Surface-Centered Prediction of Nanoparticle–Protein Interactions Using Molecular Dynamics Simulations and Interpretable Features

作者：Y Huang, Reid C. Van Lehn · 发表于：ChemRxiv · 年份：2026 · DOI：10.26434/chemrxiv.15006572/v1 · 研究领域：Nanoparticle-Based Drug Delivery、Nanoparticles: synthesis and applications、Computational Drug Discovery Methods

Gold nanoparticles are of broad interest for biomedical applications because their surface properties can be tuned through functionalization by organic ligands. In biological environments, proteins can non-specifically adsorb on nanoparticle surfaces to form a corona that redefines nanoparticle surface properties and governs biological fate. Understanding and controlling the nanoparticle-protein interactions that shape the protein corona is therefore essential, but such interactions are difficult to anticipate. To address this gap, we develop a computational framework that integrates atomistic molecular dynamics simulations and machine learning methods to predict nanoparticle-protein interactions. Inspired by methods that learn protein–protein interaction patterns from molecular surfaces, we develop a unified surface representation for both gold nanoparticles and proteins. We extract nanoparticle and protein surface features and pairwise complementarity features, with ligand-shell dynamics incorporated into the nanoparticle features through molecular dynamics simulations. We then train a classification model to distinguish unbound, moderate, and strong binding behaviors across 264 distinct nanoparticle-protein pairs, allowing the features that govern binding to be learned. The classification model achieves a one-vs-rest receiver-operator-characteristic area under the curve of 0.739 on held-out proteins, demonstrating predictive transfer across diverse systems. We validate the...