{ "id": "AIBD-0043", "title": "AI-based structure prediction doubled structural coverage of the human nuclear pore complex scaffold", "summary": "Researchers combined AI predictions with electron-microscopy maps to model more than 90% of the human nuclear pore scaffold.", "date_observed": "2021-10-27", "date_basis": "source publication", "date_precision": "day", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Researchers at EMBL, the Max Planck Institute of Biophysics, and collaborating institutions used AlphaFold and RoseTTAfold to predict structures of nuclear-pore proteins. They used ColabFold to model complexes and fitted the predictions to electron-microscopy maps. Their model covered more than 90% of the scaffold, roughly twice the coverage of the previous model. The authors checked the predictions against existing and new experimental structures. 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That is a material advance for those groups' research programs." }, { "group": "societal", "depth": 2, "reason": "A 70-MDa atomically resolved model covering over 90% of the scaffold is a durable public resource for nuclear transport research. The source reports no direct patient or population benefit." } ], "ai_role": { "status": "assessed", "statement": "AI structure prediction generated the nucleoporin and subcomplex models that formed the input to integrative modeling. The authors state that the predictions were benchmarked against existing and new experimental structures. The resulting model was validated against earlier and new experimental data, so the AI output was independently checked rather than accepted as given." }, "evidence": [ { "kind": "preprint", "citation": "Mosalaganti S, Obarska-Kosinska A, Siggel M, et al. (2021). Artificial intelligence reveals nuclear pore complexity. bioRxiv 2021.10.26.465776, posted 27 October 2021.", "title": "Artificial intelligence reveals nuclear pore complexity", "url": "https://www.biorxiv.org/content/10.1101/2021.10.26.465776v1.full", "date": "2021-10-27", "written_by": "deployer", "quote": null }, { "kind": "peer-reviewed study", "citation": "Mosalaganti S, Obarska-Kosinska A, Siggel M, et al. (2022). AI-based structure prediction empowers integrative structural analysis of human nuclear pores. Science.", "title": "AI-based structure prediction empowers integrative structural analysis of human nuclear pores", "url": "https://doi.org/10.1126/science.abm9506", "date": "2022-06-10", "written_by": "deployer", "quote": "we used artificial intelligence (AI)-based prediction to generate an extensive repertoire of structural models of human NUPs and their subcomplexes" }, { "kind": "report", "citation": "Google DeepMind. “AlphaFold.” Timeline of a breakthrough, entries for 2020 and 2021.", "title": "AlphaFold", "url": "https://deepmind.google/science/alphafold/", "date": null, "written_by": "makers", "quote": null }, { "kind": "other", "citation": "Baek, Minkyung, Frank DiMaio, Ivan Anishchenko, Justas Dauparas, Sergey Ovchinnikov, Jue Wang, and David Baker. “RoseTTAFold: The First Release of RoseTTAFold.” Version 1.0.0. Zenodo, July 3, 2021. doi:10.5281/zenodo.5068265.", "title": "RoseTTAFold: The first release of RoseTTAFold", "url": "https://zenodo.org/records/5068265", "date": "2021-07-03", "written_by": "makers", "quote": null } ], "replication": { "produced_by": [ "Cryo-electron tomographic maps of the human nuclear pore complex in two states", "AI structure predictions for nucleoporins and subcomplexes, benchmarked against experimental structures", "Integrative modeling by groups holding prior structural data on the complex" ], "must_hold": [ "Cryo-electron tomographic maps of sufficient resolution for fitting", "Nucleoporin sequences and homologues available for prediction", "Independent experimental structures available for benchmarking" ], "echoes": [] }, "paired_incident": null, "headline": "AlphaFold models cover over 90% of the human NPC scaffold", "metric": "covers more than 90% of the NPC scaffold, roughly doubling previous structural coverage", "comparator": { "against": "the previous structural model of the human NPC scaffold", "before": "16 Nups and 35 MDa (46% of the scaffold) of the previous model", "after": "25 out of ~30 Nups, and account for 70 MDa of the molecular weight of the NPC (>90% of the scaffold molecular weight)", "basis": "measured", "quote": "The resulting models (Fig. 1) include 25 out of ~30 Nups, and account for 70 MDa of the molecular weight of the NPC (>90% of the scaffold molecular weight) in comparison to 16 Nups and 35 MDa (46% of the scaffold) of the previous model, and largely account for the EM density observed in the constricted and dilated states.", "url": "https://www.biorxiv.org/content/10.1101/2021.10.26.465776v1.full" }, "later": { "status": "unknown", "date": null, "note": "No later development found.", "quote": null, "url": null }, "value": null, "developer_country": null, "deployer_country": "Germany", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-20", "change": "Removed the unverified AlphaFold/DeepMind attribution from the title, system, developer and the account's last sentence in favor of the source's own wording “AI-based structure prediction”, because the abstract names no tool and the full text could not be read (science.org returned HTTP 403 and the DESY and Goethe University Frankfurt repository copies were unreadable), and added Hamburg to the location because the Kosinski group's affiliations (EMBL Hamburg, Centre for Structural Systems Biology) are in Hamburg.", "source": "Mosalaganti S, et al. 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