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The coronavirus disease-2019 (COVID-19) pandemic, caused by the pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), started in China during late 2019 and swiftly spread worldwide. Since COVID-19 emergence, many therapeutic regimens have been relentlessly explored, and although two vaccines have just received emergency use authorization by different governmental agencies, antiviral therapeutics based neutralizing antibodies and small-drug inhibitors can still be vital viable options to prevent and treat SARS-CoV-2 infections. The viral spike glycoprotein (S-protein) is the key molecular player that promotes human host cellular invasion via recognition of and binding to the angiotensin-converting enzyme 2 gene (ACE2). In this work, we report the results obtained by mutating in silico the 18 ACE2 residues and the 14 S-protein receptor binding domain (S-RBDCoV-2) residues that contribute to the receptor/viral protein binding interface. Specifically, each wild-type protein-protein interface residue was replaced by a hydrophobic (isoleucine), polar (serine and threonine), charged (aspartic acid/glutamic acid and lysine/arginine), and bulky (tryptophan) residue, respectively, in order to study the different effects exerted by nature, shape, and dimensions of the mutant amino acids on the structure and strength of the resulting binding interface. The computational results were next validated a posteriori against the corresponding experimental data, yielding an overall agreement of 92%. Interestingly, a non-negligible number of mis-sense variations were predicted to enhance ACE2/S-RBDCoV-2 binding, including the variants Q24T, T27D/K/W, D30E, H34S7T/K, E35D, Q42K, L79I/W, R357K, and R393K on ACE2 and L455D/W, F456K/W, Q493K, N501T, and Y505W on S-RBDCoV-2, respectively.

Nowadays, computer simulations have been established as a fundamental tool in the design and development of new dendrimer-based nanocarriers for drug and gene delivery. Moreover, the level of detail contained in the information that can be gathered by performing atomistic-scale simulations cannot be obtained with any other available experimental technique. In this chapter we describe the main computational toolbox that can be exploited in the different stages of novel dendritic nanocarrier production-from the initial conception to the stage of biological intermolecular interactions.

Gene therapy using small interfering RNA (siRNA) is emerging as a novel therapeutic approach to treat various diseases. However, safe and efficient siRNA delivery still constitutes the major obstacle for clinical implementation of siRNA therapeutics. Here we report an ionizable supramolecular dendrimer vector, formed via self-assembly of a small amphiphilic dendrimer, as an effective siRNA delivery system with a favorable safety profile. By virtue of the ionizable tertiary amine terminals, the supramolecular dendrimer has a low positively charged surface potential and no notable cytotoxicity at physiological pH. Nonetheless, this ionizable feature imparted sufficient surface charge to the supramolecular dendrimer to enable formation of a stable complex with siRNA via electrostatic interactions. The resulting siRNA/dendrimer delivery system had a surface charge that was neither neutral, thus avoiding aggregation, nor too high, thus avoiding cytotoxicity, but was sufficient for favorable cellular uptake and endosomal release of the siRNA. When tested in different cancer cell lines and patient-derived cancer organoids, this dendrimer-mediated siRNA delivery system effectively silenced the oncogenes Myc and Akt2 with a potent antiproliferative effect, outperforming the gold standard vector, Lipofectamine 2000. Therefore, this ionizable supramolecular dendrimer represents a promising vector for siRNA delivery. The concept of supramolecular dendrimer nanovectors via self-assembly is new, yet easy to implement in practice, offering a new perspective for supramolecular chemistry in biomedical applications. [Figure not available: see fulltext.]

The recent emergence of the pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent for the coronavirus disease 2019 (COVID-19), is causing a global pandemic that poses enormous challenges to global public health and economies. SARS-CoV-2 host cell entry is mediated by the interaction of the viral transmembrane spike glycoprotein (S-protein) with the angiotensin-converting enzyme 2 gene (ACE2), an essential counter-regulatory carboxypeptidase of the renin-angiotensin hormone system that is a critical regulator of blood volume, systemic vascular resistance, and thus cardiovascular homeostasis. Accordingly, this work reports an atomistic-based, reliable in silico structural and energetic framework of the interactions between the receptor-binding domain of the SARS-CoV-2 S-protein and its host cellular receptor ACE2 that provides qualitative and quantitative insights into the main molecular determinants in virus/receptor recognition. In particular, residues D38, K31, E37, K353, and Y41 on ACE2 and Q498, T500, and R403 on the SARS-CoV-2 S-protein receptor-binding domain are determined as true hot spots, contributing to shaping and determining the stability of the relevant protein-protein interface. Overall, these results could be used to estimate the binding affinity of the viral protein to different allelic variants of ACE2 receptors discovered in COVID-19 patients and for the effective structure-based design and development of neutralizing antibodies, vaccines, and protein/protein inhibitors against this terrible new coronavirus.

