Computational Protein Structure Prediction and Design
3.0
creditsAverage Course Rating
The 2024 Nobel Prize in Chemistry was awarded for protein structure prediction and design. With recent research, it is now possible to blindly predict most protein structures and to design many new structures from scratch. This class will introduce the fundamental concepts in protein structure, biophysics, optimization and deep learning that have enabled the breakthroughs in computational structure prediction and design. Problems covered will include protein folding and docking, design of proteins and interfaces, and physical and deep learning methods. Class will consist of lectures and hands-on computer workshops. Students will learn to use molecular visualization tools, write programs with the PyRosetta protein structure software suite, use deep learning programs (e.g. AlphaFold2, ESM, IgLM, ProteinMPNN, RFDiffusion), review recent literature, and complete a project.
Fall 2014
Professor: Jeffrey Gray
Students appreciated the opportunity to gain relevant skills from this course. Students also praised the expertise and effective teaching of the instructor. Students found that the greatest difficulty with the course was the ability to gain the necessary programming skills, and they thought that additional guidance in this area would have improved the class. Students also thought the course could have benefitted from better guidance for homework assignments. Students thought it would be valuable for potential future participants to know that prior experience with programming in Python was especially useful for this class.