{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T08:20:16Z","timestamp":1788164416413,"version":"build-2803163510"},"reference-count":48,"publisher":"American Chemical Society (ACS)","issue":"16","license":[{"start":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T00:00:00Z","timestamp":1785456000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T00:00:00Z","timestamp":1785456000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T00:00:00Z","timestamp":1785456000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-045"}],"funder":[{"DOI":"10.13039\/501100018537","name":"National Science and Technology Major Project","doi-asserted-by":"publisher","award":["2024ZD0531200"],"award-info":[{"award-number":["2024ZD0531200"]}],"id":[{"id":"10.13039\/501100018537","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/na","name":"Innovative Research Team of High-Level Local Universities in Shanghai","doi-asserted-by":"publisher","award":["NA"],"award-info":[{"award-number":["NA"]}],"id":[{"id":"10.13039\/na","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,8,24]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Intrinsically disordered proteins (IDPs) drive diverse cellular processes through broad conformational ensembles, but experimental characterization of these ensembles is sparse and high-quality training data is scarce, holding back artificial intelligence (AI) approaches to ensemble prediction. Single-bead coarse-grained (CG) force fields such as CALVADOS, parametrized directly against experimental observables, currently provide a more reliable route to disordered ensembles than direct AI prediction. CG sampling lacks atomistic resolution and must be paired with backmapping; the atomistic information recoverable from this two-step process is shaped jointly by the CG representation and the backmapping algorithm, and the interplay between these contributions is not well characterized. We benchmarked CODLAD, a recently published latent-diffusion backmapping pipeline, on 23 Protein Ensemble Database (PED) systems using a dual-input design: the same architecture receives either PED-reference C\u03b1 coordinates or independently sampled CALVADOS C\u03b1 trajectories. This design separates paired reconstruction error, measurable for PED+CODLAD, from unpaired CG-input-associated ensemble deviations, measurable for CG+CODLAD at the distribution level. Reconstruction from PED conformers achieved 0.56 \u00b1 0.08 \u00c5 backbone root-mean-square deviation and preserved local geometry. Reconstruction from CALVADOS-sampled C\u03b1 trajectories preserved the C\u03b1 framework and bond geometry, while ensemble-level deviations relative to PED were localized to proline backbone geometry and cooperative secondary structure, both consistent with information not carried by an unconstrained single-bead representation. The benchmark quantifies which atomistic properties can be recovered after projecting a CG ensemble into all-atom space and identifies improved CG geometric encoding and sequence-conditioned AI priors as the directions for further progress.<\/jats:p>","DOI":"10.1021\/acs.jcim.6c01701","type":"journal-article","created":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T14:03:23Z","timestamp":1785852203000},"page":"10287-10301","source":"Crossref","is-referenced-by-count":0,"title":["Local Geometry\nRecovers but Cooperative Structure\nDoes Not: Residue-Resolved Limits of All-Atom Reconstruction from\nSingle-Bead Coarse-Grained Disordered Protein Ensembles"],"prefix":"10.1021","volume":"66","author":[{"given":"Jianxiang","family":"Huang","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University School of Medicine , , ,","place":["Shanghai, China, 200336"]},{"name":"Ningxia Medical University , , ,","place":["Yinchuan, China, 750004"]},{"name":"Shanghai Jiao Tong University School of Medicine , , ,","place":["Shanghai, China, 200025"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Qiao","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University School of Medicine , , ,","place":["Shanghai, China, 200025"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Liu","sequence":"additional","affiliation":[{"name":"Ningxia Medical University , , ,","place":["Yinchuan, China, 750004"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zongtao","family":"Chai","sequence":"additional","affiliation":[{"name":"Shanghai Geriatric Medical Center , , ,","place":["Shanghai, China, 201104"]},{"name":"Fudan University , , ,","place":["Shanghai, China, 200032"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1334-6292","authenticated-orcid":true,"given":"Shaoyong","family":"Lu","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University