Accurate calculation of second osmotic virial coefficients of proteins using mixed Poisson–Boltzmann and extended DLVO theory

The state of proteins in aqueous solution is determined by weak, nonspecific interactions affected by pH, solvent composition, and ionic strength. Protein–protein interactions play a crucial role in determining protein stability and solubility. The second osmotic coefficient (B$_{22}$) provides insight into effective interactions between proteins in solution. Models for calculating B$_{22}$ are valuable for estimating interactions, explaining measured phenomena, and reducing experimental time. However, existing models, like the Derjaguin–Landau–Verwey–Overbeek (DLVO) theory, assume a simple spherical shape for proteins. Owing to the fact that proteins exhibit diverse shapes and charge distributions, influencing their electrostatic properties and overall interactions, DLVO accuracy is significantly reduced for nonspherical proteins. To address this limitation, we introduce the xDLVO-CGhybr model, which combines Poisson–Boltzmann (PB) and Debye–Hückel (DH) theories to account for electrostatic interactions between proteins. PB is used for short intermolecular distances (<2 nm) with an all-atom resolution, while DH is employed for longer distances on a coarse-grained level. Additionally, xDLVO-CGhybr incorporates an improved coarse-grained Lennard-Jones (LJ) potential derived directly from the all-atom potential to capture dispersion interactions. This model improves the calculated B22 values compared to existing models and can be applied to proteins with arbitrary shape and charge under various solvent conditions (up to 1 M monovalent salt concentration). We demonstrate the application of xDLVO-CGhybr to bovine trypsin inhibitor, ribonuclease A, chymotrypsinogen, concanavalin A, bovine serum albumin, and human immunoglobulin type I proteins, validating the model against experimental data

A coarse-grained xDLVO model for colloidal protein–protein interactions

Colloidal protein–protein interactions (PPIs) of attractive and repulsive nature modulate the solubility of proteins, their aggregation, precipitation and crystallization. Such interactions are very important for many biotechnological processes, but are complex and hard to control, therefore, difficult to be understood in terms of measurements alone. In diluted protein solutions, PPIs can be estimated from the osmotic second virial coefficient, B$_{22}$, which has been calculated using different methods and levels of theory. The most popular approach is based on the Derjaguin–Landau–Verwey–Overbeek (DLVO) theory and its extended versions, i.e. xDLVO. Despite much efforts, these models are not fully quantitative and must be fitted to experiments, which limits their predictive value. Here, we report an extended xDLVO-CG model, which extends existing models by a coarse-grained representation of proteins and the inclusion of an additional ion–protein dispersion interaction term. We demonstrate for four proteins, i.e. lysozyme (LYZ), subtilisin (Subs), bovine serum albumin (BSA) and immunoglobulin (IgG1), that semi-quantitative agreement with experimental values without the need to fit to experimental B$_{22}$ values. While most likely not the final step in the nearly hundred years of research in PPIs, xDLVO-CG is a step towards predictive PPIs calculations that are transferable to different proteins

Hierarchical Coarse-Grained Strategy for Macromolecular Self-Assembly: Application to Hepatitis B Virus-Like Particles

Macromolecular self-assembly is at the basis of many phenomena in material and life sciences that find diverse applications in technology. One example is the formation of virus-like particles (VLPs) that act as stable empty capsids used for drug delivery or vaccine fabrication. Similarly to the capsid of a virus, VLPs are protein assemblies, but their structural formation, stability, and properties are not fully understood, especially as a function of the protein modifications. In this work, we present a data-driven modeling approach for capturing macromolecular self-assembly on scales beyond traditional molecular dynamics (MD), while preserving the chemical specificity. Each macromolecule is abstracted as an anisotropic object and high-dimensional models are formulated to describe interactions between molecules and with the solvent. For this, data-driven protein–protein interaction potentials are derived using a Kriging-based strategy, built on high-throughput MD simulations. Semi-automatic supervised learning is employed in a high performance computing environment and the resulting specialized force-fields enable a significant speed-up to the micrometer and millisecond scale, while maintaining high intermolecular detail. The reported generic framework is applied for the first time to capture the formation of hepatitis B VLPs from the smallest building unit, i.e., the dimer of the core protein HBcAg. Assembly pathways and kinetics are analyzed and compared to the available experimental observations. We demonstrate that VLP self-assembly phenomena and dependencies are now possible to be simulated. The method developed can be used for the parameterization of other macromolecules, enabling a molecular understanding of processes impossible to be attained with other theoretical models

