Orientation-Dependent Protein Binding at Nanoparticle Interfaces

TL;DR

This study develops a computational framework combining coarse-grained models and docking to analyze protein orientation-dependent binding on nanoparticles, bridging energetics and docking scores.

Contribution

It introduces a systematic method to compare protein-nanoparticle binding geometries using heatmaps and Jensen-Shannon divergence, enhancing predictive modeling.

Findings

Encouraging agreement between docking scores and adsorption energies in several cases.

Identification of limitations and potential improvements in angular resolution and interaction parameters.

Framework provides a quantitative link between energetics and docking outputs at interfaces.

Abstract

Accurate quantification of protein-nanoparticle interactions is essential for applications in nanobiotechnology, nanomedicine, and drug delivery. Motivated by recent computational and experimental work, we combine coarse-grained united-atom (UA) models with molecular docking to characterize protein adsorption on SiO_2 nanoparticles. We construct orientation-resolved heatmaps in which polar and azimuthal angles uniquely specify the relative protein-nanoparticle pose, and the map amplitude reports binding propensity via the minimum UA adsorption energy or the docking score. Each angular bin corresponds to a distinct docked complex, enabling systematic comparison of binding geometries across models. To relate docking score landscapes to Boltzmann-averaged UA adsorption energetics, we analyze eight birch pollen allergen proteins previously studied experimentally. Similarity between the two orientational distributions is quantified using the Jensen-Shannon divergence (JSD). We find encouraging agreement between the two approaches in several cases, while also identifying limitations and routes for improvement, including optimized angular resolution and iterative refinement of interaction parameters. Overall, this framework provides a quantitative bridge between coarse-grained energetics and docking outputs at protein-nanoparticle interfaces, supporting improved predictive modeling and mechanistic insight into protein-nanoparticle binding landscapes.