One of the major goals of computational materials science is the rapid and accurate prediction of properties of new materials. In order to design new materials and compositions for specific, high-performance applications, it is essential to rely on predictive tools for the estimation of the desired properties before material preparation, characterization and processing. In the future, new materials and systems will be characterized by progressively higher degrees of complexity, due to the strong relationship between nanotechnology, biotechnology, computer science and cognitive disciplines. However, computer power and simulation algorithms are also quickly evolving, opening new avenues for novel material and system design based on virtual (aka in silico) experiments. Notwithstanding the great advances achieved in the simulation of structural, thermal, mechanical and transport properties of materials at the macroscopic level, the accurate property prediction for complex nanostructured materials remains a critical issue in the material design strategy. This hurdle arises from the strong dependence of the material properties on the underlying nanostructure. Atomistic simulations based on molecular dynamics or Monte Carlo methods allow such structure–property relationships to be derived already for systems of noticeable size and time scales; yet, the study of highly complex systems (e.g., polymer nanocomposites and self-assembled nanomaterials) is still out of current reach at such a fine level of detail. Indeed, the fast advancement of high-performance computing has already expanded these time/scale windows, and the upcoming advent of hexascale and/or quantum-based computers will indeed further contribute to this expansion; yet, the investigation of many critical phenomena will remain inaccessible to atomistic-based simulations. To circumvent this limitation, multiscale simulation techniques have been developed to create a seamless bridge across different time/scale domains (from electronic to continuum methods), thereby providing reliable predictive tools to design engineers. Accordingly, in this review work we present a selection of case studies based on our own experience in multiscale molecular modeling of nanostructured, complex systems of industrial interest. The main goal of this objective is to show how multiscale in silico experiments in molecular systems design and engineering is at the same time theoretically sound and mature enough for its full exploitation.

International audience; Bioimaging has revolutionized medicine by providing accurate information for disease diagnosis and treatment. Nanotechnology‐based bioimaging is expected to further improve imaging sensitivity and specificity. In this context, supramolecular nanosystems based on self‐assembly of amphiphilic dendrimers for single photon emission computed tomography (SPECT) bioimaging are developed. These dendrimers bear multiple In3+ radionuclides at their terminals as SPECT reporters. By replacing the macrocyclic 1,4,7,10‐tetraazacyclododecane‐1,4,7,10‐tetraacetic acid cage with the smaller 1,4,7‐triazacyclononane‐1,4,7‐triacetic acid scaffold as the In3+ chelator, the corresponding dendrimer exhibits neutral In3+‐complex terminals in place of negatively charged In3+‐complex terminals. This negative‐to‐neutral surface charge alteration completely reverses the zeta‐potential of the nanosystems from negative to positive. As a consequence, the resulting SPECT nanoprobe generates a highly sought‐after biodistribution profile accompanied by a drastically reduced uptake in liver, leading to significantly improved tumor imaging. This finding contrasts with current literature reporting that positively charged nanoparticles have preferential accumulation in the liver. As such, this study provides new perspectives for improving the biodistribution of positively charged nanosystems for biomedical applications.

The sensing of small molecules poses the challenge to develop devices able to discriminate between compounds that may be structurally very similar. Here, attention has been paid to the use of self-assembled monolayer (SAM)-protected gold nanoparticles since they enable a modular approach to tune single-molecule affinity and selectivity simply by changing functional moieties (i.e. covering ligands), alongside with multivalent molecular recognition. To date, the discovery of monolayers suitable for a specific molecular target relies on trial-and-error approaches, with ligand chemistry being the main criteria used to modulate selectivity and sensitivity. By using molecular dynamics, we showcase that either individual molecular characteristics and/or collective features such as ligand flexibility, monolayer organization, ligand local ordering, and interfacial solvent properties can also be exploited conveniently. The knowledge of the molecular mechanisms that drive the recognition of small molecules on SAM covered nanoparticles will critically expand our ability to manipulate and control such supramolecular systems.

