DFPC Lipid Nanoparticle Interactions with Biofilms: Role of Gold Core Size, Kinetics, and Predictive Modeling

Abstract

Nanoparticles (NPs) play a crucial role in biomedical applications, especially as drug delivery carriers due to their versatility and modifiability. This study investigates the interactions between DFPC lipid-wrapped gold nanoparticles (LNPs) of varying core sizes and biofilms at the molecular level using coarse-grained molecular dynamics simulations. Results demonstrate that smaller gold core LNPs embed fully into biofilms, increasing membrane fluidity and disorder, while larger core LNPs remain adsorbed on the membrane surface without penetrating the bilayer. To enhance the predictive power and accelerate real-time evaluation of NP-biofilm interactions, this study further proposes a deep learning-assisted prediction framework. By training convolutional neural networks (CNNs) on molecular dynamics trajectory data, the model can predict nanoparticle penetration, embedding probability, and membrane response under varying particle sizes, lipid compositions, and environmental conditions. The combined simulation and deep learning framework establishes a scalable and adaptive tool for optimizing lipid nanoparticle design in drug delivery and nanomedicine.