Current Proteomics Research Articles

Dual-species proteomics and targeted intervention of animal-pathogen interactions

IntroductionHost-microbe interactions are important to human health and ecosystems globally, so elucidating the complex host-microbe interactions and associated protein expressions drives the need to develop sensitive and accurate biochemical techniques. Current proteomics techniques reveal information from the point of view of either the host or microbe, but do not provide data on the corresponding partner. Moreover, it remains challenging to simultaneously study host-microbe proteomes that reflect the direct competition between host and microbe. This raises the need to develop a dual-species proteomics method for host-microbe interactions. ObjectivesWe aim to establish a forward + reverse Stable Isotope Labeling with Amino acids in Cell culture (SILAC) proteomics approach to simultaneously label and quantify newly-expressed proteins of host and microbe without physical isolation, for investigating mechanisms in direct host-microbe interactions. MethodsUsing Caenorhabditis elegans-Pseudomonas aeruginosa infection model as proof-of-concept, we employed SILAC proteomics and molecular pathway analysis to characterize the differentially-expressed microbial and host proteins. We then used molecular docking and chemical characterization to identify chemical inhibitors that intercept host-microbe interactions and eliminate microbial infection. ResultsBased on our proteomics results, we studied the iron competition between pathogen iron scavenger and host iron uptake protein, where P. aeruginosa upregulated pyoverdine synthesis protein (PvdA) (fold-change of 5.2313) and secreted pyoverdine, and C. elegans expressed ferritin (FTN-2) (fold-change of 3.4057). Targeted intervention of iron competition was achieved using Galangin, a ginger-derived phytochemical that inhibited pyoverdine production and biofilm formation in P. aeruginosa. The Galangin-ciprofloxacin combinatorial therapy could eliminate P. aeruginosa biofilms in a fish wound infection model, and enabled animal survival. ConclusionOur work provides a novel SILAC-based proteomics method that can simultaneously evaluate host and microbe proteomes, with future applications in higher host organisms and other microbial species. It also provides insights into the mechanisms dictating host-microbe interactions, offering novel strategies for anti-infective therapy.

Metabolic labeling based methylome profiling enables functional dissection of histidine methylation in C3H1 zinc fingers

Protein methylation is a functionally important post-translational modification that occurs on diverse amino acid residues. The current proteomics approaches are inefficient to discover the methylation on residues other than Arg and Lys, which hinders the deep understanding of the functional role of rare protein methylation. Herein, we present a methyl-specific metabolic labeling approach for global methylome mapping, which enable the acquisition of methylome dataset covering diverse methylation types. Interestingly, of the identified methylation events, His methylation is found to be preferably occurred in C3H1 zinc fingers (ZFs). These His methylation events are determined to be Nπ specific and catalyzed by CARNMT1. The His methylation is found to stabilize the structure of ZFs. U2AF1 is used as a proof-of-concept to highlight the functional importance of His methylation in ZFs in RNA binding and RNA metabolism. The results of this study enable novel understanding of how protein methylation regulates cellular processes.

Exploring the feasibility of a single-protoplast proteomic analysis

BackgroundRecent advances in high-resolution mass spectrometry have now enabled the study of proteomes at the single-cell level, offering the potential to unveil novel aspects of cellular processes. Remarkably, there has been no prior attempt to investigate single-plant cell proteomes. In this study, we aimed to explore the feasibility of conducting a proteomic analysis on individual protoplasts.FindingsAs a result, our analysis identified 978 proteins from the 180 protoplasts, aligning with well-known biological processes in plant leaves, such as photosynthetic electron transport in photosystem II. Employing the SCP package in the SCoPE2 workflow revealed a notable batch effect and extensive missing values in the data. Following correction, we observed the heterogeneity in single-protoplast proteome expression. Comparing the results of single-protoplast proteomics with those of bulk leaf proteomics, we noted that only a small fraction of bulk data was detected in the single-protoplast proteomics data, highlighting a technical limitation of the current single-cell proteomics method.ConclusionsIn summary, we demonstrated the feasibility of conducting a single-protoplast proteomic experiment, revealing heterogeneity in plant cellular proteome expression. This underscores the importance of analyzing a substantial number of plant cells to discern statistically significant changes in plant cell proteomes upon perturbation such as abscisic acid treatment in future studies. We anticipate that our study will contribute to advancing single-protoplast proteomics in the near future.

