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质子N-乙六胺的形态分析:来自第一原则和机器学习的意想不到的甲基化效应

Kenee Kaiser Suyo Custodio¹, Truc Quyen Vo Thi^1,2, Huu Trong Phan¹

¹Institute of Atomic and Molecular Sciences, Academia Sinica, Taipei, 10617, Taiwan. kcustodio@gate.sinica.edu.tw.

Physical chemistry chemical physics : PCCP

|September 15, 2025

概括

机器学习有效地绘制了碳水化合物的3D结构,揭示了意想不到的甲基化效应. 这种计算方法,将神经网络潜力与DFT结合起来,有助于解决复杂的甘氨酸构造,并通过光谱学识别新的结构.

科学领域:

计算化学计算化学
结构生物学结构生物学
葡萄糖科学 (Glycoscience) 是一种科学.

背景情况:

了解碳水化合物的3D结构对于它们的生物作用至关重要,但在实验上很难.
传统的ab initio方法如DFT是准确的,但对于灵活的分子来说计算成本昂贵.
现有的方法很难完全描述复杂碳水化合物的结构格局.

研究的目的:

开发高效的机器学习 (ML) 方法来确定碳水化合物3D结构.
为了研究质子化N-乙六胺 (HexNAcH+) 和它们的甲基化衍生物的构造性行为.
创建一个全面的,第一原则准确的相关单糖的结构数据库.

主要方法:

开发了基于机器学习的方法,利用本地最小值数据库来有效地定位低能耗符合性器件.
训练有素的神经网络潜力 (NNP) 模型模仿密度函数理论 (DFT) 潜能表面.
使用DFT重新优化候选结构以获得高精度,应用于HexNAcH+和甲基化形式.

主要成果:

创建了32个单糖结构的数据库,其准确度达到第一原则.
确定了对HexNAcH+形状的意想不到的甲基化效应,挑战了目前的理解.
模拟的振动光谱与IRMPD实验数据有很好的相关性,这表明解决尚未探索的结构的潜力.

结论:

通过ML加速的DFT方法可以有效地阐明复杂的碳水化合物3D结构.
甲基化显著影响HexNAcH+的形状偏好,需要修订模型.
红外多光子解离 (IRMPD) 光谱是一种可行的方法,用于表征多种不同的HexNAcH + 符合性.

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