AI-Powered Protein Engineering: From Molecular Discovery to Therapeutic Innovation
From Protein Sequences to Therapeutic Candidates — Harness AI, Generative Design and Structural Bioinformatics to Engineer Next-Generation Proteins.
Skills you will gain:
About Workshop:
This 3-day mentor-led workshop introduces AI-powered approaches for modern protein engineering and therapeutic protein discovery. Participants will explore protein sequence analysis, structure prediction, functional-site identification, generative protein design, sequence optimisation, antibody and peptide engineering, molecular interaction analysis, and computational validation. The workshop follows a practical research workflow from protein sequence and structure to AI-assisted design, candidate evaluation, and therapeutic prioritisation.
Aim: To provide practical, research-oriented training in AI-assisted protein engineering, combining structural bioinformatics, deep learning, generative AI, protein design, and computational validation for therapeutic and industrial protein development.
Workshop Objectives:
- Understand the principles of computational and AI-driven protein engineering.
- Analyse protein sequences, evolutionary conservation, and structure–function relationships.
- Apply AI-based approaches for protein structure prediction.
- Identify functional sites, binding pockets, and potential engineering regions.
- Understand protein language models and AI-based sequence representations.
- Explore generative AI for de novo protein and scaffold design.
- Perform protein mutation analysis and computational sequence optimisation.
- Explore AI-assisted antibody and therapeutic peptide engineering.
- Evaluate protein stability, solubility, specificity, and binding properties.
- Analyse protein–protein and protein–ligand interactions.
- Understand molecular docking and molecular-dynamics-based validation.
- Explore AI-driven enzyme engineering for biomedical and industrial applications.
What you will learn?
📅 Day 1: AI for Protein Structure, Function & Molecular Discovery
Focus: Analyse protein sequences and structures to identify functional and engineering opportunities.
- Protein engineering and computational protein-design fundamentals
- AI and deep learning in structural biology
- Protein sequence analysis and evolutionary conservation
- Structure–function relationships
- AI-based protein structure prediction
- Structural visualisation and analysis
- Functional-site and binding-pocket identification
- Engineering-target selection
- Disease-associated proteins and enzyme-engineering case studies
🛠️ Hands-on
Analyse a target protein sequence, predict/explore its structure, visualise structural features, and identify potential engineering sites.
Tools: AlphaFold, ColabFold, BLAST, Clustal Omega, PyMOL, ChimeraX, Python, Google Colab
📅 Day 2: Generative AI & Next-Generation Protein Design
Focus: Use AI and generative approaches to create and optimise novel protein sequences and therapeutic candidates.
- Protein language models and sequence representations
- Generative AI for de novo protein design
- Novel protein scaffolds and sequence generation
- Mutation analysis and rational sequence redesign
- Structure-conditioned protein design
- Computational sequence optimisation
- AI-assisted antibody and therapeutic peptide engineering
- Prediction of stability, solubility, specificity, and binding affinity
- Candidate sequence evaluation and prioritisation
🛠️ Hands-on
Generate or redesign candidate protein sequences, compare variants, evaluate predicted properties, and prioritise promising candidates.
Tools: ProteinMPNN, RFdiffusion, DiffAb, AntiFold, Immune Builder, SAbPred, Python, Google Colab
📅 Day 3: Therapeutic Protein Engineering & Translational Validation
Focus: Evaluate engineered proteins for molecular interactions, therapeutic potential, and translational feasibility.
- AI-guided therapeutic protein and antibody development
- Protein–protein and protein–ligand interaction analysis
- Molecular docking and computational validation
- Molecular-dynamics concepts for structural assessment
- AI-driven enzyme engineering for biomedical and industrial applications
- Therapeutic proteins for cancer, infectious diseases, and rare disorders
- Stability, specificity, solubility, and developability assessment
- Immunogenicity and biomanufacturing considerations
- Candidate ranking and computational validation
- Translational applications and experimental validation strategies
🛠️ Hands-on
Evaluate engineered candidates using structural and interaction evidence and develop an integrated computational protein-engineering workflow for candidate prioritisation.
Tools: AlphaFold, ColabFold, PyMOL, ChimeraX, ProteinMPNN, RFdiffusion, Python, Google Colab
Mentor Profile
Fee Plan
Important Dates
16 Nov 2026 Indian Standard Timing 4:30 PM
16 Nov 2026 to 18 Nov 2026 Indian Standard Timing 5:30 PM
Get an e-Certificate of Participation!
Intended For :
- Graduate & Postgraduate Students – Biotechnology, Bioinformatics, Biochemistry, Molecular Biology, Chemistry, Pharmacy, Life Sciences, and related fields.
- PhD Scholars & Researchers – Protein engineering, structural biology, computational biology, drug discovery, antibody engineering, enzyme engineering, and therapeutic proteins.
- Academicians & Faculty – Professionals interested in AI-driven protein design, structural bioinformatics, and computational biotechnology.
- Biotechnology Professionals – Protein design, biologics, enzyme engineering, and biotechnology R&D.
- Pharmaceutical Professionals – Drug discovery, therapeutic proteins, antibody development, and biologics R&D.
- Bioinformatics & Computational Researchers – AI/ML, structural bioinformatics, molecular modelling, and computational protein design.
- R&D Professionals – Industrial enzyme development, protein optimisation, and next-generation biologics.
Career Supporting Skills
Workshop Outcomes
- Analyse protein sequences and evolutionary conservation.
- Predict and interpret protein structures using AI-based tools.
- Identify functional sites and potential engineering targets.
- Apply generative AI concepts to protein sequence design.
- Generate and evaluate protein variants.
- Explore computational antibody and peptide engineering.
- Assess protein stability, solubility, specificity, and binding properties.
- Analyse protein–protein and protein–ligand interactions.
- Understand docking and molecular-dynamics-based validation.
- Apply computational approaches to enzyme engineering.
- Evaluate therapeutic protein developability and immunogenicity considerations.
- Prioritise engineered protein candidates for experimental validation.
- Build an end-to-end computational protein-engineering workflow.