Effector-Binding-Directed Dimerization and Dynamic Communication

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Effector Binding-Directed Dimerization and Dynamic Communication between Allosteric Sites of Ribonucleotide Reductase Bill Pham, Richard Justin Lindsay, and Tongye Shen Biochemistry, Just Accepted Manuscript • DOI: 10.1021/acs.biochem.8b01131 • Publication Date (Web): 20 Dec 2018 Downloaded from http://pubs.acs.org on December 26, 2018

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Biochemistry

Effector Binding-Directed Dimerization and Dynamic Communication between Allosteric Sites of Ribonucleotide Reductase Bill Pham1, Richard J. Lindsay2, and Tongye Shen1* 1Department

of Biochemistry & Cellular and Molecular Biology, University of Tennessee, Knoxville, TN, 37996, USA Graduate School of Genome Science and Technology, Knoxville, TN, 37996, USA *Corresponding author: [email protected] 2UT-ORNL

Abstract Proteins forming dimers or larger complexes can be strongly influenced by their effector-binding status. We investigated how the effector-binding event is coupled with interface formation via computer simulations, and we quantified the correlation of two types of contact interactions: between the effector and its binding pocket, and between protein monomers. This was achieved by connecting the protein dynamics at the monomeric level with the oligomer interface information. We applied this method to ribonucleotide reductase (RNR), an essential enzyme for de novo DNA synthesis. RNR contains two important allosteric sites, the s-site (specificity site) and the a-site (activity site), which bind different effectors. We studied these different binding states with atomistic simulation and used their coarse-grained contact information to analyze the protein dynamics. The results reveal that the effectorprotein dynamics at the s-site and the dimer interface formation are positively coupled. We further quantify the resonance level between these two events, which can be applied to other similar systems. At the a-site, different effector-binding states (ATP vs. dATP) drastically alter the protein dynamics and affect the activity of the enzyme. Based on these results, we propose a new mechanism of how the a-site regulates enzyme activation.

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I. Introduction A class of proteins contain effector binding pocket(s) which may influence their dimerization and oligomerization status, including well-known examples from structural proteins such as actin and tubulin and allosteric enzymes such as ribonucleotide reductase (RNR). Effector binding can trigger protein conformational change and thus alter protein-protein interaction.1, 2 These effectors do not necessarily bind directly to the interface; instead, they function allosterically and influence interface formation. For RNR, an essential enzyme for DNA production, elegant allosteric regulation is realized through dimeric and multimeric protein complex formation. RNR reduces ribonucleoside diphosphates (NDPs, N=A,U,G,C) to their corresponding deoxy forms.3, 4 This process, controlled allosterically, balances the pool of DNA base types for de novo DNA synthesis5, 6 and contributes to the survival of all living organisms.7-9 Due to the universal presence of RNR, it has been considered a drug target through exploiting specific RNR regulation mechanism and inhibiting cell growth from cancer cells to bacteria and parasites.10-14 A subset of RNR inhibitors strongly affect the interface formation,9, 15 as the oligomerization status is essential for RNR functions.16-18 Thus, it is important to study how oligomerization is influenced by effector binding for this reason. The regulation of RNR varies widely from species to species. A particularly intriguing class of RNRs (class Ia), including human RNR (hRNR), has a sophisticated regulation scheme involving two allosteric sites: the activity (a-site) and specificity sites (s-site). The consensus view on how they function is that the a-site controls the overall activity of the enzyme, while the s-site controls the specific type reduction reaction among choices of NDPs. As the regulation mechanisms of different species are distinct, especially for the activity regulation,19, 20 we focus on hRNR and make connections to bacterial RNR and yeast RNR when possible. It turns out that nucleotide binding at the s-site can also promote dimer formation.21 The dimerization of RNR is an essential step for enzyme function, as the dimer is regarded as an activated form of RNR. The protein can further form a hexamer (a trimer of these dimers) through effector binding at the a-site, but only dATP binding at this site has been reported to inactivate the enzyme.5, 21, 22 These results were largely obtained from extensive studies of RNR from a structural biology prospective, especially the dimer crystal structures with different effector-binding states21 and hexamer structures obtained from SAXS and cryo-EM methods.18, 23 There were also reports on the communication between these two allosteric sites.24 However, the detail of the regulation mechanism, especially from a protein dynamics viewpoint, is elusive and several puzzles remain unaddressed. Atomistic computer simulation followed by statistical analysis offers a unique perspective on protein dynamics. In this work, we applied atomistic simulations to generate conformational ensembles of the protein at different effector-binding states. We obtained not only how proteins respond to perturbations by changing their mean structures but also the intricate correlated dynamics among different parts of the protein. The atomistic resolution of the simulation is desirable to keep effector-protein interactions accurate. In order to examine the protein dynamics and the allosteric mechanism,25 in addition to the common analyses for all-atom simulations, we also studied the correlated motions expressed by contact dynamics. The results confirmed that s-site binding promotes dimerization and demonstrated that different effector-binding states at the a-site (ATP versus dATP) communicate differently with the s-site. Our general framework (from a contact dynamics viewpoint) can also be applied to explore the mechanisms of other effector-protein complexes and offer an insight into drug design or protein engineering of effector binding-directed oligomerization in the future.26

