Coarse grained simulations of peptide nanoparticle formation : the role of local structure and nonbonded interactions

Abstract

Biocompatible nanostructures play an important role in drug delivery and tissue engineering applications. A controlled growth of peptide based nanoparticles with specific morphology needs an understanding of the role of the sequence and solvation properties. In a previous combined experimental-computational study we identified factors that govern the formation of well-defined aggregates by self-assembled pentapeptides, using single amino acid substitution (Mishra, N. K.; Jain, A.; Peter, C.; Verma, S. J. Phys. Chem. B 2017, 121, 8155-8161). The atomistic simulation study suggested a subtle interplay between various peptide properties like igidity/flexibility, hydrogen-bonding, partitioning of aromatic residues and dimerization of peptides that determine the different morphologies, while the overall aggregation propensity was mostly determined by the composition of the methanol/water solvent mixture. The size of the simulated aggregates and the timescales were rather restricted due to the atomistic character of the study. Here, we present an extension to a coarse grained representation which allows for much larger system sizes and longer time scales. To this end, we have optimized a MARTINI model so that it can deal with a system that relies on local structure formation. We combine information on local behavior from atomistic studies and apply supportive dihedral angles together with local adjustment of the bead types to find the right interplay of solvent and peptides. Finally, to mimic the dimers, an introduction of additional bonds between the monomers was necessary. By adding the modifications stepwise we were able to disentangle the influences of the various contributions, like rigidity/flexibility of the peptides, the dimer formation, or the non-bonded properties of the beads, on the overall aggregation propensity and morphology of the nanoparticles. The obtained models resemble the experimental and atomistic behavior and are able to provide mechanistic insight into peptide nanoparticle formation.