Nanoparticle-induced assembly of hydrophobically modified chitosan

<p>Hydrophobically modified chitosan (HMC) self-assembles in solution to form gels, making it suitable for applications in oil dispersion, hydrogel design and wound dressing. The self-assembly of HMC is driven by the association of hydrophobic moieties that are attached to chitosan monomers along the polymer chain. We present the results of discontinuous molecular dynamics simulations aimed at understanding how the length and density of the hydrophobic modification chains attached to HMC affect self-assembly and the structure of the resulting network. Long modification chains are required to promote the formation of a stable network in solution at a modification density of 5%; the networks form more readily at a modification density of 10%. The pore size distribution of the resulting HMC network is relatively independent of the modification chain length and density. Insertion of different sized hydrophobic nanoparticles into HMC has a significant impact on network formation, with the particles acting as junction points that promote the association of several HMC chains. The networks form faster in the presence of many small nanoparticles than in the presence of few large nanoparticles. We conclude that HMC could be a viable candidate to form hydrogels in solution.</p

Binding Preferences of Amino Acids for Gold Nanoparticles: A Molecular Simulation Study

A better understanding of the binding preference of amino acids for gold nanoparticles of different diameters could aid in the design of peptides that bind specifically to nanoparticles of a given diameter. Here we identify the binding preference of 19 natural amino acids for three gold nanoparticles with diameters of 1.0, 2.0, and 4.0 nm, and investigate the mechanisms that govern these preferences. We calculate potentials of mean force between 36 entities (19 amino acids and 17 side chains) and the three gold nanoparticles in explicit water using well-tempered metadynamics simulations. Comparing these potentials of mean force determines the amino acids’ nanoparticle binding preferences and if these preferences are controlled by the backbone, the side chain, or both. Twelve amino acids prefer to bind to the 4.0 nm gold nanoparticle, and seven prefer to bind to the 2.0 nm one. We also use atomistic molecular dynamics simulations to investigate how water molecules near the nanoparticle influence the binding of the amino acids. The solvation shells of the larger nanoparticles have higher water densities than those of the smaller nanoparticles while the orientation distributions of the water molecules in the shells of all three nanoparticles are similar. The nanoparticle preferences of the amino acids depend on whether their binding free energy is determined mainly by their ability to replace or to reorient water molecules in the nanoparticle solvation shell. The amino acids whose binding free energy depends mainly on the replacement of water molecules are likely to prefer to bind to the largest nanoparticle and tend to have relatively simple side chain structures. Those whose binding free energy depends mainly on their ability to reorient water molecules prefer a smaller nanoparticle and tend to have more complex side chain structures

Development of a Coarse-Grained Model of Chitosan for Predicting Solution Behavior

A new coarse-grained (CG) model of chitosan has been developed for predicting solution behavior as a function of degree of acetylation (DA). A multiscale modeling approach was used to derive the energetic and geometric parameters of this implicit-solvent, CG model from all-atom simulations of chitosan and chitin molecules in explicit water. The model includes representations of both protonated d-glucosamine (GlcN⁺) and <i>N</i>-acetyl-d-glucosamine (GlcNAc) monomers, where each monomer consists of three CG sites. Chitosan molecules of any molecular weight, DA, and monomer sequence can be built using this new CG model. Discontinuous molecular dynamics simulations of chitosan solutions show increased self-assembly in solution with increasing DA and chitosan concentration. The chitosan solutions form larger percolated networks earlier in time as DA and concentration increase, indicating “gel-like” behavior, which qualitatively matches experimental studies of chitosan gel formation. Increasing DA also results in a greater number of monomer–monomer associations, which has been predicted experimentally based on an increase in the storage modulus of chitosan gels with increasing DA. Our model also gives insight into how the monomer sequence affects self-assembly and the frequency of interaction between different pairs of monomers

β–strand content and solvent accessible area.

<p>Probability that each residue is in β-strand conformation. Data are averaged over three time windows (A) t* = 2153~6727, (B) 19648~24266 and (C) 38120~42736. (D) Solvent accessible area for each residue. Data are averaged over time window (t* = 38120~42736).</p

Time evolution of the interaction energy.

<p>The total interaction energy in units of ɛ_HB for (A) 1st, 2nd, 3rd, (B) 4th, 5th, 6th, (C) 7th, 8th, 9th, 10th trajectories. The 3rd (green), 5th (red), 10th (blue) trajectories show lower energies than the others. (D) P_max (max population) within each Δt* = 5000 interval which is defined in text.</p

Snapshots for the 5^th run.