II. Method and Systems


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Biochemistry

A. Contact correlation analysis Besides the standard methods of examining simulation trajectories, we utilize contact correlation analysis25, 27 to study the connection between the effector-binding site and the dimer interface. Going a step further, we propose a quantitative way of analyzing the level of coupling between effector binding and protein-protein association from contact analysis. As detailed in a previous work,25 the computational method CAMERRA tracks the dynamics of contact breaking and forming between four regions (𝑖, 𝑗, 𝑘, and 𝑙) of the protein (or protein complex) and expresses the results as a covariance matrix with the element 𝐶𝑖𝑗𝑘𝑙 = 〈(𝑢𝑖𝑗 ― 〈𝑢𝑖𝑗〉)(𝑢𝑘𝑙 ― 〈𝑢𝑘𝑙〉)〉, that is, how contact formed between 𝑖 and 𝑗 is correlated with the contact formed between 𝑘 and 𝑙. Specifically, the contact variable 𝑢𝑖𝑗 represents the level of contact interaction between 𝑖 and 𝑗, whereas 〈𝑢𝑖𝑗〉 indicates an ensemble average of 𝑢𝑖𝑗. The resolution of the initial work is that each region is an amino acid residue, and the value of 𝑢𝑖𝑗 is assigned as 1 or 0 depending on whether a contact is made between residues 𝑖 and 𝑗.28 For better handling larger proteins (as is the case of the current study) and for comparing protein families, we have further extended this concept and created a coarse-grained version in which each region is a segment of multiple adjacent residues.27 The coarse-grained contact matrix contains the elements 𝑢𝑖𝑗 that indicate the contact strength between segments 𝑖 and 𝑗. A coarse-grained contact strength definition “L”, which is the sum of residue-residue contacts normalized by the square root of the area on a contact map, was used among several choices27 for the current study. Principal component analysis (PCA) takes as input the resulting covariance matrix 𝐶𝑖𝑗𝑘𝑙 and extracts the dominating motions in the contact dynamics space. For contact PCA, we can identify any eigenvector component as an element of a matrix according to which pair of residues (or segments, in a coarse-grained version in the current case) 𝑖 and 𝑗 form that contact, with 𝑖 and 𝑗 representing row and column indices. Thus, the α-th mode of the eigenvector (also termed principal component, PC) can be expressed as a matrix. We call this matrix a contact displacement matrix and describe it as dαij, denoting a normalized specific mode of fluctuation around mean values, i.e. displacement proportional to contact fluctuation dij ∝ uij ― 〈𝑢𝑖𝑗〉. Parallel to the fact that a traditional cartesian PCA reveals orthogonal “vibrational” modes of protein dynamics, contact PCA reveals modes of contact dynamics expressed by these displacement matrices.25 For the current study, we further process the extracted motions from the contact PCA to assess and quantify the correlation between the effector-binding and the dimer formation events. To achieve this, we first define the onebody displacement 𝐷𝛼𝑘 = ∑𝑗𝑑α𝑗𝑘 , i.e., a partial summation of the α-th principal component interacting with a certain region (residue or segment) 𝑘. Next, we sum all the one-body displacements for the effector-binding site as 𝐸 = ∑𝑘 𝐷1𝑘. Here the effector binding region is defined by the residues that form the binding pocket. A ∈ effector binding

slight complication for coarse-grained contact analysis is that each segment can potentially contain more than one residue that belong to the binding pocket. Here, we use a weight factor 𝑛𝐿(𝑘) to indicate the number of the residues, whereas 𝑛𝐿(𝑘) = 1 or 0 for the residue-residue contact description. Thus, we can write 𝐸 = ∑𝑘 𝑛𝐿(𝑘) × 𝐷1𝑘 in general. ∑𝑘

Similarly, the term associated with dimer interfacial interaction is 𝐼 = 𝐷1𝑘 = ∑𝑘 𝑛𝐼(𝑘) × 𝐷1𝑘. Each of these equations specifies the overall contact dynamics (forming and

∈ dimer interface

breaking) at a certain region described by the dominant dynamic motion, eigenvector PC1. Finally, the score function, defined by 𝑅 = 𝐸 × 𝐼, is used to quantify synchronization of the contact motions between the regions at the effector-binding site and that at the dimer interface. As the result of this synchronization calculation, a positive 𝑅 value shows that when effector-protein contacts are made, the protein dimer interface is formed and vice versa, a negative 𝑅 value indicates that the contact forming of


