In silico design, building and gas adsorption of nano-porous graphene scaffolds.

Graphene-based nano-porous materials (GNM) are potentially useful for all those applications needing a large specific surface area (SSA), typical of the bidimensional graphene, yet realized in the bulk dimensionality. Such applications include for instance gas storage and sorting, catalysis and electrochemical energy storage. While a reasonable control of the structure is achieved in micro-porous materials by using nano-micro particles as templates, the controlled production or even characterization of GNMs with porosity strictly at the nano-scale still raises issues. These are usually produced using dispersion of nano-flakes as precursors resulting in little control on the final structure, which in turn reflects in problems in the structural model building for computer simulations. In this work, we describe a strategy to build models for these materials with predetermined structural properties (SSA, density, porosity), which exploits molecular dynamics simulations, Monte Carlo methods and machine learning algorithms. Our strategy is inspired by the real synthesis process: starting from randomly distributed flakes, we include defects, perforation, structure deformation and edge saturation on the fly, and, after structural refinement, we obtain realistic models, with given structural features. We find relationships between the structural characteristics and size distributions of the starting flake suspension and the final structure, which can give indications for more efficient synthesis routes. We subsequently give a full characterization of the models versus H2 adsorption, from which we extract quantitative relationship between the structural parameters and the gravimetric density. Our results quantitatively clarify the role of surfaces and edges relative amount in determining the H2 adsorption, and suggest strategies to overcome the inherent physical limitations of these materials as adsorbers. We implemented the model building and analysis procedures in software tools, freely available upon request.

Engineering 3D Graphene-Based Materials: State of the Art and Perspectives.

Graphene is the prototype of two-dimensional (2D) materials, whose main feature is the extremely large surface-to-mass ratio. This property is interesting for a series of applications that involve interactions between particles and surfaces, such as, for instance, gas, fluid or charge storage, catalysis, and filtering. However, for most of these, a volumetric extension is needed, while preserving the large exposed surface. This proved to be rather a hard task, especially when specific structural features are also required (e.g., porosity or density given). Here we review the recent experimental realizations and theoretical/simulation studies of 3D materials based on graphene. Two main synthesis routes area available, both of which currently use (reduced) graphene oxide flakes as precursors. The first involves mixing and interlacing the flakes through various treatments (suspension, dehydration, reduction, activation, and others), leading to disordered nanoporous materials whose structure can be characterized a posteriori, but is difficult to control. With the aim of achieving a better control, a second path involves the functionalization of the flakes with pillars molecules, bringing a new class of materials with structure partially controlled by the size, shape, and chemical-physical properties of the pillars. We finally outline the first steps on a possible third road, which involves the construction of pillared multi-layers using epitaxial regularly nano-patterned graphene as precursor. While presenting a number of further difficulties, in principle this strategy would allow a complete control on the structural characteristics of the final 3D architecture.

Low-Resolution Models for the Interaction Dynamics of Coated Gold Nanoparticles with ß2-microglobulin.

A large number of low-resolution models have been proposed in the last decades to reduce the computational cost of molecular dynamics simulations for bio-nano systems, such as those involving the interactions of proteins with functionalized nanoparticles (NPs). For the proteins, "minimalist" models at the one-bead-per residue (Cα-based) level and with implicit solvent are well established. For the gold NPs, widely explored for biotechnological applications, mesoscale (MS) models treating the NP core with a single spheroidal object are commonly proposed. In this representation, the surface details (coating, roughness, etc.) are lost. These, however, and the specificity of the functionalization, have been shown to have fundamental roles for the interaction with proteins. We presented a mixed-resolution coarse-grained (CG) model for gold NPs in which the surface chemistry is reintroduced as superficial smaller beads. We compared molecular dynamics simulations of the amyloid ß2-microglobulin represented at the minimalist level interacting with NPs represented with this model or at the MS level. Our finding highlights the importance of describing the surface of the NP at a finer level as the chemical-physical properties of the surface of the NP are crucial to correctly understand the protein-nanoparticle association.

