Bottom-up Proteomics Research Articles

Development of an Automated, Ultra-Rapid Bottom-Up Proteomics Workflow Utilizing Alginate-Based Hydrogels.

A new approach to sample preparation and enzymatic digestion in bottom-up proteomics has been developed using alginate-based hydrogel entrapment of enzymes. This hydrogel facilitates rapid and room-temperature digestions with multienzyme capabilities. Three methodologies were tested: within microcentrifuge tubes, in situ pipette tips, and automated robotic liquid handling. Factorial experimental design identified a 1 h, room temperature, pepsin-trypsin dual-enzyme digestion as optimal for sequence coverage and protein group identification, comparable to a gold-standard overnight proteomic protocol. This method promises significant advancements in proteomic analysis by enhancing reusability, speed, throughput, convenience, and cost-effectiveness, without hindering digestion efficiency.

Strategies for Top-Down Hydrogen Deuterium Exchange-Mass Spectrometry: A Mini Review and Perspective.

Hydrogen deuterium-exchange mass spectrometry (HDX-MS) is commonly used in the study of protein dynamics and protein interactions. By measuring the isotopic exchange of backbone amide hydrogens in solution, HDX-MS offers valuable structural insights into challenging biological systems. Traditional HDX-MS approaches utilize bottom-up (BU) proteomics, in which deuterated proteins are digested before MS analysis. BU-HDX enables the characterization of proteins with various sizes in simple protein mixtures or complex biological samples such as cell lysates. However, BU methods are inherently limited by the inability to resolve protein sub-populations arising from different protein conformations, such as those arising from post-translational modifications (PTMs). Alternatively, top-down (TD) HDX-MS detects the global deuterium uptake at the intact proteoform level, allowing direct probing of structural changes due to protein-protein interactions, PTMs, or conformational changes. Combining TD-HDX-MS with electron-based fragmentation techniques, such as electron capture dissociation (ECD) and electron transfer dissociation (ETD), has demonstrated the feasibility of studying intact protein interactions with amino acid-level resolution. Here, we present a brief overview of methodologies, limitations, and applications of TD-HDX-MS using direct infusion techniques and LC-based approaches. Furthermore, we conclude with a perspective on the future directions for TD-HDX-MS.

MALDI MSI Protocol for Spatial Bottom-Up Proteomics at Single-Cell Resolution.

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) started with spatial mapping of peptides and proteins. Since then, numerous bottom-up protocols have been developed. However, achievable spatial resolution and sample preparation with many wet steps hindered the development of single cell-level workflows for bottom-up spatial proteomics. This study presents a protocol optimized for MALDI-MSI measurements of single cells within the context of their 2D culture. Sublimation of CHCA, followed by a dip in ice-cold ammonium phosphate monobasic (AmP), produced peptide-rich mass spectra while maintaining matrix crystal sizes around 400 nm. This enables MALDI-MSI imaging of proteins in single cells grown on an ITO slide with a throughput of approximately 7800 cells per day. 89 peptide-like features corresponding to a single MDA-MB-231 breast cancer cell were detected. Furthermore, by combining the MALDI-MSI data with LC-MS/MS data obtained on cell pellets, we have successfully identified 24 peptides corresponding to 17 proteins, including actin, vimentin, and transgelin-2.

Influence of different sample preparation approaches on proteoform identification by top-down proteomics.

Top-down proteomics using mass spectrometry facilitates the identification of intact proteoforms, that is, all molecular forms of proteins. Multiple past advances have lead to the development of numerous sample preparation workflows. Here we systematically investigated the influence of different sample preparation steps on proteoform and protein identifications, including cell lysis, reduction and alkylation, proteoform enrichment, purification and fractionation. We found that all steps in sample preparation influence the subset of proteoforms identified (for example, their number, confidence, physicochemical properties and artificially generated modifications). The various sample preparation strategies resulted in complementary identifications, substantially increasing the proteome coverage. Overall, we identified 13,975 proteoforms from 2,720 proteins of human Caco-2 cells. The results presented can serve as suggestions for designing and adapting top-down proteomics sample preparation strategies to particular research questions. Moreover, we expect that the sampling bias and modifications identified at the intact protein level will also be useful in improving bottom-up proteomics approaches.

