Learning Sequence Conservation from Functional Constraints in Intrinsically Disordered Regions

Links evolutionary conservation with structural and functional signatures in intrinsically disordered regions. Integrates sequence alignments, molecular simulations, and functional annotations to create a curated database enabling AI models of regulatory function.

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Author Jeetain Mittal
Last Updated September 10, 2026, 18:10 (UTC)
Published August 5, 2026, 18:11 (UTC)
Citation Jeetain Mittal 2026. Learning Sequence Conservation from Functional Constraints in Intrinsically Disordered Regions. CyVerse Data Commons.
Description Links evolutionary conservation with structural and functional signatures in intrinsically disordered regions. Integrates sequence alignments, molecular simulations, and functional annotations to create a curated database enabling AI models of regulatory function.
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 intrinsically disordered regions, sequence conservation, molecular dynamics, functional annotation, regulatory proteins
de_created_date 2026-03-04T17:26:20Z
de_modified_date 2026-07-28T22:07:47Z