Encounter complexes and dimensionality reduction in protein–protein association
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Abstract
An outstanding challenge has been to understand the mechanism whereby proteins associate. We report here the results of exhaustively sampling the conformational space in protein–protein association using a physicsbased energy function. The agreement between experimental intermolecular paramagnetic relaxation enhancement (PRE) data and the PRE profiles calculated from the docked structures shows that the method captures both specific and nonspecific encounter complexes. To explore the energy landscape in the vicinity of the native structure, the nonlinear manifold describing the relative orientation of two solid bodies is projected onto a Euclidean space in which the shape of low energy regions is studied by principal component analysis. Results show that the energy surface is canyonlike, with a smooth funnel within a two dimensional subspace capturing over 75% of the total motion. Thus, proteins tend to associate along preferred pathways, similar to sliding of a protein along DNA in the process of proteinDNA recognition.
https://doi.org/10.7554/eLife.01370.001eLife digest
Proteins rarely act alone. Instead, they tend to bind to other proteins to form structures known as complexes. When two proteins come together to form a complex, they twist and turn through a series of intermediate states before they form the actual complex. These intermediate states are difficult to study because they don’t last for very long, which means that our knowledge of how complexes are formed remains incomplete.
One promising approach for studying the formation of complexes is called paramagnetic relaxation enhancement. In this technique certain areas in one of the proteins are labelled with magnetic particles, which produce signals when the two proteins are close to each other. Repeating the measurement several times with the magnetic particles in different positions provides information about the overall structure of the complex. Computational modelling can then be used to work out the fine details of the structure, including the shapes of the intermediate structures made by the proteins as they interact.
A computer method called docking can be used to predict the most favourable positions that the proteins can take, relative to one another, in a complex. This involves calculating the energy contained in the system, with the correct structure having the lowest energy. Docking methods also predict protein models with slightly higher energies, but with structures that are radically different. Modellers usually ignore these structures, but comparing the docking results to paramagnetic relaxation enhancement data, Kozakov et al. found that these structures actually represent the intermediate states.
Analysing the structure of the intermediate states revealed that the movement of the two proteins relative to one another is severely restricted as they form the final complex. Kozakov et al. found that proteins associate along preferred pathways, similar to the way a protein slides along DNA in the process of proteinDNA recognition. Knowing that the movement of the proteins is restricted in this way will enable researchers to improve the efficiency of docking calculations.
https://doi.org/10.7554/eLife.01370.002Introduction
Interactions between proteins play a central role in various aspects of the structural and functional organization of the cell. To recognize its partner, a protein must align its binding interface, usually a small fraction of the total surface, with a similarly small binding interface on the other protein (Berg and von Hippel, 1985; Ubbink, 2009). Since all interactions are of relatively short range, the process must start with a diffusive search governed by Brownian motion, which brings the proteins to a ‘macrocollision’ to yield a transition state also known as the encounter complex (Berg and von Hippel, 1985; von Hippel and Berg, 1989). The encounter complex can be thought of as an ensemble of conformations in which the two molecules can rotationally diffuse along each other, or participate in a series of ‘microcollisions’ that properly align the reactive groups. The second step of association consists of conformational rearrangements to the native complex. While it has been generally recognized that association proceeds through a transition state, little was known of the encounter complex structures and configurations as their populations are low, their lifetimes are short, and they are difficult to trap, rendering them essentially invisible to conventional structural and biophysical methods (Iwahara and Clore, 2006).
Novel experimental and improved computational methods, developed during the last decade, have the potential to provide information leading to better understanding of the nature of encounter complexes. On the experimental side, the major progress is due to the application of NMR paramagnetic relaxation enhancement (PRE), a technique that is exquisitely sensitive to the presence of lowly populated states in the fast exchange regime (Clore, 2008; Clore and Iwahara, 2009; Fawzi et al., 2010). The detection of such intermediates requires introducing paramagnetic labels, one at a time, at a few sites on one of the interacting proteins, and measuring the transverse paramagnetic relaxation enhancement (PRE) rates, Γ_{2}, of the backbone amide protons (^{1}H_{N}) of the partner protein (Tang et al., 2006). In a fast exchanging system, the observed value of Γ_{2} is the weighted average of the values for the various states present in solution (Iwahara et al., 2004; Iwahara and Clore, 2006). Because Γ_{2} is dependent on the inverse sixth power of the distance (<r^{−6}>) between the unpaired electron on the paramagnetic center and the observed proton, and because the Γ_{2} rates at short distances are very large owing to the large magnetic moment of the unpaired electrons, lowpopulation intermediates can be detected. In particular, the observed intermolecular ^{1}H_{N}−Γ_{2} rates and those backcalculated from the structure of the native complex generally differ for a number of residues, revealing regions that participate in transitional interactions (Tang et al., 2006).
Paramagnetic relaxation enhancement (PRE) techniques can provide distributions of distances between a paramagnetic ion and protons, indicating the presence and relative ratio of conformational subensembles (Tang et al., 2006; Suh et al., 2007; Clore and Iwahara, 2009; Fawzi et al., 2010), but determining the detailed structure of encounter complexes requires computational approaches. A semiquantitative depiction of the minor species can be obtained by using restrained rigidbody simulated annealing refinement to minimize the difference between observed and calculated ^{1}H_{N}−Γ_{2} rates (Tang et al., 2006; Kim et al., 2008). However, with the development of docking methods it is possible to globally sample the entire conformational space of two interacting proteins, generating all low energy states. Although in some cases binding is inherently coupled with folding (Shoemaker et al., 2000; Zheng et al., 2012), a large class of protein complexes can be adequately described by a model that assumes essentially rigid association, possibly followed by refinement that allows for local changes in side chains and interacting loops (Smith and Sternberg, 2002; Kozakov et al., 2006; Vajda and Kozakov, 2009). As demonstrated by the results of CAPRI (Critical Assessment of Prediction of Interactions) communitywide protein docking experiment, for such cases modern computational docking methods, including automated servers, are capable of generating docked conformations that agree well with the Xray structure of the complex (Lensink and Wodak, 2013). In particular, our program PIPER, based on the Fast Fourier transform (FFT) correlation approach, globally and systematically samples the conformational space of two interacting proteins on a dense grid using a physics based energy function (Kozakov et al., 2006). The program is implemented in the heavily used server ClusPro (Comeau et al., 2007), which yields good results when docking Xray structures of two proteins with at most moderate backbone conformational changes upon binding (Kozakov et al., 2010). Based on the results of CAPRI, Cluspro has been the best protein–protein docking server for the last 5 years (Kozakov et al., 2013; Lensink and Wodak, 2013).
It is well known that, in addition to nearnative structures, docking generally yields a large number of models that are similar to nearnative ones in terms of energy, but may substantially differ in terms of geometry (Vajda and Kozakov, 2009). Since such ‘false positive’ models do not predict the final bound complex, they are usually regarded as artifacts. However, using molecular mechanics energy functions without ‘builtin’ information on the native state it is reasonable to assume that the alternative low energy models represent encounter complexes that, in view of their favorable interactions, may occur along association pathways. Accordingly, we show here that using the large ensemble of low energy structures generated by docking provides better approximation of experimental PRE profiles than the one calculated only from the coordinates of the final complex. Since in docking calculations we start from unbound protein structures and systematically sample the entire conformational space, based on this result we can easily generate ensembles of encounter complexes for any pair of associating proteins.
