Lying at the heart of many vital cellular processes such as photosynthesis and respiration, biological electron transfer (ET) is mediated by transient interactions among proteins that recognize multiple binding partners. Accurate description of the ET complexes – necessary for a comprehensive understanding of the cellular signaling and metabolism – is compounded by their short lifetimes and pronounced binding promiscuity. Here, we used a computational approach relying solely on the steric properties of the individual proteins to predict the ET properties of protein complexes constituting the functional interactome of the eukaryotic cytochrome c (Cc). Cc is a small, soluble, highly-conserved electron carrier protein that coordinates the electron flow among different redox partners. In eukaryotes, Cc is a key component of the mitochondrial respiratory chain, where it shuttles electrons between its reductase and oxidase, and an essential electron donor or acceptor in a number of other redox systems. Starting from the structures of individual proteins, we performed extensive conformational sampling of the ET-competent binding geometries, which allowed mapping out functional epitopes in the Cc complexes, estimating the upper limit of the ET rate in a given system, assessing ET properties of different binding stoichiometries, and gauging the effect of domain mobility on the intermolecular ET. The resulting picture of the Cc interactome 1) reveals that most ET-competent binding geometries are located in electrostatically favorable regions, 2) indicates that the ET can take place from more than one protein-protein orientation, and 3) suggests that protein dynamics within redox complexes, and not the electron tunneling event itself, is the rate-limiting step in the intermolecular ET. Further, we show that the functional epitope size correlates with the extent of dynamics in the Cc complexes and thus can be used as a diagnostic tool for protein mobility.
Many vital cellular processes such as photosynthesis, respiration, and a variety of metabolic reactions rely on long-range electron transfer (ET) between macromolecules. In a biological context, the intermolecular ET involves formation of a redox complex, electron tunneling across a biomolecular interface, and dissociation of the reduced and oxidized products. While the electron tunneling is, arguably, the best described step in this sequence of events [1], [2], our understanding of the first and the last steps is often obscured by the macromolecular dynamics and the uncertainty of the binding geometries in reactive complexes [3]. Experimental study of the biomolecular ET in solution is further complicated by two factors. First, in order to maximize the overall turnover in the ET chain, redox complexes have short lifetimes and, consequently, low binding affinities, making structural characterization of these weak, transient, highly dynamic complexes difficult [4]. Second, unlike in other biomolecular interactions where formation of a functional complex requires precise alignment of the partner molecules, an efficient intermolecular ET can occur from more than one protein-protein orientation. This possible multitude of active binding geometries presents an additional challenge for an accurate description of the biomolecular ET. The observation that most of the transient ET complexes are formed by oppositely-charged proteins spurred numerous modeling studies, the earliest of which aimed at optimizing intermolecular electrostatic interactions and minimizing the separation between the binding partners' redox cofactors [5], [6]. These were followed by more elaborate simulations [7]–[11], which revealed regions of attractive electrostatic potential containing a range of favorable protein-protein orientations [7]–[9], [12] or indicated possible binding-induced conformational changes [7], [11]. However, crystal structures of ET protein complexes show very few charge-charge or polar contacts across the interfaces, with most of the interactions dominated by van der Waals (vdW) forces [13]. Moreover, in several systems, mutagenesis of the charged residues expected to stabilize transient protein-protein complexes shows little or no effect on the binding affinity and the ET rate [14]–[16]. Thus, contrary to the conclusions of early modeling studies [5], [6], it appears that ET complexes are not optimized for intermolecular electrostatic interactions, which is thought to facilitate rapid dissociation required for a high turnover [13]. The