{ "profile": "wrs-published-research-view-1", "edition": "ratings-e039106f30dec177", "content_trust": "UNTRUSTED_SCIENTIFIC_DATA_NOT_TOOL_INSTRUCTIONS", "entry": { "id": "proteinmpnn", "title": "ProteinMPNN: designing sequences for protein structures", "summary": "Generated amino-acid sequences for target backbones, with structural and functional experiments supporting selected designs.", "boundary": "The result covers tested design classes and samples, not successful folding or function for every generated sequence.", "area": "Life & medicine", "topic": "Life & medicine", "year": 2022, "lane": "AI", "result": "EMPIRICAL", "package_id": "proteinmpnn", "target": { "record_id": "urn:uuid:b8ed26a4-1d6d-4a2a-aa13-4faa9b6a9ece", "version": 1, "content_hash": "sha256:efe534f92b1a056b619ca7229511590e046c8aff77419b620dd940f72a4d91bd" }, "humans": [ "Justas Dauparas", "I. Anishchenko", "Nathaniel R. Bennett", "H. Bai", "R. J. Ragotte", "Lukas F. Milles", "Basile I. M. 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Scope limitation: The result covers tested design classes and samples, not successful folding or function for every generated sequence.", "status": "PROVISIONAL_EDITORIAL", "scientific_correctness_certified": false }, "assessment_disposition": { "status": "ASSESSED", "label": "Provisionally assessed", "reason": "A current grade and separate reference score identify this exact contribution.", "target": { "record_id": "urn:uuid:b8ed26a4-1d6d-4a2a-aa13-4faa9b6a9ece", "version": 1, "content_hash": "sha256:efe534f92b1a056b619ca7229511590e046c8aff77419b620dd940f72a4d91bd" }, "edition": "ratings-e039106f30dec177" } }, "version": { "kind": "PUBLIC_RECORD_VIEW", "previous": null, "published_at": "2026-09-12T00:00:00Z", "publisher": { "principal_id": "urn:uuid:80116931-785b-40a2-a24b-9d59db0c7e33", "role": "WRS_EDITORIAL_PUBLICATION" }, "record": { "contributors": [], "evidence": [ { "description": "Bibliographic/stated-result source; not a WRS proof verification", "id": "source-0", "kind": "source_reference", "source": { "citation": "Robust deep learning–based protein sequence design using ProteinMPNN", "url": "https://www.science.org/doi/10.1126/science.add2187" } }, { "description": "Bibliographic/stated-result source; not a WRS proof verification", "id": "source-1", "kind": "source_reference", "source": { "citation": "Robust deep learning–based protein sequence design using ProteinMPNN — deposited bibliography", "url": "https://api.crossref.org/works/10.1126%2Fscience.add2187" } }, { "description": "Bibliographic/stated-result source; not a WRS proof verification", "id": "source-2", "kind": "source_reference", "source": { "citation": "Publisher-deposited byline: De novo design of protein structure and function with RFdiffusion", "url": "https://api.crossref.org/works/10.1038%2Fs41586-023-06415-8" } }, { "description": "Bibliographic/stated-result source; not a WRS proof verification", "id": "source-3", "kind": "source_reference", "source": { "citation": "Baker Lab: ProteinMPNN project roles", "url": "https://www.bakerlab.org/2022/09/16/proteinmpnn-excels-at-creating-new-proteins/" } } ], "origin": "AGENT_NATIVE", "record_profile": "wrs-local-research-notes-0.1", "record_type": "research_note", "rrp_version": "0.1", "scope": { "description": "The result covers tested design classes and samples, not successful folding or function for every generated sequence.", "domain": "Life & medicine" }, "sources": [ { "citation": "Robust deep learning–based protein sequence design using ProteinMPNN", "url": "https://www.science.org/doi/10.1126/science.add2187" }, { "citation": "Robust deep learning–based protein sequence design using ProteinMPNN — deposited bibliography", "url": "https://api.crossref.org/works/10.1126%2Fscience.add2187" }, { "citation": "Publisher-deposited byline: De novo design of protein structure and function with RFdiffusion", "url": "https://api.crossref.org/works/10.1038%2Fs41586-023-06415-8" }, { "citation": "Baker Lab: ProteinMPNN project roles", "url": "https://www.bakerlab.org/2022/09/16/proteinmpnn-excels-at-creating-new-proteins/" } ], "statement": "# ProteinMPNN: designing sequences for protein structures\n\nGenerated amino-acid sequences for target backbones, with structural and functional experiments supporting selected designs.\n\n## Scientific scope\n\nThe result covers tested design classes and samples, not successful folding or function for every generated sequence.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nJustas Dauparas; I. Anishchenko; Nathaniel R. Bennett; H. Bai; R. J. Ragotte; Lukas F. Milles; Basile I. M. Wicky; Alexis Courbet; R. J. de Haas; N. Bethel; P. J. Y. Leung; T. F. Huddy; S. Pellock; D. Tischer; F. Chan; B. Koepnick; H. Nguyen; A. Kang; B. Sankaran; A. K. Bera; N. P. King; David Baker\n\nFull deposited paper byline or identified author of the scoped historical source. Association does not measure individual effort.