Search Results (116)

The rapid emergence and evolution of avian viral pathogens present a major challenge to global poultry health and food security. Traditional vaccine development is often slow, costly, and limited by antigenic diversity. In this study, we present a comprehensive artificial intelligence (AI)-driven pipeline for the rational design, modeling, and optimization of multi-epitope vaccines targeting economically important RNA and DNA viruses affecting poultry, including H5N1, NDV, IBV, IBDV, CAV, and FPV. We utilized advanced machine learning and deep learning tools for epitope prediction, antigenicity assessment, and structural modeling (via AlphaFold2), and codon optimization. B-cell and T-cell epitopes were selected based on binding affinity, conservation, and immunogenicity, while adjuvants and linker sequences enhanced construct stability and immune response. In silico immune simulations forecasted robust humoral and cellular responses, including cytokine production and memory cell activation. The study also highlights challenges such as data quality, model interpretability, and ethical considerations. Our work demonstrates the transformative potential of AI in veterinary vaccinology and offers a scalable model for rapid, data-driven vaccine development against avian diseases. Full article

Rotavirus (RV) is one of the main etiologic agents associated with diarrheal diseases (DDs), being responsible for approximately 200 thousand deaths annually. Currently, there are still many aspects regarding the virus biology, cell cycle, and pathophysiology of RV that need further elucidation. Therefore, the present work aimed to investigate whether the triggering receptor expressed on myeloid cells 1 (TREM-1) might be associated with RV infection. This immune receptor has been observed as an amplifier of inflammatory responses in different infectious and non-infectious diseases, including inflammatory bowel disease and celiac disease. Initially, we searched for public transcriptomic data regarding RV infection and the expression of TREM-1 and its associated genes, which were significantly upregulated in infected mice and children. Then, we infected monocytes with the virus, with or without a TREM-1 inhibitor. The inhibition of the receptor’s activity resulted in a significant decrease in IL-1β production. We also observed a reduction in cytopathic effects when MA104 cells were treated with TREM-1 inhibitors and then infected with simian RV. To further elucidate the interactions between the virus and TREM-1, in silico tools were used to simulate interactions between the receptor and RV proteins. These simulations suggested the occurrence of interactions between TREM-1 and VP5*, a protein involved in viral attachment to target cells, and also between the receptor and NSP4, a viral enterotoxin with immunostimulant properties. Hence, our results indicate that TREM-1 is involved in RV infection, both as a mediator of inflammatory responses and as a player in the host–virus relationship. Full article

Introduction: Tumor necrosis factor-α (TNF-α) is a key regulator of inflammatory responses, and its biological activity is dependent on proteolytic processing by the tumor necrosis factor-α-converting enzyme (TACE), also known as ADAM17. Aberrant TACE activity has been associated with various inflammatory and immune-mediated diseases, positioning it as a compelling target for therapeutic intervention. Methods: While our previous study explored TACE inhibition via repositioned FDA-approved drugs, the present study aims to examine previously untested chemical scaffolds from the Enamine compound library, seeking first-in-class TACE inhibitors. We employed an integrated in silico workflow that combined ligand-based virtual screening using a graph convolutional network (GCN) model trained on known TACE inhibitors with structure-based methodologies, including molecular docking, molecular dynamics (MD) simulations, and binding free energy calculations. Results: Several enamine-derived compounds demonstrated strong predicted inhibitory potential, favorable docking scores, and stable interactions with the TACE active site. Among them, Z1459964184, Z2242870510, and Z1450394746 emerged as lead candidates based on their highly stable 300 ns RMSD and robust hydrogen bonding profile as compared to the reference compound BMS-561392. Conclusions: This study highlights the utilization of deep learning-driven screening combined with extended 300 ns molecular simulations to identify novel small-molecule scaffolds for TACE inhibition and supports further exploration of these hits as potential anti-inflammatory therapeutics. Full article

