Structure and Phase Transformations of DPPC Lipid Bilayers in the Presence of Nanoparticles: Insights from Coarse-Grained Molecular Dynamics Simulations

In this article, we investigate fluid-gel transformations of a DPPC lipid bilayer in the presence of nanoparticles, using coarse grained molecular dynamics. Two types of nanoparticles are considered, specifically a 3 nm hydrophobic nanoparticle located in the core of the bilayer and a 6 nm charged nanoparticle located at the interface between the bilayer and water phase. Both negatively and positively charged nanoparticles at the bilayer interface are investigated. We demonstrate that the presence of all types of nanoparticles induces disorder effects in the structure of the lipid bilayer. These effects are characterized using computer visualization of the gel phase in the presence of nanoparticles, radial distribution functions, and order parameters. The 3 nm hydrophobic nanoparticle immersed in the bilayer core and the positively charged nanoparticle at the bilayer surface have no effect on the temperature of the fluid-gel transformation, compared to the bulk case. Interestingly, a negatively charged hydrophobic nanoparticle located at the surface of the bilayer causes slight shift of the fluid-gel transformation to a lower temperature, compared to the bulk bilayer case

Biomolecule surface patterning may enhance membrane association

Under dehydration conditions, amphipathic Late Embryogenesis Abundant (LEA) proteins fold spontaneously from a random conformation into alpha-helical structures and this transition is promoted by the presence of membranes. To gain insight into the thermodynamics of membrane association we model the resulting alpha-helical structures as infinite rigid cylinders patterned with hydrophobic and hydrophilic stripes oriented parallel to their axis. Statistical thermodynamic calculations using Single Chain Mean Field (SCMF) theory show that the relative thickness of the stripes controls the free energy of interaction of the alpha-helices with a phospholipid bilayer, as does the bilayer structure and the depth of the equilibrium penetration of the cylinders into the bilayer. The results may suggest the optimal thickness of the stripes to mimic the association of such protein with membranes.Comment: Published in ACS Nano http://pubs.acs.org/doi/pdf/10.1021/nn204736

Transmembrane helix dynamics of bacterial chemoreceptors supports a piston model of signalling.

Transmembrane α-helices play a key role in many receptors, transmitting a signal from one side to the other of the lipid bilayer membrane. Bacterial chemoreceptors are one of the best studied such systems, with a wealth of biophysical and mutational data indicating a key role for the TM2 helix in signalling. In particular, aromatic (Trp and Tyr) and basic (Arg) residues help to lock α-helices into a membrane. Mutants in TM2 of E. coli Tar and related chemoreceptors involving these residues implicate changes in helix location and/or orientation in signalling. We have investigated the detailed structural basis of this via high throughput coarse-grained molecular dynamics (CG-MD) of Tar TM2 and its mutants in lipid bilayers. We focus on the position (shift) and orientation (tilt, rotation) of TM2 relative to the bilayer and how these are perturbed in mutants relative to the wildtype. The simulations reveal a clear correlation between small (ca. 1.5 Å) shift in position of TM2 along the bilayer normal and downstream changes in signalling activity. Weaker correlations are seen with helix tilt, and little/none between signalling and helix twist. This analysis of relatively subtle changes was only possible because the high throughput simulation method allowed us to run large (n = 100) ensembles for substantial numbers of different helix sequences, amounting to ca. 2000 simulations in total. Overall, this analysis supports a swinging-piston model of transmembrane signalling by Tar and related chemoreceptors

Allostery in Its Many Disguises: From Theory to Applications.

Allosteric regulation plays an important role in many biological processes, such as signal transduction, transcriptional regulation, and metabolism. Allostery is rooted in the fundamental physical properties of macromolecular systems, but its underlying mechanisms are still poorly understood. A collection of contributions to a recent interdisciplinary CECAM (Center Européen de Calcul Atomique et Moléculaire) workshop is used here to provide an overview of the progress and remaining limitations in the understanding of the mechanistic foundations of allostery gained from computational and experimental analyses of real protein systems and model systems. The main conceptual frameworks instrumental in driving the field are discussed. We illustrate the role of these frameworks in illuminating molecular mechanisms and explaining cellular processes, and describe some of their promising practical applications in engineering molecular sensors and informing drug design efforts

A virtual screening approach for identifying plants with anti H5N1 neuraminidase activity

Recent outbreaks of highly pathogenic and occasional drug-resistant influenza strains have highlighted the need to develop novel anti-influenza therapeutics. Here, we report computational and experimental efforts to identify influenza neuraminidase inhibitors from among the 3000 natural compounds in the Malaysian-Plants Natural-Product (NADI) database. These 3000 compounds were first docked into the neuraminidase active site. The five plants with the largest number of top predicted ligands were selected for experimental evaluation. Twelve specific compounds isolated from these five plants were shown to inhibit neuraminidase, including two compounds with IC50 values less than 92 μM. Furthermore, four of the 12 isolated compounds had also been identified in the top 100 compounds from the virtual screen. Together, these results suggest an effective new approach for identifying bioactive plant species that will further the identification of new pharmacologically active compounds from diverse natural-product resources

Identifying small-molecules binding sites in RNA conformational ensembles with SHAMAN

Abstract The rational targeting of RNA with small molecules is hampered by our still limited understanding of RNA structural and dynamic properties. Most in silico tools for binding site identification rely on static structures and therefore cannot face the challenges posed by the dynamic nature of RNA molecules. Here we present SHAMAN, a computational technique to identify potential small-molecule binding sites in RNA structural ensembles. SHAMAN enables exploring the conformational landscape of RNA with atomistic molecular dynamics and at the same time identifying RNA pockets in an efficient way with the aid of probes and enhanced-sampling techniques. In our benchmark composed of large, structured riboswitches as well as small, flexible viral RNAs, SHAMAN successfully identified all the experimentally resolved pockets and ranked them among the most favorite probe hotspots. Overall, SHAMAN sets a solid foundation for future drug design efforts targeting RNA with small molecules, effectively addressing the long-standing challenges in the field