arXiv subjects

Ian Rouse

Publications and source records attributed to Ian Rouse.

4 recordsLinked to original sources

arXiv2026

Orientation-Dependent Protein Binding at Nanoparticle Interfaces

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.

physics.bio-ph↗

arXiv2024

NPCoronaPredict: A computational pipeline for the prediction of the nanoparticle-biomolecule corona

The corona of a nanoparticle immersed in a biological fluid is of key importance to its eventual fate and bioactivity in the environment or inside live tissues. It is critical to have insight into both the underlying bionano interactions and the corona composition to ensure biocompatibility of novel engineered nanomaterials. A prediction of these properties in silico requires the successful spanning of multiple orders of magnitude of both time and physical dimensions to produce results in a reasonable amount of time, necessitating the development of a multiscale modelling approach. Here, we present the NPCoronaPredict open-source software package: a suite of software tools to enable this prediction for complex multi-component nanomaterials in essentially arbitrary biological fluids, or more generally any medium containing organic molecules. The package integrates several recent physics-based computational models and a library of both physics-based and data-driven parameterisations for nanomaterials and organic molecules. We describe the underlying theoretical background and the package functionality from the design of multi-component NPs through to the evaluation of the corona.

cond-mat.mes-hall↗

arXiv2022

Machine-learning based prediction of small molecule -- surface interaction potentials

Predicting the adsorption affinity of a small molecule to a target surface is of importance to a range of fields, from catalysis to drug delivery and human safety, but a complex task to perform computationally when taking into account the effects of the surrounding medium. We present a flexible machine-learning approach to predict potentials of mean force (PMFs) and adsorption energies for chemical -- surface pairs from the separate interaction potentials of each partner with a set of probe atoms. We use a pre-existing library of PMFs obtained via atomistic molecular dynamics for a variety of inorganic materials and molecules to train the model. We find good agreement between original and predicted PMFs in both training and validation groups, confirming the predictive power of this approach, and demonstrate the flexibility of the model by producing PMFs for molecules and surfaces outside the training set.

physics.chem-ph↗

arXiv2020

Advanced in silico characterization of nanomaterials for nanoparticle toxicology

Nanomaterials possess a wide range of potential applications due to their novel properties compared to bulk matter, but these same properties may represent an unknown risk to health. Experimental safety testing cannot keep pace with the rate at which new nanoparticles are developed and, being lengthy and expensive, often hinders the development of technology. An economic alternative to in vitro and in vivo testing is offered by nanoinformatics, potentially enabling the quantitative relation of the nanomaterial properties to their crucial biological activities. Recent research efforts have demonstrated that such activities can be successfully predicted from the physicochemical characteristics of nanoparticles, especially those related to the bionano interface, by means of statistical models. In this work, as a step towards in silico prediction of toxicity of nanomaterials, an advanced computational characterization of these materials has been proposed and applied to titanium dioxide nanoparticles. The characteristics of nanoparticles and bionano interface are computed using a systematic multiscale approach relying only on information on chemistry and structure of the nanoparticles.

cond-mat.mtrl-sci↗