Once it is established that the energy function used for sampling the conformational space enables us to accurately predict both the native state and the ensemble of encounter complexes, and thus the energy function is valid beyond selecting the native structure of the complex, we proceed to characterizing the energy landscape in the 6D translational/rotational space near the native state (McCammon, 1998; Camacho et al., 1999; Zhang et al., 1999; Tovchigrechko and Vakser, 2001). We focus on the main binding funnel in a neighborhood of the native state, which is the most important region of the conformational space, containing over 90% of complex structures observed in the PRE experiments (Iwahara and Clore, 2006; Tang et al., 2006; Clore and Iwahara, 2009; Fawzi et al., 2010). Since rigid body association occurs in a low dimensional space, the shape of the binding energy landscape can be studied in detail, in contrast to protein folding, which occurs in a very high dimensional space (Dill and Chan, 1997). In spite of the low dimensionality, the analysis is far from simple in this highly curved space due to the interdependence of the coordinates (Park, 1995; Park and Ravani, 1997; Shen et al., 2008; Mirzaei et al., 2012). However, one can transform the rotational space into a product of axisangle representations using complex exponentials (Park and Ravani, 1997). These socalled exponential parameters create a local onetoone mapping between the nonlinear manifold of potential conformations and an Euclidean space (Mirzaei et al., 2012), and thus the shape of low energy regions in the conformational space can be studied by classical principal component analysis (PCA) in the Euclidean space. Using PCA we will be able to determine whether any subspace can accommodate a large fraction of the structures, and whether there are energy barriers that restrict the distribution of encounter complexes in the vicinity of the native state.
The most important result of our analysis is that the region of the space in a neighborhood of the native state invariably includes high energy barriers preventing the ligand from moving into a one or twodimensional restrictive subspace. Orthogonal to the restrictive subspace is a permissive subspace, in which the energy is relatively flat. Based on these results one can visualize the energy landscape as resembling a canyonlike terrain where the low energy areas (at the bottom of the canyon) lie in a lower dimensional subspace. Thus, within the range of physical interactions, two proteins sample only relatively small fractions of the conformational space, and converge toward the native state along preferred pathways. This result represents information that, in principle, could have been obtained by running molecular dynamics or Brownian dynamics simulations. However, a sufficiently dense sampling of conformational space by molecular dynamics is computationally very demanding, even when restricting considerations to a neighborhood of the native state (Wang and Wade, 2003), whereas Brownian dynamics simulations usually rely on highly simplified protein models (Camacho et al., 2000; Spaar and Helms, 2005). In contrast, we use detailed allatom models and map the energy surface using two different physicsbased energy functions. As will be shown, these differences do not significantly affect the results of the principal component analysis, suggesting that the reduction of dimensionality is an inherent property of the free energy landscape in protein–protein association.
Results
Prediction of encounter complex ensembles
We focused on the modeling of the association between the Nterminal domain of Enzyme I (EIN) and the histidinecontaining phosphocarrier protein (HPr) (Figure 1A), because the complex has been studied in a series of PRE titration experiments (Fawzi et al., 2010). Specific association between the two proteins occurs in the first step of the bacterial phosphotransfer system, resulting in phosphoryl transfer between EIN and HPr upon proper alignment of active site histidines of the two sides of the interface (Garrett et al., 1999). The binding has an equilibrium dissociation constant of 4.3 μM (Suh et al., 2008). For the computational study of encounter complexes we have placed the center of EIN at the origin of a coordinate system, and systematically sampled the entire rotational/translational space of HPr. Unbound structures were used both for the receptor, EIN (chain A from PDB entry 1ZYM) and for the ligand, HPr (chain P from PDB entry 2JEL). Sampling was performed using the docking program PIPER, which performs exhaustive evaluation of a physicsbased energy function in discretized 6D space of mutual orientations of two proteins using the Fast Fourier transform (FFT) correlation approach (Kozakov et al., 2006). We sample 70,000 rotations, which approximately correspond to sampling at every 5° in the space of Euler angles. In the translational space, the sampling is defined by the 1.0 Å grid cell size. PIPER is used with a ‘smooth’ energy function that includes terms describing attractive and repulsive van der Waals interactions, electrostatic interactions calculated by a simplified generalized Borntype expression, and a desolvation terms, the latter represented by a pairwise interaction potential (Chuang et al., 2008). We call the energy function ‘smooth’ because the repulsive contributions to the van der Waals interaction are selected to allow for a certain amount of overlaps.
Since the generated structures will be used for calculating PRE profiles to compare them to experimental PRE data (Fawzi et al., 2010), two sets of docking calculations were performed using HPr structures that included the paramagnetic label EDTAMn^{2+}, placed either at E5C, which is distal to the EIN/HPr interface in the native state, or at E32C, which is close to the edge of the interface (Figure 1A). Each CEDTAMn^{2+} label has three potential rotameric states, and hence Mn^{2+} can occupy three different positions (Figure 1—figure supplement 1). A separate docking was performed for each rotameric state of each EDTAMn^{2+} probe. We retained the 10,000 lowest energy structures from each docking simulation, thus a total of 30,000 low energy structures for each of the two probes. These structures were then used for the analysis of encounter complexes and for the calculation of intermolecular PRE rates.
Figure 1B shows the center of each low energy HPr structure, generated by the docking, as a small sphere, and indicates that these structures form three major clusters. Figure 1C shows the Interface Root Mean Square Deviation (IRMSD) from the native complex vs the PIPER energy score of the docked structures. For the calculation of IRMD we first select the interface residues of HPr that are within 10 Å of any EIN atom in the native complex. For each docked structure we than superimpose EIN onto EIN in the Xray structure of the complex, and calculate the RMSD between the Cα atoms of the HPr interface residues in docked and native structures. The structures in the largest cluster (shown in blue in Figure 1B) overlap with the native state, with the lowest energy conformations being within 5 Å IRMSD from the native (Figure 1C). The structures in this cluster, termed Class I, are the results of rigid body rotations and small translations around the native binding mode. Two representative Class I structures are shown in Figure 1D. Some Class I structures have less than 1 Å IRMSD, but the cluster extends as far as 15 Å IRMSD from the native. The two other clusters, termed Class II patch 1 and Class II patch 2, consist of structures that can coexist with the native complex. We note that while the three clusters clearly separate in the 3D representation shown in Figure 1B, they substantially overlap when projected into one dimension as a function of their IRMSD values. Nevertheless, Figure 1C shows at least three distinguishable energy minima. Class II patch 1 (magenta in Figure 1B) centers around a local energy minimum at around 17 Å IRMSD (Figure 1C). Figure 1E shows two representative conformations for this patch. The third large cluster, Class II patch 2 (red in Figure 1B), is located on the opposite side of the Class I cluster, and is better seen after rotating the complex by 180° around its vertical axis (Figure 1F). The local energy minimum in this cluster is located at about 30 Å IRMSD from the native state. Figure 1G shows two representative conformations for the Class II patch 2. In addition to the complexes that belong to Class I and the two patches of Class II, there are a number of smaller patches, shown in pink in Figure 1B.
PRE experiments and theoretical PRE profiles based on structure
To detect encounter complexes in the EIN/HPr system by PRE titration experiments, HPr was labeled with a paramagnetic EDTAMn^{2+} moiety conjugated via a disulfide bond to surface cysteine mutations at specific sites (Fawzi et al., 2010). We consider the mutations E5C and E32C that are both located outside the specific interaction surface with EIN (Figure 1A) and thus the labels do not interfere with the formation of the native complex. Intermolecular ^{1}H_{N}−Γ_{2} rates for the backbone amide protons of U[^{2}H,^{15}N]labeled EIN were measured in the presence of 150 mM NaCl to eliminate potential spurious nonspecific interactions not relevant at physiological ionic strength. PRE measurements were carried out at six different concentrations of the paramagnetically labeled HPr (ranging from 60 to 450 μM), corresponding to HPr:EIN molar ratios of 0.2–1.5. At each point in the titration, the intermolecular PREs were summed over their respective residues and normalized to the highest value of each titration curve. The data points in Figure 2 show the normalized intermolecular PRE values and their standard errors observed in these titration experiments (Fawzi et al., 2010).
Given the coordinates of a complex, one can backcalculate theoretical PRE profiles (‘Materials and methods’). As shown in Figure 2, the theoretical profile calculated from the coordinates in the Xray structure of the native complex (black curve) substantially deviates from the experimental values for a number of residues. For HPrE5C the largest differences occur at positions 59–97 and 160–167 of EIN, with smaller deviations at 23–37 and 183–189 (Figure 2A). For HPrE32C the large differences are at positions 22–51, 56–74, 96–106, and 160–167 of EIN, with smaller differences at 184–189 (Figure 2B). These deviations show that the observed PRE rates cannot be explained well on the basis of the native binding mode of HPr alone, and provide at least qualitative evidence for the existence of lowly populated encounter states in rapid exchange with the final native complex (Tang et al., 2008).