SecStAnT: secondary structure analysis tool for data selection, statistics and models building.

MOTIVATION: Atomistic or coarse grained (CG) potentials derived from statistical distributions of internal variables have recently become popular due to the need of simplified interactions for reaching larger scales in simulations or more efficient conformational space sampling. However, the process of parameterization of accurate and predictive statistics-based force fields requires a huge amount of work and is prone to the introduction of bias and errors. RESULTS: This article introduces SecStAnT, a software for the creation and analysis of protein structural datasets with user-defined primary/secondary structure composition, with a particular focus on the CG representation. In addition, the possibility of managing different resolutions and the primary/secondary structure selectivity allow addressing the mapping-backmapping of atomistic to CG representation and study the secondary to primary structure relations. Sample datasets and distributions are reported, including interpretation of structural features. AVAILABILITY AND IMPLEMENTATION: SecStAnT is available free of charge at secstant.sourceforge.net/. Source code is freely available on request, implemented in Java and supported on Linux, MS Windows and OSX.

Diffusion within the cytoplasm: a mesoscale model of interacting macromolecules.

Recent experiments carried out in the dense cytoplasm of living cells have highlighted the importance of proteome composition and nonspecific intermolecular interactions in regulating macromolecule diffusion and organization. Despite this, the dependence of diffusion-interaction on physicochemical properties such as the degree of poly-dispersity and the balance between steric repulsion and nonspecific attraction among macromolecules was not systematically addressed. In this work, we study the problem of diffusion-interaction in the bacterial cytoplasm, combining theory and experimental data to build a minimal coarse-grained representation of the cytoplasm, which also includes, for the first time to our knowledge, the nucleoid. With stochastic molecular-dynamics simulations of a virtual cytoplasm we are able to track the single biomolecule motion, sizing from 3 to 80 nm, on submillisecond-long trajectories. We demonstrate that the size dependence of diffusion coefficients, anomalous exponents, and the effective viscosity experienced by biomolecules in the cytoplasm is fine-tuned by the intermolecular interactions. Accounting only for excluded volume in these potentials gives a weaker size-dependence than that expected from experimental data. On the contrary, adding nonspecific attraction in the range of 1-10 thermal energy units produces a stronger variation of the transport properties at growing biopolymer sizes. Normal and anomalous diffusive regimes emerge straightforwardly from the combination of high macromolecular concentration, poly-dispersity, stochasticity, and weak nonspecific interactions. As a result, small biopolymers experience a viscous cytoplasm, while the motion of big ones is jammed because the entanglements produced by the network of interactions and the entropic effects caused by poly-dispersity are stronger.

A multi-scale-multi-stable model for the rhodopsin photocycle.

We report a multi-scale simulation study of the photocycle of the rhodopsins. The quasi-atomistic representation ("united atoms" UA) of retinal is combined with a minimalist coarse grained (CG, one-bead-per amino acid) representation of the protein, in a hybrid UA/CG approach, which is the homolog of QM/MM, but at lower resolution. An accurate multi-stable parameterization of the model allows simulating each state and transition among them, and the combination of different scale representation allows addressing the entire photocycle. We test the model on bacterial rhodopsin, for which more experimental data are available, and then also report results for mammalian rhodopsins. In particular, the analysis of simulations reveals the spontaneous appearance of meta-stable states in quantitative agreement with experimental data.

Quantum sensing of microRNAs with nitrogen-vacancy centers in diamond.

Label-free detection of nucleic acids such as microRNAs holds great potential for early diagnostics of various types of cancers. Measuring intrinsic biomolecular charge using methods based on field effect has been a promising way to accomplish label-free detection. However, the charges of biomolecules are screened by counter ions in solutions over a short distance (Debye length), thereby limiting the sensitivity of these methods. Here, we measure the intrinsic magnetic noise of paramagnetic counter ions, such as Mn2+, interacting with microRNAs using nitrogen-vacancy (NV) centers in diamond. All-atom molecular dynamics simulations show that microRNA interacts with the diamond surface resulting in excess accumulation of Mn ions and stronger magnetic noise. We confirm this prediction by observing an increase in spin relaxation contrast of the NV centers, indicating higher Mn2+ local concentration. This opens new possibilities for next-generation quantum sensing of charged biomolecules, overcoming limitations due to the Debye screening.

