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The advent of computational vaccine design has revolutionized the field of immunology, enabling the development of protein and mRNA vaccines at an accelerated pace. With the success of COVID-19 mRNA vaccines, there is a growing need for computational tools that can predict, optimize, and validate the efficacy of novel vaccine candidates. This workshop introduces participants to in silico methods used to design vaccine candidates, including the selection of appropriate antigens, epitopes, adjuvants, and delivery systems.
The workshop covers the design of protein and mRNA vaccines, focusing on techniques like epitope prediction, protein folding (using tools such as MODELLER), codon optimization for mRNA, and mRNA vaccine construct design. Participants will also learn how to simulate vaccine immunogenicity, assess immune response using immune system modeling, and explore delivery methods for optimal efficacy. The program combines theory with dry-lab practical sessions on popular computational tools, enabling participants to create design-ready vaccine candidates for preclinical testing.
This workshop aims to provide participants with the skills and knowledge to design protein and mRNA vaccines using computational tools. It will cover the complete workflow from sequence analysis, antigen selection, and vaccine construct design to mRNA synthesis and delivery system modeling. Participants will also learn how in silico approaches can accelerate vaccine development by identifying potential epitopes, improving immune response, and reducing experimental costs.
Participants will learn to:
02/01/2026
IST 07:00 PM
02/01/2026 – 02/03/2026
IST 08:00 PM
Participants will be able to:
Prof. Kumud Malhotra, Dean of the University Institute of Physical and Life Sciences with 30 years of experience is an academician and administrator and has attained the highest echelons in the educational sector by managing senior positions, like Director, Dean, Managing Editor, or Editor-in-Chief . . .
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PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.
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