Background: Helcococcus kunzii, a facultative anaerobe and Gram-positive coccus, has been documented as a cunning pathogen, mainly in immunocompromised individuals, as evidenced by recent clinical and microbiological reports. It has been associated with a variety of polymicrobial infections, comprising diabetic foot ulcers, prosthetic joint infections, osteomyelitis, endocarditis, and bloodstream infections. Despite its emerging clinical relevance, no licensed vaccine or targeted immunotherapy currently exists for H. kunzii, and its rising resistance to conventional antibiotics presents a growing public health concern. Objectives: In this study, we employed an integrated subtractive proteomics and immunoinformatics pipeline to design a multi-epitope subunit vaccine (MEV) candidate against H. kunzii. Initially, pan-proteome analysis identified non-redundant, essential, non-homologous, and virulent proteins suitable for therapeutic targeting. Methods/Results: From these, two highly conserved and surface-accessible proteins, cell division protein FtsZ and peptidoglycan glycosyltransferase FtsW, were selected as promising vaccine targets. Comprehensive epitope prediction identified nine cytotoxic T-lymphocyte (CTL), five helper T-lymphocyte (HTL), and two linear B-cell (LBL) epitopes, which were rationally assembled into a 397-amino-acid-long chimeric construct. The construct was designed using appropriate linkers and adjuvanted with the cholera toxin B (CTB) subunit (NCBI accession: AND74811.1) to enhance immunogenicity. Molecular docking and dynamics simulations revealed persistent and high-affinity ties amongst the MEV and essential immune receptors, indicating a durable ability to elicit an immune reaction. In silico immune dynamic simulations predicted vigorous B- and T-cell-mediated immune responses. Codon optimization and computer-aided cloning into the E. coli K12 host employing the pET-28a(+) vector suggested high translational efficiency and suitability for bacterial expression. Conclusions: Overall, this computationally designed MEV demonstrates favorable immunological and physicochemical properties, and presents a durable candidate for subsequent in vitro and in vivo validation against H. kunzii-associated infections. Full article

Background: While most Escherichia coli strains are harmless members of the gastrointestinal microbiota, certain pathogenic variants can cause severe intestinal and extraintestinal diseases. A notable outbreak of E. coli O104:H4, involving both enteroaggregative (EAEC) and enterohemorrhagic (EHEC) strains, occurred in Europe, resulting in symptoms ranging from bloody diarrhea to life-threatening colitis and hemolytic uremic syndrome (HUS). Since treatment options remain limited and have changed little over the past 40 years, there is an urgent need for an effective vaccine. Such a vaccine would offer major public health and economic benefits by preventing severe infections and reducing outbreak-related costs. A multiepitope vaccine approach, enabled by advances in immunoinformatics, offers a promising strategy for targeting HUS-causing E. coli (O104:H4 and O157:H7 serotypes) with minimal disruption to normal microbiota. This study aimed to design an immunogenic multiepitope vaccine (MEV) construct using bioinformatics and immunoinformatic tools. Methods and Results: Comparative proteomic analysis identified 672 proteins unique to E. coli O104:H4, excluding proteins shared with the nonpathogenic E. coli K-12-MG1655 strain and those shorter than 100 amino acids. Subcellular localization (P-SORTb) identified 17 extracellular or outer membrane proteins. Four proteins were selected as vaccine candidates based on transmembrane domains (TMHMM), antigenicity (VaxiJen), and conservation among EHEC strains. Epitope prediction revealed ten B-cell, four cytotoxic T-cell, and three helper T-cell epitopes. Four MEVs with different adjuvants were designed and assessed for solubility, stability, and antigenicity. Structural refinement (GALAXY) and docking studies confirmed strong interaction with Toll-Like Receptor 4 (TLR4). In silico immune simulations (C-ImmSim) indicated robust humoral and cellular immune responses. In Conclusions, the proposed MEV construct demonstrated promising immunogenicity and warrants further validation in experimental models. Full article

Organic acids, as natural metabolites, play crucial roles in human metabolism and health. Tumor Necrosis Factor (TNF), a pivotal mediator in immune regulation and inflammation, is a key therapeutic target. We evaluated ten organic acids as TNF modulators using in silico molecular docking, followed by detailed ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiling and molecular dynamics (MD) simulations for three lead candidates: gallic, aconitic, and crocetin acids. Their effects on TNF gene expression were then assessed in vivo using a mouse leukocyte model. The in silico results indicated that crocetin had the highest TNF binding affinity (−5.6 to −4.6 kcal/mol), while gallic acid formed the most stable protein-ligand complex during MD simulations, and aconitic acid established hydrogen bond interactions. ADMET analysis suggested potential pharmacokinetic limitations, including low permeability. Contrasting its high predicted binding affinity, in vivo gene expression analysis revealed that crocetin stimulated TNF synthesis, whereas gallic and aconitic acids acted as inhibitors. This research explores organic acids as potential TNF modulators, highlighting their complex interactions and providing a foundation for developing these compounds as anti-inflammatory agents targeting TNF-mediated diseases. Full article

