The study represents the first comprehensive in silico drug-target prioritisation for T. foetus in roughly a decade. The previous analysis, published ten years ago, proposed 113 candidate targets based solely on homology to compounds catalogued in the ChEMBL database. Since then, both the genomic resources available for the parasite and the pharmacological landscape have changed considerably. The new work, led by Joanna Dąbrowska with colleagues Maciej Kochanowski and Jacek Sroka, set out to close that gap with a far more layered approach, drawing on multiple independent lines of evidence rather than a single homology filter.

The pipeline began with the predicted proteome of T. foetus strain K, which contains 25,030 proteins. From this starting point, the researchers applied a subtractive strategy designed to strip away anything unlikely to make a safe and effective drug target. Proteins with close counterparts in the feline host were filtered out, reducing the risk that an inhibitor would also attack the cat’s own biology. The remaining proteins were then cross-referenced against three pharmacological reference databases, which flag targets already known to be druggable, and against essential-gene resources drawn from three model eukaryotic organisms, which highlight genes that parasites of this kind cannot survive without.

A further layer of prioritisation drew on PHI-base, a curated virulence reference database that catalogues genes experimentally shown to contribute to pathogenicity in other organisms. Proteins appearing in this resource are attractive candidates because disrupting them could blunt the parasite’s ability to cause disease, not merely its survival in culture. When all of these filters were combined, the pipeline returned 433 priority candidates from strain K. To test whether the result was an artefact of one particular genome assembly, the team ran the identical pipeline on a chromosome-scale assembly of a second strain, KV-1, which yielded 436 candidates. Sixty-six percent of the candidates were recovered as reciprocal best hits between the two strains, a substantial cross-strain overlap that strengthens confidence in the robustness of the target list.

Expression data added a third dimension to the prioritisation. A candidate protein may look perfect on paper, but if the gene encoding it is silent in the disease-relevant life stage, it offers little therapeutic purchase. Using gene-level transcriptomic data from the feline isolate G10/1, the researchers found that 82.7 percent of the priority candidates were actively transcribed in that isolate, measured in transcripts per million. This high proportion of expressed targets suggests that the subtractive pipeline is not merely cataloguing genomic relics but is surfacing proteins the parasite genuinely deploys during infection of its feline host.

The most striking finding emerged when the candidates were ranked by expression level. Nine of the ten most highly expressed priority candidates belong to protein families for which clinically advanced small-molecule inhibitors are already available. These families include the molecular chaperones Hsp70 and Hsp90, the CDC48/p97 AAA+ ATPase, components of the ubiquitin-proteasome pathway, and serine/threonine kinases. Each of these protein classes has been the subject of intensive pharmaceutical development in human medicine, particularly in oncology, where inhibitors of Hsp90, p97, the proteasome, and various kinases have advanced through clinical trials or reached approval. The implication is direct: medicinal chemistry against these families is mature, and existing compounds or close analogues could be screened for activity against T. foetus without the need to invent inhibitor scaffolds from scratch.

The tenth member of that top-ten list stands out for a different reason. It is a Rab4B GTPase, a member of the broader Ras/GTPase superfamily, which is generally considered pharmacologically tractable because GTP-binding proteins have well-defined nucleotide pockets that small molecules can target. However, no Rab4B-specific inhibitor has been characterised to date. This makes the Rab4B candidate both an intriguing opportunity and an open challenge: it would require de novo inhibitor development rather than repurposing, but it sits within a protein superfamily with a long track record of successful drug discovery.

To demonstrate that the candidate list can support structure-based work, the team carried out a proof-of-concept molecular docking screen against three of the top-ranked candidates. Docking computationally fits small ligands into the three-dimensional structures of target proteins and scores the predicted binding affinity. Using a ligand library that included positive controls, negative control analogues, selective inhibitors, and natural ligand controls, each annotated with its clinical or pre-clinical development status, the researchers recovered internally consistent, structure-based affinity rankings across the three targets. The authors are careful to note that these computational results await experimental validation, but the internal consistency of the rankings suggests the protein structures and docking workflow are behaving as expected and can serve as a foundation for laboratory follow-up.

The practical significance of the work lies in the disease it addresses. Feline tritrichomonosis is a chronic large-bowel diarrhoea that persists in infected cats, and with no approved therapy, veterinarians are left using ronidazole off-label. The drug’s narrow therapeutic window means the margin between an effective dose and a toxic one is slim, and the documented spread of resistance across three continents makes the search for alternatives urgent. By identifying targets in protein families with existing clinical-grade inhibitors, the study offers what the authors describe as an immediate opportunity for drug-repurposing studies. Repurposing, in which drugs developed for one indication are tested against another, can compress the timeline and cost of drug development dramatically, because safety, pharmacokinetic, and manufacturing data for the original compounds may already exist.

