Hidden water's influence on rhodopsin activation

Abstract

Structural biology relies on several powerful techniques, but these tend to be limited in their ability to characterize protein fluctuations and mobility. Overreliance on structural approaches can lead to omission of critical information regarding biological function. Currently there is a need for complementary biophysical methods to visualize these mobile aspects of protein function. Here, we review hydrostatic and osmotic pressure-based techniques to address this shortcoming for the paradigm of rhodopsin. Hydrostatic and osmotic pressure data contribute important examples, which are interpreted in terms of an energy landscape for hydration-mediated protein dynamics. We find that perturbations of rhodopsin conformational equilibria by force-based methods are not unrelated phenomena; rather they probe various hydration states involving functional proton reactions. Hydrostatic pressure acts on small numbers of strongly interacting structural or solvent-shell water molecules with relatively high energies, while osmotic pressure acts on large numbers of weakly interacting bulk-like water molecules with low energies. Local solvent fluctuations due to the hydration shell and collective water interactions affect hydrogen-bonded networks and domain motions that are explained by a hierarchical energy landscape model for protein dynamics.

Figure 1

Protein interactions with soft matter are potentially hidden in static structures created by x-ray crystallography or cryogenic electron microscopy. (A) Representative cryo-EM structure of visual rhodopsin used as a membrane protein archetype (26) (PDB: 6OFJ). Cryogenic temperatures or crystal lattice forces can restrain conformational fluctuations that occur in solution, within a membrane lipid bilayer, or a detergent micelle. (B) Rhodopsin interactions with soft matter (lipids, detergents, water) include conformational transitions associated with changes in hydration that involve water (blue) influx into the protein core (5,7). A comprehensive picture of protein dynamics includes both equilibrium structure and fluctuations. Adapted with permission from (5). Copyright 2014 American Chemical Society.

Figure 2

Energy landscape model describes functional dynamics of proteins. (A) Side view of a hierarchical multidimensional energy landscape (EL) where different protein states occupy basins of attraction separated by energy barriers. The EL manifold comprises various tiers: movements of entire protein domains encompass tier-0 fluctuations; local interconversion between amino acid side-chain conformations corresponds to tier-1 fluctuations; and tier-2 fluctuations consist of movements of larger structures such as helices, loops, and turns. These fluctuations are contained within a basin of the same tier, e.g., a tier-1 basin contains multiple tier-1 fluctuations, and is separated from other tier-1 basins by tier-0 fluctuations. Top right inset: expansion of a single β-basin (synonymous with a tier-1 basin). (B) View from the top of multidimensional energy landscape, showing how a single β-basin fits into the larger energy landscape. A tier-0 basin contains all macrostates (represented as β-basins) of the protein. An individual β-basin contains numerous α-basins. Within an α-basin the protein undergoes rapid α-fluctuations and randomly samples all conformations contained in the α basin. Less frequently, the protein experiences β-fluctuations and undergoes movement among different α-basins. Previously, the larger-scale protein conformational changes were assigned to tier-1 and coupled to the bulk-like solvent (α-fluctuations); the smaller-scale changes were assigned to tier-2 and coupled to the hydration shell (β-fluctuations) (49,52). However, these assignments are now reversed (51). The smaller-scale β-fluctuations are assigned to tier-1 while larger-scale α-fluctuations are assigned to tier-2 (51) (confer figure). Although the α-fluctuations are lower in energy than β-fluctuations, they cause larger-scale conformational changes due to the greater numbers of bulk-like solvent molecules compared with the hydration shell.

Figure 3

Dependence of metarhodopsin activation equilibrium on osmotic pressure reveals back shifting to preactive MI state. Dehydration of the protein by large polymer osmolytes occurs due to nonspecific colligative properties. The logarithm of the MI-MII equilibrium constant (K = [MII]/[MI]) is plotted against osmotic pressure for a series of large polyethylene glycol polymer osmolytes (PEGs) showing an approximately second-order relation (T = 15°C). Universal colligative behavior is found for PEGs with M_r between 1000 and 6000 Da, where the linear term is proportional to the hydrated volume change. At higher osmotic pressure the second-order curvature term arises from the compressibility of the hydration volume and/or specific PEG interactions with the protein. Inset: illustration of how rhodopsin activation equilibrium is back shifted to the preactive MI (closed) state by hydrophilic polymers that are entropically excluded and dehydrate the protein. Reprinted with errors bars from (7). Copyright (2022) National Academy of Sciences.

