Multidrug-resistant (MDR) Streptococcus pasteurianus is an emerging pathogen causing severe hospital and community-acquired infections. However, there is currently no vaccine, prompting the need for new vaccine strategies. Here, we used a systematic computational workflow combining pangenome analysis, subtractive proteomics, immunoinformatics and molecular dynamics to develop a multi-epitope vaccine (MEV) targeting common antigenic epitopes among different strains of S. pasteurianus . Pangenome analysis of 11 strains identified 1,436 core gene clusters (34.2%) conserved within the 11 genomes analyzed. Subtractive proteomics analysis yielded three predicted candidate proteins, DisA, FtsX and FtsW, based on essentiality, limited human sequence similarity and predicted antigenicity; their native surface accessibility remains unconfirmed. Computational epitope prediction identified six cytotoxic T-lymphocyte (CTL), four helper T-lymphocyte (HTL), and three linear B-cell (LBL) epitopes from these proteins with high antigenicity and immunogenicity. The 344-amino acid multi-epitope vaccine (MEV) design, comprising the Mycobacterium tuberculosis L7/L12 adjuvant and peptide linkers, exhibited enhanced stability and physicochemical characteristics. Docking and molecular dynamics simulations a putative interaction between the MEV and a TLR1–TLR2 ectodomain model derived from PDB 2Z7X; the contact-based predicted was 1.3 × 10⁻²⁴ M, and MM/PBSA binding free energies were − 32.15 and − 16.83 kcal/mol for Replicates 1 and 2, respectively, consistent with a favorable predicted interaction. These computational findings provide a structural basis for further experimental investigation of the proposed vaccine construct. Structural stability was assessed using two independent 100 ns molecular dynamics replicates (200 ns aggregate), and the energetics of the predicted complex were further evaluated using MM/PBSA binding free-energy calculations. In silico population coverage analysis estimated 76.87% global population coverage across various geographical and ethnic groups. In silico immune analysis predicted humoral and cellular immune-response patterns, with increased IgG antibody response, prolonged cytokine secretion (IFN-γ and IL-2) and generation of memory cells. Optimized gene sequence and in silico cloning into the pET28a(+) vector provided a proposed construct for experimental expression testing in Escherichia coli. This in silico study presents a computationally designed multi-epitope vaccine candidate against S. pasteurianus that requires experimental evaluation before any conclusions about immunogenicity or protection.

Background/Objectives: Streptococcus suis is an important zoonotic pathogen responsible for severe infections in animals and humans, and the emergence of diverse strains has reduced the effectiveness of conventional antimicrobial therapies. Since there is no broadly protective vaccine, there is a need for new vaccination strategies that focus on conserved antigens from a variety of strains. This study aimed to design and evaluate a multi-epitope vaccine candidate against diverse S. suis strains using an integrated pangenome-guided reverse vaccinology approach. Methods: To design a multi-epitope vaccine (MEV) candidate against diverse S. suis, an integrated computational framework was employed, incorporating pangenome analysis, subtractive proteomics, reverse vaccinology, immunoinformatics, structural modeling, molecular docking, molecular dynamics simulation, immune simulation, and in silico cloning. The conserved core proteins were systematically screened for essential, non-homologous, antigenic, non-allergenic and non-toxic vaccine candidates for epitope prediction. Results: A total of 7421 gene families, including 1169 conserved core genes, were identified through pangenome analysis of 24 complete S. suis genomes. Three computationally prioritized candidate proteins were identified through sequential subtractive proteomics: sucrose phosphorylase, peptidoglycan hydrolase PcsB and an RND transporter-associated adaptor protein, annotated in the source database as an RND efflux transporter periplasmic adaptor subunit. We selected eight cytotoxic T-lymphocyte (CTL) epitopes, five helper T-lymphocyte (HTL) epitopes, and three linear B-cell epitopes with favorable predicted immunological properties to develop a 397-amino acid multi-epitope vaccine construct that contains the S. suis 50S ribosomal protein L7/L12 adjuvant with rationally designed peptide linkers. The vaccine construct exhibited favorable physicochemical properties, predicted structural stability, and high antigenicity scores. The predicted combined HLA population coverage of the selected CTL and HTL epitopes was 90.77% across the populations included in the analysis. Immune simulation predicted patterns consistent with humoral and cellular immune activation, including sustained IgG production, elevated IFN-γ and IL-2 secretion, efficient antigen clearance, and generation of immunological memory, whereas molecular docking and molecular dynamics simulations characterized the predicted interaction and conformational behavior of the MEV–TLR1/TLR2 complex. Codon optimization (CAI = 0.996) and in silico cloning into the pET-30a(+) expression vector supported the potential feasibility of recombinant expression in Escherichia coli. Conclusions: In this study, a rationally designed multi-epitope vaccine candidate against diverse S. suis strains was developed using an integrated pangenome-guided reverse vaccinology approach. Based on these computational analyses, the proposed vaccine candidate showed favorable predicted immunogenicity, predicted structural quality, predicted HLA population coverage, and expression feasibility, providing a foundation for future experimental validation and development of a vaccine against diverse S. suis.

