{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T20:33:55Z","timestamp":1787085235077,"version":"build-2736575974"},"posted":{"date-parts":[[2026,8,15]]},"group-title":"Biophysics","reference-count":97,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,8,15]],"date-time":"2026-08-15T00:00:00Z","timestamp":1786752000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"award":["W911NF-22-2-0246"],"award-info":[{"award-number":["W911NF-22-2-0246"]}],"id":[{"id":"https:\/\/ror.org\/05epdh915","id-type":"ROR","asserted-by":"publisher"}]},{"award":["R35GM142745"],"award-info":[{"award-number":["R35GM142745"]}],"id":[{"id":"https:\/\/ror.org\/01cwqze88","id-type":"ROR","asserted-by":"publisher"}]},{"award":["OISE-2435360"],"award-info":[{"award-number":["OISE-2435360"]}],"id":[{"id":"https:\/\/ror.org\/021nxhr62","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,8,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>Proteins are critical biomolecular machines that populate ensembles of interconverting conformations. Many biological processes depend on transitions between metastable states. Although molecular dynamics (MD) simulations provide a physically grounded route to characterize these motions, routine sampling of large-scale conformational transitions remains computationally demanding. Recent advances in protein structure prediction have created new opportunities for ensemble generation, but many existing approaches require noising inputs, task-specific training, supervised fitting on extensive MD data, or experimentally-informed restraints. Here, we introduce Pi-Ensemble (Predicting Interpolated Ensemble), a sequence-guided framework for generating protein conformational ensembles interpolating between two structural anchor states. Unlike previous methods, Pi-Ensemble alternately leverages inverse-folding and structure-prediction models to propose intermediate conformations between known protein states, generating diverse ensembles without additional training. We evaluate Pi-Ensemble across diverse protein systems, including enzymes, transporters, receptors, and benchmark cases with reference MD simulations or experimental Double Electron-Electron Resonance (DEER) data. Pi-Ensemble recovers physically plausible intermediate conformations, captures transition pathways observed in large-scale MD simulations, and generates structures consistent with experimental distance distributions. Furthermore, Pi-Ensemble-generated conformations provide effective starting seeds for parallel MD simulations, improving conformational exploration and accelerating convergence relative to simulations initiated only from endpoint structures. These results establish sequence-guided structural interpolation as a practical strategy for probing protein conformational landscapes. By generating diverse and physically reasonable conformational proposals without long-timescale MD or model retraining, Pi-Ensemble provides an extensible framework for studying protein flexibility, guiding adaptive sampling, and accelerating mechanistic investigations of protein function.<\/jats:p>","DOI":"10.64898\/2026.08.12.744498","type":"posted-content","created":{"date-parts":[[2026,8,16]],"date-time":"2026-08-16T05:40:12Z","timestamp":1786858812000},"source":"Crossref","is-referenced-by-count":0,"title":["Pi-Ensemble: Sequence-guided generation of interpolated protein conformational ensembles"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8718-8940","authenticated-orcid":false,"given":"Hassan","family":"Nadeem","sequence":"first","affiliation":[{"name":"Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3833-5872","authenticated-orcid":false,"given":"Diego E.","family":"Kleiman","sequence":"additional","affiliation":[{"name":"Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4224-5246","authenticated-orcid":false,"given":"Yuming","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA"},{"name":"Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6251-024X","authenticated-orcid":false,"given":"Andrew D. B.","family":"Leakey","sequence":"additional","affiliation":[{"name":"Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA"},{"name":"Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4079-5381","authenticated-orcid":false,"given":"Diwakar","family":"Shukla","sequence":"additional","affiliation":[{"name":"Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA"},{"name":"Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA"},{"name":"Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA"},{"name":"Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA"},{"name":"Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2026081813200596000_2026.08.12.744498v1.1","doi-asserted-by":"publisher","DOI":"10.1038\/S41586-021-03819-2"},{"key":"2026081813200596000_2026.08.12.744498v1.2","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-024-02272-z"},{"key":"2026081813200596000_2026.08.12.744498v1.3","doi-asserted-by":"publisher","DOI":"10.1038\/S41586-024-07487-W"},{"key":"2026081813200596000_2026.08.12.744498v1.4","doi-asserted-by":"publisher","DOI":"10.1101\/2024.10.10.615955"},{"key":"2026081813200596000_2026.08.12.744498v1.5","doi-asserted-by":"publisher","DOI":"10.1101\/2024.11.19.624167"},{"key":"2026081813200596000_2026.08.12.744498v1.6","doi-asserted-by":"publisher","DOI":"10.1101\/2025.06.14.659707"},{"key":"2026081813200596000_2026.08.12.744498v1.7","doi-asserted-by":"publisher","DOI":"10.1126\/science.ade2574"},{"key":"2026081813200596000_2026.08.12.744498v1.8","doi-asserted-by":"publisher","DOI":"10.1126\/science.ads0018"},{"key":"2026081813200596000_2026.08.12.744498v1.9","doi-asserted-by":"publisher","DOI":"10.1016\/S0022-2836(65)80285-6"},{"key":"2026081813200596000_2026.08.12.744498v1.10","doi-asserted-by":"publisher","DOI":"10.1021\/j100223a002"},{"key":"2026081813200596000_2026.08.12.744498v1.11","doi-asserted-by":"publisher","DOI":"10.1021\/ja202726y"},{"key":"2026081813200596000_2026.08.12.744498v1.12","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbc.2023.105456"},{"key":"2026081813200596000_2026.08.12.744498v1.13","doi-asserted-by":"publisher","DOI":"10.1016\/j.sbi.2023.102768"},{"key":"2026081813200596000_2026.08.12.744498v1.14","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2025520118"},{"key":"2026081813200596000_2026.08.12.744498v1.15","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1038\/s42003-024-06291-6","article-title":"The Arabidopsis AtSWEET13 transporter discriminates sugars by selective facial and positional substrate recognition","volume":"7","year":"2024","journal-title":"Commun. 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