Publikation Detail

Abstract: Coarse-grained (CG) simulation models have become very popular tools to study complex molecular systems with great computational efficiency on length and timescales that are inaccessible to simulations at atomistic resolution. In so-called bottom up coarse-graining strategies, the interactions in the CG model are devised such that an accurate representation of an atomistic sampling of configurational phase space is achieved. This means the coarse-graining methods use the underlying multibody potential of mean force (i.e. free-energy surface) derived from the atomistic simulation as parametrization target. Here, we present a new method where a neural network (NN) is used to extract high-dimensional free energy surfaces (FES) from molecular dynamics (MD) simulation trajectories. These FES are used for simulations on a CG level of resolution. The method is applied to simulating homo-oligo-peptides (oligo-glutamic-acid (oligo-glu) and oligo-aspartic-acid(oligo-asp)) of different lengths. We show that the NN is not only able to correctly describe the free-energy surface for oligomer lengths that it was trained on, but is also able to predict the conformational sampling of longer chains.