current view is that charged residues – often located at the periphery of the binding sites – play a critical role in the early stages of protein-protein interactions by steering binding partners to form a loose encounter complex, but are less important for the ensuing formation of the specific, ET-active form, dominated by short-range forces [4], [13], [17], [18]. In addition, electrostatic interactions can guide the partner proteins to productive docking geometries, without contributing to the overall binding affinity [19]. The discrepancies between the experimental results and the outcome of the electrostatics-based simulations highlight the need for alternative computational approaches. In this work, we used a modeling protocol based solely on steric properties of protein molecules to describe the ET network of eukaryotic cytochrome c (Cc) – a small, soluble, highly conserved hemoprotein. Cc is a key component of the eukaryotic respiratory chain, where it shuttles electrons between Cc reductase (Cbc1) and Cc oxidase (CCO) [20]. Its other physiological redox partners include Cc peroxidase (CcP) [21], sulfite oxidase (SOX) [22], flavocytochrome b2 (Fcb2) [23], a flavin-dependent sulfhydryl oxidase Erv1 [24], and possibly cytochrome b5 (Cb5) [20], [25], [26]. In each of these systems, Cc acts as an electron donor or acceptor and serves as a hub coordinating the electron flow in the mitochondrial intermembrane space [26]. Starting from the structures of individual proteins, we performed extensive conformational sampling of possible ET-active binding geometries, which allowed mapping out functional epitopes in the Cc complexes, estimating the upper limit of the ET rate in a given system, assessing ET properties of different binding stoichiometries and, in the case of Cc complexes with SOX and Fcb2, gauging the effect of domain mobility on the intermolecular ET.
Protein coordinates were taken from the PDB; cofactor topologies and parameters were obtained from the HIC-Up database [27]. The ScErv1 structure was modeled with Modeller [28] based on the X-ray coordinates of the homologous protein AtErv1 [29]. Missing protein loops were built with Modeller [28]. Hydrogen atoms were added in Xplor-NIH using standard topology and parameter sets [30]. The ensembles of Fcb2 and SOX domain-domain orientations were generated in Xplor-NIH by simulated annealing in torsion angle space with standard protein geometry and vdW parameters and knowledge-based dihedral angle potentials [31]. Two domains were treated as rigid bodies, while the intervening linker segment (residues 99–100 or 89–99 for Fcb2; and residues 83–94 for SOX) was allowed full flexibility. Detailed description of the systems studied is provided in the Table S1. Conformational sampling was performed in Xplor-NIH [30] using a modified version of the published protocol [32]. The molecular system was oriented such that the centers of mass (CM) of the two interacting proteins appeared at the origin of the coordinate system and on the positive z axis. In a typical run, the Cc binding partner was kept stationary, while Cc was systematically rotated around x and z axes, corresponding to the θ and φ rotations in the spherical coordinate space (Figure 1A). The desired spatial resolution, δd – defined here as the separation between neighboring CMs of the rotated protein and typically set to 1 Å – determines the δθ and δφ rotation increments and the total number of steps required to sample the entire conformational space (Equations 1–3). (1) where δd and d are the desired spatial resolution and the distance between CMs of the two proteins in the starting structure. Number of δθ rotational increments is given by nθ = π/δθ, with the value rounded to the last digit before the decimal so that. For θ rotation around the x axis, the rotation matrix is given by: (2) Upon θ rotation, the ordinate of the Cc CM becomes y′ = d·sinθ. By symmetry with the Equation 1, the δφi rotational increment at each θi is given by: (3) At each θi, the number of δφi rotational increments is given by nφ, i = 2π/δφi, with the value rounded to the last digit before the decimal so that. For φ rotation around the z axis, the rotation matrix is given by: (4) The total number of the CMs defining the coverage of the conformational space is given by (5) To sample flat, irregular surfaces of the selected CCO and Cbc1 regions, the protocol was modified to include the initial translation of Cc along the long axis of the molecular system, followed by rotation around the same