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COS597N MLSB Week 11 Pre-lecture Questions
Readings:
1. Yim, Trippe, Bortoli, Mathieu et al.
SE(3) diffusion model with application to protein backbone generation.
ICML 2023.
2. Watson, Juergens, Bennett et al.
De novo design of protein structure and function with RFdiffusion.
Nature 2023.
3. Ingraham et al.
Illuminating protein space with a programmable generative model.
bioxRiv 2022.
Due before class on 11/16.
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Q1: (Recap) What is “designability” in the context of protein design? What is a potential problem in using methods like Alphafold2 refolding to measure designability?
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Q2:
This week’s papers each describe a diffusion-based protein generative model p_theta(x). What specifically is x, i.e. what is the data distribution, and how are proteins represented?
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Q3:
Choose one of the following algorithmic contributions of the Chroma model and describe how it works: 1) polymer diffusion process, 2) random graph neural network architecture, or 3) low-temperature sampling algorithm.
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Q4:
What application of protein design are you most excited about?
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