The researchers have made their full candidate list and supporting data openly available as supplementary tables, including UniProt and RefSeq identifiers, locus tags, protein descriptions, Pfam and InterPro annotations, Gene Ontology terms, pathway counts, expression values, the complete docking ligand library, and the cross-strain reciprocal best hit pairs. The work was supported by the Internal Research Fund of the National Veterinary Research Institute under project F/123, and the authors acknowledge the Galaxy Europe community and the European Bioinformatics Institute for maintaining the public bioinformatic resources on which the analysis relied, including Ensembl Protists, InterProScan, and the AlphaFold Protein Structure Database. For a parasite that has received far less research attention than its importance to feline health would justify, the study provides both a starting point for experimental target validation and a template for how integrative omics can accelerate drug discovery in neglected veterinary pathogens.

Subject of Research: Integrative omics identification of drug-repurposing targets in the feline parasite Tritrichomonas foetus

Article Title: Integrative omics reveals candidate drug-repurposing targets in Tritrichomonas foetus

Article References: Dąbrowska, J., Kochanowski, M., & Sroka, J. (2026). Integrative omics reveals candidate drug-repurposing targets in Tritrichomonas foetus. BMC Genomics. https://doi.org/10.1186/s12864-026-13330-5

Image Credits: AI Generated

DOI: 10.1186/s12864-026-13330-5

Keywords: Tritrichomonas foetus, feline tritrichomonosis, drug repurposing, drug-target prioritisation, subtractive proteomics, comparative genomics, molecular docking, Hsp90, ubiquitin-proteasome pathway, kinases, Rab4B GTPase, ronidazole resistance

In a study published in International Microbiology, Rasha Assad Assiri of Princess Nourah bint Abdulrahman University and colleagues describe a computational systems immunology framework that integrates subtractive proteomics, reverse vaccinology and immunoinformatics to design a multi-epitope vaccine, or MEV, against K. michiganensis strain ATCC 8724. The approach begins with the bacterium’s complete proteome of 5,483 proteins and systematically filters it down to a handful of promising vaccine targets. Essentiality screening with the Geptop 2.0 server identified 398 proteins the bacterium cannot live without, and comparison against the human proteome using BLASTP, with sequences showing at least 30 percent identity excluded, left 199 non-homologous essential candidates. Subcellular localization prediction, antigenicity scoring with VaxiJen, allergenicity screening with AllerTOP and transmembrane-helix analysis with TMHMM narrowed the field further, ultimately yielding four envelope-associated proteins: the outer-membrane beta-barrel proteins BamA and LptD, the periplasmic lipopolysaccharide transport component LptA, and the outer-membrane lipoprotein LolB.

The choice of targets is biologically deliberate. LptA shuttles lipopolysaccharide, the inflammatory outer-membrane molecule of Gram-negative bacteria, across the periplasm, while LptD delivers LPS to the cell surface. BamA anchors the beta-barrel assembly machinery that builds outer-membrane proteins, and LolB sorts lipoproteins into the outer membrane. All four are essential for bacterial survival, predicted to be antigenic and non-allergenic, and lack similarity to human proteins, reducing the risk of autoimmune cross-reaction. Homologs of these proteins have been reported as conserved and immunogenic targets in several other Gram-negative pathogens, lending external support to their selection. By focusing on envelope-associated machinery rather than variable surface structures, the designers aimed for a vaccine less vulnerable to the antigenic drift that undermines single-antigen subunit vaccines.

From these four proteins, the team predicted cytotoxic T-lymphocyte, helper T-lymphocyte and linear B-cell epitopes using the Immune Epitope Database analysis tools. Eight CTL epitopes survived filtering for non-toxicity, antigenicity, MHC-I binding within the top two percent of predicted binders and non-allergenicity, although six of them carried negative immunogenicity scores on the IEDB scale and were retained only because they met the other predefined criteria. Four HTL epitopes were selected, three of which were predicted to induce interferon-gamma, a cytokine central to cellular immunity against intracellular pathogens; the fourth, ARFNIDSTQVSLTPD, was kept despite its non-inducer prediction because it satisfied all other binding and safety thresholds. Three linear B-cell epitopes, predicted with an artificial-neural-network-based server, completed the immunological repertoire. Population coverage analysis across 78 global population groups estimated that the combined MHC class I and II epitope set would cover 73.80 percent of the world’s population, peaking at 83.22 percent in South Africa and 82.90 percent in England, but dipping to 67.86 percent in South Asia, a regional variation the authors suggest could inform future vaccination prioritization.

Assembling fifteen epitopes into a single molecule requires architectural finesse. Isolated epitopes are weak immunogens, so the researchers grafted the 130-amino-acid 50S ribosomal protein L7/L12 from Mycobacterium tuberculosis, a known immunostimulatory adjuvant, onto the N-terminus of the construct via a rigid, alpha-helical EAAAK linker that spatially separates the adjuvant from the epitope payload. Within the epitope region, AAY linkers separate CTL epitopes to support proteolytic processing, flexible GPGPG linkers rich in glycine and proline space out the HTL epitopes, and short lysine-rich KK linkers keep the B-cell epitopes accessible. The result is a 386-amino-acid chimeric protein with a predicted molecular weight of 40.53 kilodaltons, a theoretical pI of 8.97, an instability index of 28.71 and a GRAVY value of minus 0.334 indicating overall hydrophilicity. VaxiJen assigned the full construct an antigenicity score of 0.9564, comfortably above the 0.5 threshold, and the in silico safety screens classified it as non-toxic and non-allergenic.