Plant Disease: A Growing Threat to Global Food Security

The escalating global population has led to an increased demand for both quantity and quality in food production. Throughout history, plant diseases have posed significant threats to agricultural output by causing substantial food losses annually while also compromising product quality. Accurate identification of pathogens, clarifying the pathogenic mechanism of pathogens, and understanding the interaction between pathogens and hosts are important for the control of plant diseases. This Special Issue, “Research Progress on Pathogenicity of Fungi in Crops”, belongs to the section “Pest and Disease Management” of Agronomy. It contains research papers on the identification and phylogeny of fungal pathogens, the molecular genetics of plant fungal pathogens, the molecular mechanisms of fungal pathogenicity, and the molecular basis of the interaction between fungi and crops. These studies encapsulate efforts to understand disease systems within current genomics, transcriptomics, proteomics, and metabolomics studies, highlighting research findings that could be future targets for crop disease and pest control. The studies presented in this Special Issue promote the progress of fungal pathogenicity research in crops and provide a scientific basis for future disease control, which is of great significance for sustainable agricultural development and global food security.

Short Peptide Amyloids Are a Potential Sequence Pool for the Emergence of Proteins

Under prebiotic conditions, peptides are capable of self-replication through a structure-based template-assisted mechanism when they form amyloids. Furthermore, peptide amyloids can spontaneously form inside fatty acid vesicles creating membrane enclosed complex structures of variable morphologies. This is possible because fatty acid vesicle membranes act as filters allowing passage of activated amino acids while some amino acids derived from the activated species become non-permeable and trapped in the vesicles. Similarly, nascent peptides derived from the condensation of the activated amino acids are also trapped in the vesicles. It is hypothesized that such preselected peptide amyloids become a sequence pool for the emergence of proteins in life and that after billions of years of cellular evolution, the sequences in the current proteome have diverged significantly from these original seed peptides. If this hypothesis is correct, it could be possible to detect the traces of these seed sequences in current proteomes.Here, we show for all possible 3, 6, 7, 8 or 9 residue sequence motifs that those motifs that are most amyloidogenic/aggregation prone are over-represented in extant proteomes compared to a sequence-randomized proteome. Furthermore, we find that there is a greater proportion of amyloidogenic sequence motifs in archaea proteomes than in the larger primate proteomes. This suggests that the evolution towards larger proteomes leads to smaller proportion of amyloidogenic sequences.

Robust, Precise, and Deep Proteome Profiling Using a Small Mass Range and Narrow Window Data-Independent-Acquisition Scheme.

In recent years, a plethora of different data-independent acquisition methods have been developed for proteomics to cover a wide range of requirements. Current deep proteome profiling methods rely on fractionations, elaborate chromatography, and mass spectrometry setups or display suboptimal quantitative precision. We set out to develop an easy-to-use one shot DIA method that achieves high quantitative precision and high proteome coverage. We achieve this by focusing on a small mass range of 430-670 m/z using small isolation windows without overlap. With this new method, we were able to quantify >9200 protein groups in HEK lysates with an average coefficient of variance of 3.2%. To demonstrate the power of our newly developed narrow mass range method, we applied it to investigate the effect of PGC-1α knockout on the skeletal muscle proteome in mice. Compared to a standard data-dependent acquisition method, we could double proteome coverage and, most importantly, achieve a significantly higher quantitative precision, as compared to a previously proposed DIA method. We believe that our method will be especially helpful in quantifying low abundant proteins in samples with a high dynamic range. All raw and result files are available at massive.ucsd.edu (MSV000092186).

Application of Liquid-Liquid Extraction for N-terminal Myristoylation Proteomics

Proteins can be modified by lipids in various ways, for example, by myristoylation, palmitoylation, farnesylation, and geranylgeranylation—these processes are collectively referred to as lipidation. Current chemical proteomics using alkyne lipids has enabled the identification of lipidated protein candidates but does not identify endogenous lipidation sites and is not readily applicable to in vivo systems. Here, we introduce a proteomic methodology for global analysis of endogenous protein N-terminal myristoylation sites that combines liquid-liquid extraction of hydrophobic lipidated peptides with liquid chromatography-tandem mass spectrometry using a gradient program of acetonitrile in the high concentration range. We applied this method to explore myristoylation sites in HeLa cells and identified a total of 75 protein N-terminal myristoylation sites, which is more than the number of high-confidence myristoylated proteins identified by myristic acid analog-based chemical proteomics. Isolation of myristoylated peptides from HeLa digests prepared with different proteases enabled the identification of different myristoylated sites, extending the coverage of N-myristoylome. Finally, we analyzed in vivo myristoylation sites in mouse tissues and found that the lipidation profile is tissue-specific. This simple method (not requiring chemical labeling or affinity purification) should be a promising tool for global profiling of protein N-terminal myristoylation.

