Diffusion probabilistic modelling of protein backbones in 3D for the motif-scaffolding problem | Brian Trippe & Jason Yim

[DISCLAIMER] - For the full visual experience, we recommend you tune in through our YouTube channel to see the presented slides.

If you enjoyed this talk, consider joining the Molecular Modeling and Drug Discovery (M2D2) talks live.

Also consider joining the M2D2 Slack

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.

Full Paper

Speakers: Brian Trippe and Jason Yim

Twitter Prudencio   

Twitter Therence

Twitter Cas  

Twitter Valence Discovery