Figure 4

Light-activation equilibrium of visual rhodopsin is back shifted to preactive MI state by application of hydrostatic pressure. Equilibrium constant for MI-MII transition of rhodopsin in native retinal disk membranes is plotted versus hydrostatic pressure (T = 3°C). Note that relatively large hydrostatic pressures correspond to strong interaction energies of the protein with water. Inset: summary of equilibrium constant at 0.1 MPa (≈0.99 atm) and ΔV showing dependence on lipid or detergent environment of rhodopsin. Data and errors bars are replotted from (77).

Figure 5

Dependence of metarhodopsin equilibrium on osmotic pressure for small osmolytes shows that stabilization of active MII rhodopsin occurs until saturation is reached. The equilibrium constant for the MI-MII transition is plotted versus increasing concentration (osmotic pressure) of polyethylene glycol (PEG) solutes of various molar masses (T = 15°C). Lines with positive slope indicate that the osmolyte shifts the equilibrium toward active MII state; lines with a negative slope indicate shifting of the equilibrium toward the preactive MI state. Inset: depiction of how metarhodopsin equilibrium is forward shifted to active (open) MII state by penetration of small osmolytes into the protein interior. Reprinted with errors bars from (7). Copyright (2022) National Academy of Sciences.

Figure 6

Protonatable groups of visual rhodopsin control its activation equilibrium. (A) Schematic pH titration curves showing fraction of MII versus pH highlighting protonation states of amino acid residues and the retinal ligand that control rhodopsin activity. Near physiological pH the protonation reaction of Glu¹³⁴ of the conserved E(D)RY sequence motif dominates the equilibrium. At acidic pH, the Schiff base linkage of the retinal cofactor begins to protonate, even in MII, thus reducing the MII fraction. At alkaline pH values the endpoint is due to MII substates that coexist with Glu¹³⁴ fully deprotonated. (B) Experimental pH titration curves from UV-visible spectroscopy show that controlled hydration governs rhodopsin activation in retinal disk membranes (T = 15°C). Applied osmotic stress stemming from large PEG osmolytes removes bulk-like water from rhodopsin, back shifting the pH titration curve and decreasing the pK_A of Glu¹³⁴ favoring the preactive MI state. Conversely small PEGs can penetrate into rhodopsin and dehydrate microdomains, increasing the pK_A of Glu^l34 and forward shifting the reaction by favoring the active MII state. Note also that an effect on the alkaline endpoint is observed: small osmolytes stabilize the open MII conformation even when Glu¹³⁴ is fully deprotonated, increasing the alkaline endpoint, while dehydrating large osmolytes reduce the deprotonated MII population and alkaline endpoint. Dehydration of rhodopsin by large PEG osmolytes reduces the pH range and hydration by small osmolytes extends the pH region over which rhodopsin is active. Adapted incuding errors bars from (7). Copyright (2022) National Academy of Sciences.

Figure 7

Side view of the hierarchical multidimensional energy landscape (EL) of rhodopsin, where different protein states (MI, MII, etc.) occupy basins of attraction separated by surmountable energy barriers. The macrostates of MI and MII comprise β-basins, and light activation would correspond to tier-0 fluctuations. Bottom left inset: the energy landscape is rough; within each tier the peak heights and energy of each substate differ. Bottom right inset: collective α-fluctuations are coupled (slaved) to bulk-like water about the protein, while local β-fluctuations are coupled to the solvent shell and structural water. The α- and β-fluctuations arise from the same waters probed by osmotic and hydrostatic pressure, respectively.

Figure 8

Schematic cartoon of how hydrostatic pressure and osmotic pressure shift the rhodopsin activation equilibrium from the active MII state (right) to the preactive MI state (left) by perturbing structural or bulk-like water. (A) Osmotic pressure removes bulk-like water (blue) surrounding the protein from the active MII state thus dehydrating rhodopsin and back shifting to the preactive MI state. (B) Hydrostatic pressure forces water (red) into the solvent shell or small internal cavities where interactions with key amino acid residues can shift the equilibrium between active MII and the preactive MI state. Influences of both hydrostatic pressure and osmotic pressure are unified by a hierarchical energy landscape mechanism (ELM).