Heavy crude oil consists of thousands of compounds and much of them have a fairly large relative molar mass and complex structure. It is hard to learn the dynamic behavior of this fluid system at all atom models. The present study aims at constructing a mesoscale platform to explore aggregate behavior of asphaltenes in heavy crude oil. The aggregate structure in heavy crude oils was investigated by introducing rigid body fragments, which represents the significant presence of structures of fused aromatic rings in fractions such as asphaltenes and resins into dissipative particle dynamics (DPD). Another pressing task about how to determine the structure of the average model molecules and conservative force parameters was discussed in detail. With some regularity concerning the number of rings, the distribution of side chains and heteroatoms in average model molecules are revealed. Finally, we integrated the modified DPD program, model molecules, and the parameters selected for the preliminarily simulation of the heavy crude oil and emulsion system. The interlayer distance and the number of layers of the well-ordered structure in heavy crude oil are similar to some molecular dynamics works and supported by X-ray and transmission electron microscopy (TEM) experimental data. The relationship between the stability and the mass ratio among components of heavy crude oil is explored, and the result of our simulations fits the regularity Shell once published. In the emulsion system, the surfactant-like feature of asphaltenes and resins are observed. The preliminary simulation results demonstrate the validity of the rotational algorithm and parameters employed and encourage us to extend this platform to study the rheological and colloidal characteristics of heavy crude oils in the future.
In addition to continuous rapid progress in RNA structure determination, probing, and biophysical studies, the past decade has seen remarkable advances in the development of a new generation of RNA folding theories and models. Here, we review RNA structure prediction models and models for ion-RNA and ligand-RNA interactions. These new models are becoming increasingly important for mechanistic understanding of RNA function and quantitative design of RNA nanotechnology. We focus on new methods for physics-based, knowledge-based, and experimental data-directed modeling for RNA structures, and explore the new theories for the predictions of metal ion and ligand binding sites and metal ion-dependent RNA stabilities. The integration of these new methods with theories for the cellular environment effects, such as molecular crowding and cotranscriptional folding, may ultimately lead to an all-encompassing RNA folding model.
Hybridizing graphitic nanoplatelets (GNP) with commercially functionalized multi-walled carbon nanotubes (MWCNTs) in a polyetherimide (PEI) composite at a total loading of 0.5 wt% resulted in considerable improvements in electrical conductivity, thermal conductivity and dynamic mechanical properties, compared to solely GNP or solely MWCNT composites at the same total loading. The results reveal a synergistic interaction between the GNPs and MWCNTs based on GNP protection against fragmentation of the MWCNTs during high power sonication, while still allowing full dispersion of both fillers, by providing a shielding mechanism against MWCNT damage during dispersion processing. A new process for molecular level dispersion of exfoliated GNPs in PEI is also reported. Field emission scanning electron microscopy revealed strong interactions between PEI and the flat surfaces of GNPs and effectively intercalated GNP morphology within the matrix. GNPs alone can also produce excellent electrical conductivity improvements: at 1.0 wt% of GNP, electrical conductivity of the composite increased by 11 orders of magnitude and the percolation threshold was determined to be between 0.5 and 1.0 wt% of GNP.
Experiments have suggested that ion correlation and fluctuation effects can be potentially important for multivalent ions in RNA folding. However, most existing computational methods for the ion electrostatics in RNA folding tend to ignore these effects. The previously reported Tightly Bound Ion (TBI) model can treat ion correlation and fluctuation but its applicability to biologically important RNAs is severely limited by the low computational efficiency. Here, based on Monte Carlo sampling for the many-body ion distribution, we develop a new computational model, Monte Carlo Tightly Bound Ion (MCTBI) model, for ion binding properties around an RNA. Due to an enhanced sampling algorithm for ion distribution, the model leads to significant improvement in computational efficiency. For example, for a 160-nt RNA, the model causes more than 10-fold increase in the computational efficiency, and the improvement in computational efficiency is more pronounced for larger systems. Furthermore, unlike the earlier model, which describes ion distribution using the number of bound ions around each nucleotide, the current MCTBI model is based on the three-dimensional coordinates of the ions. The higher efficiency of the model allows us to treat the ion effects for medium to large RNA molecules, RNA-ligand complexes, and RNA-protein complexes. This new model, together with proper RNA conformational sampling and energetics model, may serve as a starting point for further development for the ion effects in RNA folding and conformational changes and for large nucleic acids systems.
The ability to accurately predict the binding site, binding pose, and binding affinity for ligand–RNA binding is important for RNA-targeted drug design. Here, we describe a new computational method, RLDOCK, for predicting the binding site and binding pose for ligand–RNA binding. By developing an energy-based scoring function, we sample exhaustively all of the possible binding sites with flexible ligand conformations for a ligand–RNA pair based on the geometric and energetic scores. The model distinguishes from other approaches in three notable features. First, the model enables exhaustive scanning of all of the possible binding sites, including multiple alternative or coexisting binding sites, for a given ligand–RNA pair. Second, the model is based on a new energy-based scoring function developed here. Third, the model employs a novel multistep screening algorithm to improve computational efficiency. Specifically, first, for each binding site, we use a gird-based energy map to rank the binding sites according to the minimum Lennard-Jones potential energy for the different ligand poses. Second, for a given selected binding site, we predict the ligand pose using a two-step algorithm. In the first step, we quickly identify the probable ligand poses using a coarse-grained simplified energy function. In the second step, for each of the probable ligand poses, we predict the ligand poses using a refined energy function. Tests of the RLDOCK for a set of 230 RNA–ligand-bound structures indicate that RLDOCK can successfully predict ligand poses for 27.8, 58.3, and 69.6% of all of the test cases with the root-mean-square deviation within 1.0, 2.0, and 3.0 Å, respectively, for the top three predicted docking poses. The computational method presented here may enable the development of a new, more comprehensive framework for the prediction of ligand–RNA binding with an ensemble of RNA conformations and the metal-ion effects.
The effect of crowded environment with static obstacles on the translocation of a three-dimensional self-avoiding polymer through a small pore is studied using dynamic Monte Carlo simulation. The translocation time τ is dependent on polymer-obstacle interaction and obstacle concentration. The influence of obstacles on the polymer translocation is explained qualitatively by the free energy landscape. There exists a special polymer-obstacle interaction at which the translocation time is roughly independent of the obstacle concentration at low obstacle concentration, and the strength of the special interaction is roughly independent of chain length N. Scaling relation τ ~ N(1.25) is observed for strong driving translocations. The diffusion property of polymer chain is also influenced by obstacles. Normal diffusion is only observed in dilute solution without obstacles or in a crowded environment with weak polymer-obstacle attraction. Otherwise, subdiffusion behavior of polymer is observed.
Free energy landscapes for polymer chain translocating through an interacting pore are calculated by using exact enumeration method. A potential barrier exists at weak attractive or repulsive polymer-pore interaction and it changes to a potential well with the increase in the attraction. The result reveals that there is a free translocation point where polymer is free of energy barrier. Using the free energy landscape, the translocation time tau for polymer worming through the pore and the migration time tau(m) for polymer migrating from cis side to trans side are calculated with the Fokker-Plank equation. It shows that a moderate attractive polymer-pore interaction accelerates the migration of polymer from cis side to trans side.
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