FFLUX: Transferability of polarizable machine-learned electrostatics in peptide chains
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Updated: Mar 6, 2026

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
Published on: January 26, 2024
Timothy L Fletcher1,2, Paul L A Popelier1,2
1Manchester Institute of Biotechnology (MIB), 131 Princess Street, Manchester, M1 7DN, Great Britain.
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.
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