title: Multipolar Electrostatic Energy Prediction for all 20 Natural Amino Acids Using Kriging Machine Learning.
abstract: 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.
authors.affiliation: Manchester Institute of Biotechnology (MIB) , 131 Princess Street, Manchester M1 7DN, Great Britain.|School of Chemistry, University of Manchester , Oxford Road, Manchester M13 9PL, Great Britain.
authors.forename: Timothy L
authors.initials: TL
authors.lastname: Fletcher
authors.affiliation: Manchester Institute of Biotechnology (MIB) , 131 Princess Street, Manchester M1 7DN, Great Britain.|School of Chemistry, University of Manchester , Oxford Road, Manchester M13 9PL, Great Britain.
authors.forename: Paul L A
authors.initials: PL
authors.lastname: Popelier
mesh_terms: D000596:Amino Acids / Q000737:chemistry*; D000069550:Machine Learning; D008958:Models, Molecular; D011789:Quantum Theory; D055672:Static Electricity; D013816:Thermodynamics
chemical_list: D000596:Amino Acids