Our hypothesis is that the nonnative structures generated by the docking represent encounter complexes, and hence accounting for these structures predicts the experimental PRE values better than the profile calculated only from the coordinates of the native complex. Therefore we calculate the theoretical PRE profiles based on all 30,000 low energy structures obtained by the docking for each position of the paramagnetic label (blue lines in Figure 2). The details of the calculation are given in the ‘Materials and methods’. We emphasize that these curves are based only on the docked structures, and the native binding mode in the Xray structure of the complex is not used. The results show that the hypothesis is certainly true for EIN/HPrE5C, because the full encounter ensemble provides much better approximation than the native complex, particularly for residues 59–97 and 160–167 (Figure 2A). The correlation coefficient between the experimental PRE rates and the ones based on the encounter complexes is 0.705. In contrast, the correlation coefficients between the experimental PRE rates and the ones backcalculated from the native structure (black line in Figure 2A) is only 0.47. We note that the agreement improves even for the interface residues in the 67–85 region of EIN (indicated by red ticks in Figure 2). The explanation is that the E5CEDTAMn^{2+} label is on the far side of HPr from the interface, and in some encounter complexes this label is much closer to interface residues than in the native complex. For HPr with the E32CEDTAMn^{2+} label, accounting for the encounter complexes generated by docking (blue curve in Figure 2B) improves the correlation coefficient more moderately, from 0.709 to 0.77. This is due to the fact that E32C is close to the edge of the interface in the native complex, already providing a strong PRE signal for residues 67–85, resulting in a high correlation coefficient with the experimental PRE values. As will be discussed, since the PRE data are sensitive to small conformational changes and thus are inherently noisy, it is difficult to further improve an already high correlation coefficient. However, even for HPrE32C, the PRE profile backcalculated from the low energy models still yields better prediction than considering only the native binding mode. Since it is well known that the PRE profiles heavily depend on the location of the paramagnetic tag relative to the native interface (Fawzi et al., 2010), this result does not contradict to our hypothesis that accounting for all structures generated by the docking improves the prediction of PRE rates.
To provide a control and to demonstrate that the use of an accurate energy function is very important for generating a meaningful ensemble of encounter complexes we have also performed docking calculations using a scoring function without longrange energy terms, that is, considering only the attractive and repulsive components of the van der Waals energy. This simplified energy function yields docked structures that have good shape complementarity, but have no favorable electrostatic or chemical interactions. The 30,000 structures with the lowest van der Waals energy from this ‘shapecomplementarity only’ docking were then used for backcalculating theoretical PRE profiles. The results of these calculations, shown in Figure 2—figure supplement 1, make it absolutely clear that the backcalculated PRE profiles based on the ensemble of structures generated without a proper energy function do not show any resemblance to the observed PRE data. In fact, both correlation coefficients between theoretical and experimental PRE rates are negative, −0.36 and −0.58, respectively, for the probes at positions E5C and E32C.
Considering encounter complexes generated by PIPER using its physics based energy function we also obtained good agreement with experimental PRE data for other pairs of proteins. The first is the IIA^{Mannitol}/HPr complex (Cornilescu et al., 2002; Tang et al., 2006). Figure 2—figure supplement 2,B show the native structure of the complex and the ensemble of docked structures generated by PIPER. The paramagnetic label is placed at E5C of HPr, colored red and indicated by a small circle in Figure 2—figure supplement 2A. It is important that, similarly to the EIN/HPr system, the E5CEDTAMn^{2+} label is on the far side of HPr from the interface in the native structure of the complex. Figure 2—figure supplement 2C shows the experimental PRE data, the theoretical PRE profile based on the native complex (black line), and the theoretical profile obtained by considering the 30,000 low energy structures generated by the docking (blue line). The correlation coefficient between the experimental PRE rates and the ones backcalculated from the native structure is 0.58, whereas using the docked structures for the PRE calculation increases the correlation coefficient to 0.78, demonstrating substantially improved prediction. Although some of the improvements occur at the interface residues, it is clearly helpful that the HPrE5CEDTAMn^{2+} label is far from the interface. The distance between the label and a number of IIA^{Mannitol} residues is substantially reduced in some of the encounter complexes, which makes the presence of minor species more pronounced. We also show observed PRE data and theoretical profiles calculated from the ensemble of structures generated by docking for the complexes HPr/HPr (Tang et al., 2008; Figure 2—figure supplement 3), and cytochrome c/cytochrome c peroxidase (Bashir et al., 2010) (Figure 2—figure supplement 4), demonstrating good qualitative agreement in both cases.
Energy landscapes of encounter complexes
Having established that the sampling algorithm and energy function are accurate enough for predicting ensembles of encounter complexes, we proceeded to the characterization of the energy landscape in a neighborhood of the native complex conformation. As in the previous section, we focused on the rigidbody motions of the ligand protein in the space fixed on the receptor protein, although local structural adjustments of the proteins were allowed for more accurate energy calculation. Geometrically the 6D translational/rotational space is the socalled Special Euclidean Group SE(3), which is the semidirect product of R^{3} of the translations and SO(3) of the rotations (Park, 1995; Park and Ravani, 1997; Shen et al., 2008; Mirzaei et al., 2012). Restricting considerations to encounter complexes in which the surfaces of the two proteins touch each other removes the distance of the two proteins as a variable, and the space can be parameterized in terms of 5 angular coordinates. Two angles are needed to define the direction from the center of the receptor to the center of the ligand interface, and the other three angles specify the rotation of the ligand. Although the resulting space is nonlinear and thus the 5 angular coordinates are interdependent, a 5D Euclidean space can be mapped onto this nonlinear space using exponential maps (Shen et al., 2008), and hence we will be able to use analysis tools such as PCA, developed for application in Euclidean space. Further details justifying the need for the use of exponential maps will be given in ‘Materials and methods’. Once an appropriate coordinate system was defined, we selected and densely sampled a region in the neighborhood of the native state to obtain information on the shape of the binding funnel (Camacho et al., 1999; Selzer and Schreiber, 2001; Wang and Wade, 2003; Miyashita et al., 2004). Since the apparent properties of the landscape depend both on the energy evaluation model and the method of sampling, to assess the generality of the results we used both PIPER (Kozakov et al., 2006) and the very different docking program RosettaDock (Gray et al., 2003), which is based on Monte Carlo minimization and rebuilds side chain conformations during the search. From each sampling calculation, performed either by PIPER or by RosettaDock, we selected the conformations below a certain energy threshold to delineate the floor of the energy funnel.
Encounter complexes were generated from unbound protein structures (Chen et al., 2003) for a diverse set of 42 interacting protein pairs (Table 1). For each of these complexes, selected from the protein docking benchmark (Chen et al., 2003), both PIPER and RosettaDock found an energy funnel near the native state. Since this is generally not the case for complexes involving multiple subunits or large conformational changes upon binding, such complexes in the benchmark set were not considered. Structures were retained within 10 Å IRMSD from the native state. After sampling, the exponential coordinates were normalized to ensure that the variances in the sample set are the same along each coordinate axis, and the shape of the energy landscape over the selected region was studied by applying principal component analysis (PCA) to 5% of conformations with the lowest energy values. As will be emphasized in ‘Materials and methods’, the use of exponential maps, resulting in independent coordinates, is crucial for the success of our study, as only in this case can PCA separate the essential hyperspaces that bound the low energy ensemble.
The eigenvalues obtained by PCA are normalized to add to 100%. Each eigenvalue λ _{i} can be then interpreted as the percentage of the total variance that is accounted for by the variance along the corresponding eigenvector v_{i}. The smallest eigenvalue, λ_{5}, is less than 5% for almost all complexes (Table 1). In many cases both λ_{5} and λ_{4} are small (their sum is less than 10%), indicating that the eigenvectors v_{4} and v_{5} span a ‘restrictive’ subspace where the low energy structures barely deviate from the native complex. In contrast, λ_{1} and λ_{2} typically sum up to more than 75% of variance. Thus, it is expected that in the ‘permissive’ subspace spanned by v_{1} and v_{2} the low energy structures may substantially differ from the native conformation.
