Deriving Transferable Parameters for the Coarse-Grained Martini Model: Application to Amyloid-Like and Elastin-Like Peptides

Abstract

Coarse-grained (CG) models provide a computationally efficient means for investigating complex biological processes over relatively long periods of time and length scales at a reduced level of detail. We present recent advances on our extension of the CG MARTINI model to more accurately describe backbone flexibility of proteins by introducing in the energy function a term that accounts for the dihedral potentials on the peptide backbone. The modified model is applied to amyloid-like and elastin-like peptides, and its performance is assessed from its ability to reproduce structural properties calculated from atomistic trajectories of peptides in water. The transferability of dihedral potentials is addressed in terms of peptide length since it is significantly more challenging to obtain complete conformational sampling for longer peptides compared to shorter peptides. We test the transferability of dihedral parameters by employing parameters derived from the atomistic trajectories of shorter fragments in simulations of longer peptides. Our results show that transferable parameters can be derived to model long peptides with the modified CG MARTINI model.