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effector-binding regions is negatively coupled with that of the dimer-associated regions. Our comparison also focus on comparing the relative 𝑅 values to make a conclusion, but not their absolute values (compared to 0). That is, a positive increase in 𝑅 value upon effector binding indicates that effector binding is “in sync” and thus promotes dimer interface formation. In fact, the value of 𝑅 depends on many other thermodynamic factors (such as effector concentration) that are absent in our simplistic scheme, while the comparative analysis is aligned with the spirit of a perturbation study. To cluster the conformations using a dendrogram and thus compare the conformations in contact degrees of freedom, we also define a measure of distance between two contact matrices. Here, the level of similarity between two symmetric matrices 𝑢 and 𝑢’ is defined as a normalized Euclidean distance

(∑′(𝑢𝑖𝑗 ― 𝑢′𝑖𝑗)2/∑′1). The primed

summation restricts the sum to upper diagonal elements.

B. System setup To demonstrate our method, we created four monomeric systems of human RNR with different effector-binding states: hRNR(0), hRNR(T), hRNR(TA), hRNR(TdA), as listed in Table I. The characters inside parentheses indicate the presence of small effector molecules at allosteric binding sites. For example, hRNR(TdA) indicates that the system contains dTTP bound at the specificity site and dATP at the activity site. The apo system, hRNR(0), was created using the crystal structure of human ribonucleotide reductase 1 (pdb: 3hnc, chain B) with the effector dTTP removed. The two small regions with missing structural data (residues 292-293, 630631) were added using ModLoop.29 The protonation status was checked using the H++ web server with pH=7, the neutral pH of water.30 All amino acid residues are in their default protonated state, except K88 and E434 are assigned as neutral. Most histidine residues have protonated ε-nitrogen (assigned as HIE), while H200 is protonated at δnitrogen (HID). Identical protonation state was used for all four systems. We solvated the system in a water box without added counterions, since the net charge of the solute is zero. Although sulfate ions SO42- (as divalent anions of the buffer) were reported in the crystal structure, we did not include them in our simulations. They appear to be non-essential for RNR biophysics and are not always present at allosteric sites. The same crystal structure was also used to set up the system hRNR(T) containing hRNR with dTTP (-4e) bound at the specificity site with 1 Mg2+. Another system, hRNR(TA), was built using the crystal structure of human RNR 1 bound to ATP at the activity site and dTTP at the specificity site (pdb: 3hne, chain B). The system hRNR(TdA) was created using the crystal structure of human RNR bound to dATP at the activity site and dTTP at the specificity site (pdb: 3hnf, chain B). All systems were set up using leap31 and their simulations were performed using NAMD.32 The amber14SB,31 TIP3P,33 and nucleotide force-fields34 were adopted for this system. The parameters of dATP were modified from that of ATP. Particularly, we used the parameters provided by AMBER for the base and the deoxyribose ring of dA (DA3). We used Antechamber to generate the optimal atomic positions and partial charges for dTTP. Total simulation time for each system is 120 ns. We deemed the first 20 ns as relaxation dynamics and excluded them from the subsequent production analysis. Simulations were run using NAMD with standard protocols such as the Langevin thermostat (300 K), the Berendsen barostat (1 atm), and the SHAKE algorithm for bond constraints.32 The simulation timestep is 2 fs and snapshots were taken at the interval of 1 ps.


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Biochemistry

C. Structure of RNR and definition of segments for coarse-grained contact matrix The fold of RNR belongs to the class of TIM-like barrels (α/β barrel).35, 36 The barrel (roughly 480 residues) can be divided into two halves with five central β-strands each. The N-terminus of this protein contains about 220 additional residues including an important allosteric site, the a-site. A direct analysis of contact statistics can be challenging for large systems, and a coarse-grained contact matrix can simplify the problem.27 In this case, the RNR system is comprised of 742 residues at the monomeric level. In our coarse-grained description, the protein contains 87 total segments, including 34 α-helices, 21 β-strands, and 32 other structural regions.37 In Figure 1a, each segment is displayed as a sphere whose size corresponds to the segment size, and they are color coded from red to blue (N- to C-terminus) according to their segment index. The residue-segment index conversion can be found in Supporting Information (SI) Table SI and Figure S1. The two effector-binding sites (a-site and s-site, as shown in Figure 2) regulate the catalysis of RNR at the active site (catalytic site, c-site). The a-site is located at the N-terminal ATP cone,38 which contains a characteristic set of regions: the “β-cap” loop (B1-G1-B2) and four subsequent helices (H1-H4).39 These regions altogether hold the signaling ATP in place. In comparison, the s-site consists of three regions: a loop (B8-G11-B9) and two helices (H13, H14). Effector binding at the s-site directs the substrate specificity at the c-site.40 How monomers oligomerize is shown in Figure 2 (both 3D structures and the contact map), where a contact model was built based on the dimer (pdb: 3hnc) and the hexamer (pdb: 5d1y) structure.23 The intra-protein interaction is shown in (i) and contacts across dimer and hexamer interfaces are shown in (ii) and (iii), respectively. The contact map for the dimer interface (contact map ii) includes the interaction between the s-site and the rest of the protein. Particularly, the dimer interface is formed by the strong contact of helices H13, H13*, H14, and H14*. We use an asterisk symbol (*) to distinguish between a monomer and its symmetric counterpart across the interface. Additionally, the interface is stabilized by the interaction between two flexible loops enclosing the s-site effector, B8-G11-B9 and G12*. Since these two loop regions play many important roles, they are often discussed and commonly referred to as Loop 1 and Loop 2 in scientific literature.18, 35, 39 In contrast, the contact interaction at the hexamer interface (contact map iii) is weaker and centers around four segments of the ATP cone (H1, H1*, B2, B2*).

