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

Previous exploration of the conformational space of sodiated mono-saccharides using a random search algorithm leads to ∼10 structurally distinct conformers covering an energy range of ∼150 kJ mol. Thus, it is reasonable to expect that the number of distinct conformers for a given disaccharide would be on the order of 10. 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 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 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.