In Silico Prediction of Protein Adsorption Energy on Titanium Dioxide and Gold Nanoparticles

Abstract

The free energy of adsorption of proteins onto nanoparticles offers an insight into the biological activity of these particles in the body, but calculating these energies is challenging at the atomistic resolution. In addition, structural information of the proteins may not be readily available. In this work, we demonstrate how information about adsorption affinity of proteins onto nanoparticles can be obtained from first principles with minimum experimental input. We use a multiscale model of protein–nanoparticle interaction to evaluate adsorption energies for a set of 59 human blood serum proteins on gold and titanium dioxide (anatase) nanoparticles of various sizes. For each protein, we compare the results for 3D structures derived from experiments to those predicted computationally from amino acid sequences using the I-TASSER methodology and software. Based on these calculations and 2D and 3D protein descriptors, we develop statistical models for predicting the binding energy of proteins, enabling the rapid characterization of the affinity of nanoparticles to a wide range of proteins.

Between two walls: Modeling the adsorption behavior of β-glucosidase A on bare and SAM-functionalised gold surfaces

“…We will use the term rigidity profile to describe the ordered set of force constants for all the residues of the protein. simulation studies on proteins adsorbed on SAMs 43,49,58 or bare-gold surfaces, 48,[59][60] are significantly more favorable for the protein-bare gold interaction (down to -400 kcal.mol -1 ) as compared to the protein-SAM-OH interaction (down to -150 kcal.mol -1 ), thus accounting for the increased stability of βGA adsorbed on gold. These results also surface, seem to converge toward the same area of the graph (highlighted by a black circle in Figure 3).…”

First principles characterisation of bio–nano interface

“…The characteristic radii of the amino acids and the Hamaker constants were calculated as described elsewhere. 35 Once the PMFs and Hamaker constants for the set of amino acids and other fragments of interests have been calculated, we employ the UA methodology 12 to calculate the binding energies of a set of reference proteins (see ESI †) onto spherical titania NPs. In this model, the protein is represented as a set of beads, with each bead representing one amino acid.…”

Recent Advances in Immunosafety and Nanoinformatics of Two-Dimensional Materials Applied to Nano-imaging

“…The scarcity of quality data and comprehensive databases is the major bottleneck in the application of ML to predict nanomaterials immune reactions (175,176). Data-driven strategies have been making important advances in modeling biological phenomena that have potential usage to evaluate nano-immune interactions, such as predicting biomolecular corona compositions (177)(178)(179)(180)(181), and nanomaterials and cell interactions (e.g., cell uptake, cytotoxicity, membrane integrity, oxidative stress) (182)(183)(184)(185). Furthermore, the exploration of omics approaches (e.g., genomics, transcriptomics, and metabolomics) has promoting the development of ML models to process the complex data generated by these techniques and enables a better understanding of the molecular mechanisms of nanomaterials adverse effects in a systemic context, defining and predicting adverse outcome pathways (186)(187)(188)(189).…”