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Lecture 11B
Protein structure prediction

Methodology

Date: Mar 27, 2025

DRAFT

This page is a work in progress and is subject to change at any moment.

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.

Supplementary material

Relevant content for today's lecture.

  • None! Just the lecture.

DRAFT

This page is a work in progress and is subject to change at any moment.

Building on the foundations from the previous lecture, this session focuses on the practical aspects of setting up and running MD simulations. We'll walk through the steps involved in preparing a system for simulation.

Learning objectives

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

  1. Explain why DHFR is a promising drug target.
  2. Select and prepare a protein structure for molecular simulations.
  3. Explain the importance of approximating molecular environments.
  4. Describe periodic boundary conditions and their role in MD simulations.
  5. Explain the role of force field selection and topology generation.
  6. Outline the process of energy minimization and its significance.

Supplementary material

Relevant content for today's lecture if you are interested.

This final lecture in the MD series focuses on the analysis and interpretation of MD simulation data. We'll explore common analysis techniques and how to extract meaningful biological insights from simulation trajectories.

Learning objectives

After today, you should better understand:

  1. Molecular ensembles and their relevance.
  2. Maintaining thermodynamic equilibrium.
  3. Relaxation and production MD simulations.
  4. RMSD and RMSF as conformational changes and flexibility metrics.
  5. Relationship between probability and energy in simulations.

Supplementary material

Relevant content for today's lecture.

  • Any scientific literature.