandrin mammoth and provided insight into its diet. The data have been deposited to the ProteomeXchange with identifier < PXD025518 > .

Proteins involved in antibiotic resistance (resistome) and with antimicrobial activity are present in biological specimens. This study aims to explore the presence and abundance of antimicrobial peptides (AMPs) and resistome proteins in bovine milk from diverse breeds and from intensive (Pezzata rossa, Bruna alpina, and Frisona) and non-intensive farming (Podolica breeds). Liquid atmospheric pressure matrix-assisted laser desorption/ionization (LAP-MALDI) mass spectrometry (MS) profiling, bottom-up proteomics, and metaproteomics were used to comprehensively analyze milk samples from various bovine breeds in order to identify and characterize AMPs and to investigate resistome proteins. LAP-MALDI MS coupled with linear discriminant analysis (LDA) machine learning was employed as a rapid classification method for Podolica milk recognition against the milk of other bovine species. The results of the LAP-MALDI MS analysis of milk coupled with the linear discriminant analysis (LDA) demonstrate the potential of distinguishing between Podolica and control milk samples based on MS profiles. The classification accuracy achieved in the training set is 86% while it reaches 98.4% in the test set. Bottom-up proteomics revealed approximately 220 quantified bovine proteins (identified using the Bos taurus database), with cathelicidins and annexins exhibiting higher abundance levels in control cows (intensive farming breeds). On the other hand, the metaproteomics analysis highlighted the diversity within the milk’s microbial ecosystem with interesting results that may reflect the diverse environmental variables. The bottom-up proteomics data analysis using the Comprehensive Antibiotic Resistance Database (CARD) revealed beta-lactamases and tetracycline resistance proteins in both control and Podolica milk samples, with no relevant breed-specific differences observed.

Mass-spectrometry-based wheat proteomics is challenging because the current interpretation of mass spectrometry data relies on public databases that are not exhaustive (UniProtKB/Swiss-Prot) or contain many redundant and poor or un-annotated entries (UniProtKB/TrEMBL). Here, we report the development of a manually curated database of the metabolic proteins of Triticum aestivum (hexaploid wheat), named TriMet_DB (Triticum aestivum Metabolic Proteins DataBase). The manually curated TriMet_DB was generated in FASTA format so that it can be read directly by programs used to interpret the mass spectrometry data. Furthermore, the complete list of entries included in the TriMet_DB is reported in a freely available resource, which includes for each protein the description, the gene code, the protein family, and the allergen name (if any). To evaluate its performance, the TriMet_DB was used to interpret the MS data acquired on the metabolic protein fraction extracted from the cultivar MEC of Triticum aestivum. Data are available via ProteomeXchange with identifier PXD037709.