As an example, Figure 3A shows IRMSD and energy distributions along the five eigenvectors, calculated from the low energy structures generated by PIPER, for the complex between the retinoid Xreceptor α (RXRα) and the peroxisome proliferatoractivated receptor γ (PPARγ), considered here as the receptor and the ligand, respectively. The PDB entry of the complex is 1K74, but we docked the unbound (separately crystallized) RXRα and PPARγ structures rather than the components from the complex. Figure 3B shows the distributions of the same quantities, but based on the low energy structures generated by RosettaDock. The largest eigenvalue, λ_{1}, is close to 50% for both energy functions. The corresponding movements along v_{1} are rotations of helix H12 of PPARγ around a hydrophobic patch, formed by the side chains of F432, A433, and L436, which binds to a large hydrophobic pocket of RXRα and remains almost at the same position in all low energy encounter complexes (Figure 3C, Figure 3—figure supplement 1; Video 1). As helix H12 rotates, the entire PPARγ moves with it until a loop formed by PPARγ residues 394 to 403 reaches a favorable position on the surface of RXRα. We note that hydrophobic patch on helix H12 and the residues connecting it to the rest of the protein (residues 413–433) are known to be essential for forming the heterodimer (Chan and Wells, 2009). Along the eigenvector v_{2} that correspond to the second largest eigenvalue λ_{2} we can observe how the amino end of helix H12 with the hydrophobic patch on it moves into its binding pocket (Video 2). Based on the eigenvalues λ_{1} and λ_{2} (Table 1), over 75% of all movement of PPARγ approaching RXRα occurs in the subspace spanned by the eigenvectors v_{1} and v_{2}. Thus, this subspace can be regarded as the essential consensus of a very large number of association trajectories. In contrast to the permissive subspace, changes are very small along v_{5} (Figure 3D). Since the higher energy structures (not included in the data considered for PCA) can be substantially further from the native state than the ones with low energy, we conclude that the valley based on energy is much narrower than the valley based on geometry.
As a second example, we show IRMSD and energy distributions and PCA results for an enzyme–inhibitor complex, subtilisin Carlsberg and its protein inhibitor, OMTKY3 (Figure 4). The PDB entry of the complex is 1R0R. For this pair of proteins, the low energy encounter complexes along the eigenvectors v_{4} and v_{5} show even narrower distributions than in the previous example, both for PIPER and RosettaDock (Figure 4A,B). Since the essentially planar inhibitor loop (residues 13 to 19 of OMTKY3) is locked into the crevice at the enzyme’s active site, we expected that the motion along the most permissive direction would be the rigid body rotation of the entire inhibitor, possibly with slight readjustments of the loop. However, we have found that the motion along v_{1} is the move of the loop, and particularly the primary specificity residue L18, deeper into the binding pocket of the enzyme (Figure 4C, Figure 4—figure supplement 1; Video 3). The rotation along the loop is also present, but along the eigenvector v_{2} rather than v_{1} (Video 4). Based on the eigenvectors λ_{1} and λ_{2} (Table 1), 81.8% of the movements of OMTKY3 upon binding occurs in the subspace spanned by eigenvectors v_{1} and v_{2} for this complex. In contrast, the motion along the most restrictive direction v_{5} is a very small translation along the bottom of the active site (Figure 4D). It is important to note that, in principle, small eigenvalues identified by PCA could have also occur by chance due to undersampling a subspace. We performed simple Monte Carlo analyses to exclude this possibility (‘Materials and methods’).
As shown by the eigenvalues in Table 1 and by Figures 3 and 4, the energy funnels derived from the PCA of the energy landscapes generated by PIPER and RosettaDock slightly differ. This is not surprising, because we specifically selected two docking programs that are very different both in terms of their sampling algorithms and scoring functions (‘Materials and methods’). PIPER performs systematic rigid body sampling on a dense grid using a ‘smooth’ potential that allows for some overlaps (Kozakov et al., 2006). In contrast, RosettaDock samples the region of interest using a Monte Carlo minimization algorithm, which biases the search toward low energy regions, and thus the sampling is less exhaustive than the systematic sampling by PIPER. The method periodically rebuilds the complete set of interface side chains, followed by the optimization of the rigid body displacement. The energy is locally minimized in every iteration cycle of a Monte Carlo search algorithm (Gray et al., 2003), and since the clashes are continuously removed, RosettaDock can use an energy function that is more sensitive to small changes in the coordinates than the energy function used in PIPER. Accordingly, Panels A and B of Figures 3 and 4 display somewhat different shapes of the energy distributions around the native state placed at the center of the coordinate system. Using rigid structures without local minimization, the minimum of the PIPER energy may be somewhat shifted from the native state, resulting in a more skewed energy landscape than the one obtained with RosettaDock, which generally places the energy minimum closer to the native structure and hence has a more symmetric energy landscape. In view of the differences between the two energy functions we consider it important that the PCA analyses of low energy structures generated by either PIPER or RosettaDock yield similar distributions of the eigenvalues for all 42 complexes. For each complex, both programs result in two small eigenvalues λ_{5} and λ_{4}. Although these eigenvalues are generally somewhat smaller for PIPER, because the rigid body approximation leads to a steeper increase in energy as we move away from the minimum along the most restrictive direction, both programs clearly indicate the existence of a restrictive subspace. In addition, Monte Carlo studies, described in the ‘Materials and methods’, show that the restrictive subspaces predicted PIPER and RosettaDock are fairly similar. The similarity of these subspaces obtained by two very different energy functions for all 42 complexes indicates that the reduction of dimensionality is an inherent property of protein–protein association. Both programs predict that, on the other end of the spectrum, the two largest eigenvalues, λ_{1} and λ_{2}, together exceed 75% of the total variance for most complexes. Thus, in a neighborhood of the native state the encounter complexes are essentially restricted to a two dimensional permissive subspace in the rotational/translational space, and this conclusion is independent of the docking program used.
Discussion
Generation of encounter complexes by docking
Assuming moderate conformational changes and using grid approximation, the FFT based global and systematic sampling of the configurational space of two interacting proteins using a physics based energy function converts the docking problem into an exactly solvable problem of statistical mechanics (Kozakov et al., 2013). According to the CAPRI communitywide protein–protein docking experiment, this type of approximation gives good results for a large fraction of complexes (Lensink and Wodak, 2013). However, it has been well known that, for most protein pairs, such global search yields low energy structures in several regions of the conformational space, some of which are far from the structure of the native complex. Physicsbased energy functions are expected to be globally valid for modeling interactions between proteins, including the nonnative states. Thus, one can assume the energy values that are low relative to the average energy but still exceed the energy at the global minimum may lead to the formation of relatively shortlived encounter complexes along the association pathways. As shown in this paper, the agreement between experimental PRE data and theoretical PRE profiles calculated from the ensemble of structures generated by docking confirms this hypothesis, and thus structures of encounter complexes can be obtained simply as byproducts of docking without any further computational expense. While this result is not unexpected, in view of the limited structural information available on encounter complexes it is potentially significant.
Identification of encounter complexes using PRE
To detect intermediate structures in the association of proteins EIN and HPr, paramagnetic labels were introduced at two sites on HPr, one at a time, and the transverse paramagnetic relaxation enhancement (PRE) rates, Γ_{2}, of the backbone amide protons (^{1}H_{N}) of EIN were measured. Since the population of the intermediate structures is generally much lower than the population of the native complex, it is important to discuss why PRE can detect the presence of encounter complexes. The major factor is that the magnitude of the PREs is proportional to < r^{−6}>, where r is the distance between the nucleus of interest and the paramagnetic center, and <> denotes averaging over the ensemble of structures. Due to the large magnetic moment of an unpaired electron, the effect is detectable for sizeable separations (up to ∼34 Å for Mn^{2+}). A hypothetical example can be used to explain why the method can detect states with very low populations. We consider an ensemble that includes a major species A with the population, p_{A}, of 99%, and with a paramagnetic center to proton distance of 30 Å, and a minor species B with the population, p_{B}, of 1%, and with a paramagnetic center to proton distance of 8 Å. We calculate Γ_{2} for this proton in a twosite exchange system between A and B, where Γ_{2} is defined as the difference in the transverse relaxation rates of the paramagnetic and diamagnetic states (Iwahara and Clore, 2006). For a ∼30kDa complex, for species A the ^{1}HΓ_{2} arising from Mn^{2+} is ∼2 s^{−1} (Γ_{2,A}), and for species B it is ∼5.6 × 10^{3} s^{−1} (Γ_{2,B}). Considering B as a shortlived encounter complex and A as the native state, and assuming that the system is in the fast exchange regime, the apparent PRE rate, Γ_{2}, is the population weighted average of the Γ_{2} rates of the two species, that is, Γ_{2} = p_{A} Γ_{2,A} + p_{B} Γ_{2,B} (Iwahara and Clore, 2006). Based on this expression Γ_{2} is ∼30fold larger than Γ_{2,A}, thereby permitting one to both infer the presence of, and obtain some structural information on, the minor species, because the PRE is a highly distancedependent quantity. Thus, according to this simple explanation, the PREs can clearly capture the footprint of minor species that exchange rapidly with the native complex, in spite of their much lower concentration.