Mass Spectrometry-Based Proteomics for Assessing Epitranscriptomic Regulations.

Epitranscriptomics is a rapidly evolving field that explores chemical modifications in RNA and how they contribute to dynamic and reversible regulations of gene expression. These modifications, for example, N6-methyladenosine (m6A), are crucial in various RNA metabolic processes, including splicing, stability, subcellular localization, and translation efficiency of mRNAs. Mass spectrometry-based proteomics has become an indispensable tool in unraveling the complexities of epitranscriptomics, offering high-throughput, precise protein identification, and accurate quantification of differential protein expression. Over the past two decades, advances in mass spectrometry, including the improvement of high-resolution mass spectrometers and innovative sample preparation methods, have allowed researchers to perform in-depth analyses of epitranscriptomic regulations. This review focuses on the applications of bottom-up proteomics in the field of epitranscriptomics, particularly in identifying and quantifying epitranscriptomic reader, writer, and eraser (RWE) proteins and in characterizing their functions, posttranslational modifications, and interactions with other proteins. Together, by leveraging modern proteomics, researchers can gain deep insights into the intricate regulatory networks of RNA modifications, advancing fundamental biology, and fostering potential therapeutic applications.

Gender- and Age-Based Characterization and Comparison of the Murine Primary Peritoneal Mesothelial Cell Proteome.

Organs in the abdominal cavity are covered by a peritoneal membrane, which is comprised of a monolayer of mesothelial cells (MC). Diseases involving the peritoneal membrane include peritonitis, primary cancer (mesothelioma), and metastatic cancers (ovarian, pancreatic, colorectal). These diseases have gender- and/or age-related pathologies; however, the impact of gender and age on the peritoneal MC is not well evaluated. To address this, we identified and characterized gender- and age-related differences in the proteomes of murine primary peritoneal MC. Primary peritoneal MC were isolated from young female (FY) or male (MY) mice (3-6 months) and aged female (FA) or male (MA) mice (20-23 months), lysed, trypsin digested using S-Traps, then subjected to bottom-up proteomics using an LC-Orbitrap mass spectrometer. In each cohort, we identified >1000 protein groups. Proteins were categorized using Gene Ontology and pairwise comparisons between gender and age cohorts were conducted. This study establishes baseline information for studies on peritoneal MC in health and disease at two physiologic age/gender points. Segregation of the data by gender and age could reveal novel factors to specific disease states involving the peritoneum. [This in vitro primary cell model has utility for future studies on the interaction between the mesothelium and foreign materials.].

Effects of spray drying and freeze drying on the protein profile of whey protein concentrate.

Whey protein concentrate (WPC) is consumed for its high protein content. The structure and biological functionality of whey proteins in WPC powders may be affected by the drying technique applied. However, the specific impact of spray drying and freeze drying on the overall protein profile of whey protein derived from sweet whey streams at scale is unknown. Herein, we examine the effects of commercial-scale freeze drying and spray drying on WPC to determine which method better preserves bioactive whey proteins, with the goal of helping the dairy industry create high-value products that meet the growing consumer demand for functional dairy products. WPCs were produced from pasteurized liquid whey using either a commercial spray dryer or freeze dryer. A variety of analytical techniques, including enzyme-linked immunosorbent assay, polyacrylamide gel electrophoresis, and bottom-up proteomics using liquid chromatography-tandem mass spectroscopy were used to identify, quantify, and compare the retention of bioactive proteins in WPC before and after spray drying and freeze drying. In addition, the extent of denaturation was studied via solubility testing, differential scanning calorimetry, and hydrophobicity assessment. There was little to no difference in the retention or denaturation of key bioactive proteins between spray-dried and freeze-dried WPC powders. There was a higher percentage of select Maillard modifications in freeze-dried and spray-dried powders than in the control. The lack of significant differences between spray drying and freeze drying identified herein indicates that freeze drying does not meaningfully improve retention of bioactive proteins compared with spray drying when performed after multiple pasteurization steps. PRACTICAL APPLICATION: This study aimed to provide insight into the impacts of spray drying versus freeze drying on whey proteins. Overall, our results indicate that for commercial dairy processing that involves multiple rounds of pasteurization, freeze drying does not meaningfully improve the retention of bioactive proteins compared with spray drying. These findings may help the food and dairy industry make informed decisions regarding the processing of its whey protein products to optimize nutritional value.

