Nanoscale in silico classification of ligand functionalised surfaces for protein adsorption resistance

Structure, Properties, and Applications of Silica Nanoparticles: Recent Theoretical Modeling Advances, Challenges, and Future Directions

Silica nanoparticles (SNPs), one of the most widely researched materials in modern science, are now commonly exploited in surface coatings, biomedicine, catalysis, and engineering of novel self-assembling materials. Theoretical approaches are invaluable to enhancing fundamental understanding of SNP properties and behavior. Tremendous research attention is dedicated to modeling silica structure, the silica-water interface, and functionalization of silica surfaces for tailored applications. In this review, the range of theoretical methodologies are discussed that have been employed to model bare silica and functionalized silica. The evolution of silica modeling approaches is detailed, including classical, quantum mechanical, and hybrid methods and highlight in particular the last decade of theoretical simulation advances. It is started with discussing investigations of bare silica systems, focusing on the fundamental interactions at the silica-water interface, following with a comprehensively review of the modeling studies that examine the interaction of silica with functional ligands, peptides, ions, surfactants, polymers, and carbonaceous species. The review is concluded with the perspective on existing challenges in the field and promising future directions that will further enhance the utility and importance of the theoretical approaches in guiding the rational design of SNPs for applications in engineering and biomedicine.

Graphitic nanoflakes modulate the structure and binding of human amylin

Human amylin is an inherently disordered protein whose ability to form amyloid fibrils is linked to the onset of type II diabetes. Graphitic nanomaterials have potential in managing amyloid diseases as they can disrupt protein aggregation processes in biological settings, but optimising these materials to prevent fibrillation is challenging. Here, we employ bias-exchange molecular dynamics simulations to systematically study the structure and adsorption preferences of amylin on graphitic nanoflakes that vary in their physical dimensions and surface functionalisation. Our findings reveal that nanoflake size and surface oxidation both influence the structure and adsorption preferences of amylin. The purely hydrophobic substrate of pristine graphene (PG) nanoflakes encourages non-specific protein adsorption, leading to unrestricted lateral mobility once amylin adheres to the surface. Particularly on larger PG nanoflakes, this induces structural changes in amylin that may promote fibril formation, such as the loss of native helical content and an increase in β-sheet character. In contrast, oxidised graphene nanoflakes form hydrogen bonds between surface oxygen sites and amylin, and as such restricting protein mobility. Reduced graphene oxide (rGO) flakes, featuring lower amounts of surface oxidation, are amphiphilic and exhibit substantial regions of bare carbon which promote protein binding and reduced conformational flexibility, leading to conservation of the native structure of amylin. In comparison, graphene oxide (GO) nanoflakes, which are predominantly hydrophilic and have a high degree of surface oxidation, facilitate considerable protein structural variability, resulting in substantial contact area between the protein and GO, and subsequent protein unfolding. Our results indicate that tailoring the size, oxygen concentration and surface patterning of graphitic nanoflakes can lead to specific and robust protein binding, ultimately influencing the likelihood of fibril formation. These atomistic insights provide key design considerations for the development of graphitic nanoflakes that can modulate protein aggregation by sequestering protein monomers in the biological environment and inhibit conformational changes linked to amyloid fibril formation.

