Determination of the Dipole Moment Variation Upon Excitation in the Chromophore of Green Fluorescent Protein From Molecular Dynamic Trajectories with QM/MM Potentials Using Machine Learning Methods

Abstract Quantum and molecular mechanics (QM/MM) potentials are used to calculate molecular dynamics trajectories for the EYFP protein of the green fluorescent protein family. Machine learning models are constructed to establish the relationship between the geometric parameters of the chromophore in the frame of its trajectory and the properties of its electronic excitation. It is shown that it is not enough to use only bridging bonds between the phenyl and imidazolidone fragments of the chromophore as a geometric parameter, and at least two more neighboring bonds must be added to the model. The proposed models allow determination of the dipole moment variation upon excitation with an average error of 0.11 a.u.