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Designing of a multi-epitope vaccine targeting enterovirus D68: An integrated immunoinformatic and reverse
Hailah M Almohaimeed1, Amany I Almars2, Nada Alkhorayef3
1Department of Basic Science, College of Medicine, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh, 11671, Saudi Arabia.
Abstract:
Enterovirus D68 (EV-D68) is an enterovirus known for causing respiratory infections, as well as flaccid myelitis, meningitis and encephalitis. Despite the efforts, no licensed vaccine against EV-D68 is currently available. Vaccine development efforts are ongoing; however, the process is complex and requires extensive clinical validation. In contrast, immunoinformatic is a rapidly expanding area with the potential to significantly influence the therapeutic interventions and vaccine development for infectious diseases. Herein, immunoinformatic and reverse vaccinology strategies were utilized to design a multi-epitope vaccine construct targeting EV-D68 virus. In this connection, three virulent proteins were selected for analysis based on their immunogenic characteristics. Further, B-cells and T-cells epitopes were predicted and connected through suitable linkers and adjuvant. The predicted T-cell epitopes within the vaccine construct exhibited a significant worldwide population coverage. Moreover, Robetta was utilized to predict the 3D structure of the vaccine construct. Subsequently the molecular docking simulation of construct was employed to study the molecular interactions by using Toll-like receptors as target proteins and further subjected to MD simulation. The results reveal the stability of the vaccine-receptor complex throughout the simulation. Finally, in silico cloning showed potential for the predicted vaccine within the Escherichia coli expression system. These findings provide valuable insights that may guide subsequent experimental studies and contribute meaningfully to the early phases of EV-D68 vaccine research and development. By streamlining candidate selection and optimizing design parameters, our findings holds promise for accelerating the transition from computational predictions to effective vaccine formulations.
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