“Predicting Protein Structure with Meta AI: Practical Guide

About this course

Unlock the power of AI-driven bioinformatics by mastering Predicting Protein Structure  using Meta AI! This comprehensive, beginner-friendly course will teach you how to predict, analyze, and visualize protein structures using Meta AI’s ESMFold without requiring expensive lab equipment or heavy computational resources.Protein structure plays a critical role in understanding biological functions, designing drugs, and advancing research in biotechnology and healthcare. Traditional methods like X-ray crystallography and NMR are time-consuming and costly. With AI breakthroughs from Meta AI, you can now predict protein structures quickly and accurately to accelerate your research and project workflows.In this course, you will start with the fundamentals of protein structure, learning about primary, secondary, tertiary, and quaternary structures and why they are important in biology and drug design. You will then dive into the exciting world of AI-based protein structure prediction, understanding how tools like Meta AI’s ESMFold are revolutionizing structural biology.Through hands-on tutorials using Google Colab, you will learn how to:Fetch protein sequences from databases like UniProt,Predict their structures using ESMFold,Visualize and interpret these structures using tools like PyMOL, Chimera, and Mol*.You will work on practical mini-projects, such as predicting the structure of a disease-related protein and comparing predicted structures with experimental data, gaining insights into drug discovery and protein engineering applications.This course also covers the limitations of AI in structure prediction, best practices, and ethical considerations, equipping you to apply these skills responsibly in your research or professional projects.Whether you are a student, researcher, bioinformatics enthusiast, or a professional i

Course objectives

• Familiarize students with essential bioinformatics concepts and tools
• Equip students to apply AI in structural biology responsibly