axis, both in increments required to produce 1 Å separation between neighboring Cc CMs. To simulate the rotational freedom, Cc was rotated around orthogonal χ, ψ, ξ axes originating at its CM (Figure 1A). The rotational axes were defined relative to the plane determined by the two proteins' CMs and the Fe atom of Cc with the corresponding Cartesian coordinates (x0, y0, z0), (x1, y1, z1), and (x2, y2, z2) (Equation 6): (6) Solving the determinant and noticing that one of the CMs is at the origin of the coordinate system, i. e. (x0, y0, z0) = (0,0, 0), gives A = y1z2−y2z1, B = x2z1−x1z2, C = x1y2−y1x2, and D = 0. The χ and ξ axes are defined along the (A, B, C) normal to the plane and the (x1, y1, z1) vector joining the two CMs, respectively, while the φ axis is perpendicular to the latter two and is given by the vector (A′, B′, C′) = (Cy1−Bz1, Az1−Cx1, Bx1−Ay1). Two rotational sampling protocols were employed in this work. In the first protocol, the rotational coordinates were systematically varied in δχ = δψ = δξ increments in the full rotational space (0°≤χ, ψ<360°, 0°≤ξ<180°). In the second protocol, the conformational search was performed with composite rotation increments (δχ = δψ = 5°, δξ = 15°) in the reduced rotational space (−45°≤χ, ψ≤45°, 0°≤ξ<360°) (Figure 1B, C). In the latter case, the Cc in the starting protein-protein orientation was rotated around its CM in δχ = δψ = δξ = 5° increments (0°≤χ, ψ<360°, 0°≤ξ<180°) in the search for the position with the smallest separation between the ET cofactors (black circles in Figure 1B). In this way, the frontal orientation of the Cc heme was ensured during subsequent θ and φ rotations (grey circle in Figure 1B), enabling sampling of short intermolecular ET distances during the reduced rotational search (Figure 1C). For each rotamer, the intermolecular vdW energy term (fvdW) was calculated with a repulsive quartic potential (Equation 7) [33], [34]: (7) where rij is the interatomic distance, rmin adopts the values of the standard vdW radii as represented by the Lennard-Jones potential [33], s is the scaling factor (the value of 0. 75, frequently used in Xplor rigid-body docking [33], [34], was employed throughout), and kvdW is a force-constant (20 kcal·mol−1Å−4). The sum is carried over all pairwise intermolecular interactions between the atoms that are below a specified non-bonded cut-off, typically set to a value between 5. 5 and 8. 5 Å. The vdW potential was set to zero for all side-chain atoms extending beyond Cβ, and Cc translated along the vector joining two proteins' CMs until the vdW energy reached the value between zero and a chosen cut-off, thus producing a rigid-body mimic of the protein complex. The separation between the proteins' CMs determines the other translational coordinate, r. In control runs, side-chain optimization with full vdW potential was performed after each translational step (Figure 2). For each (χ, ψ, ξ) set, the smallest edge-to-edge distance between the redox cofactors, dmin, was calculated, and the rotamer with the smallest dmin selected. The dmin at each sampled (θ, φ) position was used in the subsequent distance distribution analysis and the ET-rate calculations. In the Cc-CCO system, the proximity of the CCO residue W104 to the protein surface necessitated side-chain optimization of the final docking solutions. Simulations of ternary complexes were performed by sampling binding geometries of the second Cc molecule disregarding the Cc bound in the 1∶1 complex and subsequent evaluation of the vdW term between the two Cc molecules. Only the solutions with the vdW terms below a defined cut-off were retained. A similar approach was used for modeling Cc binding to the multidomain proteins Fcb2 and SOX: the interaction of Cc with the Fcb2 or SOX heme domain was followed by evaluation of the overall vdW energies and selection of the allowed solutions. The annotated Xplor-NIH scripts for the conformational sampling are provided in the Dataset S1; their detailed description is given in the Text S1. The ET rate constants were calculated from Equation 8 [2]: (8) where k0 = 1013 s−1 is the nuclear frequency, β = 1. 4 Å−1 is the decay coefficient of the electronic coupling, r is the edge-to-edge distance between the redox centers, r0 = 3. 6 Å is the vdW contact distance, ΔGo is the free energy difference between reactant and product states, λ is the reorganization energy, kB is the Boltzmann constant, and T is the temperature. The values of ΔGo and λ were taken from the literature (see Text S2).