Monkeypox virus (MPXV) has caused 148,892 confirmed cases and 341 deaths from 137 countries worldwide, as reported by the World Health Organization (WHO), highlighting the urgent need for effective vaccines to prevent the spread of MPXV. Traditional vaccine development is low-throughput, expensive, time consuming, and susceptible to reversion to virulence. Alternatively, a reverse vaccinology approach offers a rapid, efficient, and safer alternative for MPXV vaccine design. Here, MPXV proteins associated with viral infection were analyzed for immunogenic epitopes to design multi-epitope vaccines based on B-cell, CD4+, and CD8+ epitopes. Epitopes were selected based on allergenicity, antigenicity, and toxicity parameters. The prioritized epitopes were then combined via peptide linkers and N-terminally fused to various protein adjuvants, including PADRE, beta-defensin 3, 50S ribosomal protein L7/12, RS-09, and the cholera toxin B subunit (CTB). All vaccine constructs were computationally validated for physicochemical properties, antigenicity, allergenicity, safety, solubility, and structural stability. The three-dimensional structure of the selected construct was also predicted. Moreover, molecular docking and molecular dynamics (MD) simulations between the vaccine and the TLR-4 immune receptor demonstrated a strong and stable interaction. The vaccine construct was codon-optimized for high expression in the E. coli and was finally cloned in silico into the pET21a (+) vector. Collectively, these results could represent innovative tools for vaccine formulation against MPXV and be transformative for other infectious diseases. Full article

This study focuses on exploring potential inhibitors of the Marburg virus interferon inhibitory domain protein (MARV-VP35), which is responsible for immune evasion and immunosuppression during viral manifestation. A combination of in silico techniques was applied, including structure-based pharmacophore virtual screening, molecular docking, absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis, molecular dynamics (MD), and molecular stability assessment of the identified hits. The docking scores of the 14 selected ligands ranged between −6.88 kcal/mol and −5.28 kcal/mol, the latter being comparable to the control ligand. ADMET and drug likeness evaluation identified Mol_01 and Mol_09 as the most promising candidates, both demonstrating good predicted antiviral activity against viral targets. Density functional theory (DFT) calculations, along with relevant quantum chemical descriptors, correlated well with the docking score hierarchy, and molecular electrostatic potential (MEP) mapping confirmed favorable electronic distributions supporting the docking orientation. Molecular dynamics simulations further validated complex stability, with consistent root mean square deviation (RMSD), root mean square fluctuation (RMSF), and secondary structure element (SSE) profiles. These findings support Mol_01 and Mol_09 as viable candidates for experimental validation. Full article

Necrotic enteritis (NE), caused by pathogenic Clostridium perfringens, poses a significant threat to global poultry health, with estimated annual losses exceeding USD 6 billion. The rising incidence of NE has been associated with the reduced use of antibiotic growth promoters, underscoring the urgent need for alternative control measures such as vaccination. Collagen adhesin protein (CNA), a key virulence factor in NE pathogenesis, represents a promising vaccine target. The US Food and Drug Administration has begun phasing out animal testing requirements for biologics and monoclonal antibody drugs. In this study, a computational multi-epitope vaccine (MEV) targeting CNA was designed by integrating predicted Cluster of Differentiation (CD)4⁺ helper T lymphocyte (Th), CD8⁺ cytotoxic T lymphocyte (CTL), and B-cell epitopes. Bioinformatics tools were used to identify immunogenic, antigenic, and non-allergenic epitopes assembled into a 115-amino-acid peptide vaccine construct. The candidate demonstrated strong stability and solubility. In silico immune simulation predicted robust immune responses, including elevated IgG and IgM antibody levels, plasma cell proliferation, Th memory formation, and CTL activation, comparable to responses elicited by a full-length CNA. These findings support the potential of the designed peptide as one of the multiple effective NE vaccine components, offering a promising alternative to antibiotic-based approaches in poultry disease management. Full article

PD-L1 (programmed cell death ligand-1) is a protein located on the surface of regulatory cells. It has an immunosuppressive role as it binds specifically to the protein programmed cell death-1 (PD-1), a checkpoint glycoprotein, present on the surface of immune cells such as T and B lymphocytes. Many tumor cells block the immune response by overexpressing PD-L1 on their surface; therefore, targeting PD-L1 represents a powerful strategy that allows tumor localization. To determine the presence of PD-L1 in cells, the use of ad hoc functionalized peptides that bind to PD-L1 can be exploited. One of them is the peptide CLP002 (Trp-His-Arg-Ser-Tyr-Tyr-Thr-Trp-Asn-Leu-Asn-Thr), which, bound to surface-enhanced Raman scattering (SERS) gold nanostructures via a suitable linker, was shown to be highly effective in recognizing MDA-MB-231 breast cancer cells and, importantly, this recognition can be measured by SERS experiments. To characterize, on a molecular scale, the interaction between PD-L1 and peptide functionalized nanostructures, we performed molecular dynamics (MDs) simulations, studying the features of peptide monolayers bound on gold surfaces in the absence and presence of PD-L1. The results obtained allow us to explain why the nature of the linker plays a fundamental role in the binding and why a peptide carrying the same amino acids as CPL002 but with a different sequence (scrambled) is much less active than CLP002. These results open the way to an in silico evaluation of the key parameters that regulate the binding of PD-L1 useful for cancer recognition. Full article

