Prediction of conformationally dependent atomic multipole moments in carbohydrates.
Prediction of conformationally dependent atomic multipole moments in carbohydrates.
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DOI：
10.1002/jcc.24215
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发表时间：
2015-12-15
影响因子：
3
通讯作者：
Popelier, Paul L. A.
中科院分区：
文献类型：
--
作者：
Cardamone, Salvatore;Popelier, Paul L. A.
关键词：
碳水化合物的构象灵活性在计算化学领域具有挑战性。这种灵活性导致电子密度的变化，从而导致原子多极矩的波动。量子化学拓扑（QCT）允许“分子中的原子”的划分，从而将电子密度定位到有限的原子域，从而允许对原子多极矩进行明确的评估。通过选择一个化学系统的物理上真实的构象的集合，可以计算构象空间中定义点上的各种多极矩。机器学习方法kriging的后续实现提供了分析函数的评估，该函数在这些点之间平滑地插值。这允许预测原子多极矩在构象空间的新点，没有训练，但在预测范围内。在这项工作中，我们证明了碳水化合物红糖和蔗糖适用于上述方法。我们研究了当训练集合包含多个能量极小值及其在构象空间中的环境时，克里格模型如何响应。此外，我们评估了随着训练集合规模的增加，模型预测能力的增益。我们相信这种方法在碳水化合物领域是全新的。对于600个中等规模的训练集，超过90%的外部测试配置对于开链的总（预测）静电能量（相对于从头算）的误差最大为1 kJ mol - 1，而对于环的误差超过90%，最大为4 kJ mol - 1。©2015 Wiley期刊公司
The conformational flexibility of carbohydrates is challenging within the field of computational chemistry. This flexibility causes the electron density to change, which leads to fluctuating atomic multipole moments. Quantum Chemical Topology (QCT) allows for the partitioning of an “atom in a molecule,” thus localizing electron density to finite atomic domains, which permits the unambiguous evaluation of atomic multipole moments. By selecting an ensemble of physically realistic conformers of a chemical system, one evaluates the various multipole moments at defined points in configuration space. The subsequent implementation of the machine learning method kriging delivers the evaluation of an analytical function, which smoothly interpolates between these points. This allows for the prediction of atomic multipole moments at new points in conformational space, not trained for but within prediction range. In this work, we demonstrate that the carbohydrates erythrose and threose are amenable to the above methodology. We investigate how kriging models respond when the training ensemble incorporating multiple energy minima and their environment in conformational space. Additionally, we evaluate the gains in predictive capacity of our models as the size of the training ensemble increases. We believe this approach to be entirely novel within the field of carbohydrates. For a modest training set size of 600, more than 90% of the external test configurations have an error in the total (predicted) electrostatic energy (relative to ab initio) of maximum 1 kJ mol−1 for open chains and just over 90% an error of maximum 4 kJ mol−1 for rings. © 2015 Wiley Periodicals, Inc.
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