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

If you enjoyed this talk, consider joining the Molecular Modeling and Drug Discovery (M2D2) talks live: https://m2d2.io/talks/m2d2/about/ Also consider joining the M2D2 Slack: https://m2d2group.slack.com/join/shar... 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   ~ 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