Generative AI for Protein Design, Antibodies, and Therapeutic Discovery

Aim

Course Objectives

Syllabus

Module 1: Foundations of Protein AI and Structure Prediction 

• Protein databases and sequence annotation
• Structure confidence, pLDDT, and basic model interpretation
• Search a protein sequence using UniProt
• Explore known structures using RCSB PDB
• View predicted structures using AlphaFold DB
• Predict or explore a protein structure using ColabFold / ESMFold demo
• Visualize protein structure using Mol* / PyMOL
• Tools: Google Colab, NCBI, PubMed / literature search & demo & UniProt

Module 2: Generative AI for Protein Design and Engineering 

• Generate or explore protein sequence embeddings using a demo notebook
• Understand RFdiffusion-based protein scaffold/binder design workflow
• Use ProteinMPNN concept workflow for sequence redesign
• Compare native vs designed protein sequences
• Visualize designed protein structures
• Discuss developability checks: stability, solubility, immunogenicity, and manufacturability
• Tools: Google Colab, NCBI, PubMed / literature search & demo & UniProt

Module 3: Generative AI for Antibodies and Therapeutic Discovery 

• Explore antibody sequence and structure basics
• Predict or view antibody structure using IgFold demo
• Identify CDR regions and binding-relevant regions
• Visualize antibody–antigen interaction concept
• Perform a basic docking workflow demonstration
• Create a therapeutic discovery workflow map
• Prepare a simple AI model validation checklist
• Tools: Google Colab, NCBI, PubMed / literature search & demo & UniProt

Course Outcomes

Meet Your Mentor(s)

DR. HARISHCHANDER ANANDARAM

Assistant Professor

Dr Harishchander Anandaram is an Assistant Professor at Centre for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, India. He received his Ph.D. Degree in Bioengineering from Sathyabama Institute of Science and Technology, Chennai, in 2020. . . . In his PhD thesis, he worked on pharmacogenomics and miRNA- regulated networks in psoriasis, a joint project with Georgetown University, USA, JIPMER, INDIA, CIBA, INDIA, and ILS, INDIA. His thesis illustrated a multi-disciplinary approach by combining computational biophysics and molecular biology machine learning. Also, he had the opportunity to collaborate with international researchers and have publications in reputed international journals in bioinformatics and systems biology simultaneously. He has received the prestigious “Young Scientist Award” from “The Melinda Gates Foundation” for his research abstract on “The Implications of miRNA Dynamics in Infectious Diseases”. To date, he has reviewed more than 200 manuscripts in systems biology. He is currently working on predicting Novel lead molecules and biomarkers using computational techniques to target inflammatory pathways associated with infectious and autoimmune disorders.