In a realistic protein–protein association the PRE rate, Γ_{2}, is the population weighted average of the Γ_{2} rates over the native state and the entire ensemble of encounter complexes. The strong distance dependence of Γ_{2} implies that the observed values are sensitive even to small conformational changes that may occur, for example, due to changes in the rotameric state of the EDTAMn^{2+} paramagnetic probe. Thus, as shown in Figure 2, the PRE data, while sensitive to the presence of minor species, are also fairly noisy (‘Materials and methods’). In spite of their substantial variance, the data are informative, since PREs generally also occur at residues that are far from the paramagnetic label in the native complex but are getting closer to it in some members of the encounter ensemble, clearly indicating the presence of nonnative transition states. As the examples studied in this paper show, the minor species can be better detected if the label is far from the interface. In fact, a label placed close to interface generates a strong PRE signal, and thus the PRE profile backcalculated from the native structure already correlates well with the data. However, accounting for the encounter complex ensemble most likely improves the prediction even in such cases, but the improvement is generally smaller than the one with the paramagnetic label placed far from the interface.
Reduction of dimensionality in protein–protein association
The reduction of dimensionality in molecular association was originally proposed to explain high binding rates (von Hippel and Berg, 1989), particularly the ability of proteins to locate their target sites along DNA (Riggs et al., 1970). Dimensionality reduction is caused by interaction forces that are nonspecific and thus do not lead to binding at a specific site, but keep the macromolecules in proximity for a prolonged time, allowing an extensive search of the surface along certain directions while restraining the search along others (Ubbink, 2009). This is clearly the case for DNA, whose negative charge attracts positively charged proteins without providing a specific interaction site (Iwahara et al., 2006; Gorman and Greene, 2008). It is well known that longrange electrostatic interactions can also increase the rates of association of two proteins with net opposite charges or with strong charge dipoles, as the search for the reactive patches is facilitated by dipolar preorientation of the proteins upon their approach (Schreiber et al., 2009). Such charge interactions prolong the lifetime of the transition state and increase the fraction of productive complexes, and thus can reduce dimensionality. However, it is frequently assumed that, due to specific charge–charge interactions and their irregular surface, proteins do not have ensembles of orientations having similar energies and thus allowing for search along the surface. We have shown here that this is definitely not the case because the energy landscape of interacting proteins, at least within the 10 Å IRMSD neighborhood of the native state, always includes a permissive subspace along which the conformation of the complex can substantially change without crossing significant energy barriers. Thus, there is no reason to assume that the interactions are nonspecific in proteinDNA association but are specific when two proteins associate. In fact, for all 42 protein pairs, some of which have strong electrostatic interactions, the energy landscape is smooth funnel in a two dimensional permissive subspace. In all cases this subspace captures at least 75% of the total motion as the two molecules approach the native state. For each of the 42 complexes we also detect a high energy subspace, which reduces the dimensionality of the space available to encounter complexes along the association pathways. Thus, there is much less difference between proteinDNA and protein–protein association than it was previously believed.
Finally we note that the reduced dimensionality of the search space can potentially simplify docking calculations, and thus the results of PCA provide several opportunities for improving the efficiency of docking methods. First, it is well known that any type of optimization is more efficient along the principal components, as large steps can be taken along the permissive directions. This is particularly the case for secondorder methods such as the Newton–Raphson optimization that uses a local quadratic approximation of the energy function to find the next minimum in each iteration (Fletcher, 1981). It is also important that such methods require the inversion of the Hessian matrix of the energy function, and in the case of reduced dimensionality the matrix is nearly singular, leading to numerical difficulties and loss of accuracy. Once such directions are known, the problem can be avoided by regularization methods (Fletcher, 1981). In fact, after developing and testing a mediumrange optimization method SDU, which employs quadratic semidefinite underestimation in the 5D angular space with the exponential parameters also used in this work (Shen et al., 2008), we understood that the approach can be made more efficient by accounting for the reduced dimensionality of the search space and adding regularization based on PCA. Another potential use is optimally selecting perturbation vectors using the relative magnitudes of the eigenvalues in biased Monte Carlo methods (Lee et al., 1996).
Materials and methods
Global sampling by the Fast Fourier transform (FFT) correlation approach
In order to fully explore the conformational space in protein–protein association we perform exhaustive evaluation of an energy function in the discretized space of mutual orientations of the two proteins using the docking program PIPER, which is based on the Fast Fourier transform (FFT) correlation approach (Kozakov et al., 2006). The center of mass of the first protein, defined here as the receptor, is fixed at the origin of the coordinate system, whereas the second protein (usually the smaller of the two), defined as the ligand, is rotated and translated. The translational space is represented as a grid of 1.0 Å displacements of the ligand center of mass, and the rotational space is sampled using 70,000 rotations based on a deterministic layered Sukharev grid sequence, which quasiuniformly covers the space. The energy expression used for the FFT based sampling includes simplified van der Waals energy E_{vdw} with attractive (E_{attr}) and repulsive (E_{rep}) contributions, the electrostatic interaction energy E_{elec}, and a statistical pairwise potential Epair, representing other solvation effects (Chuang et al., 2008):
The individual energy terms are calculated by the ${E}_{vdw}={E}_{attr}+{w}_{1}{E}_{rep}\text{,\hspace{0.17em}}{E}_{elec}={\displaystyle {\sum}_{\mathrm{i}}{\displaystyle {\sum}_{\mathrm{j}}\left[{q}_{i}{q}_{j}/{\left\{{r}^{2}+{D}^{2}\text{\hspace{0.17em}}\mathrm{exp}\left({r}^{2}/4{D}^{2}\right)\right\}}^{1/2}\right]}}$, and E_{pair} = Σ_{i} Σ_{j} ε_{ij}, where r is the distance between atoms i and j, D is an atomtype independent approximation of the generalized Born radius, and ε_{ij} is a pairwise interaction potential between atoms i and j. All energy expressions are defined on the grid. The coefficients w_{1} = 4, w_{2} = 600, w_{3} = 5, weight the different contributions to the scoring function, and are based on calorimetric considerations. In order to evaluate the energy function E by FFT, it must be written as a sum of correlation functions. The first two terms, E_{vdw} and E_{elec}, satisfy this condition, whereas E_{pair} is written as a sum of a few correlation functions, using an eigenvalueeigenvector decomposition (Kozakov et al., 2006). For each rotation, this expression can be efficiently calculated using P forward and one inverse Fast Fourier transforms. The calculations are performed for each of the 70,000 rotations, and one or several lowest energy translations for each rotation are retained. The results are clustered with a 10 Å IRMSD radius around the native coordinate.
Generating encounter complexes
Unbound structures were used both for the receptor, EIN (chain A from PDB (Berman et al., 2000) entry 1ZYM) and for the ligand, HPr (chain P from PDB entry 2JEL). Encounter complexes were generated using the global systematic Fast Fourier Transform based docking program PIPER (Kozakov et al., 2006). The docking was performed with each of the three conformers of EDTAMn^{2+} group, both at positions E5C and E32C of HPr (Fawzi et al., 2010). For each conformer, the 10,000 lowest energy complex structures were retained for the calculation of PRE rates.
Transverse paramagnetic relaxation enhancement (PRE) rate calculation
To calculate the transverse PRE rates (Γ_{2}) from the ensemble of encounter complexes generated by the FFT based sampling we use the N_{st} = 30,000 (10,000 for each of the three conformers of EDTAMn^{2+}) lowest energy structures. The observed PRE values are the PRE rates averaged over a population (Iwahara et al., 2004; Tang et al., 2006), and hence
where Γ_{ji} is the PRE rate for residue i of the jth structure in the ensemble. Each individual Γ_{ji} value is proportional to the inverse sixth power of distance r_{ij} between the backbone amide proton (directly bonded to ^{15}N) of the ith residue and the paramagnetic ion Mn^{2+}. To account for magnetic trap flexibility, we use the three state EDTAMn^{2+} SolomonBloembergen approximation (Iwahara et al., 2004; Tang et al., 2006). With these assumptions the PRE rates are given by Γ_{ji} = C × Σ_{k} (r_{ikj})^{−6}, where C = 1.2 × 10^{10} Å^{6}/s and r_{ikj} is the distance between residue i and the kth state of EDTAMn^{2+} in the jth structure in the low energy ensemble of docked configurations. The Γ_{ji} values are limited to 90s^{−1},that is, Γ_{ji} = 90 if Γ_{ji} ≥ 90. Introducing this threshold is based on the observation that both theoretical and experimental Γ_{ji} values become very uncertain over this threshold because small distance variations strongly affect the result (Kim et al., 2008; Fawzi et al., 2010).
Sampling by Monte Carlo minimization
