COMPUTATIONAL IMMUNOLOGY FOR NEXTGEN VACCINE

Week 01: Vaccine Science & AI Foundations

1. Day 01: Introduction to vaccine types – live attenuated, inactivated, subunit, mRNA and viral vector vaccines.
2. Day 02: Traditional vaccine development pipeline – major stages, timelines, challenges and bottlenecks.
3. Day 03: Immunology fundamentals – antigens, antibodies, B-cells, T-cells and immune responses.
4. Day 04: Applications of AI in vaccine development – target identification, antigen design, clinical trials and manufacturing.
5. Day 05: AI, ML and Deep Learning fundamentals for biological applications – supervised learning, unsupervised learning and deep learning architectures.

Week 02: Immunoinformatics & Biological Databases

1. Day 06: Introduction to immunoinformatics – epitopes, MHC binding and B-cell/T-cell epitopes.
2. Day 07: Biological databases – IEDB, ViPR, UniProt and PDB; pathogen and antigen data retrieval.
3. Day 08: B-cell epitope prediction using BepiPred and ABCpred.
4. Day 09: T-cell epitope prediction using NetMHCpan and MHCflurry.
5. Day 10: Antigenicity, allergenicity and toxicity prediction; introduction to HLA allele frequencies and population coverage analysis.

Week 03: Biological Data Processing & Python for Vaccine Design

1. Day 11: Python fundamentals for bioinformatics – Biopython and biological sequence handling.
2. Day 12: Biological data formats – FASTA, VCF and epitope datasets; data cleaning and preprocessing.
3. Day 13: Pandas for epitope datasets – feature extraction and feature engineering.
4. Day 14: Hands-on epitope prediction – SARS-CoV-2 Spike protein sequence retrieval and B-cell/T-cell epitope analysis.
5. Day 15: HLA population coverage analysis and preparation of epitope datasets for machine learning.

Week 04: Machine Learning for Epitope Prediction

1. Day 16: Introduction to machine learning applications in immunoinformatics and epitope classification.
2. Day 17: Random Forest and Support Vector Machine (SVM) for epitope versus non-epitope classification.
3. Day 18: Model evaluation – accuracy, precision, recall, F1-score, ROC curves and PR curves for imbalanced datasets.
4. Day 19: Regression approaches for immunogenicity prediction and feature selection.
5. Day 20: Clustering and dimensionality reduction – UMAP and t-SNE; hands-on ML model development for epitope classification.

Week 05: Deep Learning & Structural Vaccinology

1. Day 21: Protein structure fundamentals – PDB, primary to quaternary structure and AlphaFold2.
2. Day 22: Structural vaccinology – 3D epitope mapping and visualization of antigen structures.
3. Day 23: CNN architectures for protein sequence analysis and antigenicity prediction.
4. Day 24: Graph Neural Networks (GNNs) for protein representation and protein graph analysis; introduction to GraphProt concepts.
5. Day 25: Protein language models – ESM-2 and ProtBERT embeddings; antigen-antibody molecular docking using HADDOCK and hands-on AlphaFold2/PyMOL visualization.

Week 06: mRNA Vaccine Design & Generative AI

1. Day 26: Fundamentals of mRNA vaccine design – codon optimization, UTRs and RNA structure.
2. Day 27: AI-assisted mRNA optimization – LinearDesign and codon usage models.
3. Day 28: RNA secondary structure prediction, stability analysis and machine learning applications.
4. Day 29: Lipid nanoparticle (LNP) formulation, delivery systems and AI-based optimization concepts.
5. Day 30: Generative AI for protein design – RFdiffusion and ProteinMPNN; LLM-based literature mining for vaccine target identification; hands-on optimized mRNA sequence design for a selected epitope.

Week 07: Clinical Trials, Manufacturing & Regulatory Science

1. Day 31: AI in clinical trials – patient recruitment, trial optimization and adverse-event prediction.
2. Day 32: Real-world data analysis – vaccine effectiveness modelling and outcome prediction.
3. Day 33: AI in vaccine manufacturing – bioreactor optimization, quality control and process monitoring.
4. Day 34: Vaccine ethics, biosafety and regulatory frameworks – FDA, WHO and AI model validation.
5. Day 35: Case study – Moderna's mRNA vaccine platform and AI-assisted vaccine development pipeline.

Week 08: Advanced Vaccine Case Studies & Capstone Development

1. Day 36: Case study – reverse vaccinology and development of the MenB Bexsero vaccine.
2. Day 37: Capstone Project – pathogen selection, literature review and antigen identification.
3. Day 38: Multi-epitope vaccine design – B-cell/T-cell epitope selection, HLA coverage and linker/adjuvant selection.
4. Day 39: Development of an ML/DL-based epitope prediction pipeline and candidate prioritization.
5. Day 40: Structural analysis, molecular docking and visualization of selected vaccine candidates.

Week 09: Capstone Completion, Deployment & Research Presentation

1. Day 41: Capstone – integration of immunoinformatics, machine learning and structural analysis results.
2. Day 42: Result interpretation, validation strategy and comparison with published vaccine research.
3. Day 43: Research report writing, scientific figures, molecular visualization and GitHub documentation.
4. Day 44: Final presentation preparation, project demonstration and scientific discussion.
5. Day 45: Final project presentation, future trends – pan-virus vaccines, AI agents and next-generation computational vaccine design.