Background: Influenza D Virus (IDV) presents a possible threat to animal and human health, necessitating the development of effective vaccines. Although no human illness linked to IDV has been reported, the possibility of human susceptibility to infection remains uncertain. Hence, there is a need for an animal vaccine to be designed. Such a vaccine will contribute to preventing and controlling IDV outbreaks and developing effective countermeasures against this emerging pathogen. This study, therefore, aimed to design an mRNA vaccine construct against IDV using immunoinformatic methods and evaluate its potential efficacy. Methods: A comprehensive methodology involving epitope prediction, vaccine construction, and structural analysis was employed. Viral sequences from six continents were collected and analyzed. A total of 88 Hemagglutinin Esterase Fusion (HEF) sequences from IDV isolates were obtained, of which 76 were identified as antigenic. Different bioinformatics tools were used to identify preferred CTL, HTL, and B-cell epitopes. The epitopes underwent thorough analysis, and those that can induce a lasting immunological response were selected for the construction. Results: The vaccine prototype comprised nine epitopes, an adjuvant, MHC I-targeting domain (MITD), Kozaq, 3′ UTR, 5′ UTR, and specific linkers. The mRNA vaccine construct exhibited antigenicity, non-toxicity, and non-allergenicity, with favourable physicochemical properties. The secondary and tertiary structure analyses revealed a stable and accurate vaccine construct. Molecular docking simulations also demonstrated strong binding affinity with toll-like receptors. Conclusions: The study provides a promising framework for developing an effective mRNA vaccine against IDV, highlighting its potential for mitigating the global impact of this viral infection. Further experimental studies are needed to confirm the vaccine’s efficacy and safety.

Breast cancer remains a leading cause of mortality among women globally, underscoring the urgent need for novel therapies that combine high efficacy with minimal adverse effects. This study examines the bioinformatic design of animal-derived therapeutic peptides targeting two key breast cancer biomarkers: matrix metalloproteinase 1 (MMP1) and epidermal growth factor receptor (EGFR). Approximately 1,500 antimicrobial peptides were retrieved from the antimicrobial peptide database 3 (APD3). They were screened for hemolytic properties using the Hemolytik database. This screening resulted in 255 non-hemolytic candidates, which were further screened through VaxiJen 2.0 and ToxinPred2 for antigenicity and allergenicity. Moreover, physicochemical properties were analyzed using the Expasy ProtParam server. We applied thresholds of molecular weight (1.7–3.1 kDa), instability index (≤ 28), and net charge (+ 2 to + 7). Five peptides met all criteria and were screened for anticancer potential using AntiCP 2.0 and ACPred, identifying three overlapping anticancer peptides (ACPs), including Metalnikowin IIA (AP00363) from Palomena prasina , Guentherin (AP00584) from Hylarana guentheri , and P-10 (AP02393) from Ciona intestinalis . Furthermore, peptide structures were modeled using Colab AlphaFold2. Tertiary structures of target proteins were obtained from UniProt, and Buserelin (FDA-approved ACP) was obtained from the DCTPep database. All peptides were docked against MMP1 and EGFR using HDock and ClusPro. AP00363 showed the highest docking affinity with MMP1 and EGFR, scoring −235.61 and −271.70, respectively. Molecular dynamics simulations supported the stability and interaction potential of these peptides. Overall, this study presents promising bioinformatically designed, animal-derived ACPs for targeted breast cancer therapy. Further in vitro and in vivo validation is necessary to assess clinical relevance.