Astrocyte-derived SerpinA3N promotes neuroinflammation and epileptic seizures by activating the NF-κB signaling pathway in mice with temporal lobe epilepsy

Impaired activation and regulation of the extinction of inflammatory cells and molecules in injured neuronal tissues are key factors in the development of epilepsy. SerpinA3N is mainly associated with the acute phase response and inflammatory response. In our current study, transcriptomics analysis, proteomics analysis, and Western blotting showed that the expression level of Serpin clade A member 3N (SerpinA3N) is significantly increased in the hippocampus of mice with kainic acid (KA)-induced temporal lobe epilepsy, and this molecule is mainly expressed in astrocytes. Notably, in vivo studies using gain- and loss-of-function approaches revealed that SerpinA3N in astrocytes promoted the release of proinflammatory factors and aggravated seizures. Mechanistically, RNA sequencing and Western blotting showed that SerpinA3N promoted KA-induced neuroinflammation by activating the NF-κB signaling pathway. In addition, co-immunoprecipitation revealed that SerpinA3N interacts with ryanodine receptor type 2 (RYR2) and promotes RYR2 phosphorylation. Overall, our study reveals a novel SerpinA3N-mediated mechanism in seizure-induced neuroinflammation and provides a new target for developing neuroinflammation-based strategies to reduce seizure-induced brain injury.

Potential of Negative-Ion-Mode Proteomics: An MS1-Only Approach.

Current proteomics approaches rely almost exclusively on using the positive ionization mode, resulting in inefficient ionization of many acidic peptides. This study investigates protein identification efficiency in the negative ionization mode using the DirectMS1 method. DirectMS1 is an ultrafast data acquisition method based on accurate peptide mass measurements and predicted retention times. Our method achieves the highest rate of protein identification in the negative ion mode to date, identifying over 1000 proteins in a human cell line at a 1% false discovery rate. This is accomplished using a single-shot 10 min separation gradient, comparable to lengthy MS/MS-based analyses. Optimizing separation and experimental conditions was achieved by utilizing mobile buffers containing 2.5 mM imidazole and 3% isopropanol. The study emphasized the complementary nature of data obtained in positive and negative ion modes. Combining the results from all replicates in both polarities increased the number of identified proteins to 1774. Additionally, we analyzed the method's efficiency using different proteases for protein digestion. Among the four studied proteases (LysC, GluC, AspN, and trypsin), trypsin and LysC demonstrated the highest protein identification yield. This suggests that digestion procedures utilized in positive-mode proteomics can be effectively applied in the negative ion mode. Data are deposited to ProteomeXchange: PXD040583.

HyperSpec: Ultrafast Mass Spectra Clustering in Hyperdimensional Space.

As current shotgun proteomics experiments can produce gigabytes of mass spectrometry data per hour, processing these massive data volumes has become progressively more challenging. Spectral clustering is an effective approach to speed up downstream data processing by merging highly similar spectra to minimize data redundancy. However, because state-of-the-art spectral clustering tools fail to achieve optimal runtimes, this simply moves the processing bottleneck. In this work, we present a fast spectral clustering tool, HyperSpec, based on hyperdimensional computing (HDC). HDC shows promising clustering capability while only requiring lightweight binary operations with high parallelism that can be optimized using low-level hardware architectures, making it possible to run HyperSpec on graphics processing units to achieve extremely efficient spectral clustering performance. Additionally, HyperSpec includes optimized data preprocessing modules to reduce the spectrum preprocessing time, which is a critical bottleneck during spectral clustering. Based on experiments using various mass spectrometry data sets, HyperSpec produces results with comparable clustering quality as state-of-the-art spectral clustering tools while achieving speedups by orders of magnitude, shortening the clustering runtime of over 21 million spectra from 4 h to only 24 min.

