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Lecture 12
Protein structure prediction

Date: Oct 10, 2024

We'll explore how amino acid sequences are transformed into three-dimensional structures through computational methods. The session will cover various approaches, from traditional homology modeling to cutting-edge deep learning techniques like AlphaFold. We'll examine the principles underlying these methods, their applications, and their impact on biological research.

Learning objectives

What you should be able to do after today's lecture:

  1. Why are we learning about protein structure prediction?
  2. Identify what makes structure prediction challenging.
  3. Explain homology modeling.
  4. Know when to use threading instead of homology modeling.
  5. Interpret a contact map for protein structures.
  6. Comprehend how coevolution provides structural insights.
  7. Explain why ML models are dominate protein structure prediction.

Readings

Relevant content for today's lecture.

  • None! Just the lecture.

Presentation