III. Results Before comparing the specific structural and dynamic differences between different systems in detail, we assess the overall conformational differences among all four simulated systems. This allows us to ascertain the differences that we captured reflecting the setup (different effector-binding states) and are not from the stochastic nature of the simulation. This also serves as a demonstration of the level of convergence of the sampling. For each system, we first obtained 10 block-mean contact matrices, which are time-averaged with a window size of 10 ns. We then computed the pairwise Euclidean distances between all 40 block-mean contact matrices. These distances are shown in Figure 3. One can further apply hierarchical cluster analysis to sort out the similarity between these block-mean contact matrices by using a dendrogram. As expected, the results illustrate that the sampled conformations of the same setup are more similar, while different systems can be easily distinguished from each other, even when they dynamically evolve over time.

A. Communication between s-site and dimer interface: effector binding-induced correlated motion promotes interface formation


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We first examine the conformational transition of RNR from apo form to an effector-bound form. This effectorbinding event at the s-site triggers the dimerization of RNR, which is essential for RNR enzymatic function while the protein’s monomeric form is inactive.21, 41, 42 In this subsection, we inspect (1) the difference of mean structures and (2) the change of fluctuation around the corresponding mean, which can be viewed as the first and second order effects of effector-binding induced conformation changes, respectively. To quantify the change between mean structures of apo RNR and dTTP-bound forms (hRNR(0) and hRNR(T), respectively), we superimposed their mean structures of 100 ns and calculated the distances between corresponding CA atoms of each residue. Displayed in Figure 4a, the difference of most residues is within 2 Å except for three regions: B1-G1-B2 (β-cap) of the a-site, B8-G11-B9 of the s-site, and G12 at the dimer interface. In the presence of effector dTTP at the s-site, the loop G12 moves closer to the interface, which allows the loop to form a stronger contact with the s*-site (the mirror binding pocket across the dimer interface) and promotes the formation of dimer interface. The 3D representation of RNR with the regions that change upon binding are color-coded are shown in supplementary Figure S2. This observation, that s-site binding induces dimerization via G12 loop communication, is consistent with a previous phylogenetic sequence study of RNR.43 As reported in Ref.10, the movement of G12 toward the interface also assists substrate catalysis by preventing this loop from blocking the catalytic site, thus indicating a direct communication between the s- and the c-site. It is interesting to point out that in bacterial RNR, there is a similar mechanism of communication for substrate specificity between s-site binding and G12 loop conformation.16 As dTTP binds to the s-site, a couple key regions are observed to change their flexibilities, which is demonstrated by the B-factor plot in Figure 4b. From the apo form to the effector-bound form, the B-factor values decrease for most regions, especially the ones at the ATP cone (B1-G1-B2) and at the dimer interface (B8-G11-B9, G12), indicating that these regions become more rigid and ordered. This shows that s-site binding contributes to the stability of the a-site while supporting a dimer configuration. The antiparallel β-sheet B17-B18, however, displays an increase in flexibility when the effector is bound to the s-site. Since B17-B18 also forms contact with the ATP cone, the flexibility change of this region suggests its role in supporting the stability at the a-site. As mentioned in System Setup, we built the apo form of human RNR by removing dTTP from a dTTP-bound dimer crystal structure. To ascertain such a computer predicted apo form can be used to deduce the realistic structure and dynamic differences between apo and effector-bound forms, we validate the prediction using the yeast RNR (yRNR) system where data from both forms were reported. Particularly, we compare our predicted hRNR with the corresponding yRNR results, in which case both apo (pdb: 2cvs) and holo (pdb: 2cvy, dTTP-bound at s-site) crystal structures are available. Besides yRNR, there are also apo and complex structures of E. coli RNR available.36 We chose yRNR for comparison since this eukaryotic system shares a strong sequence homology (83.7% similarity) with its human counterpart hRNR, and a significant number of residues are conserved at the s-site.43, 44 Our comparison is how flexibility changes upon effector binding in yRNR vs. that in the simulated hRNR systems. We used experimental B-factor to express the local flexibility in the two systems of yRNR, and the results in Figure 4c show that their B-factor values are largely consistent. Typically, the overall amplitude of experimental B-factors (Figure 4c) can be more subdued compared to that of the computational ones (Figure 4b) due to crystal packing and data fitting procedure, especially for termini and loop regions. Additionally, the computational result was obtained from the more flexible, monomer forms in this case. Rather, we focus on comparing the changes of B-factor due to effector binding. Only two regions (B8-G11-B9 and G12) of yRNR display a significant localization upon effector binding, which is the same feature observed in the computational hRNR results. 36 Although mean structural changes and flexibility changes revealed many of the local features of effector-binding induced conformational changes, the dynamic communication across different sites of the protein (e.g. between the s-site and the dimer interface region) requires a correlation analysis in which we choose the contact degrees of


