Papers › Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem

Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem

8 Jun 2022arXiv:2206.04119archive 2025-07-28

Brian L. Trippe, Jason Yim, Doug Tischer, David Baker, Tamara Broderick, Regina Barzilay, Tommi Jaakkola

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.

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flatten_cfg blt2114/ProtDiff_SMCDiff/experiments/torch_train_diffusion.py official repository ran · our draft was wrong no licence file found · pointer only · dd06ac4c46b0b80b · report
residual_resample blt2114/protdiff_smcdiff/inpainting/particle_filter.py official repository ran · our draft was wrong no licence file found · pointer only · 4fa016d543e7ded1 · report
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Tasks

Graph Neural Network

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Methods

ALIGNDiffusion

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