The cell membrane is "stuffed" with proteins, whose transmembrane (TM) helical domains spontaneously associate to form functionally active complexes. For a number of membrane receptors, a modulation of TM domains' oligomerization has been shown to contribute to the development of severe pathological states, thus calling for detailed studies of the atomistic aspects of the process. Despite considerable progress achieved so far, several crucial questions still remain: How do the helices recognize each other in the membrane? What is the driving force of their association? Here, we assess the dimerization free energy of TM helices along with a careful consideration of the interplay between the structure and dynamics of protein and lipids using atomistic molecular dynamics simulations in the hydrated lipid bilayer for three different model systems - TM fragments of glycophorin A, polyalanine and polyleucine peptides. We observe that the membrane driven association of TM helices exhibits a prominent entropic character, which depends on the peptide sequence. Thus, a single TM peptide of a given composition induces strong and characteristic perturbations in the hydrophobic core of the bilayer, which may facilitate the initial "communication" between TM helices even at the distances of 20-30 Å. Upon tight helix-helix association, the immobilized lipids accommodate near the peripheral surfaces of the dimer, thus disturbing the packing of the surrounding. The dimerization free energy of the modeled peptides corresponds to the strength of their interactions with lipids inside the membrane being the lowest for glycophorin A and similarly higher for both homopolymers. We propose that the ability to accommodate lipid tails determines the dimerization strength of TM peptides and that the lipid matrix directly governs their association. © 2015 American Chemical Society.
In order to fully understand the microscopic origins of binding specificity between nucleic acids and proteins, it is imperative to study the dependence of the binding preferences between nucleobases and protein side chains on the properties of the environment. Here, we employ molecular dynamics simulations and umbrella sampling to derive the potentials of mean force and the associated absolute binding free energies between the four standard RNA nucleobases and the side chains of aspartic acid and tryptophan in water/methanol mixtures exhibiting a wide range of dielectric constants. In addition to their opposing character when it comes to hydrophobicity, aspartate and tryptophan side chains were chosen because they exhibit the greatest change in binding free energies with nucleobases between pure water and methanol environments. We exploit a strong linear dependence of the derived ΔG values on the mole fraction of methanol to estimate the binding free energies of all possible combinations of different standard RNA nucleobases and side chains at multiple values of dielectric constants. Finally, we critically assess the recently proposed complementarity hypothesis concerning direct, coaligned binding between mRNAs and their cognate proteins in light of the present results.
The recently developed NMR techniques enable estimation of protein configurational entropy change from the change in the average methyl order parameters. This experimental observable, however, does not directly measure the contribution of intramolecular couplings, protein main-chain motions, or angular dynamics. Here, we carry out a self-consistent computational analysis of the impact of these missing contributions on an extensive set of molecular dynamics simulations of different proteins undergoing binding. Specifically, we compare the configurational entropy change in protein complex formation as obtained by the maximum information spanning tree approximation (MIST), which treats the above entropy contributions directly, and the change in the average NMR methyl and NH order parameters. Our parallel implementation of MIST allows us to treat hard angular degrees of freedom as well as couplings up to full pairwise order explicitly, while still involving a high degree of sampling and tackling molecules of biologically relevant sizes. First, we demonstrate a remarkably strong linear relationship between the total configurational entropy change and the average change in both methyl and backbone-NH order parameters. Second, in contrast to canonical assumptions, we show that the main-chain and angular terms contribute significantly to the overall configurational entropy change and also scale linearly with it. Consequently, linear models starting from the average methyl order parameters are able to capture the contribution of main-chain and angular terms well. After applying the quantum-mechanical harmonic oscillator entropy formalism, we establish a similarly strong linear relationship for X-ray crystallographic B-factors. Finally, we demonstrate that the observed linear relationships remain robust against drastic undersampling and argue that they reflect an intrinsic property of compact proteins. Despite their remarkable strength, however, the above linear relationships yield estimates of configurational entropy change whose accuracy appears to be sufficient for qualitative applications only.