Usutu virus is an emerging mosquito-borne flavivirus with an expanding geographic distribution and increasing public health relevance, yet no licensed vaccine is currently available. This study used an integrated reverse vaccinology strategy to identify conserved immunogenic regions from the Envelope protein and NS5 protein, and construct a multi-epitope vaccine. Following sequential computational screening, the retained T-cell and B-cell epitopes satisfied the predefined selection criteria, while selected T-cell epitopes achieved an estimated 96.41% global population coverage. The final vaccine consisted of 240 amino acids and incorporated an adjuvant together with peptide linkers. Computational characterization indicated favorable physicochemical features and a refined three-dimensional model with improved stereochemical characteristics. Receptor-binding analyses predicted favorable interactions with TLR2 and TLR4, producing weighted docking scores of −1326.1 and −1230.2, respectively. Molecular dynamics simulation further characterized the temporal behavior of the vaccine–TLR2 complex, while MM-GBSA analysis yielded an estimated binding energy of −74.78 kcal/mol. C-ImmSim predicted enhanced humoral and cellular immune-response patterns following repeated antigen administration, including increased simulated antibody levels and changes in immune-cell populations. All findings in this study are based on in silico analyses and represent computational predictions rather than experimentally confirmed results. Further experimental validation is required to verify the predicted properties, immunogenicity, and protective potential of the proposed vaccine candidate.

Background/Objectives: Plasmid-mediated dissemination of antimicrobial resistance genes complicates the control of bacterial infections. Identifying bacterial hosts likely to harbour these genes could guide genomic surveillance before such associations enter public databases. We investigated whether existing plasmid and surveillance data could anticipate which resistance gene–host associations would next be recorded in those databases, and which genomic features inform that prediction. Methods: We integrated 72,556 complete plasmid records and 2,116,241 surveillance isolates, encompassing 458 resistance gene alleles, 574 replicon backbones and 222 bacterial species. A temporal graph model, MobiGraph, was trained on associations recorded before a fixed cutoff and evaluated on subsequently observed associations, using candidate hosts matched for sequencing effort and phylogenetic distance. Ranking within a candidate pool was the primary outcome; threshold-free summaries are reported as secondary. Results: The observed host ranked among the ten highest-priority candidates in 42.4% of test cases; the advantage over gradient boosting on mechanistic features was confined to the ranking metrics, the two being indistinguishable on area under the precision–recall curve. Plasmid incompatibility-group compatibility, previous gene–host associations and shared relaxase classes were prominent predictive features. Across five resistance determinants, including mcr-1, blaNDM and tet(X4), 43–62% of subsequent associations were ranked within the top ten. Among recovered events, the median interval from the prediction cutoff to first recorded observation was 22.5 months. In a separate evaluation of a fixed 500-entry watch list, 65 entries were subsequently recorded as gene–host pairs over 25 months, 13.0% against an 8.2% background rate (95% CI 10.3–16.2%); because the surveillance database does not resolve the replicon, this confirmation does not extend to the predicted backbone. Conclusions: Existing plasmid and genomic surveillance records contain information that can help anticipate which resistance gene–host associations will next be recorded. Whether a newly recorded association reflects recent transfer or earlier under-sampling cannot be resolved from these data. These findings support prioritising bacterial hosts for targeted surveillance and experimental investigation of plasmid-mediated resistance dissemination.

Staphylococcus lugdunensis has emerged as an increasingly important opportunistic pathogen associated with invasive infections and rising antimicrobial resistance, creating a need for new preventive approaches. In this study, a reverse-vaccinology strategy integrated with immunoinformatics analyses was applied to genomic datasets derived from 29 complete genomes. Pan-genome evaluation identified 1689 conserved core proteins, from which four surface-associated proteins were prioritized as potential vaccine targets based on their predicted antigenic properties and favorable safety profiles. Predicted epitopes derived from these proteins were assembled into a multi-epitope vaccine construct. Computational assessment predicted broad global population coverage of 99.47% of the selected epitopes used for multi-epitope vaccine design together with favorable physicochemical properties, including predicted solubility and a low instability index of 15.54. Molecular docking analyses predicted favorable interactions between the vaccine construct and TLR2 and TLR4. Furthermore, 100 ns molecular dynamics simulations suggested that the receptor–vaccine complexes maintained overall conformational stability throughout the simulation period. Computational immune simulations predicted adaptive immune responses characterized by antibody production, cellular immune responses, and immunological memory formation. Collectively, these computational findings suggest that the proposed multi-epitope vaccine construct represents a promising candidate for further experimental validation as a potential preventive strategy against S. lugdunensis .