title: FFLUX: Transferability of polarizable machine-learned electrostatics in peptide chains.
abstract: The fully polarizable, multipolar, and atomistic force field protein FFLUX is being built from machine learning (i.e., kriging) models, each of which predicts an atomic property. Each atom of a given protein geometry needs to be assigned such a kriging model. Such a knowledgeable atom needs to be informed about a sufficiently large environment around it. The resulting complexity can be tackled by collecting the 20 natural amino acids into a few groups. Using substituted deca-alanines, we present the proof-of-concept that a given atom's charge can be modeled by a few kriging models only. © 2017 Wiley Periodicals, Inc.
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.identifier: 0000-0001-9053-1363
authors.initials: PL
authors.lastname: Popelier
mesh_terms: D000596:Amino Acids / Q000737:chemistry; D000069550:Machine Learning; D008958:Models, Molecular; D008962:Models, Theoretical; D010455:Peptides / Q000737:chemistry; D055585:Physical Phenomena; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D055672:Static Electricity; D013816:Thermodynamics
chemical_list: D000596:Amino Acids; D010455:Peptides; D011506:Proteins
keywords: QTAIM; atomic charge; force field; machine learning; peptides; quantum chemical topology; transferability