Biomolecular Adsorprion at ZnS Nanomaterials: A Molecular Dynamics Simulation Study of the Adsorption Preferences, Effects of the Surface Curvature and Coating

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 modeling approach. Here, we present the NPCoronaPredict open-source software package: a suite of software tools to enable this prediction for complex multicomponent 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 parametrizations for nanomaterials and organic molecules. We describe the underlying theoretical background and the package functionality from the design of multicomponent NPs through to the evaluation of the corona.

Critical-sized marginal defects around implants in the rabbit mandible

BACKGROUND

The mandible of the rabbit is considered a reliable model to be used to study bone regeneration in defects. The aim of the present study was to evaluate the formation of new bone around implants installed in defects of either 5 or 10 mm in the mandible of rabbits.

MATERIALS AND METHODS

In 12 rabbits, 3 mm deep circumferential defect, either 5 or 10 mm in diameter, were prepared bilaterally and an implant was placed in the center. A collagen membrane was placed to close the entrance. After 10 weeks, biopsies were taken, histological slides were prepared, and different regions of the defects were analyzed.

RESULTS

Similar amounts of new bone were found in both defects. However, most of the 5 mm defects were filled with new bone. New bone was observed closing the entrance of the defect and laid onto the implant surface. Only in a few cases the healing was incomplete. Despite a similar percentage of new bone found within the 10 mm defects, the healing was incomplete in most of the cases, presenting a low rate of bone formation onto the implant surface within the defect. Only one case presented the closure of the entrance.

CONCLUSIONS

The dimensions of the defect strongly influenced the healing so that a circumferential marginal defect of 10 mm around an implant in the mandible body should be considered a critical-sized defect. The presence of the implant and of residues of teeth might have strongly influenced the healing.

Biomolecular Adsorption on Nanomaterials: Combining Molecular Simulations with Machine Learning

Adsorption free energies of 32 small biomolecules (amino acids side chains, fragments of lipids, and sugar molecules) on 33 different nanomaterials, computed by the molecular dynamics - metadynamics methodology, have been analyzed using statistical machine learning approaches. Multiple unsupervised learning algorithms (principal component analysis, agglomerative clustering, and K-means) as well as supervised linear and nonlinear regression algorithms (linear regression, AdaBoost ensemble learning, artificial neural network) have been applied. As a result, a small set of biomolecules has been identified, knowledge of adsorption free energies of which to a specific nanomaterial can be used to predict, within the developed machine learning model, adsorption free energies of other biomolecules. Furthermore, the methodology of grouping of nanomaterials according to their interactions with biomolecules has been presented.