Molecular Mechanism of Nucleotide-Dependent Allosteric Regulation

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Molecular Mechanism of Nucleotide-Dependent Allosteric Regulation in AMP-Activated Protein Kinase Navjeet Ahalawat, and Rajesh Kumar Murarka J. Phys. Chem. B, Just Accepted Manuscript • DOI: 10.1021/acs.jpcb.6b11223 • Publication Date (Web): 27 Mar 2017 Downloaded from http://pubs.acs.org on March 28, 2017

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The Journal of Physical Chemistry

Molecular Mechanism of Nucleotide-Dependent Allosteric Regulation in AMP-Activated Protein Kinase Navjeet Ahalawat† and Rajesh K. Murarka†*

†

Department of Chemistry, Indian Institute of Science Education and Research Bhopal, Bhopal

By-pass Road, Bhauri, Bhopal 462 066, MP, India

*

Corresponding Author

Email: [email protected] Phone: +91-755-669-1319

1 ACS Paragon Plus Environment


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ABSTRACT The AMP-activated protein kinase (AMPK), a central enzyme in the regulation of energy homeostasis, is an important drug target for type 2 diabetes, obesity and cancer. Binding of adenosine nucleotides to the regulatory γ-subunit tightly regulates the activity of this enzyme. Though recent crystal structures of AMPK have provided important insights into the allosteric activation of AMPK, molecular details of the regulatory mechanism of AMPK activation is still elusive. Here, we have performed extensive all-atom molecular dynamics (MD) simulations and shown that the kinase domain (KD) and γ-subunit comes closer resulting in a more compact heterotrimeric AMPK complex in AMP-bound state compared to the ATP-bound state. The binding of ATP at site 3 of regulatory γ-subunit allosterically inhibits AMPK by destabilizing different regulatory regions of α-subunit: the autoinhibitory domain, the linker region, and the activation loop of the kinase core. The catalytically important residues experiences a change in mechanical stress, and major rearrangements in community structure derived from residueresidue interaction energy-based network are observed in KD and α-linker region upon binding of different nucleotides. Our results also highlight the role of conserved charged residues forming an ionic network near the site 3 of γ-subunit in allosteric communications.


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INTRODUCTION The AMP-activated protein kinase (AMPK), a highly conserved heterotrimeric serine/threonine protein kinase, is responsible for energy homeostasis in eukaryotic cells.1-3 Due to its pivotal role in energy homeostasis, AMPK is a promising target for the development of drugs related to metabolic disorders such as heart disease, diabetes, cancer, inflammatory disorders and viral infection.4-7 The AMPK heterotrimer is composed of a catalytic α-subunit, a glycogen-binding βsubunit and a nucleotide-binding γ-subunit (Figure 1). The catalytic α-subunit has kinase domain (KD), followed by an autoinhibitory domain (AID) that inhibits the kinase activity of KD, and an α-linker, a critical region of extended polypeptide, that interact with the β and γ subunits8 and believed to play an instrumental role in regulating the activity of AMPK. The β-subunit has a key role in heterotrimeric complex formation by acting as a scaffold for α and γ subunits.9 The γsubunit is made up of four cystathionine β-synthase (CBS) motifs and has three functional nucleotide-binding sites (sites 1, 3, and 4); site 2 is incompetent of nucleotide binding and always empty; site 4 is considered to be occupied by AMP in a non-exchangeable manner, whereas site 1 and 3 can competitively bind AMP, ADP, or ATP.10-13 Each subunit of AMPK in mammals exists in multiple isoforms encoded by distinct genes; the α and β subunits have two isoforms (α1, α2 and β1, β2), whereas the γ subunit has three isoforms (γ1, γ2, and γ3) that differ from each other in their lengths. Theses seven gene products give rise to 12 different AMPK heterotrimeric combinations when coexpressed in cells. Multiple combinations of AMPK subunit isoforms indicate that it may have many other cellular functions.



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Figure 1. A cartoon representation of the human AMPK heterotrimer. Nucleotides are shown as sphere representation. The kinase domain is colored in cyan, autoinhibitory domain in red, α-linker region in chocolate, αCTD in brown, β-subunit in green, and γ-subunit in yellow color.

Activation of AMPK depends on both cellular adenylate concentrations and phosphorylation by upstream kinases. Once activated, AMPK phosphorylates numerous proteins to upregulate ATPgenerating pathways and inhibit ATP-consuming pathways.14, 15 The activity of AMPK increases more than 100-fold when a conserved residue Thr172 (named Thr172 because of its position in the rat sequence) in the activation loop of KD is phosphorylated by upstream kinases, including tumor suppressor kinase LKB1 (liver kinase B1)16-18 and calmodulin-dependent protein kinase 4 ACS Paragon Plus Environment

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CaMKKβ.19-21 However, binding of adenine nucleotides to the γ-subunit regulates the phosphorylation and dephosphorylation of Thr172; binding of ATP is regarded as inhibitory, while AMP and ADP appear protective and stimulatory.3,