The free energy landscape near the native complex was explored using both PIPER, based on the Fast Fourier transform (FFT) correlation approach (Kozakov et al., 2006) and RosettaDock, a docking program based on the Monte Carlo minimization (MCM) algorithm (Gray et al., 2003). In each MCM cycle, RosettaDock perturbs the position of the ligand by random translations and rotations, followed by adjusting the distance between the ligand and receptor to create a contact. Next, a fast MCM at low resolution optimizes the complex orientation with respect to features that do not depend on the explicit conformations of the side chains. Finally, the side chains are added, and an allatom optimization locates the local minimum energy conformation. The complete set of interface side chains is repacked every eight cycles, followed by the optimization the rigid body displacement. After each move, side chain packing, and minimization, an energy score is calculated. The new position is kept or rejected according to the standard Metropolis acceptance criterion (Gray et al., 2003). RosettaDock uses a detailed energy function which includes van der Waals interactions with a linear term serving as the repulsive part, a solvation term based on a pairwise Gaussian solvent exclusion model, hydrogen bonding energies using an orientationdependent empirical function, a rotamer probability term, residue–residue atom pair interactions for charged residues, and a simple electrostatic term across the protein–protein interface.
Parameterizing the conformational space
The origin of the reference frame is placed at the center of the receptor, and the zaxis is directed toward the center of the interface of the native conformation. The translation of the ligand is described by the vector from the center of the receptor to the center of the ligand interface (as opposed to the center of the ligand), and the rotation of the ligand is also defined around the center of its interface. This choice is made to decouple, as much as possible, the effects of translation on the locations of the interface atoms from those of the rotation. A translation vector y ∈ R^{3} can be represented by a triplet (r,θ,φ), where r = y is the distance, θ is the azimuth angle between the projection of y on the xyplane and the xaxis (longitude, 0 ≤ θ < 2π), and φ is the zenith angle between the zaxis and the vector y (colatitude, 0 ≤ φ < π). As will be discussed, we consider r separately, and focus on selecting appropriate parameterizations for (θ,φ) compounded with the rotational space SO(3).
Parameterizing rotations is problematic because rotations are nonEuclidean in nature (i.e., travelling infinitely far in any direction will bring you back to your starting point an infinite number of times). Any attempt to parameterize the entire set of three degreesoffreedom (DOF) rotations by an open subset of Euclidean space (as do Euler angles) will suffer from gimbal lock, the loss of rotational degrees of freedom, due to singularities in the parameter space (Grassia, 1998). Parameterizations that are themselves defined over nonEuclidean spaces (such as the set of unit quaternions embedded in R^{4}) may remain singularityfree, and thus avoid gimbal lock. Employing such parameterizations is complicated, however, since the numerical tools such as optimization and PCA assume Euclidean parameterizations; therefore we must either develop new tools whose domains are nonEuclidean, or complicate our systems by imposing explicit constraints, for example, to assure that the quaternions stay on the unit sphere (Grassia, 1998). In this paper we use exponential maps (Park, 1995; Park and Ravani, 1997; Shen et al., 2008) to project an Euclidean space onto the nonlinear rotational space. As an example, the simplest exponential map defines a local onetoone correspondence between the unit circle, which is a nonlinear space (called the circle group), centered at 0 in the complex plane, and the tangent space at 1, which can be identified with the imaginary line in the complex plane. The exponential map for the circle group is given by it → e^{it}, where i = √(−1), it specifies a point on the tangent line, and the exponential function projects it into the corresponding point of the circle (Figure 5A). Extending the map to three dimensions, the exponential map projecting the tangent plain of a 3D sphere onto the surface of the sphere is shown in Figure 5B.
For (θ,φ) the exponential coordinates are (σ_{1},σ_{2}) = (φ sinθ, φ cosθ). For SO(3), the exponential coordinates are ω = (ω_{1}, ω_{2}, ω_{3}) ∈ R^{3}, where ω_{1}, ω_{2}, and ω_{3} are elements of a skewsymmetric matrix (Park, 1995; Shen et al., 2008; Mirzaei et al., 2012). The vector (σ_{1}, σ_{2}, ω_{1}, ω_{2}, ω_{3}) defines the relative orientation of the two rigid proteins. Once this relative orientation is given, the binding distance r along the translation vector y (which connects the centers of the receptor with the center of the ligand’s interface) is determined by the assumptions that the two proteins are in contact but do not overlap (Shen et al., 2008). Complexes generated by the docking methods used in this work always have this property. In particular, given a complex structure generated by the FFT sampling approach PIPER (Kozakov et al., 2006), we can uniquely determine the corresponding (σ_{1}, σ_{2}, ω_{1}, ω_{2}, ω_{3}) coordinates and the value of r. Since RosettaDock (Gray et al., 2003) generally changes the conformations of the component proteins, to isolate the rigid body motion we have to fit the initial protein structures to the complex and determine the closest rigid body transformation, projecting the higher dimensional motion into the rotationaltranslational space.
Sampling the nearnative region and principal component analysis
We sample energy values in a sphere in the 5D parameter space around the point (0, 0, 0, 0, 0), which corresponds to the native conformation. Since we all five coordinates are angles, the radius of the sphere is also defined as an angle, and we used 22.5° as the radius, which roughly translates into a radius of 10 Å IRMSD neighborhood for relatively small proteins. After the FFT based sampling using the PIPER program (Kozakov et al., 2006) we selected 5000 structures, uniformly distributed within this sphere. Conformations with more than 10 Å IRMSD from the native structure were discarded, resulting in about 4000 structures. RosettaDock (Gray et al., 2003) was used to generate 2000 structures within the 10 Å IRMSD radius. After sampling we normalized the 5D coordinates to ensure that the variances in the sample set are the same along each coordinate axis, replacing each vector (σ_{1}, σ_{2}, ω_{1}, ω_{2}, ω_{3}) with its normalized version (x_{1}, x_{2}, x_{3}, x_{4}, x_{5}). The use of such normalized coordinates removed the potential differences in the scaling of the initial angular parameters, and also rendered the results less dependent on the relative sizes of the two component proteins. For each of the two scoring functions, principal component analysis (PCA) was applied to the socalled ‘most favorable’ conformations whose energy values were below a certain threshold, for example, in the lowest 5% for the FFTbased sampling data. Let N denote the number of such favorable structures. The normalized coordinates (x_{1}, x_{2}, x_{3}, x_{4}, x_{5}) of the N structures were arranged into a Nby5 matrix X, where each row represented a conformation. The PCA analysis computed the eigenvalues and eigenvectors of the covariance matrix
(X–μ_{X})’(X–μ_{X}), where μ_{X} is the sample mean and prime denotes transpose. We sorted the eigenvalues in descending order and normalized them such that their sum equaled 1.0 (100%). The results were denoted by λ_{1},…, λ_{5}, and v_{1},…, v_{5} denote the corresponding eigenvectors, which are mutually orthogonal.
Further validation of the energy landscape
Since small eigenvalues identified by PCA might also occur by chance due to undersampling a subspace, we performed a simple Monte Carlo analysis to show that this is not the case. PCA was based on at least 100 low energy structures for each of the 42 complexes, and thence we generated 100 random vectors in the 5D space, applied PCA to derive the eigenvalues, and performed this experiment 1000 times. Results confirmed that the probability of λ_{5} < 10% is less than 0.01. Thus, in view of the large number of sample points we used, it is very unlikely that the small eigenvalues shown in Table 1 occur due to undersampling.
The PCA analyses of the 5D exponential coordinates of the low energy complex conformations show clear distinctions between permissive and restrictive directions in all 42 cases. The rigid FFTbased method (Kozakov et al., 2006) and the Monte Carlo approach in RosettaDock (Gray et al., 2003) yield similar results, in spite of the fact that two methods implement fundamentally different sampling and scoring schemes. To perform a rigorous comparison of the results from the two methods we introduce the notations λ_{1P}, . . . , λ_{5P} and λ_{1R}, . . . , λ_{5R} for the eigenvalues based on the low energy structures generated by, respectively, PIPER and RosettaDock. Both sets of eigenvalues are ordered in descending magnitude, and v_{1P},...,v_{5P} and v_{1R},...,v_{5R} denote the corresponding eigenvectors. Since our main goal is to show that the restrictive subspace is largely independent of the method used, as a measure of discrepancy we will determine the angle between the restrictive subspace spanned by eigenvectors v_{4P} and v_{5P} based on PIPER, and the subspace spanned by v_{4R} and v_{5R} based on RosettaDock. As shown in the last column of Table 1, this angle is less than or equals to 30° for all but 5 of the complexes. Accepting that the restrictive subspaces match if they differ by less than 30° seems to be a somewhat relaxed condition. However, it is easy to show by Monte Carlo simulations that the probability of such agreement by chance for 37 of the 42 structures is negligibly small. We applied PCA to two sets of 100 random vectors in 5D, calculated the covariance matrix and its eigenvalues, for each set we selected the subspace spanned by eigenvectors corresponding to the two smallest eigenvalues, and finally determined the angle between these two ‘restrictive’ subspaces. By repeating this calculation 1000 times we could show that the probability of obtaining an angle below 30° is p=0.131. Although this is not a very small number, the probability that this occurs for 37 of the 42 complexes is less than 10^{−22}. Thus, the results overwhelmingly support the claim that the restrictive directions found by two very different methods are similar, and thus the reduction in dimensionality is an inherent property of protein–protein association. Due to the orthogonality of eigenvectors, the same similarity between PIPER and RosettaDock results also applies to the permissive subspaces spanned by the first 3 eigenvectors.
Additionally we provide Figures and Videos analogous to Figures 3,4 and Videos 1–4 for all 42 complexes studied in this paper (Kozakov et al., 2014).
References

