AI-Driven Macromolecular Design: From Proteins to Programmable Nanostructures
Design, optimize, and assemble proteins into functional nanostructures using AI-driven computational workflows.
Skills you will gain:
About Workshop:
This three-day workshop introduces participants to AI-driven macromolecular design, starting with protein sequence and structure analysis and progressing toward generative protein design, sequence optimization, structural validation, and programmable nanostructure assembly. Through guided theory and hands-on computational exercises, participants will explore modern tools and workflows used in protein engineering, inverse folding, molecular self-assembly, and nanostructure design.
Aim: To provide participants with practical and conceptual understanding of AI-based protein design and computational strategies for developing and evaluating programmable macromolecular and nanoscale structures.
Workshop Objectives:
- Understand protein sequence–structure–function relationships.
- Learn how AI is applied to protein structure prediction and design.
- Explore generative models for de novo protein engineering.
- Understand inverse folding and AI-assisted sequence optimization.
- Analyze structural confidence and protein design quality.
- Explore protein–protein interactions and molecular self-assembly.
- Understand the principles of protein nanoparticles and nanocages.
- Apply computational methods to evaluate and rank designed structures.
- Connect protein engineering workflows with nanotechnology applications.
What you will learn?
Day 1: Protein Structure Analysis & AI-Based Prediction
- Protein sequence–structure–function relationships.
- Protein domains, motifs, and functional regions.
- Structural databases: UniProt, RCSB PDB, AlphaFold DB.
- AI-based structure prediction using AlphaFold/ESMFold concepts.
- Interpret pLDDT, PAE, RMSD, and TM-score.
- Protein visualization using PyMOL/ChimeraX.
Hands-on: Retrieve, visualize, and evaluate a protein structure using confidence and structural-comparison metrics.
Day 2: Generative AI for Protein Design & Optimization
- Introduction to de novo and generative protein design.
- RFdiffusion for protein backbone generation.
- ProteinMPNN for inverse folding and sequence design.
- Analyze charge, hydrophobicity, and sequence properties.
- Validate designs using structural prediction and comparison.
- Rank candidate protein designs using multiple metrics.
Hands-on: Generate candidate protein sequences, validate their structures, and rank the best designs.
Day 3: Protein Self-Assembly & Programmable Nanostructures
- Protein–protein interactions and interface engineering.
- Oligomers, multimers, symmetry, and self-assembly.
- Protein nanocages and programmable architectures.
- Analyze inter-chain contacts, distances, and steric clashes.
- Visualize and rank protein assemblies.
- Applications in drug delivery, vaccines, biosensors, and biomaterials.
Hands-on: Build and evaluate a programmable protein assembly using structural and interface-based criteria.
Mentor Profile
Fee Plan
Important Dates
15 Oct 2026 Indian Standard Timing 4:30 PM
15 Oct 2026 to 17 Oct 2026 Indian Standard Timing 5:00 PM
Get an e-Certificate of Participation!
Intended For :
- Biotechnology and bioinformatics students
- Biochemistry and molecular biology students
- Nanotechnology and materials science students
- Computational biology researchers
- Structural biology researchers
- Pharmaceutical and drug-discovery researchers
- Biomedical engineering students
- Synthetic biology and protein-engineering researchers
- PhD scholars, faculty members, and research professionals
Career Supporting Skills
Workshop Outcomes
- Analyze protein sequences and 3D structures.
- Interpret structural confidence metrics such as pLDDT, PAE, and RMSD.
- Understand workflows involving RFdiffusion, ProteinMPNN, and structure-prediction models.
- Generate and compare AI-designed protein candidates.
- Evaluate sequence and structural properties of designed proteins.
- Analyze protein interfaces and self-assembly characteristics.
- Construct and visualize simple protein-based nanostructures.
- Compare candidate structures using computational quality metrics.
- Develop an end-to-end protein-to-nanostructure design workflow.