C.J. Bayard, Y.G. Yingling
North Carolina State University,
United States
Keywords: dissipative particle dynamics, self-assembly, DNA hybridization, biomolecular condensates, LAMMPS
Summary:
Many mesoscale phenomena targeted by Dissipative Particle Dynamics (DPD) depend on chemical specificity, including polymerization, cross-linking, DNA hybridization, and ligand–receptor recognition. Capturing these processes is critical for predicting the self-assembly pathways, stability, and functional properties of soft and biological materials. Yet conventional DPD force fields are inherently non-reactive: molecular connectivity is fixed at initialization, and bond formation or selective recognition cannot be introduced during the run. This constraint limits standard DPD to systems whose structural evolution is governed primarily by non-bonded interactions, excluding a broad class of chemically programmed assemblies. To address this limitation, we implemented an internally developed dynamic bonding framework (dyBonding) within the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS). DyBonding enables interval-based runtime formation of permanent bonds between specified bead types according to user-defined distance thresholds, timing constraints, and probabilistic rules, while remaining compatible with the core DPD formalism. This strategy introduces controlled reactivity at the mesoscale without the overhead or complexity of fully reactive force fields. We further extended dyBonding to support directional and strand-specific bonding, enabling selective, antiparallel hybridization of complementary nucleic acid strands and allowing complete DNA hybridization to be represented explicitly within a coarse-grained DPD description. As a validation and application case, we used DPD to investigate how initial conditions influence the formation of quantum dot (QD)–DNA condensates. In this system, dyBonding captures two distinct assembly mechanisms: (i) biotin–streptavidin-mediated cross-linking and (ii) ssDNA hybridization between QDs functionalized with complementary strands. This unified framework enables direct comparison of alternative assembly pathways and supports quantitative alignment with experimental observations. Overall, dyBonding provides a robust and computationally efficient route for incorporating chemical specificity into DPD simulations, bridging molecular recognition rules and emergent mesoscale structure formation across large length and time scales. Beyond QD–DNA condensates, we have applied dyBonding to nucleic acid nanostar condensates, metal–organic framework organization, and other self-assembling biofunctional materials. By enabling controlled, chemically informed bonding in a widely used simulation platform (LAMMPS), dyBonding substantially expands the functional scope of DPD and supports predictive modeling of chemically programmed soft-matter systems relevant to therapeutics, drug delivery, and advanced biomaterials design.