Molecular dynamics simulations of the gramicidin A-dimyristoylphosphatidylcholine system with an ion in the channel pore region

Electric field effects on membranes: Gramicidin A as a test ground

Electric fields due to transmembrane potential differences or ionic gradients across the membrane are presumably crucial for many reactions across membranes or close to membranes like signal transduction, control of ion channels or the generation of neural impulses. Molecular dynamics simulations have been used to study the influence of external electric fields on a mixed gramicidin/phospholipid bilayer system. At high field strengths, formation of membrane electropores occurred both close and distal to the gramicidin. Gramicidin was found to stabilize the membrane adjacent to the protein but also at larger distances of up to 2-3 nm. As a result, membrane pore formation was found to be significantly suppressed for the mixed gramicidin/DMPC system. Moderate field strengths only weakly affected the structure and dynamics of the gramicidin. Spontaneous potassium passage events in external electric fields were observed for both the head-to-head helical conformation as well as for the double helical conformation of gramicidin A. The double-helical conformation was found to facilitate ion passage compared to the head-to-head helical dimer.

Interfacial tryptophan residues: a role for the cation-pi effect?

Integral membrane proteins are characterized by having a preference for aromatic residues, e.g., tryptophan (W), at the interface between the lipid bilayer core and the aqueous phase. The reason for this is not clear, but it seems that the preference is related to a complex interplay between steric and electrostatic forces. The flat rigid paddle-like structure of tryptophan, associated with a quadrupolar moment (aromaticity) arising from the pi-electron cloud of the indole, interacts primarily with moieties in the lipid headgroup region hardly penetrating into the bilayer core. We have studied the interaction between the nitrogen moiety of lipid molecule headgroups and the pi-electron distribution of gramicidin (gA) tryptophan residues (W9, W11, W13, and W15) using molecular dynamics (MD) simulations of gA embedded in two hydrated lipid bilayers composed of 1-palmitoyl-2-oleoylphosphatidylethanolamine (POPE) and 1-palmitoyl-2-oleoylphosphatidyl-choline (POPC), respectively. We use a force field model for tryptophan in which polarizability is only implicit, but we believe that classical molecular dynamics force fields are sufficient to capture the most prominent features of the cation-pi interaction. Our criteria for cation-pi interactions are based on distance and angular requirements, and the results from our model suggest that cation-pi interactions are relevant for W(PE)1), W(PE)13, W(PE)15, and, to some extent, W(PC)11 and W(PC)13. In our model, W9 does not seem to engage in cation-pi interactions with lipids, neither in POPE nor POPC. The criteria for the cation-pi effect are satisfied more often in POPE than in POPC, whereas the H-bonding ability between the indole donor and the carbonyl acceptor is similar in POPE and POPC. This suggests an increased affinity for lipids with ethanolamine headgroups to transmembrane proteins enriched in interfacial tryptophans.

Molecular dynamics simulations of C2F6 effects on gramicidin A: implications of the mechanisms of general anesthesia

It was recently postulated that the effects of general anesthetics on protein global dynamics might underlie a unitary molecular mechanism of general anesthesia. To verify that the specific dynamics effects caused by general anesthetics were not shared by nonanesthetic molecules, two parallel 8-ns all-atom molecular dynamics simulations were performed on a gramicidin A (gA) channel in a fully hydrated dimyristoylphosphatidylcholine membrane in the presence and absence of hexafluoroethane (HFE), which structurally resembles the potent anesthetic molecule halothane but produces no anesthesia. Similar to halothane, HFE had no measurable effects on the gA channel structure. In contrast to halothane, HFE produced no significant changes in the gA channel dynamics. The difference between halothane and HFE on channel dynamics can be attributed to their distinctly different distributions within the lipid bilayer and consequently to the different interactions of the anesthetic and the nonanesthetic molecules with the channel-anchoring tryptophan residues. The study further supports the notion that anesthetic-induced changes in protein global dynamics may play an important role in mediating anesthetic actions on proteins.

