Diffusion probabilistic modelling of protein backbones in 3D | Jason Yim & Brian Trippe

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Title: Diffusion probabilistic modeling of protein backbones
in 3D for the motif-scaffolding problem
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.
Speakers: Brian Trippe - / brianltrippe
Jason Yim - / json_yim
Twitter Prudencio: / tossouprudencio
Twitter Therence: / therence_mtl
Twitter Cas: / cas_wognum
Twitter Valence Discovery: / valence_ai
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Chapters:
00:00 - Intro
02:18 - Computational protein design workflow
10:57 - Diffusion models on protein backbones
13:13 - Forward diffusion and reverse denoising
20:32 - Why do diffusion models work?
21:29 - Why do diffusion for proteins?
23:59 - Model details
33:48 - Unconditional sampling
37:38 - Model limitations and failure modes
39:06 - Sampling SMCDiff
50:21 - Motif-scaffolding case studies and failure case
53:41 - Related work and conclusion
58:23 - Q+A