Lecture 11A
Protein structure prediction
Foundations
Date: Mar 25, 2025
This foundational lecture introduces students to the concept and significance of protein structure prediction. Students will explore why predicting protein structures from sequences is essential, identify key challenges, and understand major computational strategies such as homology modeling, threading, contact maps, and machine learning approaches, including AlphaFold.
Learning objectives¶
After today, you should have a better understanding of:
- The significance and challenges of protein structure prediction.
- Principles behind homology modeling and threading.
- The concept and interpretation of contact maps.
- How coevolutionary signals inform structural predictions.
- Why machine learning is transformative in structure prediction.
Supplementary material¶
Relevant content and readings for today's lecture.
- Protein structure prediction: Schwede, Schwede, Peitsch - Chapter 1; Tripathi, Dubey - Chapter 23
- Homology modeling: Coumar - Chapter 8; Bahar, Jernigan, Dill - Chapter 11
- Profile-sequence alignment: Altschul et al. (1997)
- HMM-sequence alignment: Krogh et al. (1994); Eddy (2009)
- HMM-HMM alignment: Söding (2004); Remmert et al. (2012)
- Threading: Schwede, Schwede, Peitsch - Chapter 2; Coumar - Chapter 8; Bahar, Jernigan, Dill - Chapter 11
- Coevolution: Bahar, Jernigan, Dill - Chapter 11
- Scoring functions: Schwede, Schwede, Peitsch - Chapter 3
Presentation¶
- View: slides.com/aalexmmaldonado/biosc1540-l11a
- Live link: slides.com/d/nZdKKIA/live
- Download: biosc1540-l11a.pdf