Accounting for Digestion Enzyme Bias in Casanovo.

A key parameter of any bottom-up proteomics mass spectrometry experiment is the identity of the enzyme that is used to digest proteins in the sample into peptides. The Casanovo de novo sequencing model was trained using data that was generated with trypsin digestion; consequently, the model prefers to predict peptides that end with the amino acids "K" or "R". This bias is desirable when Casanovo is used to analyze data that was also generated using trypsin but can be problematic if the data was generated using some other digestion enzyme. In this work, we modify Casanovo to take as input the identity of the digestion enzyme alongside each observed spectrum. We then train Casanovo with data generated by using several different enzymes, and we demonstrate that the resulting model successfully learns to capture enzyme-specific behavior. However, we find, surprisingly, that this new model does not yield a significant improvement in sequencing accuracy relative to a model trained without enzyme information but using the same training set. This observation may have important implications for future attempts to make use of experimental metadata in de novo sequencing models.

Functional analysis of MS-based proteomics data: from protein groups to networks

Mass-spectrometry-based proteomics allows the quantification of thousands of proteins, protein variants, and their modifications, in many biological samples. These are derived from the measurement of peptide relative quantities, and it is not always possible to distinguish proteins with similar sequences due to the absence of protein-specific peptides. In such cases, peptide signals are reported in protein groups that can correspond to several genes. Here, we show that multi-gene protein groups have a limited impact on GO-term enrichment, but selecting only one gene per group affects network analysis. We thus present the Cytoscape app Proteo Visualizer (https://apps.cytoscape.org/apps/ProteoVisualizer) that is designed for retrieving protein interaction networks from STRING using protein groups as input and thus allows visualisation and network analysis of bottom-up MS-based proteomics data sets.

Radical Protein Footprinting in Mammalian Whole Blood.

Hydroxyl Radical Protein Footprinting (HRPF) is a powerful method to probe the solvent-accessible surface area of proteins. It is mostly used to study the higher-order structure of proteins, as well as protein-protein and protein-carbohydrate interactions. Hydroxyl radicals are generated by the photolysis of hydrogen peroxide and these radicals modify the surface amino acids. Bottom-up proteomics is then applied and peptide oxidation is calculated and correlated with solvent accessibility. It is mainly performed in vitro ; however, it has been recently used in living systems, including live cells, live nematodes, and 3D cell cultures. Mammalian tissues are still out of reach as they absorb UV strongly, hindering radical generation. Here, we describe the first example of RPF in mammalian stabilized whole blood. Using photoactivation of persulfate with a commercially available FOX Photolysis System modified for sample handling and inline mixing, we demonstrate the first labeling of proteins in whole blood. We demonstrate that the RPF protocol does not alter the blood cell gross morphology outside of a moderate hypertonicity equivalent to sodium chloride exposure prior to labeling. We detail an improved quenching protocol to limit background labeling in persulfate RPF. We describe the labeling of the top ten most abundant proteins in the blood. We demonstrate the equivalence of ex vivo labeling in whole blood with labeling of the same structure in vitro using hemoglobin as a test system. Overall, these results now open the possibility of performing RPF-based structural proteomics in pre-clinical models and using readily available clinical samples.

Pilot Study for Deciphering Post-Translational Modifications and Proteoforms of Tau Protein by Capillary Electrophoresis-Mass Spectrometry.