1
Visualization of the encounter ensemble of the transient electron transfer complex of cytochrome c and cytochrome c peroxidaseJournal of the American Chemical Society 132:241–247.https://doi.org/10.1021/Ja9064574
 2

3
Diffusioncontrolled macromolecular interactionsAnnual Review of Biophysics and Biophysical Chemistry 14:131–160.https://doi.org/10.1146/annurev.bb.14.060185.001023

4
Kinetics of desolvationmediated proteinprotein bindingBiophysical Journal 78:1094–1105.https://doi.org/10.1016/S00063495(00)766689

5
Free energy landscapes of encounter complexes in proteinprotein associationBiophysical Journal 76:1166–1178.https://doi.org/10.1016/S00063495(99)772814
 6
 7

8
DARS (Decoys as the reference state) potentials for proteinprotein dockingBiophysical Journal 95:4217–4227.https://doi.org/10.1529/biophysj.108.135814
 9
 10
 11
 12
 13

14
Mechanistic details of a proteinprotein association pathway revealed by paramagnetic relaxation enhancement titration measurementsProceedings of the National Academy of Sciences of the United States of America 107:1379–1384.https://doi.org/10.1073/pnas.0909370107
 15

16
Solution structure of the 40,000 Mr phosphoryl transfer complex between the Nterminal domain of enzyme I and HPrNature Structural Biology 6:166–173.https://doi.org/10.1038/5854

17
Visualizing onedimensional diffusion of proteins along DNANature Structural & Molecular Biology 15:768–774.https://doi.org/10.1038/Nsmb.1441

18
Practical parameterization of rotations using the exponential mapJournal of Graphics Tools 3:29–48.https://doi.org/10.1080/10867651.1998.10487493

19
Proteinprotein docking with simultaneous optimization of rigidbody displacement and sidechain conformationsJournal of Molecular Biology 331:281–299.https://doi.org/10.1016/S00222836(03)006703
 20

21
Characterization of nonspecific proteinDNA interactions by H1 paramagnetic relaxation enhancementJournal of the American Chemical Society 126:12800–12808.https://doi.org/10.1021/Ja046246b

22
NMR structural and kinetic characterization of a homeodomain diffusing and hopping on nonspecific DNAProceedings of the National Academy of Sciences of the United States of America 103:15062–15067.https://doi.org/10.1073/Pnas.0605868103

23
Replica exchange simulations of transient encounter complexes in proteinprotein associationProceedings of the National Academy of Sciences of the United States of America 105:12855–12860.https://doi.org/10.1073/pnas.0802460105
 24
 25
 26

27
Data from: Encounter complexes and dimensionality reduction in proteinprotein associationDryad, 10.5061/dryad.98n8c.

28
Protein folding by a biased Monte Carlo procedure in the dihedral angle spaceFASEB Journal: Official Publication of the Federation of American Societies for Experimental Biology 10:119–125.

29
Docking, scoring, and affinity prediction in CAPRIProteins 81:2082–2095.https://doi.org/10.1002/prot.24428

30
Theory of biomolecular recognitionCurrent Opinion In Structural Biology 8:245–249.https://doi.org/10.1016/S0959440X(98)800468

31
Rigid body energy minimization on manifolds for molecular dockingJournal of Chemical Theory and Computation 8:4374–4380.https://doi.org/10.1021/ct300272j

32
Transition state and encounter complex for fast association of cytochrome c2 with bacterial reaction centerProceedings of the National Academy of Sciences of the United States of America 101:16174–16179.https://doi.org/10.1073/pnas.0405745101

33
Distance metrics on the rigidbody motions with applications to mechanism designJournal of Mechanical Design 117:48–54.https://doi.org/10.1115/1.2826116

34
Smooth invariant interpolation of rotationsACM Transactions on Graphic 16:277–295.https://doi.org/10.1145/256157.256160

35
Lac repressoroperator interaction. 3. Kinetic studiesJournal of Molecular Biology 53:401–417.https://doi.org/10.1016/00222836(70)900744

36
Fundamental aspects of proteinprotein association kineticsChemical Reviews 109:839–860.https://doi.org/10.1021/Cr800373w

37
New insights into the mechanism of proteinprotein associationProteins Structure Function and Genetics 45:190–198.https://doi.org/10.1002/Prot.1139

38
Protein docking by the underestimation of free energy funnels in the space of encounter complexesPLOS Computational Biology 4:e1000191.https://doi.org/10.1371/journal.pcbi.1000191

39
Speeding molecular recognition by using the folding funnel: the flycasting mechanismProceedings of the National Academy of Sciences of the United States of America 97:8868–8874.https://doi.org/10.1073/Pnas.160259697

40
Prediction of proteinprotein interactions by docking methodsCurrent Opinion In Structural Biology 12:28–35.https://doi.org/10.1016/S0959440X(02)002853

41
Free energy landscape of proteinprotein encounter resulting from Brownian dynamics simulations of Barnase: BarstarJournal of Chemical Theory and Computation 1:723–736.https://doi.org/10.1021/Ct050036n
 42

43
Role of electrostatic interactions in transient encounter complexes in proteinprotein association investigated by paramagnetic relaxation enhancementJournal of the American Chemical Society 129:12954–12960.https://doi.org/10.1021/Ja0760978

44
Visualization of transient ultraweak protein selfassociation in solution using paramagnetic relaxation enhancementJournal of the American Chemical Society 130:4048–4056.https://doi.org/10.1021/Ja710493m
 45

