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Review
. 2010 May 18;43(5):652-60.
doi: 10.1021/ar9002703.

The folding energy landscape and free energy excitations of cytochrome c

Affiliations
Review

The folding energy landscape and free energy excitations of cytochrome c

Patrick Weinkam et al. Acc Chem Res. .

Abstract

The covalently bound heme cofactor plays a dominant role in the folding of cytochrome c. Because of the complicated inorganic chemistry of the heme, some might consider the folding of cytochrome c to be a special case, following principles different from those used to describe the folding of proteins without cofactors. Recent investigations, however, demonstrate that common models describing folding for many proteins work well for cytochrome c when heme is explicitly introduced, generally providing results that agree with experimental observations. In this Account, we first discuss results from simple native structure-based models. These models include attractive interactions between nonadjacent residues only if they are present in the crystal structure at pH 7. Because attractive nonnative contacts are not included in native structure-based models, their energy landscapes can be described as "perfectly funneled". In other words, native structure-based models are energetically guided towards the native state and contain no energetic traps that would hinder folding. Energetic traps are denoted sources of "frustration", which cause specific transient intermediates to be populated. Native structure-based models do, however, include repulsion between residues due to excluded volume. Nonenergetic traps can therefore exist if the chain, which cannot cross over itself, must partially unfold so that folding can proceed. The ability of native structure-based models to capture this kind of motion is partly responsible for their successful predictions of folding pathways for many types of proteins. Models without frustration describe the sequence of folding events for cytochrome c well (as inferred from hydrogen-exchange experiments), thereby justifying their use as a starting point. At low pH, the experimentally observed folding sequence of cytochrome c deviates from that at pH 7 and from models with perfectly funneled energy landscapes. Here, alternate folding pathways are a result of "chemical frustration". This frustration arises because some regions of the protein are destabilized more than others due to the heterogeneous distribution of titratable residues that are protonated at low pH. Beginning with native structure-based terms, we construct more complex models by adding chemical frustration. These more complex models only modestly perturb the energy landscape, which remains, overall, well funneled. These perturbed models can accurately describe how alternative folding pathways are used at low pH. At alkaline pH, cytochrome c populates distinctly different structural ensembles. For instance, lysine residues are deprotonated and compete for the heme ligation site. The same models that can describe folding at low pH also predict well the structures and relative stabilities of intermediates populated at alkaline pH. The success of models based on funneled energy landscapes suggest that cytochrome c folding is driven primarily by native contacts. The presence of heme appears to add chemical complexity to the folding process, but it does not require fundamental modification of the general principles used to describe folding. Moreover, its added complexity provides a valuable means of probing the folding energy landscape in greater detail than is possible with simpler systems.

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Figures

Figure 1
Figure 1
(A) The structure of cytochrome c colored by folding unit. The folding units are assigned by grouping residues that have a similar folding transition as a function of the folding reaction coordinate Q. (B) The ensemble average of the residue specific reaction coordinate Qi as a function of the global folding coordinate Q for several residues. Corresponding to the structure in (A), the curves are colored by their assigned folding unit: blue (residues 95, 96, 98, and 99), green (residue 68), yellow (residues 60 and 64), red (residues 74 and 75), and grey (residues 43, 46, and 52). Shown with dashed lines are Qi curves for several residues in the white omega loop (residues 16 to 33) that cannot be assigned to a folding unit because there is no clear single sigmoidal transition. (C) A representation of the folding funnel that depicts the sequential folding mechanism. Each level shown corresponds to the Q value at which each folding unit unfolds: (from top to bottom) blue, green, yellow, red, grey, and the native basin. The width of each line is proportional to the number of unfolded residues in the ensemble and is proportional to the configurational entropy. (D) Free energy profile as a function of the folding reaction coordinate Q calculated from a simulation of cytochrome c at 20°C.
Figure 2
Figure 2
Two folding trajectories using a native structure-based model are shown in 100 snapshot windows. The Q of the entire protein is shown in black, while the Q of each folding unit is shown in blue, green, yellow, red, and grey. The folding trajectory in A shows the dominant type of transition, which occurs greater than 90% of the time. The folding trajectory in B rarely happens.
Figure 3
Figure 3
Free energy levels of unfolding calculated from simulations based on a structure-based model at different temperatures. Overlayed structures are shown to represent a typical structural ensembles for each free energy level. Free energy levels are averages of the unfolding free energies for individual residues within a given folding unit. The unfolding free energy is calculated by matching the Q value of the unfolding transition midpoint of a specific residue (Figure 1B) to the Q value corresponding to the overall folding free energy (Figure 1D). The free energy levels and structures are colored to represent the folding units. Above the folding temperature, Tf , the free energy of the unfolded ensemble (horizontal dashed line) is less than that of the native state (black lines).
Figure 4
Figure 4
(A) Free energy levels calculated from simulations based on a model with a funneled landscape and nonnative electrostatic and hydrophobic forces. (B–C) Correlation of the unfolding free energies of individual residues determined from simulation (FQi), which are used to calculate the free energy levels in A, with the values obtained from hydrogen exchange experiments for pH 7 and pH 4 respectively.
Figure 5
Figure 5
Normalized averages of the radius of gyration (×), Trp59 fluorescence (○), and Met80 ligation (□) as a function of the folding reaction coordinate Q. The Trp59 to heme fluorescence decay is calculated using the equation k=(1+(R0r)6) in which r is the distance between the Trp59 Cα and the heme center and the Förster distance, R0, is 35 A. Met80 ligation is defined to occur when the distance between the Met80 Cα and the heme center is less than 8A.
Figure 6
Figure 6
A scheme for combining energy landscapes for folding individual chemical species with acid/base and coordination chemistry. In the above scheme, each chemical species, characterized by its protonation and ligation state, is represented by its own folding funnel of varying depths. The pH 7 folding funnel has an energy minima at the native state (N), but as the solvent conditions change, the energy landscape is perturbed giving rise to slightly different funnels with distinct minima such as misligated states M1 or M2.
Figure 7
Figure 7
(A) The fractional populations of the most stable species as a function of pH predicted using the grand canonical ensemble method: the native (●), fully unfolded (+), Lys79 misligated (*), Lys73 misligated (□), Lys72 misligated (■), and partially unfolded, hydroxide bound (△) states. (B) Free energy curves calculated from the simulation of the five most stable lysine misligated species. In addition to the three lysine misligated intermediates in A are the Lys53 (○) and Lys55 (×) misligated species.

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