Computer-aided nanodrug discovery: recent progress and future prospects

Nanodrugs, which utilise nanomaterials in disease prevention and therapy, have attracted considerable interest since their initial conceptualisation in the 1990s. Substantial efforts have been made to develop nanodrugs for overcoming the limitations of conventional drugs, such as low targeting efficacy, high dosage and toxicity, and potential drug resistance. Despite the significant progress that has been made in nanodrug discovery, the precise design or screening of nanomaterials with desired biomedical functions prior to experimentation remains a significant challenge. This is particularly the case with regard to personalised precision nanodrugs, which require the simultaneous optimisation of the structures, compositions, and surface functionalities of nanodrugs. The development of powerful computer clusters and algorithms has made it possible to overcome this challenge through in silico methods, which provide a comprehensive understanding of the medical functions of nanodrugs in relation to their physicochemical properties. In addition, machine learning techniques have been widely employed in nanodrug research, significantly accelerating the understanding of bio-nano interactions and the development of nanodrugs. This review will present a summary of the computational advances in nanodrug discovery, focusing on the understanding of how the key interfacial interactions, namely, surface adsorption, supramolecular recognition, surface catalysis, and chemical conversion, affect the therapeutic efficacy of nanodrugs. Furthermore, this review will discuss the challenges and opportunities in computer-aided nanodrug discovery, with particular emphasis on the integrated "computation + machine learning + experimentation" strategy that can potentially accelerate the discovery of precision nanodrugs.

Protein Adsorption on Solid Surfaces: Data Mining, Database, Molecular Surface-Derived Properties, and Semiempirical Relationships

Protein adsorption on solid surfaces is a process relevant to biological, medical, industrial, and environmental applications. Despite this wide interest and advancement in measurement techniques, the complexity of protein adsorption has frustrated its accurate prediction. To address this challenge, here, data regarding protein adsorption reported in the last four decades was collected, checked for completeness and correctness, organized, and archived in an upgraded, freely accessible Biomolecular Adsorption Database, which is equivalent to a large-scale, ad hoc, crowd-sourced multifactorial experiment. The shape and physicochemical properties of the proteins present in the database were quantified on their molecular surfaces using an in-house program (ProMS) operating as an add-on to the PyMol software. Machine learning-based analysis indicated that protein adsorption on hydrophobic and hydrophilic surfaces is modulated by different sets of operational, structural, and molecular surface-based physicochemical parameters. Separately, the adsorption data regarding four "benchmark" proteins, i.e., lysozyme, albumin, IgG, and fibrinogen, was processed by piecewise linear regression with the protein monolayer acting as breakpoint, using the linearization of the Langmuir isotherm formalism, resulting in semiempirical relationships predicting protein adsorption. These relationships, derived separately for hydrophilic and hydrophobic surfaces, described well the protein concentration on the surface as a function of the protein concentration in solution, adsorbing surface contact angle, ionic strength, pH, and temperature of the carrying fluid, and the difference between pH and the isoelectric point of the protein. When applying the semiempirical relationships derived for benchmark proteins to two other "test" proteins with known PDB structure, i.e., β-lactoglobulin and α-lactalbumin, the errors of this extrapolation were found to be in a linear relationship with the dissimilarity between the benchmark and the test proteins. The work presented here can be used for the estimation of operational parameters modulating protein adsorption for various applications such as diagnostic devices, pharmaceuticals, biomaterials, or the food industry.

Block Length-Dependent Protein Fouling on Poly(2-oxazoline)-Based Polymersomes: Influence on Macrophage Association and Circulation Behavior

Polymersomes are vesicular structures self-assembled from amphiphilic block copolymers and are considered an alternative to liposomes for applications in drug delivery, immunotherapy, biosensing, and as nanoreactors and artificial organelles. However, the limited availability of systematic stability, protein fouling (protein corona formation), and blood circulation studies hampers their clinical translation. Poly(2-oxazoline)s (POx) are valuable antifouling hydrophilic polymers that can replace the current gold-standard, poly(ethylene glycol) (PEG), yet investigations of POx functionality on nanoparticles are relatively sparse. Herein, a systematic study is reported of the structural, dynamic and antifouling properties of polymersomes made of poly(2-methyl-2-oxazoline)-block-poly(dimethylsiloxane)-block-poly(2-methyl-2-oxazoline) (PMOXA-b-PDMS-b-PMOXA). The study relates in vitro antifouling performance of the polymersomes to atomistic molecular dynamics simulations of polymersome membrane hydration behavior. These observations support the experimentally demonstrated benefit of maximizing the length of PMOXA (degree of polymerization (DP) > 6) while keeping PDMS at a minimal length that still provides sufficient membrane stability (DP > 19). In vitro macrophage association and in vivo blood circulation evaluation of polymersomes in zebrafish embryos corroborate these findings. They further suggest that single copolymer presentation on polymersomes is outperformed by blends of varied copolymer lengths. This study helps to rationalize design rules for stable and low-fouling polymersomes for future medical applications.

