Mentor Based
AI-Driven Macromolecular Design: From Proteins to Programmable Nanostructures
Design, optimize, and assemble proteins into functional nanostructures using AI-driven computational workflows.
About This Course
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.
Workshop Structure
Day 1: Protein Structure Analysis & AI-Based Prediction
- Fundamentals of macromolecular structure and function
- Protein sequence–structure–function relationship
- Protein domains, motifs and structural organization
- Introduction to AI in structural biology
- Protein structure prediction using AI
- AlphaFold and protein language model concepts
- Structural confidence metrics: pLDDT, PAE and RMSD
- Comparison of experimental and predicted structures
- Identification of stable, flexible and functionally important regions
- 3D visualization and interpretation of protein structures
- Hands-on: Protein sequence analysis, structure prediction and 3D structural assessment
Day 2: Generative AI for Protein Design & Optimization
- From protein structure prediction to inverse design
- Principles of de novo protein design
- Generative AI and diffusion models for protein engineering
- RFdiffusion for protein backbone generation
- ProteinMPNN for sequence design
- Inverse folding and sequence optimization
- Target- and motif-guided protein design
- Analysis of charge, hydrophobicity and amino-acid composition
- Structural validation of designed protein candidates
- Multi-parameter comparison and candidate ranking
- Hands-on: Generate, optimize, validate and rank AI-designed protein candidates
Day 3: Protein Self-Assembly & Programmable Nanostructure Design
- Principles of molecular self-assembly
- Protein–protein interactions and interface engineering
- Oligomeric and multimeric protein architectures
- Symmetry in biological assemblies
- Protein nanoparticles and nanocages
- Modular protein building blocks for nanostructure design
- Programmable biomolecular architectures
- Interface contacts, inter-chain distances and steric clashes
- Stability and assembly-quality assessment
- Computational ranking of candidate nanostructures
- Applications in nanomedicine, drug delivery, vaccines, biosensors and biomaterials
- Hands-on: Construct, visualize, evaluate and rank a protein-based programmable nanostructure
Who Should Enrol?
- 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
Important Dates
Registration Ends
October 9, 2026
IST 4:30 PM
Workshop Dates
October 9, 2026 – October 11, 2026
IST 5:00 PM
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.
Fee Structure
Student Fee
₹2499 | $65
Ph.D. Scholar / Researcher Fee
₹3499 | $75
Academician / Faculty Fee
₹4499 | $85
Industry Professional Fee
₹5499 | $105
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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