Background: The new concepts of personalized and precision medicine require the design of more and more refined delivery systems. In this frame, hydrogels can play a very important role as they represent the best surrogate of soft living tissues for what concerns rheological properties. Thus, this paper focusses on a global theoretical approach able to describe how hydrogel polymeric networks can affect the release kinetics of drugs characterized by different sizes. The attention is focused on a case study dealing with an interpenetrated hydrogel made up by alginate and poly(N-vinyl-2-pyrrolidone). Methods: Information about polymeric network characteristics (mesh size distribution and polymer volume fraction) is deduced from the theoretical interpretation of the rheological and the low field Nuclear Magnetic Resonance (NMR) characterization of hydrogels. This information is then, embodied in the mass balance equation whose resolution provides the release kinetics. Results: Our simulations indicate the influence of network characteristics on release kinetics. In addition, the reliability of the proposed approach is supported by the comparison of the model outcome with experimental release data. Conclusions: This study underlines the necessity of a global theoretical approach in order to design reliable delivery systems based on hydrogels.

Low-generation viologen-phosphorous dendrimers (VPDs) can be exploited as novel therapeutic agents, since they efficiently inhibit aggregation of amyloid-β into fibrils and are active against several strains of microorganisms. Human serum albumin (HSA), the most abundant plasma protein, is playing an increasing role as drug carrier in the clinical setting. Therefore, with the aim of exploiting HSA as a potential carrier for VPDs, in this work we performed a preliminary investigation of the interaction of six different VPDs 1–6 with HSA using a combined computational/experimental approach. First, different modeling techniques were employed to i) determine the dendrimer binding site on the HSA surface; ii) derive the free energy change ΔGb involved in each dendrimer/HSA complex formation; iii) analyze in details all molecular determinants contributing to ΔGb, and iv) evaluate the eventual HSA structural variations induced by dendrimer binding. All modeling predictions were next validated using a series of experimental techniques, including isothermal titration calorimetry (ITC), circular dichroism (CD), and fluorescence quenching and decay. In aggregate, the results from this study allowed us to rank the affinity of the different viologen-phosphorous dendrimers 1–6 towards HSA and to formulate a molecular-based rationale for the differential binding thermodynamics of the resulting dendrimer/HSA complexes. According to our data, HSA can successfully and selectively bind VPDs 1–6, dendrimer 4 being the best cargo for this endogenous protein nanocarrier.

In this work, we present Thioflavin T fluorescence, transmission electron microscopy (TEM), circular dichroism (CD), Fourier-transformed infrared (FT-IR), and oscillatory rheometry studies applied to an antineoplastic drug, 5-fluorouracil (5-FU), embedded in a heterochiral tripeptide hydrogel to obtain a drug delivery supramolecular system. The release of 5-fluorouracil was monitored over time by reverse-phase high-performance liquid chromatography (HPLC) and its interaction with the tripeptide assemblies was probed by all-atom molecular dynamics simulations.

The fast development of digitalization and computational science is opening new possibilities for a rapid design of new materials. Computational tools coupled with focused experiments can be successfully used for the design of new nanostructured materials in different sectors, particularly in the area of biomedical applications. This paper starts with a general introduction on the future of computational tools for the design of new materials and introduces the paradigm of multiscale molecular modeling. It then continues with the description of the multiscale (i.e., atomistic, mesoscale and finite element calculations) computational recipe for the prediction of novel materials and structures for biomedical applications. Finally, the comparison of in silico and experimental results on selected systems of interest in the area of life sciences is reported and discussed. The quality of the agreement obtained between virtual and real data for such complex systems indeed confirms the validity of computational tools for the design of nanostructured polymer systems for biomedical applications.

Self-assembled cationic micelles are an attractive platform for binding biologically-relevant polyanions such as heparin. This has potential applications in coagulation control, where a synthetic heparin rescue agent could be a useful replacement for protamine, which is in current clinical use. However, micelles can have low stability in human serum and unacceptable toxicity profiles. This paper reports the optimi- sation of self-assembled multivalent (SAMul) arrays of amphiphilic ligands to bind heparin in competitive conditions. Specifically, modification of the hydrophobic unit kinetically stabilises the self-assembled nanostructures, preventing loss of binding ability in the presence of human serum – cholesterol hydro- phobic units significantly outperform systems with a simple aliphatic chain. It is demonstrated that serum albumin disrupts the binding thermodynamics of the latter system. Molecular simulation shows aliphatic lipids can more easily be removed from the self-assembled nanostructures than the cholesterol ana- logues. This agrees with the experimental observation that the cholesterol-based systems undergo slower disassembly and subsequent degradation via ester hydrolysis. Furthermore, by stabilising the SAMul nano- structures, toxicity towards human cells is decreased and biocompatibility enhanced, with markedly improved survival of human hepatoblastoma cells in an MTT assay.