Abnormal accumulation of tau protein in the brain is one pathological hallmark of Alzheimer's disease (AD). Many tau protein post-translational modifications (PTMs) are associated with the development of AD, such as phosphorylation, acetylation, and methylation. Therefore, a complete picture of the PTM landscape of tau is critical for understanding the molecular mechanisms of AD progression. Here, we offered a pilot study of combining two complementary analytical techniques, capillary zone electrophoresis (CZE)-tandem mass spectrometry (MS/MS) and reversed-phase liquid chromatography (RPLC)-MS/MS, for bottom-up proteomics of recombinant human tau-0N3R. We identified 50 phosphorylation sites of tau-0N3R in total, which is about 25% higher than that from RPLC-MS/MS alone. CZE-MS/MS provided more PTM sites (i.e., phosphorylation) and modified peptides of tau-0N3R than RPLC-MS/MS, and its predicted electrophoretic mobility helped improve the confidence of the identified modified peptides. We developed a highly efficient capillary isoelectric focusing (cIEF)-MS technique to offer a bird's-eye view of tau-0N3R proteoforms, with 11 putative tau-0N3R proteoforms carrying up to nine phosphorylation sites and lower pI values from more phosphorylated proteoforms detected. Interestingly, under native-like cIEF-MS conditions, we observed three putative tau-0N3R dimers carrying phosphate groups. The findings demonstrate that CE-MS is a valuable analytical technique for the characterization of tau PTMs, proteoforms, and even oligomerization.

Decoding the impact of neighboring amino acids on ESI-MS intensity output through deep learning

Peptide-level quantification using mass spectrometry (MS) is no trivial task as the physicochemical properties affect both response and detectability. The specific amino acid (AA) sequence affects these properties, however the connection between sequence and intensity output remains poorly understood. In this work, we explore combinations of amino acid pairs (i.e., dimer motifs) to determine a potential relationship between the local amino acid environment and MS1 intensity. For this purpose, a deep learning (DL) model, consisting of an encoder-decoder with an attention mechanism, was built. The attention mechanism allowed to identify the most relevant motifs. Specific patterns were consistently observed where a bulky/aromatic and hydrophobic AA followed by a cationic AA as well as consecutive bulky/aromatic and hydrophobic AAs were found important for the prediction of the MS1 intensity. Correlating attention weights to mean MS1 intensities revealed that some important motifs, particularly containing Trp, His, and Cys, were linked with low responding peptides whereas motifs containing Lys and most bulky hydrophobic AAs were often associated with high responding peptides. Moreover, Asn-Gly was associated with low response. The model predicts MS1 response with a mean average percentage error of ∼11 % and a Pearson correlation coefficient of ∼0.64. While dimer representation of peptide sequences did not improve predictive capacity compared to single AA representation in earlier work, this work adds valuable insight for a better understanding of peptide response in MS analysis. SignificanceMass spectrometry is not inherently quantitative, and the response of a compound relies not only on its concentration but also on the molecular composition. For mass spectrometry-based analysis of peptides, such as in bottom-up proteomics, this directly implies that the response cannot be used directly to quantify individual peptides. Moreover, the dependency of the response on the amino acid sequence of individual peptides remains poorly understood. Using a deep learning model based on a recurrent neural network with an attention mechanism, we here investigate how the presence of dimer motifs within a peptide affects the MS1 response through the analysis of intended equimolar peptide pools comprising almost 200,000 unique peptides in total. Not only do we identify certain dimer classes and specific dimers that substantially affect the MS1 response, but the model is also able to predict peptide intensity with low error rates within the independent test subset. The findings not only improve our understanding of the link between sequence and response for peptides but also highlight the potential of utilizing deep learning for developing methods allowing for absolute, label-free peptide quantification.

Data-Independent Acquisition Shortens the Analytical Window of Single-Cell Proteomics to Fifteen Minutes in Capillary Electrophoresis Mass Spectrometry.

Separation in single-cell mass spectrometry (MS) improves molecular coverage and quantification; however, it also elongates measurements, thus limiting analytical throughput to study large populations of cells. Here, we advance the speed of bottom-up proteomics by capillary electrophoresis (CE) high-resolution mass spectrometry (MS) for single-cell proteomics. We adjust the applied electrophoresis potential to readily control the duration of electrophoresis. On the HeLa proteome standard, shorter separation times curbed proteome detection using data-dependent acquisition (DDA) but not data-independent acquisition (DIA) on an Orbitrap analyzer. This DIA method identified 1161 proteins vs 401 proteins by the reference DDA within a 15 min effective separation from single HeLa-cell-equivalent (∼200 pg) proteome digests. Label-free quantification found these exclusively DIA-identified proteins in the lower domain of the concentration range, revealing sensitivity improvement. The approach also significantly advanced the reproducibility of quantification, where ∼76% of the DIA-quantified proteins had <20% coefficient of variation vs ∼43% by DDA. As a proof of principle, the method allowed us to quantify 1242 proteins in subcellular niches in a single, neural-tissue fated cell in the live Xenopus laevis (frog) embryo, including many canonical components of organelles. DIA integration enhanced throughput by ∼2-4 fold and sensitivity by a factor of ∼3 in single-cell (subcellular) CE-MS proteomics.

