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

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arxiv 2206.04119 v2 pith:L33YPM2K submitted 2022-06-08 q-bio.BM cs.LGstat.ML

Brian L. Trippe , Jason Yim , Doug Tischer , David Baker , Tamara Broderick , Regina Barzilay , Tommi Jaakkola This is my paper · ORCID
classification q-bio.BMcs.LGstat.ML
keywords scaffoldsdiversemotifproteinbackbonesdesigndiffusiondistribution
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Forward citations

Cited by 5 Pith papers

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  4. Provable diffusion-based posterior sampling for linear inverse problems via DDIM

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  5. Energy-Based Flow Matching for Generating 3D Molecular Structure

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