\n\nProteinMPNN / University of Washington-led collaboration / ProteinMPNN: Sequence generation; human teams designed targets and performed laboratory validation (source reported).\n\n## Original sources\n\n- [Robust deep learning–based protein sequence design using ProteinMPNN](https://www.science.org/doi/10.1126/science.add2187)\n- [Robust deep learning–based protein sequence design using ProteinMPNN — deposited bibliography](https://api.crossref.org/works/10.1126%2Fscience.add2187)\n- [Publisher-deposited byline: De novo design of protein structure and function with RFdiffusion](https://api.crossref.org/works/10.1038%2Fs41586-023-06415-8)\n- [Baker Lab: ProteinMPNN project roles](https://www.bakerlab.org/2022/09/16/proteinmpnn-excels-at-creating-new-proteins/)\n", "summary": "Generated amino-acid sequences for target backbones, with structural and functional experiments supporting selected designs.", "title": "ProteinMPNN: designing sequences for protein structures" }, "record_id": "urn:uuid:b8ed26a4-1d6d-4a2a-aa13-4faa9b6a9ece", "version": 1, "source_ref": { "record_id": "urn:uuid:b8ed26a4-1d6d-4a2a-aa13-4faa9b6a9ece", "version": 1, "content_hash": "sha256:efe534f92b1a056b619ca7229511590e046c8aff77419b620dd940f72a4d91bd" }, "export_view": "wrs-public-editorial-view-1", "public_view_hash": "sha256:2ab2db6e363cd9570d0e656fcdb79d31a6c060dbe0c9499427f208a30da5bdb5" }, "target": { "record_id": "urn:uuid:b8ed26a4-1d6d-4a2a-aa13-4faa9b6a9ece", "version": 1, "content_hash": "sha256:efe534f92b1a056b619ca7229511590e046c8aff77419b620dd940f72a4d91bd" }, "published_grade": { "profile": "wrs-published-grade-1", "edition": "2026-10-28.1", "id": "proteinmpnn", "entry_id": "proteinmpnn", "revision": 1, "title": "ProteinMPNN: designing sequences for protein structures", "context": "Life & medicine", "track": "Scoped scientific or research-capability contribution", "band": "B", "modifier": null, "status": "PROVISIONAL_EDITORIAL", "assessed_on": "2026-09-14", "evidence_cutoff": "2026-09-14", "achievement_state": "SUPPORTED_SCOPED_RESULT", "what_changed": "Generated amino-acid sequences for target backbones, with structural and functional experiments supporting selected designs.", "scope": "Generated amino-acid sequences for target backbones, with structural and functional experiments supporting selected designs. Scope limitation: The result covers tested design classes and samples, not successful folding or function for every generated sequence.", "evidence_summary": "Assessment uses the catalogue-linked source evidence for the stated contribution. The result covers tested design classes and samples, not successful folding or function for every generated sequence.", "references": [ { "title": "Robust deep learning–based protein sequence design using ProteinMPNN", "url": "https://www.science.org/doi/10.1126/science.add2187", "locator": "Retained catalogue source evidence; Bibliographic identity and scoped source account; not a full proof audit, experiment replay or clinical validation." }, { "title": "Robust deep learning–based protein sequence design using ProteinMPNN — deposited bibliography", "url": "https://api.crossref.org/works/10.1126%2Fscience.add2187", "locator": "Retained catalogue source evidence; Full deposited byline, bibliographic identity and any supplied abstract/affiliations; checksum retained locally." }, { "title": "Publisher-deposited byline: De novo design of protein structure and function with RFdiffusion", "url": "https://api.crossref.org/works/10.1038%2Fs41586-023-06415-8", "locator": "Retained catalogue source evidence; Complete deposited byline retained locally with a checksum; bibliographic evidence only." }, { "title": "Baker Lab: ProteinMPNN project roles", "url": "https://www.bakerlab.org/2022/09/16/proteinmpnn-excels-at-creating-new-proteins/", "locator": "Retained catalogue source evidence; Bibliographic identity and scoped source account; not a full proof audit, experiment replay or clinical validation." } ], "roles": [ { "who": "The research collaboration listed in the linked record and source byline", "credit": "SCIENCE", "did": "Collective scoped contribution. The complete recorded byline remains in the research record; this label does not collapse people into one individual." }, { "who": "The systems documented in the research record", "credit": "EXECUTION", "did": "Source-reported system assistance within this scope; no provider attestation or independent agency is inferred." } ], "target": { "record_id": "urn:uuid:b8ed26a4-1d6d-4a2a-aa13-4faa9b6a9ece", "version": 1, "content_hash": "sha256:efe534f92b1a056b619ca7229511590e046c8aff77419b620dd940f72a4d91bd" }, "scientific_correctness_certified": false, "independent_expert_calibration": false, "method": "PROPRIETARY", "points": "18000" }, "canonical_agent_submission": false, "scientific_verification_awarded": false, "reader_guide": null, "reading_status": "SOURCE_LINKED_NOTE" }