To define the conformational space available to the interacting molecules and determine the shortest distance between redox cofactors at each sampled point, Cc was used as a probe to explore the surface of a partner protein in search of possible binding geometries. Proteins were treated as rigid bodies with van der Waals (vdW) potential set to zero for all atoms extending beyond Cβ to allow partial side-chain overlap, mimicking the uncertainty of the side-chain positions due to local binding-induced reorganization. Steric properties of the generated complexes were assessed by calculating the intermolecular vdW energy term. To determine the optimal parameter set for the conformational search protocol, we performed a number of control runs (Figure 2). In the first control series (where only translational freedom of Cc was simulated to accelerate the calculations), we examined the choice of the vdW cut-offs (Figure 2A, B). The value of 10 kcal/mol, found to yield realistic mimics of protein complexes in our earlier work [32], and a more permissive 300 kcal/mol cut-off were assessed. First, we performed a run with the full vdW potential and side-chain optimization for the entire complex at each translation step. The ET distance distribution in this run (black profile in Figure 2A) was used as a reference for evaluating the protocols without side-chain optimization and the full vdW potential (vdW cut-off values of 300 kcal/mol and 10 kcal/mol; red and orange, respectively in Figure 2A, B) or a reduced vdW term, set to zero for all atoms extending beyond Cβ (vdW cut-off values 300 kcal/mol and 10 kcal/mol; blue and green, respectively in Figure 2A, B). As can be seen in Figure 2A, the red and orange profiles are shifted towards higher r values, indicating that the rigid-body simulations with the full vdW potential produce binding geometries with unrealistically large separations between protein molecules. This is further evidenced by poor correlations between the corresponding distance distributions (top plots in Figure 2B). The runs with the reduced vdW term (blue and green in Figure 2A) exhibit profiles highly similar to that of the reference run and feature greatly improved linear trends in the correlation plots (bottom graphs in Figure 2B). In particular, an excellent agreement between the results of the reference run and the computationally faster simulation protocol with the reduced vdW potential and the cut-off value of 10 kcal/mol (green datasets in Figure 2A, B) indicates that the latter produces reasonable rigid-body models of protein complexes. Therefore, this protocol was used in all subsequent simulations. Having established the optimal vdW parameters, we proceeded to evaluate the choice of rotational increments to be used in the conformational search protocol. As can be seen in Figure 2C, including Cc rotations around orthogonal axes centered on its CM allows sampling shorter ET distances, with progressively shorter distances accessed at decreasing rotational increments. This is accompanied by a steady increase in the number of rotamers, reaching ca. 187,000 per (θ, φ) position for δχ = δψ = δξ = 5° (Figure 2D). Decreasing the rotational increments further quickly leads to an explosion of structures (e. g. 1. 8·1011 structures per run with δχ = δψ = δξ = 1°), rendering the computations prohibitively time-consuming. Besides, the number of shorter distances accessed is expected to reach a plateau at smaller increments, making the choice of δχ = δψ = δξ = 5° a good compromise between the density of the rotational space sampling and the computational time invested. Finally, we noticed that in protein-protein complexes with short ET distances, Cc is oriented with its heme group facing the binding partner (Figure 1B). In addition, control runs with the stationary Cc and Cb5 or CcP as the sampling probes showed that the Cc functional epitope maps onto a well-defined patch of the protein surface surrounding the exposed heme edge (Figures S6 and S7). Based on these observations, we devised an accelerated sampling scheme in a reduced rotational space, consisting of defining the frontal Cc orientation in the starting structure, usual sampling of θ and φ dimensions, and the rotational search in a reduced χ, ψ, ξ space. As can be seen in Figure 2C (compare red and black traces), the ET distance distributions obtained with the full sampling scheme (0°≤χ, ψ<360°, 0°≤ξ<180°; δχ = δψ = δξ = 5°) and the reduced rotational search (−45°≤χ, ψ≤45°, 0°≤ξ<360°; δχ = δψ = 5°, δξ = 15°) are virtually