10

Under energy stress condition

phosphorylated (at Thr172) AMPK can further be allosterically activated >10-fold by AMP.22 Important insights into allosteric regulation of AMPK by AMP have been provided by recent crystal structures of AMPK in complex with nucleotides. Crystallographic and mutational studies suggested that site 3 acts as a primary allosteric regulatory site,10,

12

where binding of AMP

enhances the kinase activity while ATP antagonizes this activation.22, 23 Understanding the molecular events that control allosteric interactions is challenging for AMPK because it has several structural elements in different subunits. Despite considerable progress made in recent years, the mechanism of allosteric activation of AMPK by AMP is still not clear at the molecular level. In this study, we have carried out extensive all-atom molecular dynamics (MD) simulations to uncover the molecular basis of allosteric regulation mechanism of AMPK due to nucleotide binding at site 3 of regulatory γ-subunit. Our simulations show that binding of ATP at site 3 of AMPK destabilizes the AID, α-linker and activation loop of KD. Further, the simulations suggest that large fluctuations near the catalytic site and in αC helix of KD could lead to a difference in catalytic activity observed between AMP and ATP bound states. The results of our study provide valuable insights into the allosteric activation mechanism of AMPK and could be useful for rational drug design.



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MATERIALS AND METHODS System Preparation and Molecular Dynamics Simulations The crystal structure (PDB ID 4RER) is used to model both AMP- and ATP-bound states of human AMPK. Site 4 of γ-subunit is bound to AMP and is considered to be non-exchangeable. Site 2 is always empty due to absence of aspartate residue that interacts with the nucleotide ribose ring. Site 1 and site 3 are only exchangeable sites. AMP is more potent than ADP to protect the dephosphorylation of Thr174, but the cellular concentration of ADP is usually higher than AMP, and site 1 has similar binding affinity for both AMP and ADP. Hence, we have considered ADP molecule at site 1 and replaced the AMP molecule by ADP at site 1. The ATPbound state was created by replacing site 3 AMP with ATP. Missing residues (less than ten consecutive residues) in the crystal structure were modeled using Modeller9.10.24, 25 As we were interested in understanding the allosteric effect due to nucleotide binding, the glycogen-binding domain of β-subunit was not considered in this work. All MD simulations were performed using Gromacs version 4.5.26, force field with CMAP corrections28,

29

27

All-atom CHARMM27

and TIP3P rigid water model30 were used. The initial

structures were solvated in a dodecahedron box with 47891 and 47887 water molecules for AMPK-AMP and AMPK-ATP complex, respectively. The minimum distance between the protein surface and the box was 10 Å. The LINCS algorithm31 was applied to constrain the bonds having H atoms, and an integration time step of 2.0 fs was used. The bonds and the angles of TIP3P water molecules were constrained using the SETTLE algorithm.32 To neutralize the systems, 3 Na+ and 5 Na+ were added to AMPK-AMP and AMPK-ATP complex, respectively. This resulted in a total of 157787 atoms for AMPK-AMP complex and 157785 atoms for 6 ACS Paragon Plus Environment

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AMPK-ATP complex. The energy minimization and equilibration were carried out as follows: (1) a 10000 steps steepest descent energy minimization with a tolerance 100 kJ mol-1 nm-1, (2) a 200 ps position restrained (all heavy atoms of AMPK, AMP, ADP, and ATP) NVT simulation with restraining force constant of 1000 kJ mol−1 nm−2 at 300 K using the Berendsen coupling33 with a coupling constant of 0.1 ps, (3) a 1 ns position restrained NPT simulation with restraining force constant of 1000 kJ mol−1 nm−2 at 300 K using the v-rescale34 temperature coupling with a coupling constant of 0.1 ps and at 1 bar pressure with Parrinello-Rahman coupling35 with a coupling constant of 2.0 ps, and (4) a 10 ns of NPT equilibration at 300 K and 1 bar pressure with parameters as mentioned in step 3. For each nucleotide-bound state, ten independent NVT production runs of 100 ns were carried out starting from different initial atomic velocities using v-rescale thermostat with a time constant of 0.1 ps. Further, to capture the long timescale motions, one trajectory of each system was extended to a simulation time of 1 µs. Thus, taken together, we have run a total of 3.8 µs simulations in this study. The electrostatic interactions were evaluated using the particle mesh Ewald (PME) method,36 with a cutoff of 10 Å and the van der Walls interactions were evaluated using a cutoff of 10 Å. Analysis of MD Trajectories Analysis of MD simulation trajectories were carried out using in-house python scripts and tools provided by Gromacs-4.5. PyMOL (https://www.pymol.org) and VMD37 were used for molecular visualization. The ten independent trajectories generated for each state (a total of 1.9 µs simulations) were used for all the analysis, unless otherwise stated. Principal component analysis



