Nanomaterial Functionalization Modulates Hard Protein Corona Formation: Atomistic Simulations Applied to Graphitic Materials

Abstract

The protein corona is an obstacle to exploiting exotic properties of nanomaterials in clinical and biotechnological settings. The atomic‐scale dynamic formation of the protein corona at the bio‐nano interface is impenetrable using conventional experimental techniques. Here, molecular dynamics simulations are used to study the effect of graphene‐oxide (GO) functionalization on apolipoprotein‐cIII (apo‐c3) adsorption. An analysis pipeline is developed, encompassing binding energy calculations to protein structure analyses employing uniform manifold approximation and projection (UMAP) dimensionality reduction and clustering. It is found that apo‐c3 is denatured by GO adsorption, driven by the large energetic contributions of electrostatic interactions; enthalpic contributions of such binding events outweigh the intraprotein bond enthalpy required to maintain the protein tertiary structure. Through denaturing and exposing buried hydrophobic residues, the protein backbone is stabilized by forming β‐bridges, which serve as binding motifs for protein–protein interactions that drive further protein aggregation on the nanomaterial surface. In contrast, adsorption on double‐clickable azide‐ and alkyne‐double functionalized GO (C2GO), apo‐c3 largely retains its tertiary structure. Binding with the nanomaterial surface is dominated by weaker van der Waals interactions that are dispersed over the protein surface, where charged protein residues are sterically hindered by azide functional groups. The apo‐c3 C‐terminus remains unchanged upon C2GO adsorption, conserving its lipid‐binding function.

Smart Humidity Sensor Based on Cs3Cu2I5 Nanorods for Real-Time Monitoring of Leakage of Respiratory Protective Equipment

“…In addition, since AlexNet is one of the older deep learning models, the pretraining effect may give it a better initial feature representation for our classification task, contributing to faster convergence and better generalization. In order to better evaluate the results of image classification in the testing set, t-SNE and UMAP algorithms were adopted for nonlinear dimensionality reduction and visualization of high-dimensional semantic features encoded by AlexNet, , and the cluster results were displayed in two-dimensional and three-dimensional spaces. Figure d shows the two-dimensional, and Figure S14a shows the three-dimensional visualization results of the test set’s semantic features by t-SNE, in which it can be seen that some data points in the different categories are close and have a high degree of similarity, but in general, there is a clear separation between the two clusters, which suggests that respirator leaks can be discriminated.…”

Exploiting the Fc base of IgG antibodies to create functional nanoparticle conjugates

“…Additional future work could be directed towards analysing the involvement of the specified residues in nanoparticle interactions by examining the binding free energy for the interaction as proposed by other researchers 23 .…”

Interactional fingerprints offer an accessible, rapid, means to characterise graphene oxide.

“…This work focusses on the rapid characterisation of GO, since it is a particularly challenging GBM, produced by a range of manufacturing methods which yield wide variation of its surface chemistry, 12,17 and with many applications which depend on the effect that this has on surfaces' molecular recognition and aggregation properties. 18,19 Here we demonstrate a new approach to QC testing for graphene oxide (GO), based on the interaction of an array of probe molecules with GO surfaces, as outlined in Figure 1. The approach is similar to other supramolecular sensor/probe arrays with optical detection: [20][21][22] when molecular probes interact with aqueous GBM dispersions, their signal (absorbance or fluorescence) is quenched, providing a ready, rapid, means to measure interaction.…”