Self-assembling dipeptides: conformational sampling in solvent-free coarse-grained simulation.

Area of Science:

• Computational chemistry
• Biomolecular modeling
• Peptide self-assembly

Background:

• Molecular dynamics (MD) simulations are crucial for understanding peptide behavior.
• Developing efficient models for complex biomolecular systems like peptides is essential.
• Hydrophobic peptides, such as diphenylalanine, present unique simulation challenges due to aggregation.

Purpose of the Study:

• To develop and validate a coarse-grained (CG) model for simulating the hydrophobic dipeptide diphenylalanine in aqueous solution.
• To enable computationally efficient simulations of peptide self-assembly processes.
• To ensure the CG model accurately reproduces conformational sampling and intermolecular interactions observed in atomistic simulations.

Main Methods:

• A bottom-up strategy was employed to derive interaction functions for CG beads.
• Peptide backbone, side groups, and end groups were represented by CG beads.
• Intra-molecular (bonded) potentials captured conformational flexibility, derived in detail.
• Nonbonded interactions were based on potentials of mean force from atomistic simulations, incorporating solvent effects.

Main Results:

• The CG model accurately reproduced the conformational properties of the diphenylalanine dipeptide compared to all-atom simulations.
• Solvent mediation effects were successfully incorporated into the effective bead-bead nonbonded interactions.
• Preliminary CG simulations of peptide self-assembly showed good agreement with all-atom, explicit solvent simulation results.

Conclusions:

• The developed CG model provides an accurate and computationally efficient method for simulating diphenylalanine.
• This approach facilitates the study of peptide self-assembly processes, including aggregation.
• The CG model effectively bridges the gap between atomistic detail and large-scale simulation capabilities.