identical. At the same time, confining rotations to a smaller space of −30°≤χ, ψ≤30°, 0°≤ξ<360° leads to reduced occurrences of small ET distances, indicating insufficient sampling of the rotational space. Thus, the former protocol – affording a 24-fold gain in the computational time (Figure 2D) – was used in all subsequent simulations. The conformational search used in this work is not optimized for speed, but rather tailored for a fine sampling of the entire conformational space in the protein complexes. However, significant reduction in the computational time can be achieved by performing the rotational search and outputting the solutions only for the CMs with short separations between the ET cofactors (e. g. <20 Å). This in effect will discard most of the ET-inactive orientations (i. e. blue areas in the CM distribution maps in Figures 3–6, see below), while sampling the functional epitopes. A further gain in the calculation speed can be achieved by decreasing the resolution of the conformational search, either by increasing the rotational increments or decreasing the spatial resolution, δd. For instance, setting δχ and δψ to 15° instead of 5° and increasing the spacing between the neighboring CMs by 1 Å afford, respectively, 9- and 4-fold decrease in the number of sampled orientations. Combining the two strategies and restricting the search to the ET-competent areas only (see above) are expected to yield a 40- to 60-fold decrease in the computational time, depending on the system. Though the suggested scheme will miss out some of the ET-competent orientations (thus lowering the accuracy of the upper-limit kET estimates), it should still provide robust functional epitope maps. The ET rate constants can be estimated from the distances between redox centers as described by Dutton and co-workers [2], [35] or calculated from the ET pathway theory developed by Beratan and Onuchic [36]. The latter, computationally more demanding, approach requires the knowledge of the side-chain contacts across the interface, while the former relies solely on the separation between the electron donor and acceptor sites and appears to be more suitable to the rigid-body search method used here. In a broad range of biological systems, the ET rate constants are described by an exponential dependence on the distances between the redox centers (Equation 8 in the Methods section) [2]. Being a measure of the electronic coupling between the electron donor and acceptor, the exponential decay constant, β, is a property of the medium through which electron tunneling occurs, with the values varying from β = 1. 0 Å−1 and β = 1. 3 Å−1 for β-strands and α-helices, respectively, to β = 2. 8 Å−1 for the vacuum [2], [3]. Analysis of biological ET systems showed that electron tunneling through a protein can be accurately described with a single value of β = 1. 4 Å−1 [2], which is used throughout this work. The functional epitopes of the physiological Cc complexes derived in this work delineate the conformational space available to all ET-competent protein-protein orientations. For the complexes studied, the epitopes map onto the negatively-charged surfaces of the Cc partner proteins. As confirmed by the simulations where the Cc surface is sampled with the Cb5 and CcP molecules (Figures S6 and S7), Cc maintains frontal orientation in all ET-competent conformations, which is in excellent agreement with the experimental studies showing that the Cc binding interfaces in redox complexes comprise the surface-exposed heme edge and the surrounding ring of lysines (Figure 7A). Taken together, these findings highlight the importance of the electrostatic steering in the ET complexes and confirm that the exposed heme edge serves as the electron exit/entry port in Cc. It is noteworthy that the charge complementarity in Cc complexes is revealed by a method relying solely on steric properties of individual proteins. The fact that most protein-protein orientations with shortest separations between redox cofactors are also highly electrostatically favorable suggests that complementary electrostatics evolved to facilitate intermolecular ET. The fact that the functional epitopes contain multiple, electrostatically favorable, ET-competent binding geometries suggests that in most cases the intermolecular ET can occur from more than one protein-protein orientation and might not require formation of a well-defined, stereospecific complex. Indeed, a recent experimental study of the interactions among a small copper protein amicyanin and several