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Biochemistry

freedom. We have previously used the method of contact PCA to effectively capture nonlocal communication in several biopolymers from protein dynamics28, 45, 46 to chromosome fluctuation,47 as we briefly recapitulated in the Method section. Each orthonormal motion (eigenvector) extracted from this method can be arranged in a matrix form or shown as a three-dimensional representation, as we will demonstrate in Figure 5 for the case of RNR. The matrix expression of the eigenvector, termed the displacement matrix, contains diagonal and non-diagonal components which respectively characterize the internal dynamics within each protein region (self-interacting, "folding" components) and the contact dynamics between different protein regions ("binding" components). In the original residue-residue contact description, the diagonal terms are absent since protein residues constantly form 100% self-contact. However, in the coarse-grained contact description used here, those terms become nontrivial. The value of each element of the displacement matrix is color-coded either blue or red, where blue represents contact forming and red represents contact breaking. In a 3D geometric representation, we display the diagonal components as spheres and non-diagonal ones as cylinders. Such eigenvectors (displacement matrices) describe a sync or anti-sync motion between different parts of the protein. For example, two red (or two blue) spheres indicate these two regions form internal contacts in sync, whereas two spheres with different colors (one red, one blue) indicates contact forming and breaking are anti-correlated between two corresponding regions. The dominant eigenvector PC1 of the apo system displays a relatively simple motion localized at the dimer interface, which involves the two loop regions B8-G11-B9 and G12. As illustrated by Figure 5a, the self-interaction of these two loops shows strong color contrast (red vs. blue), indicating an anti-sync motion between them. In other words, the folding of the loop B8-G11-B9 is correlated with the unfolding of the loop G12. The eigenvector PC1 of dTTP-bound form also shows a localized motion at these dimer interface regions. Upon s-site binding, the two loops form internal contact in a sync motion, represented by the same color spheres (red) illustrated by Figure 5b. The contact forming between B8-G11-B9 and the s-site effector makes G12 become more ordered and more likely to interact with the s*-site, thus stabilizing the dimer interface and facilitating the dimerization of RNR. In contrast, in a hypothetic dimer adopting the apo conformation, the contact between G12 and the s*-site becomes significantly weakened due to the disordered G12. Thus, the apo form of RNR does not have favorable dynamics that support the dimer configuration. Stability and conformational differences of these loop regions are corroborated by B-factor values from simulation and the distance differences between the mean structures of RNR apo and s-site effector-bound forms. To quantify the above argument on in-sync contact dynamics occurring at the binding pocket and at the dimer interface, we measure the synchronization score 𝑅 = 𝐸 × 𝐼 (defined in the Method section) for the apo and effector-binding state. The terms 𝐸 and 𝐼 express how the dynamics of effector binding and interface formation are coupled via the dominant displacement matrix, PC1. We found that 𝑅 value increased significantly upon effector binding, as listed in Table II, which indicates the contribution of effector binding to the stability of the dimer interface. Both systems have a similar value of 𝐼, whereas 𝐸 is drastically increased upon effector binding, that is, effector binding links the motion at the s-site to that of the dominant global motion.

