Multipolar Electrostatic Energy Prediction for all 20 Natural Amino Acids Using Kriging Machine Learning

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• 作者：Timothy L. Fletcher ; Paul L. A. Popelier
• 刊名：Journal of Chemical Theory and Computation
• 出版年：2016
• 出版时间：June 14, 2016
• 年：2016
• 卷：12
• 期：6
• 页码：2742-2751
• 全文大小：571K
• 年卷期：0
• ISSN：1549-9626

文摘

A machine learning method called kriging is applied to the set of all 20 naturally occurring amino acids. Kriging models are built that predict electrostatic multipole moments for all topological atoms in any amino acid based on molecular geometry only. These models then predict molecular electrostatic interaction energies. On the basis of 200 unseen test geometries for each amino acid, no amino acid shows a mean prediction error above 5.3 kJ mol^–1, while the lowest error observed is 2.8 kJ mol^–1. The mean error across the entire set is only 4.2 kJ mol^–1 (or 1 kcal mol^–1). Charged systems are created by protonating or deprotonating selected amino acids, and these show no significant deviation in prediction error over their neutral counterparts. Similarly, the proposed methodology can also handle amino acids with aromatic side chains, without the need for modification. Thus, we present a generic method capable of accurately capturing multipolar polarizable electrostatics in amino acids.