On diffusion posterior sampling via sequential Monte Carlo for zero-shot scaffolding of protein motifs

TL;DR

This paper introduces a novel approach using diffusion posterior sampling with sequential Monte Carlo to generate proteins with specific motifs, improving zero-shot scaffolding and enabling complex multi-motif and symmetry-constrained designs.

Contribution

It develops new guidance potentials and adapts SMC-aided diffusion sampling for protein motif scaffolding, demonstrating improved performance over existing methods.

Findings

Reconstruction guidance outperforms replacement methods.

Measurement tilted proposals and twisted targets enhance results.

Successful design of multi-motif and symmetric protein structures.

Abstract

With the advent of diffusion models, new proteins can be generated at an unprecedented rate. The motif scaffolding problem requires steering this generative process to yield proteins with a desirable functional substructure called a motif. While models have been trained to take the motif as conditional input, recent techniques in diffusion posterior sampling can be leveraged as zero-shot alternatives whose approximations can be corrected with sequential Monte Carlo (SMC) algorithms. In this work, we introduce a new set of guidance potentials for describing scaffolding tasks and solve them by adapting SMC-aided diffusion posterior samplers with an unconditional model, Genie, as a prior. In single motif problems, we find that (i) the proposed potentials perform comparably, if not better, than the conventional masking approach, (ii) samplers based on reconstruction guidance outperform their replacement method counterparts, and (iii) measurement tilted proposals and twisted targets improve performance substantially. Furthermore, as a demonstration, we provide solutions to two multi-motif problems by pairing reconstruction guidance with an SE(3)-invariant potential. We also produce designable internally symmetric monomers with a guidance potential for point symmetry constraints. Our code is available at: https://github.com/matsagad/mres-project.