This review work reports a collection of coupled experimental/computational results taken from our own experience in the field of self-assembled dendrimers for heparin binding. These studies present and discuss both the potentiality played by this hybrid methodology to the design, synthesis, and development of possible protamine replacers for heparin anticoagulant activity reversal in biomedical applications, and the obstacles this field has still to overcome before these molecules can be translated into nanomedicines available in clinical settings.

Gold nanoparticles (AuNPs) covered with mixtures of immiscible ligands present potentially anisotropic surfaces that can modulate their interactions at complex nano–bio interfaces. Mixed, self-assembled, monolayer (SAM)-protected AuNPs, prepared with incompatible hydrocarbon and fluorocarbon amphiphilic ligands, are used here to probe the molecular basis of surface phase separation and disclose the role of fluorinated ligands on the interaction with lipid model membranes and cells, by integrating in silico and experimental approaches. These results indicate that the presence of fluorinated amphiphilic ligands enhances the membrane binding ability and cellular uptake of gold nanoparticles with respect to those coated only with hydrogenated amphiphilic ligands. For mixed monolayers, computational results suggest that ligand phase separation occurs on the gold surface, and the resulting anisotropy affects the number of contacts and adhesion energies with a membrane bilayer. This reflects in a diverse membrane interaction for NPs with different surface morphologies, as determined by surface plasmon resonance, as well as differential effects on cells, as observed by flow cytometry and confocal microscopy. Overall, limited changes in monolayer features can significantly affect NP surface interfacial properties, which, in turn, affect the interaction of SAM-AuNPs with cellular membranes and subsequent effects on cells.

The development of efficient and safe nucleic acid delivery vectors remains an unmet need holding back translation of gene therapy approaches into bedside. Graphene oxide (GO) could help bypass such bottleneck thanks to its large surface area, versatile chemistry and biocompatibility, which could overall enhance transfection efficiency while abolishing some of the limitations linked to the use of viral vectors. Here, we aimed to assess the capacity of bare GO, without any further surface modification, to complex a short double-stranded nucleic acid of biological relevance (siRNA) and mediate its intracellular delivery. GO formed stable complexes with siRNA at 10:1, 20:1 and 50:1 GO:siRNA mass ratios. Complexation was further corroborated by atomistic molecular dynamics simulations. GO:siRNA complexes were promptly internalized in a primary mouse cell culture, as early as 4 h after exposure. At this time point, intracellular siRNA levels were comparable to those provided by a lipid-based transfection reagent that achieved significant gene silencing. Time-lapse tracking of internalized GO and siRNA evidenced a sharp decrease of intracellular siRNA from 4 to 12 h, while GO was sequestered in large vesicles, which may explain the lack of biological effect (i.e. gene silencing) achieved by GO:siRNA complexes. This study underlines the potential of non-surface modified GO flakes to act as 2D siRNA delivery platforms, without the need for cationic functionalization, but warrants further vector optimization to allow effective release of the nucleic acid and achieve efficient gene silencing.

Heparin is a sulfated glycan widely used as anticoagulant in medicine. Mallard Blue (MalB), a small cationic dye developed in our laboratories, is able to detect heparin in serum and plasma in a dose-response manner, with performance superior to its direct competitors. However, many aspects of MalB/heparin binding still remain to be explored which, once solved, may foster the clinical use of MalB. Among these, the characterization of the energetics that drives the MalB/heparin binding process, the competition for MalB binding by other polyanions (e.g., negatively-charged surfactant micelles), and the interaction of MalB with serum proteins are of particular interest. This work fills this gap by means of a combination of experimental investigations (UV-visible spectroscopy and isothermal titration calorimetry), and computational approaches based on molecular dynamics (MD) simulation techniques. In combination, the results obtained show that MalB efficiently binds to both heparin and SDS, with the binding being enthalpic in nature; yet, SDS is able to extract MalB from its complex with heparin when the surfactant is in its self-assembled form, the driving force underlying SDS-induced MalB/heparin de-complexation being entropic in nature as the two enthalpies of binding effectively cancel each other out. Once bound to SDS, the dye remains electrostatically bound to the micellar surface and does not penetrate the micelle palisade layer, as verified by steered molecular dynamics/umbrella sampling simulations. Finally, the affinity of MalB for human serum albumin (HSA), the most abundant plasma protein, is found to be lower than that for heparin, confirming the ability of the dye to work in complex physiological environments.