Ginsenosides are bioactive secondary metabolites in ginseng, which have gained popularity for their usage in traditional Oriental medicine. Many studies have reported that ginsenosides exert their effects through multiple pathways, such as GPCR-related pathways. However, focusing on their specific interactions with ADGRG3 (GPR97) can provide possible insights to inform targeted intervention strategies in oncology and immunotherapy through the tumor–immune microenvironment interactions. Thus, this study employed an integrative in silico computational strategy to investigate ginsenosides as possible targets of ADGRG3. First, gene expression was analyzed using multiple databases such as TCGA, cBioPortal, and TIMER, revealing the differential expression of ADGRG3 across cancers, with notable overexpression in leukemia. Then, the virtual screening of 128 ginsenosides identified five top candidates (Rg3, Rk3, F5, Rg7, and F1) that showed strong binding energy (−10.7 −10.6, −10.5, −10.4, and −10.3 kcal/mol, respectively) with ADGRG3, as determined through in silico molecular docking (MD). Computational approaches such as molecular dynamics simulations (MDSs), free binding energy calculations (MM-PBSA), and ADMET profiling confirmed the stability of these complexes’ favorable ADMET predictions, respectively, which warrants further experimental validation through in vitro and in vivo pharmacokinetic studies. Finally, the computational protein–protein interaction and pathway enrichment analyses of ADGRG3 demonstrated immune-related pathways, such as neutrophil degranulation and GPCR signaling, emphasizing its role in cancer progression and immune modulation. These computational findings predict ADGRG3 as a viable target for cancer and immune pathways and ginsenosides as natural ligands. Further in vitro and in vivo preclinical and clinical studies are warranted to validate the interactions of ADGRG3 with ginsenosides. Full article

Botulinum toxin A (BoNT-A) is widely used in both therapeutic and aesthetic settings; however, the formation of neutralizing antibodies (NAbs) remains a critical concern, leading to treatment failure. Immunogenic responses are known to vary between individuals due to HLA polymorphisms. Although some claim that neurotoxin-associated proteins (NAPs) shield BoNT-A from immune detection or are themselves immunogenic, there is limited molecular evidence supporting either view. This study applies computational immunogenetics to explore BoNT-A immunogenicity, focusing on HLA binding and the influence of accessory proteins. Epitope mapping, molecular docking, and HLA binding predictions were used to evaluate interactions between BoNT-A epitopes and selected class II HLA alleles (HLA-DQA1*01:02, HLA-DQA1*03:03, HLA-DQB1*06:04, HLA-DQB1*03:01, and HLA-DRB1*15:01). To assess the potential immunomodulatory role of NAPs, molecular dynamics (MD) simulations, solvent-accessible surface area (SASA) analysis, and electrostatic potential mapping were also conducted. Key epitopes—L11, N25, and C10—showed strong binding affinities to HLA-DQA1*01:02, HLA-DQB1*06:04, and HLA-DQA1*03:03, indicating a potential immunodominant role. NAPs did not obstruct these epitopes but slightly increased their exposure and appeared to stabilize the toxin structure. Electrostatic mapping and binding free energy calculations suggested no significant immunogenic shift in the presence of NAPs. BoNT-A immunogenicity appears to be influenced by HLA allele variability, reinforcing the value of patient-specific genetic profiling. The presumed immunogenic role of NAPs remains unsubstantiated at the molecular level, underscoring the need for evidence-based evaluation over commercial rhetoric. While these findings provide valuable molecular insight, it is important to acknowledge that they are derived entirely from in silico analyses. As such, experimental validation remains essential to confirm the immunological relevance of these predicted interactions. Nonetheless, this computational framework offers a rational basis for guiding future clinical research and the development of HLA-informed BoNT-A therapies. Full article

