Multi-Scale Modelling of Aggregation of TiO2 Nanoparticle Suspensions in Water

Titanium dioxide nanoparticles have risen concerns about their possible toxicity and the European Food Safety Authority recently banned the use of TiO2 nano-additive in food products. Following the intent of relating nanomaterials atomic structure with their toxicity without having to conduct large-scale experiments on living organisms, we investigate the aggregation of titanium dioxide nanoparticles using a multi-scale technique: starting from ab initio Density Functional Theory to get an accurate determination of the energetics and electronic structure, we switch to classical Molecular Dynamics simulations to calculate the Potential of Mean Force for the connection of two identical nanoparticles in water; the fitting of the latter by a set of mathematical equations is the key for the upscale. Lastly, we perform Brownian Dynamics simulations where each nanoparticle is a spherical bead. This coarsening strategy allows studying the aggregation of a few thousand nanoparticles. Applying this novel procedure, we find three new molecular descriptors, namely, the aggregation free energy and two numerical parameters used to correct the observed deviation from the aggregation kinetics described by the Smoluchowski theory. Ultimately, molecular descriptors can be fed into QSAR models to predict the toxicity of a material knowing its physicochemical properties, enabling safe design strategies.

Standard binding free energy and membrane desorption mechanism for a phospholipase C

Peripheral membrane proteins (PMPs) bind temporarily to cellular membranes and play important roles in signalling, lipid metabolism and membrane trafficking. Obtaining accurate membrane-PMP affinities using experimental techniques is more challenging than for protein-ligand affinities in aqueous solution. At the theoretical level, calculation of standard protein-membrane binding free energy using molecular dynamics simulations remains a daunting challenge owing to the size of the biological objects at play, the slow lipid diffusion and the large variation in configurational entropy that accompanies the binding process. To overcome these challenges, we used a computational framework relying on a series of potential-of-mean-force (PMF) calculations including a set of geometrical restraints on collective variables. This methodology allowed us to determine the standard binding free energy of a PMP to a phospholipid bilayer using an all-atom force field. Bacillus thuringiensis phosphatidylinositol-specific phospholipase C (BtPI-PLC) was chosen due to its importance as a virulence factor and owing to the host of experimental affinity data available. We computed a standard binding free energy of -8.2±1.4 kcal/mol in reasonable agreement with the reported experimental values (-6.6±0.2 kcal/mol). In light of the 2.3-μs separation PMF calculation, we investigated the mechanism whereby BtPI-PLC disengages from interactions with the lipid bilayer during separation. We describe how a short amphipathic helix engages in transitory interactions to ease the passage of its hydrophobes through the interfacial region upon desorption from the bilayer.

An Efficient GaMD Multi-Level Enhanced Sampling Strategy: Application to Polarizable Force Fields Simulations of Large Biological Systems

We introduce a novel multi-level enhanced sampling strategy grounded on Gaussian accelerated Molecular Dynamics (GaMD). First, we propose a GaMD multi-GPUs-accelerated implementation within the Tinker-HP molecular dynamics package. We introduce the new "dual-water" mode and its use with the flexible AMOEBA polarizable force field.By adding harmonic boosts to the water stretching and bonding terms, it accelerates the solvent-solute interactions while enabling speedups thanks to the use of fast multiple--timestep integrators. To further reduce time-to-solution, we couple GaMD to Umbrella Sampling (US). The GaMD—US/dual--water approach is tested on the 1D Potential of Mean Force (PMF) of the solvated CD2--CD58 system (168000 atoms) allowing the AMOEBA PMF to converge within 1 kcal/mol of the experimental value. Finally, Adaptive Sampling (AS) is added enabling AS-GaMD capabilities but also the introduction of the new Adaptive Sampling--US--GaMD (ASUS-GaMD) scheme. The highly parallel ASUS--GaMD setup decreases time to convergence by respectively 10 and 20 times compared to GaMD-US and US. Overall, beside the acceleration of PMF computations, Tinker-HP now allows for the simultaneous use of Adaptive Sampling and GaMD-"dual water" enhanced sampling approaches increasing the applicability of polarizable force fields to large scale simulations of biological systems.

An Efficient GaMD Multi-Level Enhanced Sampling Strategy: Application to Polarizable Force Fields Simulations of Large Biological Systems

We detail a novel multi-level enhanced sampling strategy grounded on Gaussian accelerated Molecular Dynamics (GaMD). First, we propose a GaMD multi-GPUs-accelerated implementation within the Tinker-HP molecular dynamics package. We then introduce the new "dual-water" mode and its use with the flexible AMOEBA polarizable force field. By adding harmonic boosts to the water stretching and bonding terms, it accelerates the solvent-solute interactions while enabling speedups thanks to the use of fast multiple--timestep integrators. To further reduce time-to-solution, we couple GaMD to Umbrella Sampling (US). The GaMD—US/dual-water approach is tested on the 1D Potential of Mean Force (PMF) of the CD2-CD58 system (168000 atoms) allowing the AMOEBA PMF to converge within 1 kcal/mol of the experimental value. Finally, Adaptive Sampling (AS) is added enabling AS-GaMD capabilities but also the introduction of the new Adaptive Sampling--US--GaMD (ASUS--GaMD) scheme. The highly parallel ASUS--GaMD setup decreases time to convergence by respectively 10 and 20 compared to GaMD--US and US.

