Conformational heterogeneity and vibrational signatures of dAMP in aqueous solution revealed by high-accuracy machine-learning potentials
Abstract
Nucleotides are the fundamental building blocks of life, yet many aspects of their behavior and interactions in water remain poorly understood. Here, we investigate the structural and vibrational properties of 2′ 2 ′ -deoxyadenosine 5′ 5 ′ -monophosphate (dAMP) in aqueous solution using a machine-learning approach supported by IR spectroscopy experiments. We develop highly accurate neural network potentials through an active-learning protocol that systematically samples the free-energy landscape at the GGA level and efficiently transfers this information to higher levels of theory. This potential accurately describes dAMP in water over nanosecond timescales. By explicitly learning dipole moments, the simulations capture key spectral signatures while providing molecular-level insight into their origin. Analysis of the free-energy landscape and vibrational spectra indicates that dAMP in aqueous solution populates a mixture of conformations. These findings highlight the importance of conformational heterogeneity in nucleotides and establish a general framework for high-accuracy simulations of flexible biomolecules in solution.
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BibTeX
@article{stevens2026conformational,
title = {{Conformational heterogeneity and vibrational signatures of dAMP in aqueous solution revealed by high-accuracy machine-learning potentials}},
author = {Laurie Stevens and Riccardo Martina and Indrani Bhattacharya and Asesh Bera and Rajib Kumar Mitra and Alberta Ferrarini and Marialore Sulpizi},
journal = {Communications Chemistry},
year = {2026},
month = oct,
doi = {10.1038/s42004-026-02237-7},
url = {https://www.nature.com/articles/s42004-026-02237-7},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Conformational heterogeneity and vibrational signatures of dAMP in aqueous solution revealed by high-accuracy machine-learning potentials
AU - Laurie Stevens
AU - Riccardo Martina
AU - Indrani Bhattacharya
AU - Asesh Bera
AU - Rajib Kumar Mitra
AU - Alberta Ferrarini
AU - Marialore Sulpizi
PY - 2026
DA - 2026/10/09
JO - Communications Chemistry
DO - 10.1038/s42004-026-02237-7
UR - https://www.nature.com/articles/s42004-026-02237-7
AB - Nucleotides are the fundamental building blocks of life, yet many aspects of their behavior and interactions in water remain poorly understood. Here, we investigate the structural and vibrational properties of 2^′ 2 ′ -deoxyadenosine 5^′ 5 ′ -monophosphate (dAMP) in aqueous solution using a machine-learning approach supported by IR spectroscopy experiments. We develop highly accurate neural network potentials through an active-learning protocol that systematically samples the free-energy landscape at the GGA level and efficiently transfers this information to higher levels of theory. This potential accurately describes dAMP in water over nanosecond timescales. By explicitly learning dipole moments, the simulations capture key spectral signatures while providing molecular-level insight into their origin. Analysis of the free-energy landscape and vibrational spectra indicates that dAMP in aqueous solution populates a mixture of conformations. These findings highlight the importance of conformational heterogeneity in nucleotides and establish a general framework for high-accuracy simulations of flexible biomolecules in solution.
SN - 2399-3669
ER -