Seasonal and water restriction-related changes in Eucalyptus grandis leaf proteins: Shedding light on the dark proteome

Climate change is escalating the frequency and intensity of warming and drought periods around the globe, currently representing a threat to many plant species. Understanding how plants cope with such abiotic stresses is crucial.We investigate how Eucalyptus grandis, a plant species with several industry applications, copes with seasonal variation and water restriction imposition at the proteomic level under field conditions. Therefore, we attempted to identify known proteins and novel peptides associated with the effect of seasonality and water restriction impositions, as well as to provide insights into how novel peptides behave under such conditions. The leaf proteome of E. grandis plants was studied under both a conventional proteomic workflow and a dedicated proteogenomics approach. The highest proteomic variability was identified in the summer season and the most abundant known proteins associated with seasonal variation were related to photosynthesis. Post-translational modifications, protein turnover, and chaperones were the main functional classifications identified among biological treatments. Furthermore, 144 novel peptides not predicted by current proteomics pipelines, were identified by both spectral correlations against modified databases (43) and a de novo peptide sequencing approach (101). It is predicted that most single amino acid substituted (SAS) peptides, mainly associated with the photosynthesis process, decrease protein stability by altering the quantitative change upon ΔΔG values and non-covalent interactions. Multiple reaction monitoring validation assays were performed for selected novel peptide identifications demonstrating that it is a very robust mass spectrometry-based method of validation. Data are available via ProteomeXchange with identifier PXD031100.

Posttranslational modifications of proteins in diseased retina.

Posttranslational modifications (PTMs) are known to constitute a key step in protein biosynthesis and in the regulation of protein functions. Recent breakthroughs in protein purification strategies and current proteome technologies make it possible to identify the proteomics of healthy and diseased retinas. Despite these advantages, the research field identifying sets of posttranslationally modified proteins (PTMomes) related to diseased retinas is significantly lagging, despite knowledge of the major retina PTMome being critical to drug development. In this review, we highlight current updates regarding the PTMomes in three retinal degenerative diseases-namely, diabetic retinopathy (DR), glaucoma, and retinitis pigmentosa (RP). A literature search reveals the necessity to expedite investigations into essential PTMomes in the diseased retina and validate their physiological roles. This knowledge would accelerate the development of treatments for retinal degenerative disorders and the prevention of blindness in affected populations.

Current proteomics methods applicable to dissecting the DNA damage response.

The DNA damage response (DDR) entails reorganization of proteins and protein complexes involved in DNA repair. The coordinated regulation of these proteomic changes maintains genome stability. Traditionally, regulators and mediators of DDR have been investigated individually. However, recent advances in mass spectrometry (MS)-based proteomics enable us to globally quantify changes in protein abundance, post-translational modifications (PTMs), protein localization, and protein-protein interactions (PPIs) in cells. Furthermore, structural proteomics approaches, such as crosslinking MS (XL-MS), hydrogen/deuterium exchange MS (H/DX-MS), Native MS (nMS), provide large structural information of proteins and protein complexes, complementary to the data collected from conventional methods, and promote integrated structural modeling. In this review, we will overview the current cutting-edge functional and structural proteomics techniques that are being actively utilized and developed to help interrogate proteomic changes that regulate the DDR.

Pillaging plucking plundering ransacking proteomes via CPLL technology

No proteome can be considered “democratic”, but rather “oligarchic” since a few proteins dominate the landscape and often obliterate the signal of the rare ones. That is the reason why most scientists lament that, in proteome analysis, the same set of abundant proteins is repeatedly seen. Current pre-fractionation techniques, one way or another, are besieged by problems, in that they are based on a “depletion principle”, i.e. elimination of unwanted species. Yet “democracy” calls for giving “equal rights” to everyone. One way to achieve that would be the use of libraries of combinatorial ligands coupled to spherical beads. When these beads are contacted with complex proteomes (e.g., human urines and sera, egg white, any cell or tissue lysate) of widely differing protein composition and relative abundances, they are able to “normalize” the protein population, by sharply reducing the concentration of the most abundant components while simultaneously enhancing the level of the most dilute components. It is felt that this method could offer a strong step forward in bringing the “unseen proteome” (due to either low abundance and/or presence of interferences) within the detection capabilities of current proteomics detection methods. Examples are given of the normalization of human urine and sera samples, resulting in the discovery of a host of proteins previously unreported. These beads can also be used to remove host cell proteins from purified recombinant proteins or proteins purified from natural sources that are intended for human consumption. These proteins typically reach purities of the order of 98%: higher purities often become prohibitively expensive. Yet, if incubated with Combinatorial Peptide Ligand Libraries (CPLL), even these impurities can be effectively removed with minute losses of the main, valuable product.