Layer‐By‐Layer Assembly of Asymmetric Linkers into Non‐Centrosymmetric Metal Organic Frameworks: A Thorough Theoretical Treatment

Layer-by-layer synthesis of surface-coordinated metal–organic frameworks (SURMOF) enables the assembly of asymmetric, dipolar linkers into non-centrosymmetric pillar-layered structures. Using appropriate substrate terminations can yield oriented growth with the dipoles aligned perpendicular to the surface. The aligned pillar linkers give rise to a built-in electrostatic field. In addition, the non-centrosymmetric structure of the SURMOF gives rise to intriguing nonlinear optical features, such as second harmonic generation. Previous research with methyl-functionalized bipyridine pillar linkers have demonstrated that this approach works in principle, but so far the total degree of alignment is only very small. Herein, a multiscale modelling approach is presented for in-silico SURMOF assembly to identify and overcome limitations in the growth of pillar-layered SURMOFs and to develop a strategy to maximize linker alignment. Using master equation models and kinetic Monte Carlo simulations, it is found that the formation of a highly ordered state corresponding to the thermodynamic equilibrium is often prevented by long-lasting transient effects. Based on ab initio binding energies for a wide selection of hypothetical pillar linkers, a fast-binding, slow-relaxation scheme is able to be identified during the SURMOF growth for a range of different pillar linkers. These observations allow them to derive a rational strategy for the design of novel linkers to yield SURMOF-based non-centrosymmetric materials with substantially improved properties

Multiscale Model of CVD Growth of Graphene on Cu(111) Surface

Due to its outstanding properties, graphene has emerged as one of the most promising 2D materials in a large variety of research fields. Among the available fabrication protocols, chemical vapor deposition (CVD) enables the production of high quality single-layered large area graphene. To better understand the kinetics of CVD graphene growth, multiscale modeling approaches are sought after. Although a variety of models have been developed to study the growth mechanism, prior studies are either limited to very small systems, are forced to simplify the model to eliminate the fast process, or they simplify reactions. While it is possible to rationalize these approximations, it is important to note that they have non-trivial consequences on the overall growth of graphene. Therefore, a comprehensive understanding of the kinetics of graphene growth in CVD remains a challenge. Here, we introduce a kinetic Monte Carlo protocol that permits, for the first time, the representation of relevant reactions on the atomic scale, without additional approximations, while still reaching very long time and length scales of the simulation of graphene growth. The quantum-mechanics-based multiscale model, which links kinetic Monte Carlo growth processes with the rates of occurring chemical reactions, calculated from first principles makes it possible to investigate the contributions of the most important species in graphene growth. It permits the proper investigation of the role of carbon and its dimer in the growth process, thus indicating the carbon dimer to be the dominant species. The consideration of hydrogenation and dehydrogenation reactions enables us to correlate the quality of the material grown within the CVD control parameters and to demonstrate an important role of these reactions in the quality of the grown graphene in terms of its surface roughness, hydrogenation sites, and vacancy defects. The model developed is capable of providing additional insights to control the graphene growth mechanism on Cu(111), which may guide further experimental and theoretical developments

Analytical Model of CVD Growth of Graphene on Cu(111) Surface

Although the CVD synthesis of graphene on Cu(111) is an industrial process of outstanding importance, its theoretical description and modeling are hampered by its multiscale nature and the large number of elementary reactions involved. In this work, we propose an analytical model of graphene nucleation and growth on Cu(111) surfaces based on the combination of kinetic nucleation theory and the DFT simulations of elementary steps. In the framework of the proposed model, the mechanism of graphene nucleation is analyzed with particular emphasis on the roles played by the two main feeding species, C and C$_{2}$. Our analysis reveals unexpected patterns of graphene growth, not typical for classical nucleation theories. In addition, we show that the proposed theory allows for the reproduction of the experimentally observed characteristics of polycrystalline graphene samples in the most computationally efficient way

