Numerical simulations question the long held assumption thatpacking configurations in granular systems are equally probable.

Recent single molecule experiments have reported that DNA binding proteins (DNA-BPs) can diffuse along DNA. This suggests that interactions between proteins and DNA play a role during the target search even far from their specific site on DNA. Here we show by means of Monte Carlo simulations and analytical calculations that there is a counterintuitive repulsion between the two oppositely charged macromolecules at a nanometer range. For the concave shape of DNA-BPs, and for realistic protein charge densities, we find that the DNA-protein interaction free energy has a minimum at a finite surface-to-surface separation, in which proteins can easily slide. When a protein encounters its target, the free energy barrier is completely counterbalanced by the H-bond interaction, thus enabling the sequence recognition.

The molecular support of genetic information, DNA, has to be packaged and organized inside the tiny volume of nuclei, cells, or virus capsids, in an ordered and dynamical way. Evolution has favored different strategies in different kingdoms: a liquid crystal ordering mechanism prevails in viruses; strong entanglement due to supercoiling is the main compacting strategy in bacteria; the building of a hierarchical and tunable architecture mediated by DNA–protein interaction constitutes the main compacting mechanism in archaea and eukaryotes. The interplay between these different strategies is however much more complex than at first sight and all these strategies can be used in a synergistic way. All of them have to deal with the same elementary physical constraint which hinders compaction: electrostatic repulsion due to the high line charge density of DNA. In this review, we will show how this apparent weakness can be turned into a strength in order to compact this long molecule in a functional way.

Polar liquids like water carry a characteristic nanometric length scale, the correlation length of orientation polarizations. Continuum theories that can capture this feature commonly run under the name of ``nonlocal'' electrostatics since their dielectric response is characterized by a scale-dependent dielectric function $\epsilon${}(q), where q is the wave vector; the Poisson(-Boltzmann) equation then turns into an integro-differential equation. Recently, ``local'' formulations have been put forward for these theories and applied to water, solvated ions, and proteins. We review the local formalism and show how it can be applied to a structured liquid in slit and plate geometries, and solve the Poisson-Boltzmann theory for a charged plate in a structured solvent with counterions. Our results establish a coherent picture of the local version of nonlocal electrostatics and show its ease of use when compared to the original formulation.

The goal of this work is to propose a simple continuous model that captures the dielectric properties of water at the nanometric scale. We write an electrostatic energy as a functional of the polarisation field containing a term in P⁴ and non-local Gaussian terms. Such a hamiltonian can reproduce two key properties of water: the saturation of the polarisation response of water in the presence of a strong electrostatic field and the nanometric dipolar correlations of the solvent molecules modifying the long range van der waals interaction. This model explores thus two fundamental aspects that have to be included in implicit models of electrolytes for a relevant description of electrostatic interactions at nanometric scales.

When a granular system is tapped, its volume changes. Here, using a well-defined macroscopic protocol, we prepare an ensemble of granular systems and track the statistics of volume changes as a function of the number of taps. This is in contrast to previous studies, which have focused on single trajectories and assumed ergodicity. We devise a new method to assess the convergence properties of a sequence of ensemble volume histograms and introduce a reasonable approximate version of an invariant histogram. We then compare these invariant histograms with histograms generated by sampling a long trajectory for one system and observe nonergodicity, which we quantify. Finally, we use the overlapping histogram method to assess potential compatibility with Edwards' canonical assumption. Our histograms are incompatible with this assumption.