{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/on-diffusion-posterior-sampling-via","title":"On Diffusion Posterior Sampling via Sequential Monte Carlo for Zero-Shot Scaffolding of Protein Motifs","arxiv_id":"2412.05788","date":"2024-12-08","proceeding":null,"authors":["James Matthew Young","O. Deniz Akyildiz"],"abstract":"With the advent of diffusion models, new proteins can be generated at an unprecedented rate. The \\textit{motif scaffolding problem} requires steering this generative process to yield proteins with a desirable functional substructure -- 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 to describe and solve scaffolding tasks by adapting SMC-aided diffusion posterior samplers with an unconditional model, Genie, acting as a prior. Against established benchmarks, we successfully scaffold several single-motif and multi-motif problems. The latter is possible by pairing reconstruction guidance with $\\mathrm{SE}(3)$-invariant potentials. In the single-motif case, we find these potentials perform comparably to the conventional masking approach and that reconstruction guidance outperforms replacement methods when aided with SMC. We additionally consider a guidance potential for point symmetry constraints and produce designable internally symmetric monomers with our setup. Overall, this work highlights the capabilities and areas for improvement of zero-shot posterior samplers in motif scaffolding tasks. Code is available at: https://github.com/matsagad/mres-project","url_abs":"https://arxiv.org/abs/2412.05788v1","url_pdf":"https://arxiv.org/pdf/2412.05788v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"on-diffusion-posterior-sampling-via","repo_url":"https://github.com/matsagad/mres-project","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"},{"method_slug":"set","method_name":"SET"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2412.05788","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.05788"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/matsagad/mres-project","reach":{"status":"ok"}}],"summary":{"unverified":3},"by_repo_kind":{"official":{"samples":3,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":3,"samples":[{"code_sha256_prefix":"313461580ae8dd43","entry":"compute_frenet_frames","repo":"matsagad/mres-project","repo_kind":"official","path":"protein/frames.py","file_url":"https://github.com/matsagad/mres-project/blob/HEAD/protein/frames.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"313461580ae8dd43"}},{"code_sha256_prefix":"5cf5f5c2a7684114","entry":"rmsd_kabsch","repo":"matsagad/mres-project","repo_kind":"official","path":"protein/alignment.py","file_url":"https://github.com/matsagad/mres-project/blob/HEAD/protein/alignment.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"5cf5f5c2a7684114"}},{"code_sha256_prefix":"15090528e855779d","entry":"rmsd_quaternion","repo":"matsagad/mres-project","repo_kind":"official","path":"protein/alignment.py","file_url":"https://github.com/matsagad/mres-project/blob/HEAD/protein/alignment.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"15090528e855779d"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}