On–off conduction photoswitching in modelled spiropyran-based metal-organic frameworks

Materials with photoswitchable electronic properties and conductance values that can be reversibly changed over many orders of magnitude are highly desirable. Metal-organic framework (MOF) films functionalized with photoresponsive spiropyran molecules demonstrated the general possibility to switch the conduction by light with potentially large on-off-ratios. However, the fabrication of MOF materials in a trial-and-error approach is cumbersome and would benefit significantly from in silico molecular design. Based on the previous proof-of-principle investigation, here, we design photoswitchable MOFs which incorporate spiropyran photoswitches at controlled positions with defined intermolecular distances and orientations. Using multiscale modelling and automated workflow protocols, four MOF candidates are characterized and their potential for photoswitching the conductivity is explored. Using ab initio calculations of the electronic coupling between the molecules in the MOF, we show that lattice distances and vibrational flexibility tremendously modulate the possible conduction photoswitching between spiropyran- and merocyanine-based MOFs upon light absorption, resulting in average on-off ratios higher than 530 and 4200 for p- and n-conduction switching, respectively. Further functionalization of the photoswitches with electron-donating/-withdrawing groups is demonstrated to shift the energy levels of the frontier orbitals, permitting a guided design of new spiropyran-based photoswitches towards controlled modification between electron and hole conduction in a MOF

Monte-Carlo Simulations of Soft Matter Using SIMONA: A Review of Recent Applications

Molecular simulations such as Molecular Dynamics (MD) and Monte Carlo (MC) have gained increasing importance in the explanation of various physicochemical and biochemical phenomena in soft matter and help elucidate processes that often cannot be understood by experimental techniques alone. While there is a large number of computational studies and developments in MD, MC simulations are less widely used, but they offer a powerful alternative approach to explore the potential energy surface of complex systems in a way that is not feasible for atomistic MD, which still remains fundamentally constrained by the femtosecond timestep, limiting investigations of many essential processes. This paper provides a review of the current developments of a MC based code, SIMONA, which is an efficient and versatile tool to perform large-scale conformational sampling of different kinds of (macro)molecules. We provide an overview of the approach, and an application to soft-matter problems, such as protocols for protein and polymer folding, physical vapor deposition of functional organic molecules and complex oligomer modeling. SIMONA offers solutions to different levels of programming expertise (basic, expert and developer level) through the usage of a designed Graphical Interface pre-processor, a convenient coding environment using XML and the development of new algorithms using Python/C++. We believe that the development of versatile codes which can be used in different fields, along with related protocols and data analysis, paves the way for wider use of MC methods

Stabilization of Pancake Bonding in (TCNQ)₂.⁻ Dimers in the Radical‐Anionic Salt (N−CH₃−2‐NH₂−5Cl−Py)(TCNQ)(CH₃CN) Solvate and Antiferromagnetism Induction

We report a new antiferromagnetic radical‐anion salt (RAS) formed from 7,7,8,8‐tetracyanquinonedimethane (TCNQ) anion and 2‐amino‐5‐chloro‐pyridine cation with the composition of (N−CH3−2‐NH2−5Cl−Py)(TCNQ)(CH3CN). The crystallographic data indicates the formation of (TCNQ)2.− radical‐anion π‐dimers in the synthesized RAS. Unrestricted density functional theory calculations show that the formed π‐dimers characterize with strong π‐stacking “pancake” interactions, resulting in high electronic coupling, enabling efficient charge transfer properties, but π‐dimers cannot be stable in the isolated conditions as a result of strong Coulomb repulsions. In a crystal, where (TCNQ)2.− π‐dimers bound in the endless chainlets via supramolecular bonds with (N−CH3−2‐NH2−5‐Cl−Py)+ cations, the repulsion forces are screened, allowing for specific parallel π‐stacking interactions and stable radical‐anion dimers formation. Measurements of magnetic susceptibility and magnetization confirm antiferromagnetic properties of RAS, what is in line with the higher stability of ground singlet state of the radical‐anion pair, calculated by means of the DFT. Therefore, the reported radical‐anion (N−CH3−2‐NH2−5Cl−Py)(TCNQ)(CH3CN) solvate has promising applications in novel magnetics with supramolecular structures