Epitope Prediction & Antigen Design

Week 1: Immunology Basics + Biological Databases

1. Day 01: Immune system fundamentals – antigens, antibodies, MHC-I/II, T-cells, B-cells, and immune responses
2. Day 02: Vaccine design pipeline – epitope-based vaccines vs. whole-pathogen vaccines; importance of epitopes
3. Day 03: Biological databases – IEDB, UniProt, ViPR, and PDB; retrieving pathogen proteomes
4. Day 04: Epitope types – linear vs. conformational, T-cell vs. B-cell, CTL vs. HTL epitopes
5. Day 05: Bioinformatics file formats – FASTA, PDB, and VCF; Biopython for sequence handling

Week 2: Computational Setup + B-Cell Epitope Prediction

1. Day 06: Installation and setup – Python, Biopython, PyMOL, AlphaFold Colab, and NetMHCpan
2. Day 07: Hands-on: Retrieve SARS-CoV-2, Influenza, and Dengue proteomes; sequence-length and amino-acid composition analysis
3. Day 08: B-cell epitope fundamentals – surface accessibility, flexibility, and hydrophilicity
4. Day 09: B-cell prediction tools – BepiPred 3.0, ABCpred, SVMTriP, and ElliPro
5. Day 10: Feature engineering – physicochemical properties, Boman index, charge, and aliphatic index

Week 3: Machine Learning for B-Cell Epitope Prediction

1. Day 11: Dataset preparation – IEDB positive and negative B-cell epitope datasets
2. Day 12: Machine learning models – Random Forest and SVM for epitope classification
3. Day 13: Class imbalance handling using SMOTE and model evaluation
4. Day 14: Evaluation metrics – ROC-AUC, PR-AUC, MCC, precision, and recall
5. Day 15: Hands-on: Predict B-cell epitopes from Spike protein and map candidates onto an AlphaFold structure

Week 4: T-Cell Epitope Prediction + MHC Binding

1. Day 16: MHC-I vs. MHC-II – peptide length, binding grooves, and HLA diversity
2. Day 17: MHC-I prediction tools – NetMHCpan 4.1, MHCflurry 2.0, and PickPocket
3. Day 18: MHC-II prediction using NetMHCIIpan for CD4+ helper T-cell epitopes
4. Day 19: Population coverage analysis using IEDB and global HLA allele frequencies
5. Day 20: Conservation analysis, multiple sequence alignment, and candidate filtering based on MHC binding, conservation, and accessibility

Week 5: Advanced AI – Deep Learning + Protein Language Models

1. Day 21: Hands-on: Generate a T-cell epitope map for a selected pathogen using HLA-A02:01 and HLA-DRB101:01
2. Day 22: Deep learning for epitope prediction – CNNs and 1D convolution layers
3. Day 23: Protein embeddings – ProtBERT and ESM-2 compared with k-mer features
4. Day 24: Transformer models – fine-tuning ProtBERT for epitope prediction
5. Day 25: Graph neural networks – peptide-MHC complex representation and graph-based prediction concepts

Week 6: AI Benchmarking + Multi-Epitope Antigen Design

1. Day 26: Benchmarking – compare ML, DL, and NetMHCpan using an independent IEDB test set
2. Day 27: Antigenicity, allergenicity, and toxicity assessment using VaxiJen 2.0, AllerTOP, and ToxinPred
3. Day 28: Hands-on: Train an ESM-2 + MLP model for T-cell epitope prediction
4. Day 29: Multi-epitope vaccine design – linkers GPGPG, AAY, and EAAAK with their design rationale
5. Day 30: Adjuvant selection – HBD-3 and 50S ribosomal protein; preliminary structural modelling using AlphaFold

Week 7: Antigen Construct Development + Structural Validation

1. Day 31: Codon optimization for E. coli, yeast, and human expression systems; IDT tools and LinearDesign concepts
2. Day 32: mRNA secondary structure analysis using ViennaRNA and minimum free energy (MFE)
3. Day 33: Structural validation using AlphaFold2 and pLDDT interpretation
4. Day 34: Antigen-antibody docking – HADDOCK and AlphaFold-Multimer concepts
5. Day 35: Hands-on: Assemble a multi-epitope construct and generate its 3D model using PyMOL

Week 8: Generative AI + Immune Simulation + Validation

1. Day 36: RFdiffusion – scaffold design and protein backbone generation concepts
2. Day 37: ProteinMPNN – sequence redesign for predicted stability and expression
3. Day 38: In-silico immune simulation using the C-ImmSim server
4. Day 39: Molecular dynamics simulation introduction – GROMACS and a 10 ns construct-stability workflow
5. Day 40: Research paper analysis – reproduce one figure from DeepVacPred or Vaxign-ML

Week 9: Deployment + Research Portfolio

1. Day 41: MLOps fundamentals – deploy an epitope predictor using FastAPI and Docker
2. Day 42: Scientific report writing – 1,500-word report covering Methods, Results, Discussion, and Limitations
3. Day 43: GitHub portfolio – upload code, README, and example notebooks
4. Day 44: Streamlit application – paste a protein sequence, predict top candidate epitopes, and display 3D visualization
5. Day 45: Final presentation – 10-slide project presentation and future research directions, including cancer neoantigens and pan-virus vaccine design