46
How common is the funnellike energy landscape in proteinprotein interactions?Protein Science 10:1572–1583.https://doi.org/10.1110/Ps.8701
 47

48
Convergence and combination of methods in proteinprotein dockingCurrent Opinion In Structural Biology 19:164–170.https://doi.org/10.1016/j.sbi.2009.02.008

49
Facilitated target location in biological systemsThe Journal of Biological Chemistry 264:675–678.
 50
 51

52
Predictive energy landscapes for proteinprotein associationProceedings of the National Academy of Sciences of the United States of America 109:19244–19249.https://doi.org/10.1073/Pnas.1216215109
Decision letter

Benoit RouxReviewing Editor; University of Chicago, United States
eLife posts the editorial decision letter and author response on a selection of the published articles (subject to the approval of the authors). An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent (see review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.
Thank you for sending your work entitled “Encounter Complexes and Dimensionality Reduction in ProteinProtein Association” for consideration at eLife. Your article has been evaluated by a Senior editor (John Kuriyan), a member of our Board of Reviewing Editors, and 2 reviewers. The comments were then further discussed and a consensus was determined. Prior reaching a final decision about the manuscript, we need to consider your response to address the following substantial issue that emerged from those discussions:
Docking calculations were used to thoroughly sample association modes between two proteins and identify ensembles of low energy poses. These ensembles were then used to back calculate paramagnetic relaxation enhancement (PRE) profiles, which were then compared to those derived from experimental PRE measures by NMR. The results presented show that sampling by docking is consistent with the encounters measured experimentally by PRE. The novelty of this study lies with comparing the docking ensembles with ensembles derived from PRE experiments, and using the agreement with experimentally measured PRE as a means to establish the legitimacy of the docking ensembles as representing encounter complexes. Yet little attention is devoted to the PRE data themselves and how they can be related to docking poses and their energetics. How noisy are these data? How much slack is there in associating a given binding mode with a PRE signal. While this study may be the first time the funnellike shape for the docking reaction coordinate has (some) experimental support, one should be cautious as the funnel shape is deduced from the docking calculations only. Until we understand the limitations of the PRE data it is hard to tell if they support the funnel shape. If indeed the main conclusion of the paper is that the funnel shape is supported by PRE experiments, then more must be provided about the experimental method, the uncertainty in the experimental data, and the sensitivity of the comparison between the docking results and the experiments.
Specific points to be addressed are:
1) A serious omission is the failure to include an important control where PRE back calculations are performed from docking poses with randomized energies. Would computed PRE profiles from these randomized ensembles look very different from those derived from the docking ensembles and/or experiment?
2) The Mn^{2+} probe appears to be located in only a few sites on each protein. It would be useful to show schematically where these sites actually are in Figures 2, 3, and 4. The authors should confirm that the Mn^{2+} probes really were located at E5 and E32 (it is implied but not stated), and exactly which nitrogens were labelled with ^{15}N (was it all amide nitrogens)?] In this regard it would have been extremely informative to provide a more detailed description of how PRE profiles are back calculated from association modes and their energy levels, and to discuss the inherent error rate in the experimental PRE signal itself: How does the fact that PRE is 'a technique extremely sensitive to the presence of lowly populated states...' translate in practical terms? Some of these issues are succinctly mentioned in the Introduction but not fully addressed.
https://doi.org/10.7554/eLife.01370.020Author response
The novelty of this study lies with comparing the docking ensembles with ensembles derived from PRE experiments, and using the agreement with experimentally measured PRE as a means to establish the legitimacy of the docking ensembles as representing encounter complexes. Yet little attention is devoted to the PRE data themselves and how they can be related to docking poses and their energetics. How noisy are these data? How much slack is there in associating a given binding mode with a PRE signal. While this study may be the first time the funnellike shape for the docking reaction coordinate has (some) experimental support, one should be cautious as the funnel shape is deduced from the docking calculations only. Until we understand the limitations of the PRE data it is hard to tell if they support the funnel shape. If indeed the main conclusion of the paper is that the funnel shape is supported by PRE experiments, then more must be provided about the experimental method, the uncertainty in the experimental data, and the sensitivity of the comparison between the docking results and the experiments.
We have made three main changes to address the above general criticism as follows: 1) In the Results section we added a new subsection entitled “PRE experiments and theoretical PRE profiles based on structure”. In this subsection we provide more information on the paramagnetic relaxation enhancement (PRE) experiment and its implications (see our response to the specific comments below).
2) Figure 2 now includes the error bars of the experimental PRE data points.
3) We added a new section to the Discussion, “Identification of encounter complexes using PRE”. In this section we first provide an example explaining why PRE data are capable of indicating the presence of transition states, in spite of their low population. Second, we show that although the PRE data are inherently noisy due to the strong distance dependence, they still can unambiguously reveal if minor species are present.
Specific points to be addressed are:
1) A serious omission is the failure to include an important control where PRE back calculations are performed from docking poses with randomized energies. Would computed PRE profiles from these randomized ensembles look very different from those derived from the docking ensembles and/or experiment?
We thank the reviewers for suggesting this control experiment. We have followed the advice, and to demonstrate the importance of the energy function when generating the encounter complexes performed docking calculations using a scoring function without longrange energy terms, i.e., considering only the attractive and repulsive components of the van der Waals energy. This simplified energy function yields docked structures that have good shape complementarity, but have no favorable electrostatic or chemical interactions (fourth paragraph of the Results section entitled “PRE experiments and theoretical PRE profiles based on structure”). The 30,000 structures with the lowest van der Waals energy from this “shapecomplementarity only” docking were then used for backcalculating theoretical PRE profiles. The results of these calculations, shown in the new Figure 2–figure supplement 1, make absolutely clear the PRE rates based on the ensemble of structures generated without a proper energy function do not show any resemblance to the observed PRE data. We also show that the correlation coefficients between the experimental PRE rates and the theoretical PRE profiles based on these docked structures are negative. We think that these control calculations convincingly show the need for using a meaningful physics based energy function in order to predict the observed PRE data.
2) The Mn^{2+} probe appears to be located in only a few sites on each protein. It would be useful to show schematically where these sites actually are in Figures 2, 3, and 4. The authors should confirm that the Mn^{2+} probes really were located at E5 and E32 (it is implied but not stated), and exactly which nitrogens were labelled with ^{15}N (was it all amide nitrogens)?] In this regard it would have been extremely informative to provide a more detailed description of how PRE profiles are back calculated from association modes and their energy levels, and to discuss the inherent error rate in the experimental PRE signal itself: How does the fact that PRE is 'a technique extremely sensitive to the presence of lowly populated states...' translate in practical terms? Some of these issues are succinctly mentioned in the Introduction but not fully addressed.
In two new subsections added to the revised manuscript we address the above points as follows:
A) The subsection “PRE experiments and theoretical PRE profiles based on structure” in the Results provides a more detailed description of the PRE titration experiment than in the original submission, and hopefully clarifies the issues raised by the reviewers.
B) In the subsection “Identification of encounter complexes using PRE” (Discussion) we (1) discuss the sources of errors in the PRE data; (2) show that due to its strong distance dependence, PRE is extremely sensitive to the presence of lowly populated states; and (3) explain that although the method is inherently noisy due to the strong distance dependence, it unambiguously reveals if minor species are present.
https://doi.org/10.7554/eLife.01370.021Article and author information
Author details
Funding
National Institutes of Health (GM93147)
 Dima Kozakov
 Ioannis Ch Paschalidis
National Institutes of Health (GM61867)
 Sandor Vajda
National Institutes of Health (Intramural program of NIDDK)
 G Marius Clore
National Science Foundation (DBI1147082)
 Dima Kozakov
 Sandor Vajda
US Israel Binational Science Foundation (2009418)
 Dima Kozakov
 Ora SchuelerFurman
Russian Ministry of Education and Science (14.A18.21.1973)
 Dima Kozakov
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
 Benoit Roux, University of Chicago, United States
Publication history
 Received: August 14, 2013
 Accepted: February 22, 2014
 Version of Record published: April 8, 2014 (version 1)
Copyright
This is an openaccess article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
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