School of Medicine , , ,","place":["Shanghai, China, 200336"]},{"name":"Ningxia Medical University , , ,","place":["Yinchuan, China, 750004"]},{"name":"Shanghai Jiao Tong University School of Medicine , , ,","place":["Shanghai, China, 200025"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,7,31]]},"reference":[{"key":"2026083104123744000_cit1","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","article-title":"Highly accurate protein\nstructure prediction with AlphaFold","volume":"596","author":"Jumper","year":"2021","journal-title":"Nature"},{"key":"2026083104123744000_cit2","doi-asserted-by":"publisher","first-page":"897","DOI":"10.1038\/s41586-023-07004-5","article-title":"Conformational\nensembles of the human intrinsically disordered proteome","volume":"626","author":"Tesei","year":"2024","journal-title":"Nature"},{"key":"2026083104123744000_cit3","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1038\/s41586-026-10141-2","article-title":"Accurate\npredictions of disordered protein ensembles with STARLING","volume":"652","author":"Novak","year":"2026","journal-title":"Nature"},{"key":"2026083104123744000_cit4","doi-asserted-by":"publisher","DOI":"10.1126\/science.adv9817","article-title":"Scalable emulation\nof protein equilibrium ensembles with generative deep learning","volume":"389","author":"Lewis","year":"2025","journal-title":"Science"},{"key":"2026083104123744000_cit5","doi-asserted-by":"publisher","first-page":"6503","DOI":"10.1021\/acs.chemrev.5b00590","article-title":"Protein Allostery and Conformational Dynamics","volume":"116","author":"Guo","year":"2016","journal-title":"Chem. 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Sci."},{"key":"2026083104123744000_cit30","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1012144","article-title":"Transferable deep generative\nmodeling of intrinsically disordered\nprotein conformations","volume":"20","author":"Janson","year":"2024","journal-title":"PLoS Comput. Biol."},{"key":"2026083104123744000_cit31","doi-asserted-by":"publisher","first-page":"624","DOI":"10.1021\/acs.jctc.5c01364","article-title":"Constraint\nDecoupled Latent Diffusion for Protein Backmapping","volume":"22","author":"Han","year":"2026","journal-title":"J. Chem. 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Theory\nComput."},{"key":"2026083104123744000_cit45","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1126\/science.ade2574","article-title":"Evolutionary-scale\nprediction of atomic-level protein structure with\na language model","volume":"379","author":"Lin","year":"2023","journal-title":"Science"},{"key":"2026083104123744000_cit46","doi-asserted-by":"publisher","first-page":"7112","DOI":"10.1109\/TPAMI.2021.3095381","article-title":"ProtTrans: Toward Understanding\nthe Language of Life Through Self-Supervised Learning","volume":"44","author":"Elnaggar","year":"2022","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2026083104123744000_cit47","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1186\/s12915-023-01803-y","article-title":"DisoFLAG: accurate\nprediction of protein intrinsic disorder and its\nfunctions using graph-based interaction protein language model","volume":"22","author":"Pang","year":"2024","journal-title":"BMC Biol."},{"key":"2026083104123744000_cit48","doi-asserted-by":"publisher","first-page":"na","DOI":"10.48550\/arXiv.2303.01569","article-title":"Chemically\ntransferable generative\nbackmapping of coarse-grained proteins","author":"Yang","year":"2023","journal-title":"arXiv:2303.01569\n[cs.LG]"}],"container-title":["Journal of Chemical Information\nand Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.jcim.6c01701","content-type":"application\/pdf","content-version":"vor","intended-application":"unspecified"},{"URL":"https:\/\/pubs.acs.org\/jcisd8\/article-pdf\/66\/16\/10287\/66346924\/acs.jcim.6c01701.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/pubs.acs.org\/jcisd8\/article-pdf\/66\/16\/10287\/66346924\/acs.jcim.6c01701.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T08:12:46Z","timestamp":1788163966000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/jcisd8\/article\/66\/16\/10287\/5237657\/Local-Geometry-Recovers-but-Cooperative-Structure"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,31]]},"references-count":48,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2026,7,31]]},"published-print":{"date-parts":[[2026,8,24]]}},"URL":"https:\/\/doi.org\/10.1021\/acs.jcim.6c01701","relation":{},"ISSN":["1549-9596","1549-960X"],"issn-type":[{"value":"1549-9596","type":"print"},{"value":"1549-960X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,31]]}}}