Prospects for hydrogen storage in graphene.

Hydrogen-based fuel cells are promising solutions for the efficient and clean delivery of electricity. Since hydrogen is an energy carrier, a key step for the development of a reliable hydrogen-based technology requires solving the issue of storage and transport of hydrogen. Several proposals based on the design of advanced materials such as metal hydrides and carbon structures have been made to overcome the limitations of the conventional solution of compressing or liquefying hydrogen in tanks. Nevertheless none of these systems are currently offering the required performances in terms of hydrogen storage capacity and control of adsorption/desorption processes. Therefore the problem of hydrogen storage remains so far unsolved and it continues to represent a significant bottleneck to the advancement and proliferation of fuel cell and hydrogen technologies. Recently, however, several studies on graphene, the one-atom-thick membrane of carbon atoms packed in a honeycomb lattice, have highlighted the potentialities of this material for hydrogen storage and raise new hopes for the development of an efficient solid-state hydrogen storage device. Here we review on-going efforts and studies on functionalized and nanostructured graphene for hydrogen storage and suggest possible developments for efficient storage/release of hydrogen under ambient conditions.

Deconstructing Electrostatics of Functionalized Metal Nanoparticles from Molecular Dynamics Simulations.

Gold nanoparticles (NPs) with different surface functionalizations can selectively interact with specific proteins, allowing a wide range of possible applications in biotechnology and biomedicine. To prevent their tendency to aggregate and to modulate their interaction with charged biomolecules or substrates (e.g., for biosensing applications), they can be functionalized with charged groups, introducing a mutual interaction which can be modulated by changing the ionic strength of the solvent. In silico modeling of these systems is often addressed with low-resolution models, which must account for these effects in the, often implicit, solvent representation. Here, we present a systematic conformational dynamic characterization of ligand-coated gold nanoparticles with different sizes, charges, and functionalizations by means of atomistic molecular dynamics simulations. Based on these, we deconstruct their electrostatic properties and propose a general representation of their average-long-range interactions extendable to different sizes, charges, and ionic strengths. This study clarifies in detail the role of the different features of the NP (charge, size, structure) and of the ionic strength in determining the details of the interparticle interaction and represents the first step toward a general strategy for the parametrization of NP coarse-grained models able to account for varying ionic strengths.

Heat-Driven Iontronic Nanotransistors.

Thermoelectric polyelectrolytes are emerging as ideal material platform for self-powered bio-compatible electronic devices and sensors. However, despite the nanoscale nature of the ionic thermodiffusion processes underlying thermoelectric efficiency boost in polyelectrolytes, to date no evidence for direct probing of ionic diffusion on its relevant length and time scale has been reported. This gap is bridged by developing heat-driven hybrid nanotransistors based on InAs nanowires embedded in thermally biased Na+ -functionalized (poly)ethyleneoxide, where the semiconducting nanostructure acts as a nanoscale probe sensitive to the local arrangement of the ionic species. The impact of ionic thermoelectric gating on the nanodevice electrical response is addressed, investigating the effect of device architecture, bias configuration and frequency of the heat stimulus, and inferring optimal conditions for the heat-driven nanotransistor operation. Microscopic quantities of the polyelectrolyte such as the ionic diffusion coefficient are extracted from the analysis of hysteretic behaviors rising in the nanodevices. The reported experimental platform enables simultaneously the ionic thermodiffusion and nanoscale resolution, providing a framework for direct estimation of polyelectrolytes microscopic parameters. This may open new routes for heat-driven nanoelectronic applications and boost the rational design of next-generation polymer-based thermoelectric materials.

Minimalist models for proteins: a comparative analysis.

