Towards the simulation of biomolecules: optimisation of peptide-capped glycine using FFLUX

Joseph Thacker, Alex Wilson, Zak Hughes, Matthew Burn, Peter I. Maxwell, Paul Popelier

    Research output: Contribution to journalArticlepeer-review


    The optimisation of a peptide-capped glycine using the novel force field FFLUX is presented. FFLUX is a force field based on the machine-learning method kriging and the topological energy partitioning method called Interacting Quantum Atoms. FFLUX has a completely different architecture to that of traditional force fields, avoiding (harmonic) potentials for bonded, valence and torsion angles. In this study, FFLUX performs an optimisation on a glycine molecule and successfully recovers the target density-functional-theory energy with an error of 0.89 ± 0.03 kJ mol−1. It also recovers the structure of the global minimum with a root-mean-squared deviation of 0.05 Å (excluding hydrogen atoms). We also show that the geometry of the intra-molecular hydrogen bond in glycine is recovered accurately.
    Original languageEnglish
    Early online date11 Feb 2018
    Publication statusPublished - 2018


    • FFLUX
    • Machine Learning
    • Quantum chemical topology (QCT)
    • Force Field
    • peptide
    • QTAIM
    • Kriging

    Research Beacons, Institutes and Platforms

    • Manchester Institute of Biotechnology


    Dive into the research topics of 'Towards the simulation of biomolecules: optimisation of peptide-capped glycine using FFLUX'. Together they form a unique fingerprint.

    Cite this