The role of the dielectric barrier in narrow biological channels: a novel composite approach to modeling single-channel currents

A composite continuum theory for calculating ion current through a protein channel of known structure is proposed, which incorporates information about the channel dynamics. The approach is utilized to predict current through the Gramicidin A ion channel, a narrow pore in which the applicability of conventional continuum theories is questionable. The proposed approach utilizes a modified version of Poisson-Nernst-Planck (PNP) theory, termed Potential-of-Mean-Force-Poisson-Nernst-Planck theory (PMFPNP), to compute ion currents. As in standard PNP, ion permeation is modeled as a continuum drift-diffusion process in a self-consistent electrostatic potential. In PMFPNP, however, information about the dynamic relaxation of the protein and the surrounding medium is incorporated into the model of ion permeation by including the free energy of inserting a single ion into the channel, i.e., the potential of mean force along the permeation pathway. In this way the dynamic flexibility of the channel environment is approximately accounted for. The PMF profile of the ion along the Gramicidin A channel is obtained by combining an equilibrium molecular dynamics (MD) simulation that samples dynamic protein configurations when an ion resides at a particular location in the channel with a continuum electrostatics calculation of the free energy. The diffusion coefficient of a potassium ion within the channel is also calculated using the MD trajectory. Therefore, except for a reasonable choice of dielectric constants, no direct fitting parameters enter into this model. The results of our study reveal that the channel response to the permeating ion produces significant electrostatic stabilization of the ion inside the channel. The dielectric self-energy of the ion remains essentially unchanged in the course of the MD simulation, indicating that no substantial changes in the protein geometry occur as the ion passes through it. Also, the model accounts for the experimentally observed saturation of ion current with increase of the electrolyte concentration, in contrast to the predictions of standard PNP theory.

Gramicidin A channel as a test ground for molecular dynamics force fields

We use the well-known structural and functional properties of the gramicidin A channel to test the appropriateness of force fields commonly used in molecular dynamics (MD) simulations of ion channels. For this purpose, the high-resolution structure of the gramicidin A dimer is embedded in a dimyristoylphosphatidylcholine bilayer, and the potential of mean force of a K(+) ion is calculated along the channel axis using the umbrella sampling method. Calculations are performed using two of the most common force fields in MD simulations: CHARMM and GROMACS. Both force fields lead to large central barriers for K(+) ion permeation, that are substantially higher than those deduced from the physiological data by inverse methods. In long MD simulations lasting over 60 ns, several ions are observed to enter the binding site but none of them crossed the channel despite the presence of a large driving field. The present results, taken together with many earlier studies, highlights the shortcomings of the standard force fields used in MD simulations of ion channels and calls for construction of more appropriate force fields for this purpose.

Normal mode analysis of macromolecular motions in a database framework: developing mode concentration as a useful classifying statistic

We investigated protein motions using normal modes within a database framework, determining on a large sample the degree to which normal modes anticipate the direction of the observed motion and were useful for motions classification. As a starting point for our analysis, we identified a large number of examples of protein flexibility from a comprehensive set of structural alignments of the proteins in the PDB. Each example consisted of a pair of proteins that were considerably different in structure given their sequence similarity. On each pair, we performed geometric comparisons and adiabatic-mapping interpolations in a high-throughput pipeline, arriving at a final list of 3,814 putative motions and standardized statistics for each. We then computed the normal modes of each motion in this list, determining the linear combination of modes that best approximated the direction of the observed motion. We integrated our new motions and normal mode calculations in the Macromolecular Motions Database, through a new ranking interface at http://molmovdb.org. Based on the normal mode calculations and the interpolations, we identified a new statistic, mode concentration, related to the mathematical concept of information content, which describes the degree to which the direction of the observed motion can be summarized by a few modes. Using this statistic, we were able to determine the fraction of the 3,814 motions where one could anticipate the direction of the actual motion from only a few modes. We also investigated mode concentration in comparison to related statistics on combinations of normal modes and correlated it with quantities characterizing protein flexibility (e.g., maximum backbone displacement or number of mobile atoms). Finally, we evaluated the ability of mode concentration to automatically classify motions into a variety of simple categories (e.g., whether or not they are "fragment-like"), in comparison to motion statistics. This involved the application of decision trees and feature selection (particular machine-learning techniques) to training and testing sets derived from merging the "list" of motions with manually classified ones.