B. Communication between a-site and s-site: a-site binding regulates different s-site dynamic patterns of ATPbound and dATP-bound forms Unlike the almost ubiquitous RNR regulation via the s-site,48 the existence of the a-site shows a higher level of sophistication for certain species. In RNR, a-site binding provides a prominent example of how effector binding influences the oligomer status of a protein complex. Compelling evidence suggests that dATP binding at the a-site induces the formation of the hexamer.42, 49 The dATP-bound hexamer effectively shuts down the catalytic activity of


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this protein.5, 21, 22 There is also evidence supporting that, at least in the presence of beta subunit, ATP competes for binding at the a-site and enhances the enzymatic activity of the protein complex.23 Here, we aim to understand how different effector-binding states at the a-site can affect the ground state conformations (especially how they affect hexamer interface) and whether different effectors lead to different dynamics. We focus on comparing two systems, ATP-bound and dATP-bound RNR (both with dTTP bound at the s-site) in this section. Parallel to the comparison between the apo- and the s-site bound forms of RNR in the previous subsection, differences between mean structures of ATP- and dATP-bound monomers were measured by superimposing their average structures of 100 ns. The distances of the corresponding CA atoms for the two average structures, shown in Figure 6a, are small except for four loop regions, including G12 and B17-B18. The region G12 adopts different conformations when bound to different effectors: from a disordered loop in ATP-bound form to a more structured antiparallel β-sheet in dATP-bound form, which is also consistent with the analysis of the cryo-EM structure of dATPbound hexamer (pdb: 6aui).18 This conformation of G12 (with dATP bound) allows stronger protein-effector interaction with the s*-site and stabilizes the dimer interface. The largest conformation difference, in this case, comes from the loop region B17-B18. Upon ATP binding, we found that the two β-strands (B17 and B18) lose contacts with each other while moving away from the ATP cone. We also compared the second order effect and observed the difference of the four mentioned loop regions in terms of fluctuation, as shown in Figure 6b. The flexibility of these loops shows a consistent trend with their mean structure difference. One interesting finding is that the loop G12 becomes more ordered upon a-site binding regardless of the specificity of the effector. Additionally, we found that the rigidity of the loop B17-B18 increases when ATP is bound. Looking beyond local features manifested by mean structural and flexibility changes, we again investigate the communication between different binding sites and the interface. The dominating mode (eigenvector PC1, shown in Figure 7) of the ATP-bound form shows that the dominant motion of the protein is global and dispersed throughout the structure, which indicates a dynamic communication between many parts of the protein. In contrast, the motion with bound dATP becomes much simpler and more localized at two loop regions, B8-G11-B9 and G14. Though activating effector ATP only differs from inhibitory effector dATP by a functional group (–OH versus –H at 2’ position), the dynamic modes by which these effectors interact with the a-site are distinct. In the ATP-bound form, the effector forms contact with two regions (B1-G1-B2, H1) in a sync and an additional region (G3) in an anti-sync fashion, which weakens the hexamer interface interaction. In the case of dATP-bound form, the contact dynamics between the effector and three ATP cone regions (B1-G1-B2, H1, G3) is in sync, which strengthens the hexamer interface and promotes the complex formation. Previous studies reported that the region G3 changes conformation in different a-site binding modes (ATP vs. dATP), suggesting an essential role for G3 in the hexamer interface.5 Our results further reveal that the two dynamic modes at the a-site differ in the correlated motion of effector with G3 and with other regions, thus confirming a direct correlation between conformational change of G3 and its different dynamic interactions with a-site effectors. These dynamic changes in turn modulate the interaction strength at the hexamer interface. These descriptions again can be quantified using values of the synchronization function 𝑅, as listed in Table II. Compared to ATP binding, dATP at the a-site helps the formation of the dimer interface without destabilizing much of the hexamer interface. Since a stable hexamer needs both types of interfaces, it is interesting to speculate whether dATP stabilizes the hexamer from these calculations, especially a recent study reported the overall effect of dATP stabilizes the hexamer structure.23 It would also be interesting to extend the synchronization calculation to the RNR alpha-beta (R1-R2) interface17 in the future.


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Biochemistry

We found that the a-site effectors not only affect the hexamer interface but also allosterically modulate the activity at the s-site. In comparison to the role of the s-site in directing a specific catalytic reaction, the regulatory role of the a-site was considered more complex. Overall, the function of the a-site is to activate or deactivate the enzyme, and one might think that the binding status of the a-site (apo vs. bound) should be enough to control the on/off status of RNR. From a design viewpoint, it is puzzling why two effectors ATP and dATP are needed. In fact, previous studies have shown that dATP-bound enzyme remains active in its dimer configuration (D16R mutant), while dATP-bound hexamer is the only inactive form.21 Our analysis suggests that a-site binding of ATP or dATP affects flexibility at the s-site. When ATP is bound, the s-site has a fast exchange of multiple effectors which leads to multiple catalytic reactions, whereas dATP locks the effector at the s-site and the corresponding reaction type (SI Figure S3). Thus, we propose the role of the a-site in controlling the dynamics of product switching. Specifically, in PC1, ATP enhances the dynamic contacts formed between effector and the s-site binding pocket (Figure 7a). When ATP interacts with the ATP cone region G3, the s-site effector becomes loosely bound and breaks contact with the s-site. This results in a relatively fast exchange of different effectors at the s-site, which subsequently leads to the catalysis of different substrates and a balance of the nucleotide pool. The binding of dATP, in contrast, locks an effector at the s-site. The interaction at the s-site is much more stable than that of the ATPbound form, supported by an increase in mean contact strength between effector and the s-site (SI Figure S4), and by the subdued fluctuation at the s-site in PC1 (Figure 7b). Additionally, the binding of dATP makes the loop B8-G11B9 become more ordered, which also induces a tight binding of the s-site with its effector.