The last decade has witnessed a renewed interest in the coarse-grained (CG) models for biopolymers, also stimulated by the needs of modern molecular biology, dealing with nano- to micro-sized bio-molecular systems and larger than microsecond timescale. This combination of size and timescale is, in fact, hard to access by atomic-based simulations. Coarse graining the system is a route to be followed to overcome these limits, but the ways of practically implementing it are many and different, making the landscape of CG models very vast and complex. In this paper, the CG models are reviewed and their features, applications and performances compared. This analysis, restricted to proteins, focuses on the minimalist models, namely those reducing at minimum the number of degrees of freedom without losing the possibility of explicitly describing the secondary structures. This class includes models using a single or a few interacting centers (beads) for each amino acid. From this analysis several issues emerge. The difficulty in building these models resides in the need for combining transferability/predictive power with the capability of accurately reproducing the structures. It is shown that these aspects could be optimized by accurately choosing the force field (FF) terms and functional forms, and combining different parameterization procedures. In addition, in spite of the variety of the minimalist models, regularities can be found in the parameters values and in FF terms. These are outlined and schematically presented with the aid of a generic phase diagram of the polypeptide in the parameter space and, hopefully, could serve as guidelines for the development of minimalist models incorporating the maximum possible level of predictive power and structural accuracy.

Aggregation behavior of nanoparticles: Revisiting the phase diagram of colloids.

Surface functionalization of metal nanoparticles (NPs), e.g., using peptides and proteins, has recently attracted a considerable attention in the field of design of therapeutics and diagnostics. The possibility of diverse functionalization allows them to selectively interact with proteins, while the metal core ensures solubility, making them tunable therapeutic agents against diseases due to mis-folding or aggregation. On the other hand, their action is limited by possible self-aggregation, which could be, however, prevented based on the full understanding of their phase diagram as a function of the environmental variables (temperature, ionic strength of the solution, concentration) and intrinsic characteristics (size, charge, amount, and type of functional groups). A common modeling strategy to study the phase behavior is to represent the NPs as spheres interacting via effective potentials implicitly accounting for the solvation effects. Their size put the NPs into the class of colloids, albeit with particularly complex interactions including both attractive and repulsive features, and a consequently complex phase diagram. In this work, we review the studies exploring the phases of these systems starting from those with only attractive or repulsive interactions, displaying a simpler disperse-clustered-aggregated transitions. The phase diagram is here interpreted focusing on the universal aspects, i.e., those dependent on the general feature of the potentials, and available data are organized in a parametric phase diagram. We then consider the potentials with competing attractive short range well and average-long-range repulsive tail, better representing the NPs. Through the proper combination of the attractive only and repulsive only potentials, we are able to interpret the appearance of novel phases, characterized by aggregates with different structural characteristics. We identify the essential parameters that stabilize the disperse phase potentially useful to optimize NP therapeutic activity and indicate how to tune the phase behavior by changing environmental conditions or the NP chemical-physical properties.

Evaluating the performance of ReaxFF potentials for sp2 carbon systems (graphene, carbon nanotubes, fullerenes) and a new ReaxFF potential.

We study the performance of eleven reactive force fields (ReaxFF), which can be used to study sp2 carbon systems. Among them a new hybrid ReaxFF is proposed combining two others and introducing two different types of C atoms. The advantages of that potential are discussed. We analyze the behavior of ReaxFFs with respect to 1) the structural and mechanical properties of graphene, its response to strain and phonon dispersion relation; 2) the energetics of (n, 0) and (n, n) carbon nanotubes (CNTs), their mechanical properties and response to strain up to fracture; 3) the energetics of the icosahedral C60 fullerene and the 40 C40 fullerene isomers. Seven of them provide not very realistic predictions for graphene, which made us focusing on the remaining, which provide reasonable results for 1) the structure, energy and phonon band structure of graphene, 2) the energetics of CNTs versus their diameter and 3) the energy of C60 and the trend of the energy of the C40 fullerene isomers versus their pentagon adjacencies, in accordance with density functional theory (DFT) calculations and/or experimental data. Moreover, the predicted fracture strain, ultimate tensile strength and strain values of CNTs are inside the range of experimental values, although overestimated with respect to DFT. However, they underestimate the Young's modulus, overestimate the Poisson's ratio of both graphene and CNTs and they display anomalous behavior of the stress - strain and Poisson's ratio - strain curves, whose origin needs further investigation.

