α-Helix peptides designed from EBV-gH protein display higher antigenicity and induction of monocyte apoptosis than the native peptide

We tested the hypothesis that stabilizing α-helix of Epstein–Barr virus gH-derived peptide 11438 used for binding human cells will increase its biological activity. Non-stable α-helix of peptide 11438 was unfolded in an entropy-driven process, despite the opposing effect of the enthalpy factor. Adding and/or changing amino acids in peptide 11438 allowed the designing of peptides 33207, 33208 and 33210; peptides 33208 and 33210 displayed higher helical content due to a decreased unfolding entropy change as was determined by AGADIR, molecular dynamics and circular dichroism analysis. Peptides 33207, 33208 and 33210 inhibited EBV invasion of peripheral blood mononuclear cells and displayed epitopes more similar to native protein than peptide 11438; these peptides could be useful for detecting antibodies induced by native gH protein since they displayed high reactivity with anti-EBV antibodies. Anti-peptide 33207 antibodies showed higher reactivity with EBV than anti-peptide 11438 antibodies being useful for inducing antibodies against EBV. Anti-peptide 33210 antibodies inhibit EBV invasion of epithelial cells better than anti-peptide 11438 antibodies. Peptide 33210 bound to normal T lymphocytes and Raji cells stronger than peptide 11438 and also induced apoptosis of monocytes and Raji cells but not of normal T cells in a similar way to EBV-gH. Peptide 33210 inhibited the monocytes’ development toward dendritic cells better than EBV and peptide 11438. In conclusion, stabilizing the α-helix in peptides 33208 and 33210 designed from peptide 11438 increased the antigenicity and the ability of the antibodies induced by peptides of inhibiting EBV invasion of host cells

DNA exit ramps are revealed in the binding landscapes obtained from simulations in helical coordinates.

DNA molecules are highly charged semi-flexible polymers that are involved in a wide variety of dynamical processes such as transcription and replication. Characterizing the binding landscapes around DNA molecules is essential to understanding the energetics and kinetics of various biological processes. We present a curvilinear coordinate system that fully takes into account the helical symmetry of a DNA segment. The latter naturally allows to characterize the spatial organization and motions of ligands tracking the minor or major grooves, in a motion reminiscent of sliding. Using this approach, we performed umbrella sampling (US) molecular dynamics (MD) simulations to calculate the three-dimensional potentials of mean force (3D-PMFs) for a Na+ cation and for methyl guanidinium, an arginine analog. The computed PMFs show that, even for small ligands, the free energy landscapes are complex. In general, energy barriers of up to ~5 kcal/mol were measured for removing the ligands from the minor groove, and of ~1.5 kcal/mol for sliding along the minor groove. We shed light on the way the minor groove geometry, defined mainly by the DNA sequence, shapes the binding landscape around DNA, providing heterogeneous environments for recognition by various ligands. For example, we identified the presence of dissociation points or "exit ramps" that naturally would terminate sliding. We discuss how our findings have important implications for understanding how proteins and ligands associate and slide along DNA

Methyl-guanidinium sliding along the minor groove.

<p>A) 1D-PMFs of one turn along the helical path at different radii from the DNA’s axis. Grey shadow correspond to one standard deviation (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003980#sec004" target="_blank">Methods</a> section). B) Minimum free energy path obtained by assuming that, at every angular position, the ligand will localize the radii of minimum free energy. The PMF has been color coded to show the radius using the same color scheme as A).</p

Schematic representation of the helical coordinates system.

<p>A) The helical coordinate system establishes the position of the ligand center of mass with respect to the DNA’s axis. The DNA axis was aligned to the z-axis. The helical coordinate system is defined in terms of coordinates (<i>ρ</i>, <i>ϕ</i>, <i>ξ</i>) (in yellow). Coordinates (<i>r</i>, <i>θ</i>, <i>z</i>) (in red) correspond to a cylindrical coordinate system. <i>p</i> is the pitch of the helix and <i>α</i> the pitch angle. B) The components of a vector <b>V</b> in a surface of constant <i>ρ</i> in both helical (yellow) and cylindrical coordinates (red). C) Snapshot of the initial DNA methyl-guanidinium complex.</p

Na⁺ sliding along the minor groove.

<p>A) 1D-PMFs of one turn along the helical path at different radii from the DNA’s axis. Grey shadow correspond to one standard deviation computed using the Bootstrap method (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003980#sec004" target="_blank">Methods</a> section). B) Radial distribution functions (<i>g</i>_{<i>OW</i>−<i>Na</i>⁺}) of water oxygen around the Na⁺ cations. The red line corresponds to the bulk <i>g</i>_{<i>OW</i>−<i>Na</i>⁺} and the blue line was determined considering only the Na⁺s that localized to the minor groove. Inset: snapshot from the simulations showing the localization of a hydrated Na⁺ to the minor groove.</p

Binding free-energy landscape for Na⁺ in the minor groove: The PMF was computed for one complete turn (2<i>π</i>) along the minor groove in the helical coordinates system (see S1 Fig.).

<p>The PMF was projected to the 2D plane, such that the <i>ξ</i>-axis is perpendicular to the page (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003980#sec004" target="_blank"><i>Methods</i></a> sections and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003980#pcbi.1003980.s001" target="_blank">S1 Fig.</a>) and the obtained free-energies are those of a “ribbon” passing through the middle of the minor groove’s sampled volume. In this representation the DNA’s axis is at the center of the plot. Note that in this coordinate system there is no angular periodicity.</p

Estimation of Free-Energy Differences from Computed Work Distributions: An Application of Jarzynski’s Equality

Equilibrium free-energy differences can be computed from nonequilibrium molecular dynamics (MD) simulations using Jarzynski’s equality (Jarzynski, C. <i>Phys. Rev. Lett.</i> <b>1997</b>,<i> 78</i>, 2690) by combining a large set of independent trajectories (path ensemble). Here we present the multistep trajectory combination (MSTC) method to compute free-energy differences, which by combining trajectories significantly reduces the number of trajectories necessary to generate a representative path ensemble. This method generates well-sampled work distributions, even for large systems, by combining parts of a relatively small number of trajectories carried out in steps. To assess the efficiency of the MSTC method, we derived analytical expressions and used them to compute the bias and the variance of the free-energy estimates along with numerically calculated values. We show that the MSTC method significantly reduces both the bias and variance of the free-energy estimates compared to the estimates obtained using single-step trajectories. In addition, because in the MSTC method the process is divided into steps, it is feasible to compute the reverse transition. By combining the forward and reverse processes, the free-energy difference can be computed using the Crooks' fluctuation theorem (Crooks, G. E. <i>J. Stat. Phys.</i> <b>1998</b>,<i> 90</i>, 1481 and Crooks, G. E. <i>Phys. Rev. E</i> <b>2000</b>,<i> 61</i>, 2361) or Bennett’s acceptance ratio (Bennett, C. H. <i>J. Comput. Phys</i>. <b>1976</b>,<i> 22</i>, 245), which further reduces the bias and variance of the estimates