Computational Protein Structure Prediction

Protein structure prediction has become a critical skill in modern biotechnology, pharmaceutical research, and bioinformatics, driven by advances in artificial intelligence tools. Learners are introduced to the principles of protein structure and function and the use of artificial intelligence for protein structure prediction. Students will learn how to retrieve protein sequences from biological databases and use biomolecular visualization software to model three-dimensional protein structures predicted by computational modelling software. Students will gain a foundational understanding of protein structure prediction with AlphaFold before learning about current state-of-the-art software. Course topics include review of the different levels of protein structure, mechanisms and principles driving protein folding, principles of artificial intelligence, model architecture for protein structure prediction software, methods for assessing model quality (including confidence metrics such as pLDDT and Predicted Aligned Error (PAE)), and practical experience with biomolecular visualization tools and structure prediction tools. Through handson exercises and a final report, students develop the skills needed to interpret predicted protein structures and evaluate their potential biological functions. Students who complete this micro-course gain a foundation for advanced studies in protein engineering, biotechnology, and computational biology.