<p>The time evolution of the structure for the 5^th run at T* = 0.20 in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004258#pcbi.1004258.g001" target="_blank">Fig 1C and 1D</a>. Snapshots are taken at (A) t* = 5, (B) 1244, (C) 2608, (D) 3656, (E) 4233, (F) 5442, (G) 6086, (H) 10454, (I) 11063. See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004258#pcbi.1004258.s018" target="_blank">S1 Video</a>. The β-strand contents measured by the STRIDE program are (A) 0%, (B) 12%, (C) 26%, (D) 50%, (E) 48%, (F) 66%, (G) 64%, (H) 74%, (I) 75%. The α-helix content is insignificant in these structures and the remaining portions are coil and turns.</p

Structural Conversion of Aβ_17–42 Peptides from Disordered Oligomers to U-Shape Protofilaments via Multiple Kinetic Pathways

<div><p>Discovering the mechanisms by which proteins aggregate into fibrils is an essential first step in understanding the molecular level processes underlying neurodegenerative diseases such as Alzheimer’s and Parkinson's. The goal of this work is to provide insights into the structural changes that characterize the kinetic pathways by which amyloid-β peptides convert from monomers to oligomers to fibrils. By applying discontinuous molecular dynamics simulations to PRIME20, a force field designed to capture the chemical and physical aspects of protein aggregation, we have been able to trace out the entire aggregation process for a system containing 8 Aβ17–42 peptides. We uncovered two fibrillization mechanisms that govern the structural conversion of Aβ17–42 peptides from disordered oligomers into protofilaments. The first mechanism is monomeric conversion templated by a U-shape oligomeric nucleus into U-shape protofilament. The second mechanism involves a long-lived and on-pathway metastable oligomer with S-shape chains, having a C-terminal turn, en route to the final U-shape protofilament. Oligomers with this C-terminal turn have been regarded in recent experiments as a major contributing element to cell toxicity in Alzheimer’s disease. The internal structures of the U-shape protofilaments from our PRIME20/DMD simulation agree well with those from solid state NMR experiments. The approach presented here offers a simple molecular-level framework to describe protein aggregation in general and to visualize the kinetic evolution of a putative toxic element in Alzheimer’s disease in particular.</p></div

Salt-bridge and hydrophobic interactions for the 10^th run.

<p>(A) Structure at 568 billion collision (t*≈52,000) for the 10^th run. (B)(C) Fibril axis view with ribbon diagram or with side-chain spheres. (D)~(K) Fibril axis views for each chain showing side-chain spheres; F19(purple), D23(red), K28(cyan), I32(green) and L34(pink sphere). Figs (D)~(H) have salt-bridge pairs (D23-K28) and hydrophobic interactions between I32, L34 and F19; the rest do not.</p

Molecular recognition mechanism of peptide chain bound to the tRNA^Lys3 anticodon loop <i>in silico</i>

<div><p>The mechanism by which proteins recognize and bind the post-transcriptional modifications of RNAs is unknown, yet these interactions play important functions in biology. Atomistic molecular dynamics simulations were performed to examine the folding of the model peptide chain –<i>RVTHHAFLGAHRTVG</i>– and the complex formed by the folded peptide with the native anticodon stem and loop of the human tRNA^Lys3 (hASL^Lys3) in order to explore the binding mechanism. By analyzing and comparing two folded conformations of this peptide obtained from the folding simulation, we found that the van der Waals (VDW) energy is necessary for the thermal stability of the peptide, and the charge–charge (ELE + EGB) energy is crucial for determining the three-dimensional folded structure of the peptide backbone. Subsequently, two conformations of the peptide were employed to investigate their binding behaviors to hASL^Lys3. The metastable folded peptide was found to bind to hASL^Lys3 much easier than the stable folded peptide in the binding simulations. An energetic analysis reveals that the VDW energy favors the binding, whereas the ELE + EGB energies disfavor the binding. Arginines on the peptide preferentially attract the phosphate backbone via the inter-chain ELE + EGB interaction, significantly contributing to the binding affinity. The hydrophobic phenylalanine interacts with the anticodon loop of hASL^Lys3 via the inter-chain VDW interaction, significantly contributing to the binding specificity.</p></div

Effect of Protein-like Copolymers Composition on the Phase Separation Dynamics of a Polymer Blend: A Monte Carlo Simulation

We use kinetic Monte Carlo simulation based on the bond fluctuation model to investigate the dynamics of phase separation in immiscible 80/20 A/B binary polymer blends, comprising 80% and 20% of A and B components, respectively, in the presence of ≈4.92% 30-mer protein-like copolymer (PLC) made of C and D segments. The molecular interactions are chosen such that there is an attraction between A and C and between B and D segments and no interaction between like segments; all other interaction energies have been chosen to be repulsive. The PLC migration to and presence at the A/B interface effectively slow down the process of phase separation in binary blends, thereby minimizing the unfavorable A/B contacts and reducing the A/B interfacial tension. The ability of PLCs to effectively retard the process of phase separation depends sensitively on the PLC composition. PLCs with 0.3 ≤ <i>x</i>_C ≤ 0.5, where <i>x</i>_C is the mole fraction of C, are most effective in compatibilizing the 80/20 A/B binary blend. The growth of phase-separated domains follows a dynamical scaling law for both the binary and ternary blends compatibilized by PLCs in the late stage of phase separation with universal scaling functions that are nearly independent of PLC composition