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

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.本研究报道了利用新型力场FFLUX对肽封端甘氨酸进行结构优化的工作。FFLUX是一种基于机器学习方法克里金法（kriging）以及被称为相互作用量子原子（Interacting Quantum Atoms）的拓扑能量分配方法的力场。FFLUX的架构与传统力场完全迥异，其摒弃了用于键合、价层及扭转角的（简谐）势函数。在本研究中，FFLUX对甘氨酸分子完成结构优化，并以0.89 ± 0.03 kJ·mol⁻¹的误差成功复现了目标密度泛函理论（density-functional-theory）能量。同时，其还准确复现了全局最低能量结构，除氢原子外的均方根偏差（root-mean-squared deviation）为0.05 Å。此外，本研究还证实，FFLUX能够精准复现甘氨酸分子内氢键（intra-molecular hydrogen bond）的几何构型。