Bioinformatics Study of the DNA and RNA Viruses Infecting Plants and Bacteria that Could Potentially Affect Animals and Humans

Background: From the existing knowledge of viruses, those infecting plants and bacteria and affecting animals are particularly interesting. This is because such viruses have an ability to vertically transmit to other species, including humans, and therefore could represent a public health issue of significant proportions. Objective: This study aims to bioinformatically characterize the proteins from the DNA and RNA viruses capable of infecting plants and bacteria, and affecting animals, of which there is some evidence of contact with human beings. It follows up on our previous Polanco et al., [1] “Characterization of Proteins from Putative Human DNA and RNA Viruses. Current Proteomics, 2022 19(1), 65-82 DOI: 10.2174/1570164618666210212123850”. Methods: The Polarity Index Method profile (PIM), intrinsic disorder predisposition (IDPD) profiles, and a Markov chains analysis of three DNA-viruses protein sequences and four RNA-viruses protein sequences that infect plants and bacteria and affect animals, extracted from the UniProt database, were calculated using a set of in-house computational programs. Results: Computational runs carried out in this work reveal relevant regularities at the level of the viral proteins' charge/polarity and IDPD profiles. These results enable the re-creation of the taxonomy known for the DNA- and RNA-virus protein sequences. In addition, an analysis of the entire set of proteins qualified as "reviewed" in the UniProt database was carried out for each protein viral group to discover proteins with similar PIM profiles. A significant number of proteins with such charge/polarity profiles were found. Conclusion: The bioinformatics results obtained at the level of the amino acid sequences, generated important information that contributes to the understanding of these protein groups.

Mass Spectrometry-Based Proteomics Workflows in Cancer Research: The Relevance of Choosing the Right Steps.

The qualitative and quantitative evaluation of proteome changes that condition cancer development can be achieved with liquid chromatography-mass spectrometry (LC-MS). LC-MS-based proteomics strategies are carried out according to predesigned workflows that comprise several steps such as sample selection, sample processing including labeling, MS acquisition methods, statistical treatment, and bioinformatics to understand the biological meaning of the findings and set predictive classifiers. As the choice of best options might not be straightforward, we herein review and assess past and current proteomics approaches for the discovery of new cancer biomarkers. Moreover, we review major bioinformatics tools for interpreting and visualizing proteomics results and suggest the most popular machine learning techniques for the selection of predictive biomarkers. Finally, we consider the approximation of proteomics strategies for clinical diagnosis and prognosis by discussing current barriers and proposals to circumvent them.

Potential of Omics to Control Diseases and Pests in the Coconut Tree

The coconut palm (Cocos nucifera L.) is a common crop in pantropical areas facing various challenges, one of them being the control of diseases and pests. Diseases such as bud rot caused by Phytophthora palmivora, lethal yellowing caused by phytoplasmas of the types 16SrIV-A, 16SrIV-D or 16SrIV-E, among others, and pests like the coconut palm weevil, Rhynchophorus vulneratus (Coleoptera: Curculionidae), and the horned beetle, Oryctes rhinocerus (Coleoptera: Scarabaeidae), are controlled by applying pesticides, pheromones and cultural control. These practices do not guarantee eradication since some causal agents have become resistant or are imbedded in infected tissues making them difficult to eradicate. This review condenses the current genomics, transcriptomics, proteomics and metabolomics studies which are being conducted with the aim of understanding the pathosystems associated with the coconut palm, highlighting the findings generated by omics studies that may become future targets for the control of diseases and pests in the coconut crop.

Integrative transcriptomic and TMT-based proteomic analysis reveals the mechanism by which AtENO2 affects seed germination under salt stress.

