This paper presents a physics-informed machine learning approach to the derivation of a bottom-up coarse-grained model of the SARS-CoV-2 spike glycoprotein from all-atomic molecular dynamics simulations. The machine learning procedure employs a force-matching scheme in the optimization of interaction parameters, where the force-matching scheme is combined in methodology with the initialization of the interaction parameters by the traditional iterative Boltzmann inversion method. The force-matched machine learning procedure is constructed based on two physics-informed layers: one is the Harmonic layer consisting of bond, angle, and dihedral terms as bonded potentials;the other is the Lennard-Jones layer consisting of the non-bonded Lennard-Jones potential. Coarse-grained validation simulations are performed with the learned parameters to test the derived bottom-up coarse-grained model. The simulations are able to reach the microsecond time scale with stability. The physics-informed learning approach yields simulation speeds nearly 40,000 times faster than conventional all-atomic simulations while maintaining comparable simulation accuracy. Additionally, through examination of the non-bonded Lennard-Jones parameters and the radial distribution function analysis, the learning approach matches pairwise distances of the ground-truth data with greater accuracy than the conventional iterative approach method.
Beyond the Scales: A physics-informed machine learning approach for more efficient modeling of SARS-CoV-2 spike glycoprotein (preprint)