In-cell processing enables rapid and in-depth proteome analysis of low-input Caenorhabditis elegans.

Caenorhabditis elegans is a widely used genetic model organism, however, the worm cuticle complicates extraction of intracellular proteins, a prerequisite for typical bottom-up proteomics. Conventional physical disruption procedures are not only time-consuming, but can also cause significant sample loss, making it difficult to perform proteomics with low-input samples. Here, for the first time, we present an on-filter in-cell (OFIC) processing approach, which can digest C. elegans proteins directly in the cells of the organism after methanol fixation. With OFIC processing and single-shot LCMS analysis, we identified over 9,400 proteins from a sample of only 200 worms, the largest C. elegans proteome reported to date that did not require fractionation or enrichment. We systematically evaluated the performance of the OFIC approach by comparing it with conventional lysis-based methods. Our data suggest equivalent and unbiased performance of OFIC processing for C. elegans proteome identification and quantitation. We further evaluated the OFIC approach with even lower input samples, then used this method to determine how the proteome is impacted by loss of superoxide dismutase sod-1, the ortholog of human SOD-1, a gene associated with amyotrophic lateral sclerosis (ALS). Analysis of 8,800 proteins from only 50 worms as the initial input showed that loss of sod-1 affects the abundance of proteins required for stress response, ribosome biogenesis, and metabolism. In conclusion, our streamlined OFIC approach, which can be broadly applied to other systems, minimizes sample loss while offering the simplest workflow reported to date for C. elegans proteomics analysis.

Revisiting Protein Reversed-Phase Chromatography for Bottom-Up Proteomics.

We revisited protein reversed-phase chromatography (RP), using state-of-the-art RP columns developed for biopharmaceuticals, such as monoclonal antibodies, in order to evaluate the suitability of this methodology as a prefractionation step for bottom-up proteomics. The protein RP prefractionation (Prot-RP) method was compared with two other widely used prefractionation methods, SDS-PAGE and high-pH peptide RP (Pept-RP) by using cell lysates as samples. The overlap between fractions of Prot-RP was comparable to that of SDS-PAGE, and the protein recovery was approximately 2-fold higher. On the other hand, the overlap between fractions of Prot-RP was slightly larger than that of Pept-RP, but Prot-RP was able to identify more protein termini and more isoform-specific peptides than Pept-RP. Our results indicate that the combination of highly efficient protein prefractionation with modern mass spectrometers is particularly effective for proteoform profiling from cellular samples.

An optimised method to isotopically label pure synthetic peptides 'in-house' for absolute quantification in bottom-up proteomics.

Heavy-labelled internal standards increasingly represent the gold standard for absolute quantitation in mass spectrometry (MS)-based bottom-up proteomics. The biggest drawbacks of using these standards are that they have high costs and lengthy lead times. We describe an efficient, low-cost optimised method to enable 'in-house' heavy labelling of synthetic tryptic peptides for absolute quantification using tandem LC-MS/MS mass spectrometry. Our methodology uses 18O water in a trypsin-catalysed oxygen exchange reaction at the carboxyl terminus with the overall aim of reducing the costs and lead time associated with sourcing heavy standards from commercial vendors. Step-by-step instructions are provided on how to execute this protocol with high-throughput adaptations utilising a 96-well plate and a liquid-handling robot. Detailed notes on experimental setup, tips for troubleshooting and suggested improvements to maximise labelling efficiencies are highlighted to achieve the best results. Under optimum conditions, labelling efficiencies of peptides can reach from 95% to 100%. The application of the 'in-house' labelled standards in generating calibration curves to quantify endogenous peptide concentrations is just as effective as using the synthetically sourced standards while also having great cost reduction implications as well as saving time spent waiting for peptides to arrive. The protocol is highly adaptable and can be customized to fit the specific setup of any laboratory, maximizing achievable labelling efficiencies.

