AI-Guided Epitope Prediction and Neoantigen Vaccine Design

1. Understand the principles of epitope prediction and neoantigen biology

2. Gain familiarity with key AI tools used for epitope analysis

3. Implement ML models for antigenicity and MHC binding prediction

4. Design multi-epitope vaccines and validate them using in silico tools

5. Evaluate real-world applications and challenges in AI-driven vaccinology

Day 1: Fundamentals of Epitope Prediction & Neoantigen Biology

• Introduction to Vaccinology and Immunoinformatics
• Overview of Epitopes: B-cell vs. T-cell
• Concept of Neoantigens and Personalized Vaccines
• Introduction to Key Tools: IEDB, NetMHC, VaxiJen
• Demo: Using IEDB for Basic Epitope Prediction

Day 2: AI and ML in Epitope Prediction

• Machine Learning for Epitope Prediction – An Overview
• Deep Learning Models for Antigenicity & MHC Binding
• Tools: DeepVacPred, NetMHCpan, DeepImmuno, ABCpred
• Case Study: Cancer-specific Neoantigen Prediction
• Hands-on: AI-based Workflow for Epitope Ranking

Day 3: Vaccine Design and In Silico Validation

• Constructing Multi-epitope Vaccines: Linkers, Adjuvants
• Population Coverage & Allergenicity Prediction
• Validation Tools: AllerTOP, ToxinPred, PEP-FOLD
• Real-World Applications & Challenges
• Final Demo: Design a Candidate Vaccine Using AI Tools

By the end of the workshop, participants will:

• Grasp the biological fundamentals of epitopes and neoantigens

• Use AI tools to predict and analyze antigenic peptides

• Build and validate a personalized multi-epitope vaccine model

• Analyze population coverage and allergenicity

• Gain hands-on experience with ML models and real-world datasets

• Undergraduate or postgraduate degree in Biotechnology, Bioinformatics, Computational Biology, or related life science disciplines.

• Professionals working in immunology, vaccine research, or computational drug design.

• Individuals with a keen interest in AI applications in healthcare and personalized medicine.