Cardiovascular disease remains the leading global cause of mortality, largely driven by atherosclerosis, a chronic inflammatory condition characterized by lipid accumulation and immune-cell infiltration in arterial walls. Macrophages play a central role by forming foam cells and secreting pro-atherogenic cytokines, such as TNF-α, IFN-γ, and IL-1β, which destabilize atherosclerotic plaques, expanding the lipid core and increasing the risk of thrombosis and ischemia. Despite the significant health burden of subclinical atherosclerosis, few targeted therapies exist. Current treatments, including monoclonal antibodies, are limited by high costs and immunosuppressive side effects, underscoring the urgent need for alternative therapeutic strategies. In this study, we employed in silico drug repositioning to identify multitarget inhibitors against TNF-α, IFN-γ, and IL-1β, leveraging a virtual screening of 2750 FDA-approved drugs followed by molecular dynamics simulations to assess the stability of selected cytokine–ligand complexes. This computational approach provides structural insights into potential inhibitors. Additionally, we highlight nutraceutical options, such as fatty acids (oleic, linoleic and eicosapentaenoic acid), which exhibited strong and stable interactions with key cytokine targets. Our study suggests that these bioactive compounds could serve as effective new therapeutic approaches for atherosclerosis. Full article

Background: Malaria remains a global health crisis, with the World Health Organization (WHO) reporting 241 million cases and 627,000 deaths worldwide in 2020, predominantly affecting Sub-Saharan Africa. The region accounted for 95% of cases and 96% of deaths, reflecting the immense challenges in malaria prevention and treatment. Plasmodium falciparum Schizont Egress Antigen-1 (PfSEA-1) is crucial in facilitating immune evasion and promoting the sequestration of infected red blood cells (RBCs), contributing to severe malaria symptoms, including cerebral malaria, and necessitates the urgent identification of novel or repurposed drugs targeting PfSEA1. Methods: The protein structure of PfSEA-1 (UniProt ID: A0A143ZXM2) was modelled in three dimensions, prepared, and subjected to a 50 ns molecular dynamics (MD) simulation to achieve a stable structure. The equilibrated structure was minimised for molecular docking against the DrugBank compound library. Docking analysis identified potential inhibitors, including Alparabinos, Dihycid, Ambenzyne, Amiflupipquamine, Ametchomine, and Chlobenethyzenol, with docking scores ranging from −8.107 to −4.481 kcal/mol. Advanced analyses such as interaction fingerprints, density functional theory (DFT), and pharmacokinetics evaluations were conducted. Finally, a 100 ns MD simulation in the NPT ensemble was performed to assess the stability of protein–ligand complexes, with binding free energy and total energy calculations derived from the simulation trajectories. Results and Discussion: The identified compounds exhibited satisfactory pharmacokinetic profiles and binding interactions with PfSEA-1. The MD simulations demonstrated overall stability, with minor fluctuations in some instances. Key intermolecular interactions were observed, supporting the binding stability of the identified compounds. Binding free energy calculations confirmed favourable interactions, underscoring their potential as therapeutic agents against Plasmodium falciparum. While the in silico results are promising, experimental validation is essential to confirm their efficacy and safety for clinical use. Conclusion: These findings highlight PfSEA-1 as a promising antimalarial target and identify potential inhibitors with strong binding affinities and favourable pharmacokinetics. While the computational results are encouraging, further in vitro and in vivo validation is necessary to confirm their therapeutic potential and facilitate future drug development. Full article

Background: Equine milk, including its whey proteins, is a source of nutrients and functional components in the human diet, and is especially beneficial for people with weakened immune systems, newborns, and athletes. Objectives Whey proteins in equine milk constitute approximately 20% of the total protein content and include various fractions such as albumin, globulin, and lactoferrin. Lactoferrin is one of the most extensively studied whey proteins in equine milk. Methods: HPLC-Mass analysis, enzymatic hydrolysis, modeling of 3D structure and biological activity in silico. Results: It has antioxidant, anti-inflammatory, and immunomodulatory properties, making it a promising candidate for influencing the various aspects of cardiovascular disease pathogenesis. The products of Lactoferrin hydrolysis by trypsin were confirmed using HPLC. The half-lives of the hydrolysate in the bloodstream and in an intestine-like environment were predicted in silico. Various biological activities (antihypertensive, anti-inflammatory, and antiangiogenic) were also estimated in silico and compared with the corresponding activities of lactoferrin hydrolysate amino acid sequences from camel and dromedary milk. Conclusions: The three-dimensional modeling of lactoferrin hydrolysate peptides was performed to support the development of computational models or simulations, as well as to investigate their potential antimicrobial, anti-inflammatory, or immune-modulating functions in clinical or nutritional applications. Full article