Description

AlphaFold & AI for Protein Structure Prediction

Explore the intersection of Artificial Intelligence, Deep Learning, Bioinformatics, and Protein Science through a focused course on AI-driven protein structure prediction.

The course begins with molecular biology and structural bioinformatics fundamentals before introducing Transformers, attention mechanisms, Graph Neural Networks (GNNs), protein embeddings, and the AlphaFold ecosystem.

Students progress from understanding AlphaFold fundamentals and architecture to practical inference, visualization, advanced Protein AI concepts, biomedical applications, benchmarking, ethics, and a final research-oriented capstone project.

What You’ll Learn

✓ Understand DNA, RNA, proteins, amino acids, and protein folding fundamentals.

✓ Work with structural bioinformatics concepts including PDB, UniProt, MSA, BLAST, and HHblits.

✓ Understand phylogenetics and biological sequence-analysis workflows.

✓ Learn deep-learning concepts including Transformers, attention, GNNs, and protein embeddings.

✓ Explore the history and evolution of AlphaFold 1, AlphaFold 2, and AlphaFold 3.

✓ Understand key AlphaFold 2 concepts including Evoformer, IPA, recycling, and confidence metrics.

✓ Learn practical AlphaFold installation, inference, visualization, and optimization.

✓ Explore AlphaFold-Multimer, protein language models, and OpenFold.

✓ Understand applications of Protein AI in biomedical research and drug discovery.

✓ Complete benchmarking, ethics discussions, and a research-focused final capstone.