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

  • Joseph Thacker (Creator)
  • Alex Wilson (Creator)
  • Zak Hughes (Creator)
  • Matthew J Burn (Creator)
  • Peter Maxwell (Creator)
  • Paul Popelier (Creator)
  • Tony Howell (Creator)



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<i>.</i>89 ± 0.03 kJ mol<sup>−1</sup>. It also recovers the structure of the global minimum with a <i>root</i>-<i>mean</i>-<i>squared deviation</i> of 0<i>.</i>05 Å (excluding hydrogen atoms). We also show that the geometry of the intra-molecular hydrogen bond in glycine is recovered accurately.
Date made available12 Feb 2018

Research Beacons, Institutes and Platforms

  • Manchester Institute of Biotechnology


  • Machine learning
  • Quantum chemical topology (QCT)
  • Force field
  • Peptide
  • Kriging

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