Reconciling Mediating and Slaving Roles of Water in Protein Conformational Dynamics

<div><p>Proteins accomplish their physiological functions with remarkably organized dynamic transitions among a hierarchical network of conformational substates. Despite the essential contribution of water molecules in shaping functionally important protein dynamics, their exact role is still controversial. Water molecules were reported either as mediators that facilitate or as masters that slave protein dynamics. Since dynamic behaviour of a given protein is ultimately determined by the underlying energy landscape, we systematically analysed protein self energies and protein-water interaction energies obtained from extensive molecular dynamics simulation trajectories of barstar. We found that protein-water interaction energy plays the dominant role when compared with protein self energy, and these two energy terms on average have negative correlation that increases with increasingly longer time scales ranging from 10 femtoseconds to 100 nanoseconds. Water molecules effectively roughen potential energy surface of proteins in the majority part of observed conformational space and smooth in the remaining part. These findings support a scenario wherein water on average slave protein conformational dynamics but facilitate a fraction of transitions among different conformational substates, and reconcile the controversy on the facilitating and slaving roles of water molecules in protein conformational dynamics.</p></div

The relationship between <i>r</i> (correlation coefficient between <i>E_p</i> and <i>E_p</i>_–<i>w</i>) and net effects of water molecules on local PES () for three different time scales.

<p>a) , b) and c). Data for all eight time scales studied are presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0060553#pone.0060553.s006" target="_blank">Fig. S6</a>.</p

Pearson correlation coefficient <i>r</i> between <i>E_p</i> and <i>E_p</i>_–<i>w</i> for barstar.

<p>Time scale values are obtained by first reducing time scales() with femto-second and then taking logarithm (e.g. 1 corresponds to 10 , 2 corresponds to 100 , 3 corresponds to 1 , etc.) Time scales mentioned in figures hereafter are the same. (a) ensemble average of as a function of time scales. (b) Distributions of at 10 (square), 1 (circle), 100 (upwards triangles) and 10 (downwards triangles).</p

Visualization 1: Ultrasonic detection based on polarization-dependent optical reflection

3D visualization of the mouse ear. Originally published in Optics Letters on 01 February 2017 (ol-42-3-439