Seed germination is critical for plant survival and agricultural production and is affected by many cues, including internal factors and external environmental conditions. As a key enzyme in glycolysis, enolase 2 (ENO2) also plays a vital role in plant growth and abiotic stress responses. In our research, we found that the seed germination rate was lower in the AtENO2 mutation (eno2- ) than in the wild type (WT) under salt stress in Arabidopsis thaliana, while there was no significant difference under normal conditions. However, the mechanisms by which AtENO2 regulates seed germination under salt stress remain limited. In the current study, transcriptome and proteome analyses were used to compare eno2- and the WT under normal and salt stress conditions at the germination stage. There were 417 and 4442 differentially expressed genes (DEGs) identified by transcriptome, and 302 and 1929 differentially expressed proteins (DEPs) qualified by proteome under normal and salt stress conditions, respectively. The combined analysis found abundant DEGs and DEPs related to stresses and hydrogen peroxide removal were highly down-regulated in eno2- . In addition, several DEGs and DEPs encoding phytohormone transduction pathways were identified, and the DEGs and DEPs related to ABA signaling were relatively greatly up-regulated in eno2- . Moreover, we constructed an interactive network and further identified GAPA1 and GAPB that could interact with AtENO2, which may explain the function of AtENO2 under salt stress during seed germination. Together, our results reveal that under salt stress, AtENO2 mainly affects the expression of genes and proteins related to the phytohormone signal transduction pathways, stress response factors, and reactive oxygen species (ROS), and then affects seed germination. Our study lays the foundation for further exploration of the molecular function of AtENO2 under salt stress at the seed germination stage in Arabidopsis thaliana.

Pharmacometabolomics-Based Translational Biomarkers: How to Navigate the Data Ocean.

Metabolome is the end point of the genome-environment interplay, and enables an important holistic overview of individual adaptability and host responses to environmental, ecological, as well as endogenous changes such as disease. Pharmacometabolomics is the application of metabolome knowledge to decipher the mechanisms of interindividual and intraindividual variations in drug efficacy and safety. Pharmacometabolomics also contributes to prediction of drug treatment outcomes on the basis of baseline (predose) and postdose metabotypes through mathematical modeling. Thus, pharmacometabolomics is a strong asset for a diverse community of stakeholders interested in theory and practice of evidence-based and precision/personalized medicine: academic researchers, public health scholars, health professionals, pharmaceutical, diagnostics, and biotechnology industries, among others. In this expert review, we discuss pharmacometabolomics in four contexts: (1) an interdisciplinary omics tool and field to map the mechanisms and scale of interindividual variability in drug effects, (2) discovery and development of translational biomarkers, (3) advance digital biomarkers, and (4) empower drug repurposing, a field that is increasingly proving useful in the current era of Covid-19. As the applications of pharmacometabolomics are growing rapidly in the current postgenome era, next-generation proteomics and metabolomics follow the example of next-generation sequencing analyses. Pharmacometabolomics can also empower data reliability and reproducibility through multiomics integration strategies, which use each data layer to correct, connect with, and inform each other. Finally, we underscore here that contextual data remain crucial for precision medicine and drug development that stand the test of time and clinical relevance.

A subtractive proteomics and immunoinformatics approach towards designing a potential multi-epitope vaccine against pathogenic Listeriamonocytogenes

Listeria monocytogenes is the causative agent of listeriosis, which is dangerous for pregnant women, the elderly or individuals with a weakened immune system. Individuals with leukaemia, cancer, HIV/AIDS, kidney transplant and steroid therapy suffer from immunological damage are menaced. World Health Organization (WHO) reports that human listeriosis has a high mortality rate of 20–30% every year. To date, no vaccine is available to treat listeriosis. Thereby, it is high time to design novel vaccines against L. monocytogenes. Here, we present computational approaches to design an antigenic, stable and safe vaccine against the L. monocytogenes that could help to control the infections associated with the pathogen. Three vital pathogenic proteins of L. monocytogenes, such as Listeriolysin O (LLO), Phosphatidylinositol-specific phospholipase C (PI-PLC), and Actin polymerization protein (ActA), were selected using a subtractive proteomics approach to design the multi-epitope vaccine (MEV). A total of 5 Cytotoxic T-lymphocyte (CTL) and 9 Helper T-lymphocyte (HTL) epitopes were predicted from these selected proteins. To design the multi-epitope vaccine (MEV) from the selected proteins, CTL epitopes were joined with the AAY linker, and HTL epitopes were joined with the GPGPG linker. Additionally, a human β-defensin-3 (hBD-3) adjuvant was added to the N-terminal side of the final MEV construct to increase the immune response to the vaccine. The final MEV was predicted to be antigenic, non-allergen and non-toxic in nature. Physicochemical property analysis suggested that the MEV construct is stable and could be easily purified through the E. coli expression system. This in-silico study showed that MEV has a robust binding interaction with Toll-like receptor 2 (TLR2), a key player in the innate immune system. Current subtractive proteomics and immunoinformatics study provides a background for designing a suitable, safe and effective vaccine against pathogenic L. monocytogenes.