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• Diffusion probabilistic modelling of protein backbones in 3D

Tutorial on Denoising Diffusion-based Generative Modeling

• Visit this page for the slides and more information:https://cvpr2022-tutorial-diffusion-models.github.io/

Foundations and Applications

This video presents our tutorial on Denoising Diffusion-based Generative Modeling: Foundations and Applications. This tutorial was originally presented at CVPR 2022 in New Orleans and it received a lot of interest from the research community. After the conference, we decided to record the tutorial again and broadly share it with the research community. We hope that this video can help you start your journey in diffusion models.

Diffusion Probabilistic Modelling of Protein Backbones in 3D

• Speakers: Brian Trippe - https://twitter.com/brianltrippe
• Jason Yim - https://twitter.com/json_yim

Abstract

The construction of a scaffold structure that supports a desired motif, conferring protein function, shows promise for the design of vaccines and enzymes. But a general solution to this motif-scaffolding problem remains open. Current machine-learning techniques for scaffold design are either limited to unrealistically small scaffolds (up to length 20) or struggle to produce multiple diverse scaffolds. We propose to learn a distribution over diverse and longer protein backbone structures via an E(3)-equivariant graph neural network. We develop SMCDiff to efficiently sample scaffolds from this distribution conditioned on a given motif; our algorithm is the first to theoretically guarantee conditional samples from a diffusion model in the large-compute limit. We evaluate our designed backbones by how well they align with AlphaFold2-predicted structures. We show that our method can (1) sample scaffolds up to 80 residues and (2) achieve structurally diverse scaffolds for a fixed motif.