Structural credibility was assessed with AlphaFold2 through the ColabFold implementation, followed by refinement in GalaxyRefine. The best model placed 95.6 percent of residues in favored regions of the Ramachandran plot, with 3.8 percent in allowed regions and only 0.3 percent in disallowed regions. ProSA-web returned a Z-score of minus 4.62, consistent with experimentally determined protein structures, and ERRAT reported a quality factor of 99.219. Secondary-structure prediction estimated 34.46 percent alpha helix, 47.93 percent random coil and 17.62 percent beta strand. Notably, the linker regions showed lower pLDDT confidence scores than the structured domains, a caveat the authors carried forward when interpreting downstream docking and dynamics, since flexible segments are inherently harder to model.

To probe whether the vaccine could engage innate immunity, the team docked the construct against chain B of the TLR1-TLR2 heterodimer, a pattern-recognition receptor complex, using ClusPro 2.0. The model drawn from the largest cluster, containing 72 members, was selected for analysis. PDBsum identified 24 hydrogen bonds at the interface, and the PRODIGY server estimated a binding free energy of minus 14.8 kilocalories per mole with a dissociation constant of 1.5 times ten to the minus eleventh molar, values consistent with a favorable predicted interaction. Normal mode analysis with iMODS described a comparatively rigid core with mobile peripheral regions, and a 100-nanosecond molecular dynamics simulation in GROMACS with the CHARMM36m force field showed the complex settling after roughly the first 10 nanoseconds, with the radius of gyration contracting from about 4.2 to 3.7-3.8 nanometers and the solvent-accessible surface area declining from about 580 to 500 square nanometers before stabilizing. The authors are careful to note that these metrics describe structural compatibility under simulated conditions and do not demonstrate receptor activation or downstream immune signaling.

Immune simulation with the C-ImmSim agent-based platform, run with HLA-DRB101:01, HLA-B07:02 and HLA-A*01:01, predicted a coordinated response to a single simulated vaccine dose: rapid antigen clearance accompanied by rising IgM, IgG1 and IgG2 levels, early increases in IL-2 and interferon-gamma, expansion of active and duplicating CD8-positive and CD4-positive T-cell populations, and the persistence of memory-associated helper T cells. The modeled Th1 response predominated over Th2, Th17 and regulatory populations. Codon optimization with JCat produced a Codon Adaptation Index of 0.94 and a GC content of 50 percent, both favorable for expression in Escherichia coli, and in silico cloning into the pET-30a(+) vector between NcoI and XhoI sites yielded a recombinant plasmid of 4,483 base pairs, supporting the theoretical feasibility of recombinant production.

The authors are explicit about the limits of the work. Multi-strain epitope conservation, MM-PBSA binding-energy decomposition, epitope-level cross-reactivity screening against human and microbiome proteins, replicate immune simulations and multi-dose modeling were not performed, and no recombinant expression, in vitro assay or animal study was carried out. Whether responses to the mycobacterial adjuvant might compete with pathogen-directed immunity, a phenomenon known as adjuvant immunodominance, remains an open experimental question. Still, the study represents an early application of an integrated subtractive-proteomics and immunoinformatics pipeline specifically aimed at K. michiganensis, and it arrives amid growing evidence that reverse-vaccinology-designed MEVs can be validated successfully in vitro and in vivo against related pathogens. As carbapenem resistance spreads through hospital and environmental reservoirs alike, computational designs like this one may increasingly serve as the blueprint from which the next generation of antibacterial vaccines is built, provided the laboratory work now required can keep pace with the algorithms.

Subject of Research: Computational immunoinformatics design of a multi-epitope vaccine against multidrug-resistant Klebsiella michiganensis

Article Title: Computational systems immunology and multi-scale modeling for the design of a Multi-Epitope Vaccine (MEV) against emerging multidrug-resistant Klebsiella michiganensis

Article References: Assiri, R. A., Saleh, F. M., Muhammad, S., Fenibo, E. O., & Matambo, T. (2026). Computational systems immunology and multi-scale modeling for the design of a Multi-Epitope Vaccine (MEV) against emerging multidrug-resistant Klebsiella michiganensis. International Microbiology. https://doi.org/10.1007/s10123-026-00903-3

Image Credits: AI Generated

DOI: 10.1007/s10123-026-00903-3

Keywords: Klebsiella michiganensis, multi-epitope vaccine, reverse vaccinology, immunoinformatics, subtractive proteomics, antimicrobial resistance, TLR1 docking, molecular dynamics simulation, epitope prediction, BamA, LptD, codon optimization