FFLUX: Transferability of Polarizable Machine-learned Electrostatics in Peptide Chains

Research output: Contribution to journalArticle

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.

Bibliographical metadata

Original languageEnglish
Pages (from-to)1005–1014
Number of pages10
JournalJournal of Computational Chemistry
Volume38
Issue number13
Early online date10 Mar 2017
DOIs
StatePublished - 15 May 2017