bacterial cytochromes has shown that a highly efficient ET can take place without formation of specific complexes, but rather via loose protein encounters guided by complementary electrostatics [59]. We hypothesize that the Cc ET interactome – particularly complexes with broad functional epitopes such as those in Cb5, Fcb2, SOX, and Erv1 – can exhibit a similar behavior. It is well known that dynamics regulate the ET within proteins and, together with the fluctuations in the surrounding medium, enable ET gating [60]. Similarly, heterogeneity of the binding geometries in the ET complexes – arising from multiple protein-protein orientations sampled during the initial, encounter state of the biomolecular association [61] – is expected to modulate the intermolecular ET rates. Indeed, this notion has been gaining an increasing experimental support. For example, computational analysis of the kinetics data for the interaction of bacterial cytochrome c2 (Cc2) with the photosynthetic reaction centre (RC) revealed the presence of multiple ET-active binding geometries explored in the encounter state [62], [63]. In addition, detailed molecular dynamics simulations of the Cc2-RC encounter complex showed that strong ET coupling pathways between the two proteins include interstitial water molecules and illustrated that structural fluctuations within the encounter ensemble enable fast ET rates, in agreement with the experiment [63]. In another series of studies, Hoffman, Beratan and co-workers showed that interacting Cb5 and myoglobin sample multiple ET-active protein-protein orientations, constituting a subset of the binding geometries explored in the encounter state [19], [64]. Here, we use the functional epitope mapping to delineate the extent of the conformational space containing all ET-competent geometries, including those populated in the transient encounter complex. The functional epitopes in Cc complexes differ in size and exhibit a large variation in the number of ET-competent binding geometries (Figure 7B). For example, a broad epitope in the Cc-Cb5 complex contains ten times more protein-protein orientations with kET>106 s−1 than a narrow, funnel-shaped ET patch in the Cc-Cbc1 complex. Interestingly, the size of the ET epitopes in Cc complexes correlates with the extent of protein dynamics. For instance, being a highly dynamic system consisting of multiple binding geometries [56], the Cc-Cb5 complex features a broad functional epitope, while the Cc-CcP complex – where proteins spend 70% of the time in the crystallographic binding orientation and 30% in a loose encounter state [32], [42] – has a much narrower ET-competent area. We suggest that the size of the ET epitope can be used as a diagnostic tool to discriminate between highly-dynamic, multiple-orientation complexes (e. g. Cc-Cb5, Cc-SOX, Cc-Fcb2) and their more static, predominantly single-orientation counterparts (e. g. Cc-CcP, Cc-CCO, and Cc-Cbc1) (Figure 7B). We hypothesize that differences in the widths of the ET-active areas reflect different evolutionary scenarios, contrasting the need to drive Cc binding to a well-defined, single orientation in Cbc1 and CCO complexes (served to prevent wasteful interactions with other parts of these multisubunit assemblies and the negatively-charged mitochondrial membrane) and allowing rapid ET from multiple protein-protein orientations in the Cc-Cb5 complex. Without the knowledge of the populations of the protein-protein orientations constituting the functional epitope, the occurrences of donor-acceptor distances and the corresponding ET rates derived in this work cannot be converted into the ET probability distributions, and only the upper-limit kET values can be calculated. It is conceivable that some of the highly ET-competent binding geometries are energetically unfavorable and, therefore, do not form during the interaction. On the other hand, many thermodynamically favorable orientations (be they ET-active or not) might never get populated on the protein-protein association path. Thus, accurate predictions of the ET rates from the functional epitopes would require estimating the relative thermodynamic stabilities of the ET-competent binding geometries and addressing the protein association kinetics on a microscopic level. As illustrated by early modeling efforts of the Cc-CcP complex [6], failure to consider kinetic properties of a system led to an over-optimized predicted binding geometry (exhibiting high charge complementarity, large number of intermolecular hydrogen bonds, and