New Year Offer End Date: 30th April 2024
Program
AI-Guided Therapeutic Antibody Engineering: Structure, Affinity & Developability Optimization
From Antibody Sequence to Therapeutic Candidate — Design, Predict and Optimize with AI.
Skills you will gain:
About Program:
This three-day workshop introduces an integrated computational workflow for therapeutic-antibody engineering, from sequence analysis and CDR mapping to structure prediction, antigen-binding analysis, affinity estimation, and developability assessment. Participants will explore antibody databases, structural bioinformatics tools, and machine-learning methods to analyse antibody candidates. Guided hands-on activities using SAbDab, ANARCI, AlphaFold/ColabFold, PyMOL, PRODIGY, Biopython, XGBoost, SHAP, and Google Colab will support therapeutic candidate prioritisation.
Aim:
To provide participants with practical knowledge of antibody sequence analysis, AI-assisted structure prediction, antibody–antigen interaction assessment, affinity analysis, and developability-based therapeutic candidate prioritisation.
Program Objectives:
- Understand therapeutic-antibody architecture, sequence features, CDRs, paratopes, and epitopes.
- Explore antibody sequence and structure databases for candidate retrieval and analysis.
- Analyse antibody–antigen interfaces, contact residues, and affinity-related molecular interactions.
- Apply AI and machine-learning tools for antibody structure and developability assessment.
- Develop a multi-parameter workflow to prioritise therapeutic-antibody candidates based on structure, interaction, affinity, and developability profiles.
What you will learn?
📅 Day 1: Antibody Sequence, CDR Mapping and Structure Prediction
- Therapeutic-antibody architecture: heavy and light chains, VH/VL domains, and CDRs
- Monoclonal antibodies, antibody fragments, and bispecific formats
- Antibody sequence retrieval and annotation
- CDR identification and antibody numbering schemes
- Paratopes, epitopes, and antigen-binding regions
- Principles of antibody 3D structure prediction
- AI-assisted antibody structure modelling
- Structural inspection of variable domains and binding regions
- Comparison of experimentally resolved and predicted antibody structures
🛠️ Hands-on:
- Retrieve therapeutic antibody sequences from public databases
- Identify and annotate CDR regions
- Explore antibody numbering and sequence features
- Predict or inspect antibody 3D structures
- Visualise potential antigen-binding regions
🧰 Tools Covered:
SAbDab, UniProt, RCSB PDB, ANARCI / AbNumber, AlphaFold / ColabFold, PyMOL
📅 Day 2: Antibody–Antigen Interaction and Affinity Analysis
- Principles of antibody–antigen molecular recognition
- Epitope–paratope interaction analysis
- Interpretation of antibody–antigen complex structures
- Interface-residue and contact mapping
- Hydrogen bonds, salt bridges, and hydrophobic interactions
- Structural determinants of binding specificity
- Binding-interface surface analysis
- Mutation comparison at antigen-binding regions
- Computational estimation and interpretation of binding affinity
🛠️ Hands-on:
- Retrieve and inspect an antibody–antigen complex
- Identify epitope and paratope contact residues
- Analyse hydrogen bonds and interface interactions
- Compare selected interface mutations
- Estimate and interpret antibody–antigen binding affinity
🧰 Tools Covered:
RCSB PDB, PyMOL, PDBePISA, PRODIGY, Biopython, Python
📅 Day 3: AI-Based Developability Assessment and Candidate Prioritisation
- Introduction to antibody developability assessment
- Aggregation propensity, hydrophobicity, charge, and stability
- Sequence-based physicochemical properties
- Immunogenicity considerations in therapeutic-antibody development
- Generation of antibody sequence descriptors
- Preparing antibody datasets for machine learning
- Machine-learning approaches for candidate-property prediction
- XGBoost-based prediction and model evaluation
- SHAP-based interpretation of important antibody features
- Multi-parameter therapeutic-antibody candidate ranking
🛠️ Hands-on:
- Generate sequence-based antibody descriptors
- Compare developability-related properties across candidates
- Build a basic machine-learning prediction model
- Interpret influential features using SHAP
- Rank antibody candidates using structure, interaction, and developability criteria
🧰 Tools Covered:
Biopython, ProtParam, scikit-learn, XGBoost, SHAP, Python, Google Colab
Mentor Profile
Fee Plan
INR 1999 /- OR USD 50
Get an e-Certificate of Participation!
Intended For :
- Graduate and postgraduate students in biotechnology, bioinformatics, molecular biology, biochemistry, pharmacy, and life sciences
- PhD scholars and research fellows working in antibody engineering, immunology, structural biology, or computational biology
- Academicians and faculty members interested in therapeutic-antibody research and AI-assisted biomolecular analysis
- Biotechnology and pharmaceutical researchers involved in biologics, monoclonal antibodies, and therapeutic development
- Industry professionals working in biopharmaceutical R&D, antibody discovery, protein engineering, or computational drug development
Career Supporting Skills
Program Outcomes
- Retrieve and analyse antibody sequences and identify CDRs and variable-region features.
- Predict and inspect antibody 3D structures and visualise potential antigen-binding regions.
- Analyse antibody–antigen interactions and interpret key contact residues and affinity-related features.
- Evaluate developability parameters such as aggregation propensity, hydrophobicity, charge, and stability.
- Prioritise therapeutic-antibody candidates using integrated sequence, structure, interaction, and AI-based developability analysis.