📚 Syllabus & Course Curriculum
AI & Machine Learning in Healthcare
Module-by-module breakdown of Prediction of Protein Structure Using AlphaFold: An Artificial Intelligence (AI) Program, from foundations to a certified capstone project.
Background
• Levinthal's paradox, folding principles and why prediction was hard
• Experimental methods and where they leave gaps
• CASP and the evidence behind claimed accuracy
Method
• Multiple sequence alignments and co-evolutionary signal
• Evoformer and structure module at a conceptual level
• Why MSA depth largely determines prediction quality
Running
• AlphaFold, ColabFold and the AlphaFold Database compared
• Compute, memory and runtime expectations
• Predicting complexes and the additional uncertainty this introduces
Interpretation
• pLDDT and PAE: what each measures and how to use them together
• Low-confidence regions and their frequent correspondence to disorder
• A single predicted conformation is not a description of dynamics
Application
• Docking and virtual screening against predicted models, and the caveats
• Mutation analysis and interpreting effects on stability
• When experimental structure determination remains necessary
Earn government-registered certification in Prediction of Protein Structure Using AlphaFold: An Artificial Intelligence (AI) Program
e-Certificate and e-Marksheet issued on successful completion.