IV. Concluding remarks Small-effector binding can allosterically regulate not only the catalytic site of the enzyme but also the protein-protein interfaces. In RNR, we investigated such allosteric effects using atomistic simulation and statistical analysis. Our results uncovered a detailed mechanism of how the dTTP-bound s-site promotes dimer interface formation. The dynamics of this protein influence how the two types of contacts (effector-binding and interprotein contacts) correlate. We also reveal drastic dynamics changes of the whole protein between ATP-bound versus dATP-bound states, which offer clues to how the ATP vs. dATP binding may control the function of the a-site. We suggest that the role of the a-site is more than simply turning the enzyme “on/off”, but rather it might play a role in regulating two “on” states with different dynamics (fast vs. slow switching between different products being made). Overall, we found that there was a clear communication between allosteric effector sites and interface regions, which connects the effector-binding event to the interface formation. The study of the dynamic correlation between different regions of the protein via quantitative contact analysis demonstrated here can also be generalized to other protein systems. Supporting Information: supplementary table on the definition of coarse-grained segments. Supplementary figures on additional views and representation of the data. Acknowledgement: We thank Dr. Chris Dealwis and Dr. Donald Hamelberg for very helpful discussions. We acknowledge the computational support provided by the allocations of advanced computing resources XSEDE (STAMPEDE2 at TACC). This work was also supported in parts by NIH R15 GM123469.


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References

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(33) Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W., and Klein, M. L. (1983) Comparison of simple potential functions for simulating liquid water, The Journal of Chemical Physics 79, 926-935. (34) Meagher, K. L., Redman, L. T., and Carlson, H. A. (2003) Development of polyphosphate parameters for use with the AMBER force field, Journal of Computational Chemistry 24, 1016-1025. (35) Uhlin, U., and Eklund, H. (1996) The Ten-stranded β/α Barrel in Ribonucleotide Reductase Protein R1, Journal of Molecular Biology 262, 358-369. (36) Uhlin, U., and Eklund, H. (1994) Structure of ribonucleotide reductase protein R1, Nature 370, 533. (37) Laskowski, R. A., Hutchinson, E. G., Michie, A. D., Wallace, A. C., Jones, M. L., and Thornton, J. M. (1997) PDBsum: a web-based database of summaries and analyses of all PDB structures, Trends in Biochemical Sciences 22, 488-490. (38) Aravind, L., Wolf, Y. I., and Koonin, E. V. (2000) The ATP-cone: an evolutionarily mobile, ATP-binding regulatory domain, Journal of molecular microbiology and biotechnology 2, 191-194. (39) Ahmad, F., and Dealwis, C. G. (2013) The Structural Basis for the Allosteric Regulation of Ribonucleotide Reductase, Progress in molecular biology and translational science 117, 389-410. (40) Eriksson, M., Uhlin, U., Ramaswamy, S., Ekberg, M., Regnström, K., Sjöberg, B.-M., and Eklund, H. (1997) Binding of allosteric effectors to ribonucleotide reductase protein R1: reduction of activesite cysteines promotes substrate binding, Structure 5, 1077-1092. (41) Zhang, Z., Yang, K., Chen, C.-C., Feser, J., and Huang, M. (2007) Role of the C terminus of the ribonucleotide reductase large subunit in enzyme regeneration and its inhibition by Sml1, Proceedings of the National Academy of Sciences 104, 2217-2222. (42) Kashlan, O. B., and Cooperman, B. S. (2003) Comprehensive Model for Allosteric Regulation of Mammalian Ribonucleotide Reductase: Refinements and Consequences, Biochemistry 42, 16961706. (43) Knappenberger, A. J., Grandhi, S., Sheth, R., Ahmad, M. F., Viswanathan, R., and Harris, M. E. (2017) Phylogenetic sequence analysis and functional studies reveal compensatory amino acid substitutions in loop 2 of human ribonucleotide reductase, Journal of Biological Chemistry 292, 16463-16476. (44) Xu, H., Faber, C., Uchiki, T., Racca, J., and Dealwis, C. (2006) Structures of eukaryotic ribonucleotide reductase I define gemcitabine diphosphate binding and subunit assembly, Proceedings of the National Academy of Sciences of the United States of America 103, 4028-4033. (45) Clark, A. K., Wilder, J. H., Grayson, A. W., Johnson, Q. R., Lindsay, R. J., Nellas, R. B., Fernandez, E. J., and Shen, T. (2016) The Promiscuity of Allosteric Regulation of Nuclear Receptors by Retinoid X Receptor, The Journal of Physical Chemistry B 120, 8338-8345. (46) Johnson, Q. R., Lindsay, R. J., Nellas, R. B., and Shen, T. (2016) Pressure-induced conformational switch of an interfacial protein, Proteins: Structure, Function, and Bioinformatics 84, 820-827. (47) Lindsay, R. J., Pham, B., Shen, T., and McCord, R. P. (2018) Characterizing the 3D structure and dynamics of chromosomes and proteins in a common contact matrix framework, Nucleic Acids Research 46, 8143-8152. (48) Olcott, M. C., Andersson, J., and Sjoberg, B. M. (1998) Localization and characterization of two nucleotide-binding sites on the anaerobic ribonucleotide reductase from bacteriophage T4, The Journal of biological chemistry 273, 24853-24860. (49) Kashlan, O. B., Scott, C. P., Lear, J. D., and Cooperman, B. S. (2002) A Comprehensive Model for the Allosteric Regulation of Mammalian Ribonucleotide Reductase. Functional Consequences of ATP- and dATP-Induced Oligomerization of the Large Subunit, Biochemistry 41, 462-474.