Multiscale modeling of proteins.

The activity within a living cell is based on a complex network of interactions among biomolecules, exchanging information and energy through biochemical processes. These events occur on different scales, from the nano- to the macroscale, spanning about 10 orders of magnitude in the space domain and 15 orders of magnitude in the time domain. Consequently, many different modeling techniques, each proper for a particular time or space scale, are commonly used. In addition, a single process often spans more than a single time or space scale. Thus, the necessity arises for combining the modeling techniques in multiscale approaches. In this Account, I first review the different modeling methods for bio-systems, from quantum mechanics to the coarse-grained and continuum-like descriptions, passing through the atomistic force field simulations. Special attention is devoted to their combination in different possible multiscale approaches and to the questions and problems related to their coherent matching in the space and time domains. These aspects are often considered secondary, but in fact, they have primary relevance when the aim is the coherent and complete description of bioprocesses. Subsequently, applications are illustrated by means of two paradigmatic examples: (i) the green fluorescent protein (GFP) family and (ii) the proteins involved in the human immunodeficiency virus (HIV) replication cycle. The GFPs are currently one of the most frequently used markers for monitoring protein trafficking within living cells; nanobiotechnology and cell biology strongly rely on their use in fluorescence microscopy techniques. A detailed knowledge of the actions of the virus-specific enzymes of HIV (specifically HIV protease and integrase) is necessary to study novel therapeutic strategies against this disease. Thus, the insight accumulated over years of intense study is an excellent framework for this Account. The foremost relevance of these two biomolecular systems was recently confirmed by the assignment of two of the Nobel prizes in 2008: in chemistry for the discovery of GFP and in medicine for the discovery of HIV. Accordingly, these proteins were studied with essentially all of the available modeling techniques, making them ideal examples for studying the details of multiscale approaches in protein modeling.

Covalent organic functionalization of graphene nanosheets and reduced graphene oxide via 1,3-dipolar cycloaddition of azomethine ylide.

Organic functionalization of graphene is successfully performed via 1,3-dipolar cycloaddition of azomethine ylide in the liquid phase. The comparison between 1-methyl-2-pyrrolidinone and N,N-dimethylformamide as dispersant solvents, and between sonication and homogenization as dispersion techniques, proves N,N-dimethylformamide and homogenization as the most effective choice. The functionalization of graphene nanosheets and reduced graphene oxide is confirmed using different techniques. Among them, energy-dispersive X-ray spectroscopy allows to map the pyrrolidine ring of the azomethine ylide on the surface of functionalized graphene, while micro-Raman spectroscopy detects new features arising from the functionalization, which are described in agreement with the power spectrum obtained from ab initio molecular dynamics simulation. Moreover, X-ray photoemission spectroscopy of functionalized graphene allows the quantitative elemental analysis and the estimation of the surface coverage, showing a higher degree of functionalization for reduced graphene oxide. This more reactive behavior originates from the localization of partial charges on its surface due to the presence of oxygen defects, as shown by the simulation of the electrostatic features. Functionalization of graphene using 1,3-dipolar cycloaddition is shown to be a significant step towards the controlled synthesis of graphene-based complex structures and devices at the nanoscale.

Correction: Covalent organic functionalization of graphene nanosheets and reduced graphene oxide via 1,3-dipolar cycloaddition of azomethine ylide.

[This corrects the article DOI: 10.1039/D1NA00335F.].

GCN5-dependent acetylation of HIV-1 integrase enhances viral integration.

