Conformational analysis of protonated <i>N</i>-acetyl hexosamines: unexpected methylation effects from first-principles and machine learning

Elucidating the intricate 3D conformational behavior of inherently flexible carbohydrates is crucial for understanding their biological functions, yet it remains experimentally challenging. While traditional <i>ab initio</i> computational approaches, such as density functional theory (DFT), can sample low-energy conformers, they are often resource-intensive. In this work, we developed and employed machine learning-driven methods that efficiently locate low-energy candidate structures by leveraging previously established local minima databases. These candidates were then reoptimized using a target <i>ab initio</i> method, specifically DFT, by training neural network potential (NNP) models to mimic the DFT potential energy surface. We successfully applied this approach to elucidate the 3D structures of protonated <i>N</i>-acetyl hexosamines (HexNAcH⁺) and their methylated forms, resulting in a comprehensive structural database of 32 monosaccharides with first-principles accuracy. Although our findings generally align with existing literature, the results revealed unexpected methylation effects that challenge the current understanding of HexNAcH⁺ conformational behavior. More importantly, based on the experimental vibrational spectra obtained <i>via</i> infrared multiple photon dissociation (IRMPD) from literature (for GlcNAcH⁺, GalNAcH⁺, and ManNAcH⁺) and our simulated spectra of all 16 HexNAcH⁺ structures, we find reasonable expectation that the remaining experimentally unexplored HexNAcH⁺ can be resolved <i>via</i> IRMPD.