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Construction of Multiscale Dissipative Particle Dynamics (DPD) Models from Other Coarse-Grained Models
Yinhan Wang1, Rigoberto Hernandez1
1Department of Chemistry, The Johns Hopkins University, Baltimore, Maryland 21218, United States.
ACS Omega
|April 22, 2024
Summary
This study introduces a general method to convert coarse-grained models into Dissipative Particle Dynamics (DPD) models. The new scheme accurately represents proteins and varying solvent scales, validated against detailed simulations.
Area of Science:
- Computational Chemistry and Molecular Modeling
- Soft Matter Physics
Background:
- Coarse-grained (CG) models simplify complex molecular systems for large-scale simulations.
- Dissipative Particle Dynamics (DPD) is a mesoscopic simulation technique widely used for soft matter.
- Existing DPD methods often struggle to accurately represent complex molecular structures and electrostatic interactions.
Purpose of the Study:
- To develop a general and adaptable scheme for converting existing coarse-grained models into DPD models.
- To enable accurate representation of electrostatic interactions within the DPD framework.
- To extend DPD capabilities for heterogeneous particle sizes and various underlying CG force fields.
Main Methods:
- A novel DPD coarse-graining scheme is proposed, building upon established methods (Groot & Warren, 1997).
- Incorporation of a long-range Slater Coulomb potential for accurate electrostatic interactions (González-Melchor et al., 2006).
- Conversion of MARTINI protein models to DPD, accommodating non-Lennard-Jones potentials and variable particle sizes.
Main Results:
- The generalized DPD models successfully represent various proteins, including variations in particle sizes.
- Favorable benchmarking against all-atom and MARTINI models for structural observables of peptides and proteins.
- Accurate representation of water solvent at different coarse-graining scales.
Conclusions:
- The presented general scheme provides a robust method for DPD model construction from various CG inputs.
- This approach enhances the accuracy and applicability of DPD simulations for complex biological and soft matter systems.
- The method allows for faithful representation of molecular heterogeneity and electrostatic effects in mesoscopic simulations.
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