BACKGROUND: An essential event during the replication cycle of HIV-1 is the integration of the reverse transcribed viral DNA into the host cellular genome. Our former report revealed that HIV-1 integrase (IN), the enzyme that catalyzes the integration reaction, is positively regulated by acetylation mediated by the histone acetyltransferase (HAT) p300. RESULTS: In this study we demonstrate that another cellular HAT, GCN5, acetylates IN leading to enhanced 3'-end processing and strand transfer activities. GCN5 participates in the integration step of HIV-1 replication cycle as demonstrated by the reduced infectivity, due to inefficient provirus formation, in GCN5 knockdown cells. Within the C-terminal domain of IN, four lysines (K258, K264, K266, and K273) are targeted by GCN5 acetylation, three of which (K264, K266, and K273) are also modified by p300. Replication analysis of HIV-1 clones carrying substitutions at the IN lysines acetylated by both GCN5 and p300, or exclusively by GCN5, demonstrated that these residues are required for efficient viral integration. In addition, a comparative analysis of the replication efficiencies of the IN triple- and quadruple-mutant viruses revealed that even though the lysines targeted by both GCN5 and p300 are required for efficient virus integration, the residue exclusively modified by GCN5 (K258) does not affect this process. CONCLUSIONS: The results presented here further demonstrate the relevance of IN post-translational modification by acetylation, which results from the catalytic activities of multiple HATs during the viral replication cycle. Finally, this study contributes to clarifying the recent debate raised on the role of IN acetylated lysines during HIV-1 infection.

Evolutionary Switches Structural Transitions via Coarse-Grained Models.

Transitions between different conformational states are ubiquitous in proteins. A vast class of conformation-changing proteins includes evolutionary switches, which vary their conformation as an effect of few mutations or weak environmental variations. However, modeling those processes is extremely difficult due to the need of efficiently exploring a vast conformational space to look for the actual transition path. In this study, we report a strategy that simplifies this task attacking the complexity on several sides. We first apply a minimalist coarse-grained model to the protein, based on an empirical force field with a partial structural bias toward one or both the reference structures. We then explore the transition paths by means of stochastic molecular dynamics and select representative structures by means of a principal path-based clustering algorithm. We finally compare this trajectory with that produced by independent methods adopting a morphing-oriented approach. Our analysis indicates that the minimalist model returns trajectories capable of exploring intermediate states with physical meaning, retaining a very low computational cost, which can allow systematic and extensive exploration of the multistable proteins transition pathways.

Complexes of HIV-1 integrase with HAT proteins: multiscale models, dynamics, and hypotheses on allosteric sites of inhibition.

A new and very promising strategy for HIV drug discovery consists in blocking the multiple functional interactions between HIV-1 integrase (IN) and its cellular cofactors. At present, this line of action is hindered by the absence of three-dimensional structures of IN in complex with any of them. In this article, we developed a full-length three-dimensional structure of IN, including the highly flexible terminal residues 270-288, which are not experimentally solved. Additionally, we built models of IN complexed to the human acetyltransferases GCN5 and p300 based on available structural and mutagenesis data. Then, we studied the dynamical behavior of these models by means of the Coarse-Grained Molecular Dynamics (CGMD) and Essential Dynamics (ED) to locate and characterize the nature of the largest collective motions. We found correlated motions involving distant regions of IN. Moreover, we found that these are influenced by the binding with the acetyltransferases (HATs). Taken together these findings suggest a way to affect the acetyltransferase binding by an allosteric type of inhibition and provide an important new approach for the drug design against HIV disease.

Raman study of chromophore states in photochromic fluorescent proteins.

The photophysical mechanism underlying the photochromic behavior of green fluorescent protein (GFP) mutants is investigated by means of preresonant Raman spectroscopy and model calculations. The studied molecules are reversibly switchable fluorophores that can be repeatedly converted between fluorescent and nonfluorescent states by irradiation and are attracting a broad interest for a number of new applications. Experimental results on chemically synthesized isolated chromophores are analyzed within a density functional theory approach that allows us to link the detected vibrational modes to specific ground-state configurations before and after photoconversion. These data are compared to results obtained for the case of complete folded proteins including the same chromophores. Our results highlight the impact of chromophore cis-trans isomerization and protonation state in the photophysics of these proteins and provide useful guidelines for novel mutant design.