Mass Spectrometry-Based Top-Down Proteomics in Nanomedicine: Proteoform-Specific Measurement of Protein Corona.

Conventional mass spectrometry (MS)-based bottom-up proteomics (BUP) analysis of the protein corona [i.e., an evolving layer of biomolecules, mostly proteins, formed on the surface of nanoparticles (NPs) during their interactions with biomolecular fluids] enabled the nanomedicine community to partly identify the biological identity of NPs. Such an approach, however, fails to pinpoint the specific proteoforms─distinct molecular variants of proteins in the protein corona. The proteoform-level information could potentially advance the prediction of the biological fate and pharmacokinetics of nanomedicines. Recognizing this limitation, this study pioneers a robust and reproducible MS-based top-down proteomics (TDP) technique for characterizing proteoforms in the protein corona. Our TDP approach has successfully identified about 900 proteoforms in the protein corona of polystyrene NPs, ranging from 2 to 70 kDa, revealing proteoforms of 48 protein biomarkers with combinations of post-translational modifications, signal peptide cleavages, and/or truncations─details that BUP could not fully discern. This advancement in MS-based TDP offers a more advanced approach to characterize NP protein coronas, deepening our understanding of NPs' biological identities. We, therefore, propose using both TDP and BUP strategies to obtain more comprehensive information about the protein corona, which, in turn, can further enhance the diagnostic and therapeutic efficacy of nanomedicine technologies.

A Framework for Quality Control in Quantitative Proteomics.

A thorough evaluation of the quality, reproducibility, and variability of bottom-up proteomics data is necessary at every stage of a workflow, from planning to analysis. We share vignettes applying adaptable quality control (QC) measures to assess sample preparation, system function, and quantitative analysis. System suitability samples are repeatedly measured longitudinally with targeted methods, and we share examples where they are used on three instrument platforms to identify severe system failures and track function over months to years. Internal QCs incorporated at the protein and peptide levels allow our team to assess sample preparation issues and to differentiate system failures from sample-specific issues. External QC samples prepared alongside our experimental samples are used to verify the consistency and quantitative potential of our results during batch correction and normalization before assessing biological phenotypes. We combine these controls with rapid analysis (Skyline), longitudinal QC metrics (AutoQC), and server-based data deposition (PanoramaWeb). We propose that this integrated approach to QC is a useful starting point for groups to facilitate rapid quality control assessment to ensure that valuable instrument time is used to collect the best quality data possible. Data are available on Panorama Public and ProteomeXchange under the identifier PXD051318.

Bottom-up Histone Post-translational Modification Analysis using Liquid Chromatography, Trapped Ion Mobility Spectrometry, and Tandem Mass Spectrometry.

The amino acid position within a histone sequence and the chemical nature of post-translational modifications (PTMs) are essential for elucidating the "Histone Code". Previous work has shown that PTMs induce specific biological responses and are good candidates as biomarkers for diagnostics. Here, we evaluate the analytical advantages of trapped ion mobility (TIMS) with parallel accumulation-serial fragmentation (PASEF) and tandem mass spectrometry (MS/MS) for bottom-up proteomics of model cancer cells. The study also considered the use of nanoliquid chromatography (LC) and traditional methods: LC-TIMS-PASEF-ToF MS/MS vs nLC-TIMS-PASEF-ToF MS/MS vs nLC-MS/MS. The addition of TIMS and PASEF-MS/MS increased the number of detected peptides due to the added separation dimension. All three methods showed high reproducibility and low RSD in the MS domain (<5 ppm). While the LC, nLC and TIMS separations showed small RSD across samples, the accurate mobility (1/K0) measurements (<0.6% RSD) increased the confidence of peptide assignments. Trends were observed in the retention time and mobility concerning the number and type of PTMs (e.g., ac, me1-3) and their corresponding unmodified, propionylated peptide that aided in peptide assignment. Mobility separation permitted the annotation of coeluting structural and positional isomers and compared with nLC-MS/MS showed several advantages due to reduced chemical noise.

Detection of Antimicrobial Proteins/Peptides and Bacterial Proteins Involved in Antimicrobial Resistance in Raw Cow's Milk from Different Breeds.

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.