snug steric fit of the protein sidechains), in stark contrast to the crystal structure featuring a single hydrogen bond and a loosely packed interface [37]. Used in the past to investigate Cc interactions with CcP [8], [65] and Cb5 [9], [54], Brownian dynamics (BD) simulations afford an effective weighting of various ET-competent orientations and allow extraction of bimolecular rate constants, kon, from association trajectories. When used with the ET reaction criteria (such as distances between the cofactors or the coupling strengths of ET pathways), the BD runs provide estimates for the intermolecular ET rates. Further, combination of BD and molecular dynamics (MD) allows to address the roles of structural fluctuations and the intervening solvent in the intermolecular ET process [63]. While BD and BD/MD are clearly methods of choice for accurate ET rate calculations, as a rule they require a complex force-field, adequate definition of the simulation conditions (e. g. proper partial charge and protonation state assignments for protein sidechains, boundary conditions, etc), a certain level of expertise, and considerable amount of time. Interestingly, the functional epitopes of Cc-CcP and Cc-Cb5 complexes delineated with our coarse-grained approach, relying solely on the steric properties of the interacting molecules, are in excellent agreement with those found in BD simulations [8], [9], [65]. Thus, it appears that the simple rigid-body conformational search employed in this work is sufficient for an adequate description of the ET-competent areas in Cc complexes. As can be seen from the kET distributions, the studied Cc complexes contain a large number of binding geometries with the ET rates faster than the experimentally measured values. Even though not all of these ET-competent orientations will be populated in a given protein complex (see above), it seems likely that several highly-active conformations will dominate the intermolecular ET. This finding suggests that the overall ET rates in the Cc interactome are not limited by the ET event itself, but rather controlled by alterations in protein dynamics and/or conformational changes, occurring in protein complexes on slower time scales. Such binding-induced and/or redox-dependent conformational changes, reported for free Cc [66] and its complexes with Cbc1 [11] and CCO [67], could account for the apparent discrepancies between the observed and predicted ET rates. The computational approach used here can be readily extended to other ET systems. Specifically, our analysis does not require the structures of protein complexes; can be applied in combination with homology modeling to systems with unknown protein structure (s) (as exemplified by Cc-Erv1); allows ensemble treatment of flexible groups (as shown for Cc complexes with Fcb2 and SOX); permits incorporation of side-chain optimization protocols (as done in control Cc-Cb5 runs); and enables calculations of the maximal, ‘activationless’ ET rates for the systems with unknown free energy changes (ΔG) and reorganization energies (λ). We believe that this work will engender future studies of the intermolecular ET in other biological networks.
A number of vital cellular processes such as respiration, photosynthesis, and multifarious metabolic conversions rely on a long-range electron transfer (ET) among protein molecules. Full understanding of the biological ET requires accurate description of the redox protein complexes, which is hampered by their pronounced mobility and short lifetimes. Here we used a simple computational approach to predict the ET properties of the physiological protein complexes of cytochrome c (Cc) – a small electron carrier that coordinates the electron flow among different redox partners. By performing extensive conformational sampling of the possible binding geometries, we mapped out functional epitopes in the Cc complexes and assessed their ET properties. Our study suggests that protein dynamics within redox complexes is the rate-limiting step in the intermolecular ET and indicates that the functional epitope size correlates with the extent of dynamics in the Cc complexes. We believe that the latter finding can be used as a diagnostic tool for protein mobility and expect that this work will engender future studies of the intermolecular ET in biological networks.
biochemistry
protein interactions
proteins
biophysic al simulations
biophysics simulations
biology
hemoproteins
bioenergetics
biophysics
computational biology
Electron Transfer Interactome of Cytochrome c