A first-principles exploration of the conformational space of sodiated di-saccharides assisted by semi-empirical methods and neural network potentials

Huu Trong Phan; Pei-Kang Tsou; Po-Jen Hsu; Jer-Lai Kuo

doi:10.1039/d3cp05362h

A first-principles exploration of the conformational space of sodiated di-saccharides assisted by semi-empirical methods and neural network potentials

Phys Chem Chem Phys. 2024 Mar 20;26(12):9556-9567. doi: 10.1039/d3cp05362h.

Authors

Huu Trong Phan^{1

2

3}, Pei-Kang Tsou¹, Po-Jen Hsu¹, Jer-Lai Kuo^{1

2

3

4}

Affiliations

¹ Institute of Atomic and Molecular Sciences, Academia Sinica, Taipei, 10617, Taiwan. jlkuo@pub.iams.sinica.edu.tw.
² Molecular Science and Technology Program, Taiwan International Graduate Program, Academia Sinica, Taipei, 11529, Taiwan.
³ Department of Chemistry, National Tsing Hua University, Hsinchu 30013, Taiwan.
⁴ International Graduate Program of Molecular Science and Technology (NTU-MST), National Taiwan University, Taipei 10617, Taiwan.

PMID: 38456454
DOI: 10.1039/d3cp05362h

Abstract

Previous exploration of the conformational space of sodiated mono-saccharides using a random search algorithm leads to ∼10³ structurally distinct conformers covering an energy range of ∼150 kJ mol^-1. Thus, it is reasonable to expect that the number of distinct conformers for a given disaccharide would be on the order of 10⁶. Efficient identification of distinct conformers at the first-principles level has been demonstrated with the assistance of neural network potential (NNP) with an accuracy of ∼1 kJ mol^-1 compared to DFT. Leveraging a local minima database of neutral and sodiated glucose (Glc), we develop algorithms to systematically explore the conformation landscape of 19 Glc-based sodiated disaccharides. To accelerate the exploration, the NNP method is implemented. The NNP achieves an accuracy of ∼2.3 kJ mol^-1 compared to DFT, offering a comparable quality to that of DFT. Through a multi-model approach integrating DFTB3, NNP and DFT, we can rapidly locate low-energy disaccharide conformers at the first-principles level. The methodology we show here can be used to efficiently explore the potential energy landscape of any di-saccharides when first-principles accuracy is required.