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
- 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.
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 15, 2026
IST 4:30 PM
Workshop Dates
October 15, 2026 – October 17, 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.
Recording fee
Participants in India
₹2,499
Participants outside India
$50
- All session recordings
- Study material
- Certificate after completion
- 1-year access
The live sessions of this workshop are over, so the live fees no longer apply.
Frequently asked questions
Will I get the recording?
Yes. Paid participants get the session recordings and study material in their NanoSchool dashboard (My registrations → Recorded Lectures / Study Material).
What if I miss a live session?
Watch the recording in your dashboard and send your questions to the mentor through the support desk.
Do I get a certificate?
Yes, an e-certificate of participation after the workshop, with a QR code anyone can verify and an "Add to LinkedIn" button.
Which fee category applies to me?
Choose the category that matches your current role: Student, Ph.D. Scholar / Researcher, Academician / Faculty or Industry Professional.
How can I pay?
By card, UPI or PayPal on the payment page; international bank transfer is also available there.
What is the refund policy?
See our Returns, Refunds & Cancellation policy: Returns, Refunds & Cancellation policy