Ion-mediated charge-charge interactions drive aggregation of surface-functionalized gold nanoparticles

Monolayer-protected metal nanoparticles (NPs) are not only promising materials with a wide range of potential industrial and biological applications, but they are also a powerful tool to investigate the behavior of matter at nanoscopic scales, including the stability of dispersions and colloidal systems. This stability is dependent on a delicate balance between electrostatic and steric interactions that occur in the solution, and it is described in quantitative terms by the classic Derjaguin-Landau-Vewey-Overbeek (DLVO) theory, that posits that aggregation between NPs is driven by hydrophobic interactions and opposed by electrostatic interactions. To investigate the limits of this theory at the nanoscale, where the continuum assumptions required by the DLVO theory break down, here we investigate NP dimerization by computing the Potential of Mean Force (PMF) of this process using fully atomistic MD simulations. Serendipitously, we find that electrostatic interactions can lead to the formation of metastable NP dimers. These dimers are stabilized by complexes formed by negatively charged ligands belonging to distinct NPs that are bridged by positively charged ions present in solution. We validate our findings by collecting tomographic EM images of NPs in solution and by quantifying their radial distribution function, that shows a marked peak at interparticle distance comparable with that of MD simulations. Taken together, our results suggest that not only hydrophobic interactions, but also electrostatic interactions, contribute to attraction between nano-sized charged objects at very short length scales.

Inverse Boltzmann Iterative Multi-Scale Molecular Dynamics Study between Carbon Nanotubes and Amino Acids

Our work uses Iterative Boltzmann Inversion (IBI) to study the coarse-grained interaction between 20 amino acids and the representative carbon nanotube CNT55L3. IBI is a multi-scale simulation method that has attracted the attention of many researchers in recent years. It can effectively modify the coarse-grained model derived from the Potential of Mean Force (PMF). IBI is based on the distribution result obtained by All-Atom molecular dynamics simulation, that is, the target distribution function, the PMF potential energy is extracted, and then the initial potential energy extracted by the PMF is used to perform simulation iterations using IBI. Our research results have gone through more than 100 iterations, and finally, the distribution obtained by coarse-grained molecular simulation (CGMD) can effectively overlap with the results of all-atom molecular dynamics simulation (AAMD). In addition, our work lays the foundation for the study of force fields for the simulation of the coarse-graining of super-large proteins and other important nanoparticles.

Vectorial channeling as a mechanism for translational control by functional prions and condensates

Translation of messenger RNA (mRNA) is regulated through a diverse set of RNA-binding proteins. A significant fraction of RNA-binding proteins contains prion-like domains which form functional prions. This raises the question of how prions can play a role in translational control. Local control of translation in dendritic spines by prions has been invoked in the mechanism of synaptic plasticity and memory. We show how channeling through diffusion and processive translation cooperate in highly ordered mRNA/prion aggregates as well as in less ordered mRNA/protein condensates depending on their substructure. We show that the direction of translational control, whether it is repressive or activating, depends on the polarity of the mRNA distribution in mRNA/prion assemblies which determines whether vectorial channeling can enhance recycling of ribosomes. Our model also addresses the effect of changes of substrate concentration in assemblies that have been suggested previously to explain translational control by assemblies through the introduction of a potential of mean force biasing diffusion of ribosomes inside the assemblies. The results from the model are compared with the experimental data on translational control by two functional RNA-binding prions, CPEB involved in memory and Rim4 involved in gametogenesis.

Key Factors Governing Initial Stages of Lipid Droplet Formation

Lipid droplets (LDs) are neutral lipid storage organelles surrounded by a phospholipid (PL)monolayer. LD biogenesis from the endoplasmic reticulum (ER) is driven by phase separation of neutral lipids, overcoming surface tension and membrane deformation. However, the core biophysics of the initial steps of LD formation remain relatively poorly understood. Here, we use a tunable, phenomenological coarse-grained (CG) model to study triacylglycerol (TG) nucleation in a bilayer membrane. We show that PL rigidity has a strong influence on TG lensing and membrane remodeling: When membrane rigidity increases, TG clusters remain more planar with high anisotropy but a minor degree of phase nucleation. This finding is confirmed by free energy sampling simulations that calculate the potential of mean force (PMF) as a function of the degree of nucleation and anisotropy. We also show that asymmetric tension, controlled by the number of PLs on each membrane leaflet, determines the budding direction. A TG lens buds in the direction of the monolayer containing excess PLs to allow for better PL coverage of TG, consistent with reported experiments. Finally, two governing mechanisms of the LD growth, Ostwald ripening and merging, are observed. Taken together, this study characterizes the interplay between two thermodynamic quantities during the initial LD phases, the TG bulk free energy and membrane remodeling energy.