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Biochemistry

Table I: Shorthand notation for the systems studied. System hRNR(0) hRNR(T) hRNR(TA) hRNR(TdA)

s-site Ø dTTP(-4e) dTTP(-4e) dTTP(-4e)

a-site Ø Ø ATP(-4e) dATP(-4e)

PDB origin 3HNC:B 3HNC:B 3HNE:B 3HNF:B

# H2O 27,230 27,221 27,358 27,913

Other ions Ø 1 Mg2+(at S), 2 Na+ 1 Mg2+(at S), 6 Na+ 1 Mg2+(at S), 6 Na+

Table II: Synchronization calculation for the four systems. Here s and a refer to the s- and the a-site, respectively. Id and Ih refer to the dimer and the hexamer interface, respectively. hRNR(0) hRNR(T) hRNR(TA) hRNR(TdA)

Es 0.297 1.310 -0.108 -1.418

Ea -0.225 0.227

Id 2.721 2.501 5.579 0.710

Ih -1.279 0.906

R = E s x Id 0.809 3.276 -0.605 -1.007


R = E a x Id -1.255 0.161

R = E a x Ih 0.288 0.206

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Figure Captions: Figure 1. (a) A cartoon illustration of RNR structure with the location of the a-site and the s-site labeled. The binding of effectors directs the dimerization of this protein. (b) A mathematical formula describes how effector binding (E) affects the interface formation (I). The synchronization strength of these two events is quantified using a score function (R). Figure 2. (a) 3D structure and a simplified model of RNR hexamer. (b) A contact map scheme displays the protein self-interaction (labeled i) and the interaction at the dimer (ii) or hexamer (iii) interface. Figure 3. (a) The Euclidean distances between time-average mean contact matrices (10 of 10 ns time block of four systems) form a distance matrix (40x40). (b) The hierarchical cluster analysis of the distance matrix. Figure 4. (a) Distance between the corresponding CA residues from the mean structures of hRNR(0) and hRNR(T). The regions associated with drastic deviation are labeled. (b) A comparison of computational B factor values between hRNR(0) (black) and hRNR(T) (red). (c) A comparison of experimental B factor values between yRNR(0) and yRNR(T) obtained from Ref.44. Figure 5. The dominant mode of motion obtained from contact PCA for (a) hRNR(0) and (b) hRNR(T) systems: twodimensional protein displacement matrix representation (left) and the corresponding 3D view, where colors in each sphere display a segment’s self-interaction strength and each cylinder for inter-segment correlation (right). Figure 6. (a) Distance between the corresponding CA residues from the mean structures of hRNR(TA) and hRNR(TdA). The regions associated with drastic deviation are labeled. (b) A comparison of computational B factor values between hRNR(TA) (black) and hRNR(TdA) (red). Figure 7. The dominant mode of motion obtained from contact PCA for (a) hRNR(TA) and (b) hRNR(TdA) systems: two-dimensional protein displacement matrix representation (left) and the corresponding 3D view, where colors in each sphere display a segment’s self-interaction strength and each cylinder for inter-segment correlation (right).


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Biochemistry

Figure 1


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Figure 2


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Figure 3


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Figure 4


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Figure 5 (double-column width)


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Figure 6


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Biochemistry

Figure 7 (double-column width)


Effector-Binding-Directed Dimerization and Dynamic Communication

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