Topology, Energy Landscapes and Folding Kinetics of RNA Hairpins
- 1University of Notre Dame
Abstract
Many RNAs occupy multiple inter-converting structures in order to perform key biological functions. Characterizing RNA conformational ensembles is therefore critical to illuminating the mechanisms by which RNAs fold, unfold, and undergo precise structural rearrangements in response to cellular signals. However, resolving the equilibrium populations and folding kinetics of RNA ensembles is nontrivial due to the intrinsic ruggedness of RNA conformational landscapes and wide range of timescales spanned by RNA structural dynamics. To address these challenges, we present a de novo hierarchical multiscale computational framework that integrates diverse secondary and tertiary structure generation, adaptive unbiased molecular dynamics, a generative AI model termed latent thermodynamic flows (LaTF), and Markov state modeling to efficiently map global RNA folding landscapes and associated dynamics. Applied to the GCAA tetraloop, HIV-TAR stem-loop, and microROSE RNA thermometer, this framework yields ~ 1.3 ms of cumulative sampling and resolves temperature-dependent conformational landscapes comprising numerous native-like, misfolded, partially folded, and unfolded metastable states. Across all three RNA hairpins, temperature substantially reshapes landscape ruggedness, metastable state populations, and folding mechanisms. Our predicted ensembles show overall agreement with key static and dynamic biophysical observables, including Nuclear Magnetic Resonance (NMR)-resolved structures, Nuclear Overhauser Effect (NOE)-derived distance restraints, and residual dipolar couplings (RDCs). Together, these results establish a general physics-informed AI approach for accurately modeling RNA conformational heterogeneity, equilibrium thermodynamics, and long-timescale kinetics.
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BibTeX
@misc{verma2026topology,
title = {{Topology, Energy Landscapes and Folding Kinetics of RNA Hairpins}},
author = {A. R. Verma and P. Tiwary and Y. Qiu},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.21.753325},
url = {https://www.biorxiv.org/content/10.64898/2026.09.21.753325v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - Topology, Energy Landscapes and Folding Kinetics of RNA Hairpins
AU - A. R. Verma
AU - P. Tiwary
AU - Y. Qiu
PY - 2026
DA - 2026/09/21
PB - bioRxiv
DO - 10.64898/2026.09.21.753325
UR - https://www.biorxiv.org/content/10.64898/2026.09.21.753325v1
AB - Many RNAs occupy multiple inter-converting structures in order to perform key biological functions. Characterizing RNA conformational ensembles is therefore critical to illuminating the mechanisms by which RNAs fold, unfold, and undergo precise structural rearrangements in response to cellular signals. However, resolving the equilibrium populations and folding kinetics of RNA ensembles is nontrivial due to the intrinsic ruggedness of RNA conformational landscapes and wide range of timescales spanned by RNA structural dynamics. To address these challenges, we present a de novo hierarchical multiscale computational framework that integrates diverse secondary and tertiary structure generation, adaptive unbiased molecular dynamics, a generative AI model termed latent thermodynamic flows (LaTF), and Markov state modeling to efficiently map global RNA folding landscapes and associated dynamics. Applied to the GCAA tetraloop, HIV-TAR stem-loop, and microROSE RNA thermometer, this framework yields ~ 1.3 ms of cumulative sampling and resolves temperature-dependent conformational landscapes comprising numerous native-like, misfolded, partially folded, and unfolded metastable states. Across all three RNA hairpins, temperature substantially reshapes landscape ruggedness, metastable state populations, and folding mechanisms. Our predicted ensembles show overall agreement with key static and dynamic biophysical observables, including Nuclear Magnetic Resonance (NMR)-resolved structures, Nuclear Overhauser Effect (NOE)-derived distance restraints, and residual dipolar couplings (RDCs). Together, these results establish a general physics-informed AI approach for accurately modeling RNA conformational heterogeneity, equilibrium thermodynamics, and long-timescale kinetics.
ER -