No Arabic abstract
While a significant body of investigations have been focused on the process of protein self-assembly, much less is understood about the reverse process of a filament breaking due to thermal motion into smaller fragments, or depolymerization of subunits from the filament ends. Indirect evidence for actin and amyloid filament fragmentation has been reported, although the phenomenon has never been directly observed either experimentally or in simulations. Here we report the direct observation of filament depolymerization and breakup in a minimal, calibrated model of coarse-grained molecular simulation. We quantify the orders of magnitude by which the depolymerization rate from the filament ends $k_mathrm{off}$ is larger than fragmentation rate $k_{-}$ and establish the law $k_mathrm{off}/k_- = exp [( varepsilon_| - varepsilon_bot) / k_mathrm{B}T ] = exp [0.5 varepsilon / k_mathrm{B}T ]$, which accounts for the topology and energy of bonds holding the filament together. This mechanism and the order-of-magnitude predictions are well supported by direct experimental measurements of depolymerization of insulin amyloid filaments.
During the last decade coarse-grained nucleotide models have emerged that allow us to DNA and RNA on unprecedented time and length scales. Among them is oxDNA, a coarse-grained, sequence-specific model that captures the hybridisation transition of DNA and many structural properties of single- and double-stranded DNA. oxDNA was previously only available as standalone software, but has now been implemented into the popular LAMMPS molecular dynamics code. This article describes the new implementation and analyses its parallel performance. Practical applications are presented that focus on single-stranded DNA, an area of research which has been so far under-investigated. The LAMMPS implementation of oxDNA lowers the entry barrier for using the oxDNA model significantly, facilitates future code development and interfacing with existing LAMMPS functionality as well as other coarse-grained and atomistic DNA models.
The control of biofilm formation is a challenging goal that has not been reached yet in many aspects. One is the role of van der Waals forces and another the importance of mutual interactions between the adsorbing and the adsorbed biomolecules (critical crowding). Here, a combined exeperimental and theoretical approach is presented that fundamentally probes both aspects. On three model proteins, lysozyme, {alpha}-amylase and bovine serum albumin (BSA), the adsorption kinetics is studied. Composite substrates are used enabling a separation of the short- and the long-range forces. Though usually neglected, experimental evidence is given for the influence of van der Waals forces on the protein adsorption as revealed by in situ ellipsometry. The three proteins were chosen for their different conformational stability in order to investigate the influence of conformational changes on the adsorption kinetics. Monte Carlo simulations are used to develop a model for these experimental results by assuming an internal degree of freedom to represent conformational changes. The simulations also provide data on the distribution of adsorption sites. By in situ atomic force microscopy we can also test this distribution experimentally which opens the possibility to e.g. investigate the interactions between adsorbed proteins.
We introduce the SPlit-and-conQueR (SPQR) model, a coarse-grained representation of RNA designed for structure prediction and refinement. In our approach, the representation of a nucleotide consists of a point particle for the phosphate group and an anisotropic particle for the nucleoside. The interactions are, in principle, knowledge-based potentials inspired by the ESCORE function, a base-centered scoring function. However, a special treatment is given to base-pairing interactions and certain geometrical conformations which are lost in a raw knowledge-base model. This results in a representation able to describe planar canonical and non-canonical base pairs and base-phosphate interactions and to distinguish sugar puckers and glycosidic torsion conformations. The model is applied to the folding of several structures, including duplexes with internal loops of non-canonical base pairs, tetraloops, junctions and a pseudoknot. For the majority of these systems, experimental structures are correctly predicted at the level of individual contacts. We also propose a method for efficiently reintroducing atomistic detail from the coarse-grained representation.
Single molecule force spectroscopy of DNA strands adsorbed at surfaces is a powerful technique used in air or liquid environments to quantify their mechanical properties. Although the force responses are limited to unfolding events so far, single base detection might be possible in more drastic cleanliness conditions such as ultra high vacuum. Here, we report on high resolution imaging and pulling attempts at low temperature (5K) of a single strand DNA (ssDNA) molecules composed of 20 cytosine bases adsorbed on Au(111) by scanning probe microscopy and numerical calculations. Using electrospray deposition technique, the ssDNA were successfully transferred from solution onto a surface kept in ultra high vacuum. Real space characterizations reveal that the ssDNA have an amorphous structure on gold in agreement with numerical calculations. Subsequent substrate annealing promotes the desorption of solvent molecules, DNA as individual molecules as well as the formation of DNA self assemblies. Furthermore, pulling experiments by force spectroscopy have been conducted to measure the mechanical response of the ssDNA while detaching. A periodic pattern of 0.2 to 0.3nm is observed in the force curve which arises from the stick slip of single nucleotide bases over the gold. Although an intra molecular response is obtained in the force curve, a clear distinction of each nucleotide detachment is not possible due the complex structure of ssDNA adsorbed on gold.
We propose a criterion for optimal parameter selection in coarse-grained models of proteins, and develop a refined elastic network model (ENM) of bovine trypsinogen. The unimodal density-of-states distribution of the trypsinogen ENM disagrees with the bimodal distribution obtained from an all-atom model; however, the bimodal distribution is recovered by strengthening interactions between atoms that are backbone neighbors. We use the backbone-enhanced model to analyze allosteric mechanisms of trypsinogen, and find relatively strong communication between the regulatory and active sites.