Prediction of Intramolecular Polarization of Aromatic Amino Acids Using Kriging Machine Learning
Prediction of Intramolecular Polarization of Aromatic Amino Acids Using Kriging Machine Learning
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DOI：
10.1021/ct500416k
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发表时间：
2014-09-01
影响因子：
5.5
通讯作者：
Popelier, Paul L. A.
中科院分区：
文献类型：
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作者：
Fletcher, Timothy L.;Davie, Stuart J.;Popelier, Paul L. A.
目前的计算能力使建模极化的新方法成为可能。在这里，我们展示了机器学习方法克里金准确地捕捉到拓扑原子的电子密度对周围原子位置变化的响应方式。在组氨酸、苯丙氨酸、色氨酸和酪氨酸四种芳香族氨基酸上证明了该方法的成功。描述了一种改变训练集大小以在保持准确性的同时极大地减少训练时间的新技术，并将其应用于每种氨基酸。每一种氨基酸的几何形状都被罗曼钱德兰地图中所有局部能量极小值的正常振动模式所扭曲。这些几何图形然后被用来训练克里格法模型。将克立格模型对以前未见的几何构型预测的总静电能量与真实能量进行比较，得出组氨酸、苯丙氨酸、色氨酸和酪氨酸的平均绝对误差分别为2.9、5.1、4.2和2.8kJ mol(1)。
Present computing power enables novel ways of modeling polarization. Here we show that the machine learning method kriging accurately captures the way the electron density of a topological atom responds to a change in the positions of the surrounding atoms. The success of this method is demonstrated on the four aromatic amino acids histidine, phenylalanine, tryptophan, and tyrosine. A new technique of varying training set sizes to vastly reduce training times while maintaining accuracy is described and applied to each amino acid. Each amino acid has its geometry distorted via normal modes of vibration over all local energy minima in the Ramachandran map. These geometries are then used to train the kriging models. Total electrostatic energies predicted by the kriging models for previously unseen geometries are compared to the true energies, yielding mean absolute errors of 2.9, 5.1, 4.2, and 2.8 kJ mol(1) for histidine, phenylalanine, tryptophan, and tyrosine, respectively.