Cell-membrane-inspired polymers for constructing biointerfaces with efficient molecular recognition

Fabrication of devices that accurately recognize, detect, and separate target molecules from mixtures is a crucial aspect of biotechnology for applications in medical, pharmaceutical, and food sciences. This technology has also been recently applied in solving environmental and energy-related problems. In molecular recognition, biomolecules are typically complexed with a substrate, and specific molecules from a mixture are recognized, captured, and reacted. To increase sensitivity and efficiency, the activity of the biomolecules used for capture should be maintained, and non-specific reactions on the surface should be prevented. This review summarizes polymeric materials that are used for constructing biointerfaces. Precise molecular recognition occurring at the surface of cell membranes is fundamental to sustaining life; therefore, materials that mimic the structure and properties of this particular surface are emphasized in this article. The requirements for biointerfaces to eliminate nonspecific interactions of biomolecules are described. In particular, the major issue of protein adsorption on biointerfaces is discussed by focusing on the structure of water near the interface from a thermodynamic viewpoint; moreover, the structure of polymer molecules that control the water structure is considered. Methodologies enabling stable formation of these interfaces on material surfaces are also presented.

Hydration of a small protein under carbon nanotube confinement: Adsorbed substates induce selective separation of the dynamical response

The co-involvement of biological molecules and nanomaterials has increasingly come to the fore in modern-day applications. While the "bio-nano" (BN) interface presents physico-chemical characteristics that are manifestly different from those observed in isotropic bulk conditions, the underlying molecular reasons remain little understood; this is especially true of anomalies in interfacial hydration. In this paper, we leverage atomistic simulations to study differential adsorption characteristics of a small protein on the inner (concave) surface of a single-walled carbon nanotube whose diameter exceeds dimensions conducive to single-file water movement. Our findings indicate that the extent of adsorption is decided by the degree of foldedness of the protein conformational substate. Importantly, we find that partially folded substates, but not the natively folded one, induce reorganization of the protein hydration layer into an inner layer water closer to the nanotube axis and an outer layer water in the interstitial space near the nanotube walls. Further analyses reveal sharp dynamical differences between water molecules in the two layers as observed in the onset of increased heterogeneity in rotational relaxation and the enhanced deviation from Fickian behavior. The vibrational density of states reveals that the dynamical distinctions are correlated with differences in crucial bands in the power spectra. The current results set the stage for further systematic studies of various BN interfaces vis-à-vis control of hydration properties.

Toward the Specificity of Bare Nanomaterial Surfaces for Protein Corona Formation

Aiming at creating smart nanomaterials for biomedical applications, nanotechnology aspires to develop a new generation of nanomaterials with the ability to recognize different biological components in a complex environment. It is common opinion that nanomaterials must be coated with organic or inorganic layers as a mandatory prerequisite for applications in biological systems. Thus, it is the nanomaterial surface coating that predominantly controls the nanomaterial fate in the biological environment. In the last decades, interdisciplinary studies involving not only life sciences, but all branches of scientific research, provided hints for obtaining uncoated inorganic materials able to interact with biological systems with high complexity and selectivity. Herein, the fragmentary literature on the interactions between bare abiotic materials and biological components is reviewed. Moreover, the most relevant examples of selective binding and the conceptualization of the general principles behind recognition mechanisms were provided. Nanoparticle features, such as crystalline facets, density and distribution of surface chemical groups, and surface roughness and topography were encompassed for deepening the comprehension of the general concept of recognition patterns.