Capturing Hu Antigen R Domain Closure Through Supervised Molecular Dynamics Simulations

Hu antigen R (HuR), an RNA-binding protein implicated in cancer, inflammation, and neurodegenerative disorders, represents a challenging target for drug discovery due to its large solvent-exposed RNA-binding interface and pronounced conformational flexibility. HuR contains tandem RNA recognition motifs (RRM1 and RRM2) that undergo substantial rearrangements between an apo open state and an RNA-bound closed conformation. Here, we investigated both states using molecular dynamics (MD) simulations. While the RNA-bound complex maintained a stable closed architecture, the apo form displayed extensive interdomain motions, highlighting the intrinsic flexibility of HuR. Because conventional MD simulations failed to capture the open-to-closed transition, we developed a novel application of Supervised MD (SuMD), previously employed for intermolecular recognition processes, to characterize HuR intramolecular domain closure. Using a multistep supervision protocol based on selected interdomain residue pairs, SuMD reproduced a closed conformation resembling the experimental RNA-bound structure and identified a plausible reclosure pathway. Per-residue interaction analyses revealed key contributions from the RRM1-RRM2 linker region, particularly Arg97, together with Arg136 and Arg147 of RRM2, consistent with mutagenesis data. These findings extend the applicability of SuMD to intramolecular conformational transitions, provide mechanistic insight into HuR domain closure, and offer a structural framework for identifying druggable conformational states for future HuR-targeted drug discovery.

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Journal
ChemMedChem
Published
2026-10-04
DOI
https://doi.org/10.1002/cmdc.70523
Primary Topic
Protein Structure and Dynamics
Type
article
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article

Capturing Hu Antigen R Domain Closure Through Supervised Molecular Dynamics Simulations

Stefano Moro, Veronica Salmaso, Andrea Dodaro, Gianluca Novello et al.
ChemMedChem
Protein Structure and Dynamics
article

Capturing Hu Antigen R Domain Closure Through Supervised Molecular Dynamics Simulations

Stefano Moro, Veronica Salmaso, Andrea Dodaro, Gianluca Novello, Silvia Menin, Mattia Sturlese, Chiara Cavastracci Strascia
article en

Abstract

Hu antigen R (HuR), an RNA-binding protein implicated in cancer, inflammation, and neurodegenerative disorders, represents a challenging target for drug discovery due to its large solvent-exposed RNA-binding interface and pronounced conformational flexibility. HuR contains tandem RNA recognition motifs (RRM1 and RRM2) that undergo substantial rearrangements between an apo open state and an RNA-bound closed conformation. Here, we investigated both states using molecular dynamics (MD) simulations. While the RNA-bound complex maintained a stable closed architecture, the apo form displayed extensive interdomain motions, highlighting the intrinsic flexibility of HuR. Because conventional MD simulations failed to capture the open-to-closed transition, we developed a novel application of Supervised MD (SuMD), previously employed for intermolecular recognition processes, to characterize HuR intramolecular domain closure. Using a multistep supervision protocol based on selected interdomain residue pairs, SuMD reproduced a closed conformation resembling the experimental RNA-bound structure and identified a plausible reclosure pathway. Per-residue interaction analyses revealed key contributions from the RRM1-RRM2 linker region, particularly Arg97, together with Arg136 and Arg147 of RRM2, consistent with mutagenesis data. These findings extend the applicability of SuMD to intramolecular conformational transitions, provide mechanistic insight into HuR domain closure, and offer a structural framework for identifying druggable conformational states for future HuR-targeted drug discovery.

ChemMedChemVol. 21(19)
University of Padua (IT)
Openalex Percentile: Top 21%
Protein Structure and Dynamics
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Capturing Hu Antigen R Domain Closure Through Supervised Molecular Dynamics Simulations — Stefano Moro, Veronica Salmaso, et al. · ChemMedChem (2026) | TGRS Research Map | TGRS