The need for effective and efficient design and production of sophisticated materials with advanced performances on a competitive time scale is strongly driving the integration of material modeling and simulation techniques into material selection decision processes. Specifically, for complex structural materials such as polymer-based nanocomposites (PNCs), there is a strong industrial demand for chemistry/physics-based models and modeling workflows able to predict relevant material properties in an accurate and reliable way. Under this perspective, in this work we describe the application of multiscale molecular modeling techniques for the choice of PNC materials for aerospace applications. The results are obtained in the framework of the European project Multi-scale Composite Material Selection Platform with a Seamless Integration of Materials Models and Multidisciplinary Design Framework (COMPOSELECTOR), funded by the European Commission within the H2020 call Advancing the integration of materials modeling in business processes to enhance effective industrial decision making and increase competitiveness.

The ligand shell (LS) determines a number of nanoparticles’ properties. Nanoparticles’ cores can be accurately characterized; yet the structure of the LS, when composed of mixture of molecules, can be described only qualitatively (e.g., patchy, Janus, and random). Here we show that quantitative description of the LS’ morphology of monodisperse nanoparticles can be obtained using small-angle neutron scattering (SANS), measured at multiple contrasts, achieved by either ligand or solvent deuteration. Three-dimensional models of the nanoparticles’ core and LS are generated using an ab initio reconstruction method. Characteristic length scales extracted from the models are compared with simulations. We also characterize the evolution of the LS upon thermal annealing, and investigate the LS morphology of mixed-ligand copper and silver nanoparticles as well as gold nanoparticles coated with ternary mixtures. Our results suggest that SANS combined with multiphase modeling is a versatile approach for the characterization of nanoparticles’ LS., The ligand shell of a nanoparticle remains difficult to resolve, as the available characterization methods provide only qualitative information. Here, the authors introduce an approach based on small-angle neutron scattering that can quantitatively reveal the organization of ligands in mixed-monolayer nanoparticles.

Molecular self-assembly is a topic attracting intense scientific interest. Various strategies have been developed for construction of molecular aggregates with rationally designed properties, geometries, and dimensions that promise to provide solutions to both theoretical and practical problems in areas such as drug delivery, medical diagnostics, and biosensors, to name but a few. In this respect, gold nanoparticles covered with self-assembled monolayers presenting nanoscale surface patterns—typically patched, striped or Janus-like domains—represent an emerging field. These systems are particularly intriguing for use in bio-nanotechnology applications, as presence of such monolayers with three-dimensional (3D) morphology provides nanoparticles with surface-dependent properties that, in turn, affect their biological behavior. Comprehensive understanding of the physicochemical interactions occurring at the interface between these versatile nanomaterials and biological systems is therefore crucial to fully exploit their potential. This review aims to explore the current state of development of such patterned, self-assembled monolayer-protected gold nanoparticles, through step-by-step analysis of their conceptual design, synthetic procedures, predicted and determined surface characteristics, interactions with and performance in biological environments, and experimental and computational methods currently employed for their investigation.

The spontaneous self-organization of dissimilar ligands on the surface of metal nanoparticles is a very appealing approach to obtain anisotropic "spherical" systems. In addition to differences in ligand length and end groups, a further thermodynamic driving force to control the self-assembled monolayer organization may become available if the ligands are inherently immiscible, as is the case of hydrogenated (H-) and fluorinated (F-) species. Here, we validate the viability of this approach by combining (19)F NMR experiments and multiscale molecular simulations on large sets of mixed-monolayer-protected gold nanoparticles (NPs). The phase segregation of blends of F- and H-thiolates grafted on the surface of gold NPs allows a straightforward approach to patterned mixed monolayers, with the shapes of the monolayer domains being encoded in the structure of the F/H-thiolate ligands. The results obtained from this comprehensive study offer molecular design rules to achieve a precise control of inorganic nanoparticles protected by specifically patterned monolayers.

Noble metal nanoparticles with anisotropy in shape or surface functionality hold great promise in biomedical applications. This chapter gives an introductory overview of nanoparticle interaction with biological matter, in particular related to the protein corona and cell membrane. The effect of anisotropy in nanoparticle shape and chemical functionality on the resulting material properties is then discussed in detail both from a computational viewpoint and an experimental viewpoint. While the focus lies on elucidating the role of anisotropy on the interaction between nanoparticles and biological systems, various applications are outlined and put in context.