Emergent Order Parameters of Allostery: An AI Framework for Predicting and Engineering Protein Regulation
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Additional Info
| Field | Value |
|---|---|
| Author | Banu Ozkan |
| Last Updated | September 10, 2026, 18:10 (UTC) |
| Published | August 5, 2026, 18:10 (UTC) |
| Citation | Banu Ozkan 2026. Emergent Order Parameters of Allostery: An AI Framework for Predicting and Engineering Protein Regulation. CyVerse Data Commons. |
| Description | Integrates simulations, deep mutational scanning, and evolutionary data to quantify dynamic coupling in proteins. Develops allosteric order parameters and trains models predicting how mutations rewire long-range protein control mechanisms. |
| PublicationYear | 2026 |
| Publisher | CyVerse Data Commons |
| Rights | This material is based upon work supported by the U.S. National Science Foundation under Award No. #2335029. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the U.S. National Science Foundation. |
| Subject | allostery, protein regulation, machine learning, molecular dynamics, allosteric order parameters, deep mutational scanning |
| de_created_date | 2026-03-04T17:25:49Z |
| de_modified_date | 2026-07-28T22:07:47Z |