Refining LigandPoses in RNA/Ligand Complexes of PharmaceuticalRelevance: A Perspective by QM/MM Simulations and NMR Measurements

Predicting the binding poses of ligands targeting RNAs is challenging. Here, we propose that using first-principles quantum mechanics/molecular mechanics (QM/MM) simulations, which incorporate automatically polarization effects, can help refine the structural determinants of ligand/RNA complexes in aqueous solution. In fact, recent advances in massively parallel computer architectures (such as exascale machines), combined with the power of machine learning, are greatly expanding the domain of applicability of these types of notoriously expensive simulations. We corroborate this proposal by carrying out a QM/MM-based study on a ligand targeting CAG repeat-RNA, involved in Huntington’s disease. The calculations indeed show a clear improvement in the ligand binding properties, and they are consistent with the NMR measurements, also performed here. Thus, this type of approach may be useful for practical applications in the design of ligands targeting RNA in the near future.

Publication Details

Journal
Figshare
Published
2025-02-10
DOI
https://doi.org/10.1021/acs.jpclett.4c03456.s001
Primary Topic
Mycorrhizal Fungi and Plant Interactions
Type
article
Field-Weighted Citation Impact
0.00
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article

Refining LigandPoses in RNA/Ligand Complexes of PharmaceuticalRelevance: A Perspective by QM/MM Simulations and NMR Measurements

Figshare
Mycorrhizal Fungi and Plant Interactions
article

Refining LigandPoses in RNA/Ligand Complexes of PharmaceuticalRelevance: A Perspective by QM/MM Simulations and NMR Measurements

article en

Abstract

Predicting the binding poses of ligands targeting RNAs is challenging. Here, we propose that using first-principles quantum mechanics/molecular mechanics (QM/MM) simulations, which incorporate automatically polarization effects, can help refine the structural determinants of ligand/RNA complexes in aqueous solution. In fact, recent advances in massively parallel computer architectures (such as exascale machines), combined with the power of machine learning, are greatly expanding the domain of applicability of these types of notoriously expensive simulations. We corroborate this proposal by carrying out a QM/MM-based study on a ligand targeting CAG repeat-RNA, involved in Huntington’s disease. The calculations indeed show a clear improvement in the ligand binding properties, and they are consistent with the NMR measurements, also performed here. Thus, this type of approach may be useful for practical applications in the design of ligands targeting RNA in the near future.

Figshare
Openalex Percentile: Top 52%
Mycorrhizal Fungi and Plant Interactions
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