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MD simulations generate trajectories containing a huge amount of information of atomic coordinates, and extracting information about large-scale functional motions from these trajectories is not straightforward. Principal component analysis (PCA) is a widely used dimensionality reduction technique to retrieve dominant patterns and representative distributions from large noisy data sets. PCA for both the nucleotide bound states considering only Cα atoms were performed to understand the important collective motions in the enzyme. The calculation of the covariance matrix and its diagonalization to get the associated principal components describing the directions (eigenvectors) and amplitudes (eigenvalues) of protein motions was carried out with g_covar module of Gromacs-4.5. The g_anaeig module of Gromacs-4.5 was used to project the obtained principal components (PCs) on the trajectories. Force Distribution Analysis We used the FDA code38 implemented in Gromacs-4.5.3. Forces, as defined by the force field including the contributions of all types of non-bonded (electrostatic and van der Waals) and bonded (bond, angle, dihedral) interactions, between all atoms pairs within a cutoff range 10 Å

were calculated during each step of MD simulation. Inter-residue forces were obtained from

the norm of force vector resulting from summing up atomic forces in the two residues u and v, where i is an atom of residue u, j is an atom of residue v and u ≠ v: =

∈,∈

These calculated pairwise forces from each conformation in the trajectories were averaged

over time and over the 10 independent trajectories. The stress for a residue u is defined as the sum of all residue pairs acting on the residue u:


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=

The absolute differences between per residue-summed forces of both states were used for visualization purpose. More details on FDA method can be found in the references 38-40. Network analysis of protein dynamics Allosteric networks in AMPK are examined through residue pair interaction energy based method41, 42 in which each residue represents a node and two residues are connected by residueresidue interaction energy. Edge weight, w , that connects node i and j is calculated as =

−log (ω ), where ω is defined as follows: =

, /,

! "#$ % "&' ()*"+'#,+- .)#$'$ ),ℎ'& 1'

where is 0.99 (to assigns maximum edge weight to covalent interactions), and / ≡ 0.5 ×

71 − (9 − 9: )⁄59;< >; 9 is the residue–residue non-bonded interaction energies, 9: is the

average interaction energy between all pairs of noncovalently bonded residues, and 9;< is the root-mean-square deviation of interaction energies. The / values greater than 0.99 were

reassigned to the value 0.99. The average and root-mean-square deviations of the interaction energies were −9.16 and 14.81 kJ/mol for AMPK-AMP bound complex and −9.28 and 15.45 kJ/mol for AMPK-ATP bound complex, respectively. Residue pairs with ωij < 0.01 were considered disconnected. In such a weighted network, nodes with strong and attractive interactions are closely bound with shorter distances and could exchange a large amount of information, whereas nodes with weak interaction energy have longer distances between them. Such energy based protein networks contain information of paths and nodes that are crucial for 9 ACS Paragon Plus Environment


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allosteric communication within a protein. In this graph network approach, the likely allosteric paths are described by the shortest path between spatially distant nodes. The Girvan-Newman algorithm43 is used to divide the network into communities. The communities are groups of nodes within which the nodes are densely intraconnected, and sparsely interconnected. The number of shortest paths that cross a given edge (i.e. betweenness) is calculated for all the edges and the edge with greatest betweenness is removed. This process was repeated to divide the network into optimal number of communities based on a modularity score.44 To measure the similarity between two community networks of the same state, we employed Rand index45,

46

score (R), which can be defined as: ?=

@+B @+B+C+D

where A is the number of pairs of nodes maintained in the same community, B is the number of pairs of nodes that are maintained in different communities, C is the number of pairs of nodes that were in the same community and end up in different communities, and D is number of pairs of nodes that were in different communities and end up in the same community. The Rand index gives a value between 0 and 1, where 1 means the two communities match identically and 0 means they are completely different. Generalized cross-correlations Generalized cross-correlations of residues in both states were calculated based on mutual information between all Cα atoms in the protein using the g_correlation tool, developed by Lange and Grubmüller.47 This method has the two advantages over Pearson coefficient: (1) it is independent of the relative orientation of the atomic fluctuations and (ii) it has ability to capture


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non-linear correlations. The g_correlation tool uses the mutual information (E[G , G ]) estimator

of Kraskov et al48 which estimates the marginal (I[G ] and I[G ]) and joint (I[G , G ]) entropy using k-nearest neighbor distances (k-NN) algorithm. E[G , G ] = I[G ] + I[G ] − I[G , G ]

It yields values in the range [0 ⋯ ∞]. The generalized correlation coefficients (rMI) is defined as, &LM NG , G O = {1 − 'GQ(−2E[G , G ]/$)}UV/W

where d is the dimensionality of the variable x, to get more intuitive values ranging from 0 (independent variables) to 1 (fully correlated variables).

RESULTS We employed extensive unbiased MD simulations to investigate the allosteric modulation of AMPK due to binding of different nucleotides at the regulatory γ-subunit. It is crucial to assess the convergence of a biomolecular simulation before using the trajectories for analysis. To assess the quality of conformational ensemble, we have used bootstrapped block covariance overlap method (BBCOM).49 In BBCOM multiple blocks with desired number of conformations are created by randomly drawn conformations from the trajectories and then the covariance overlap is calculated between the PCA of blocks. The BBCOM analysis shows very good covariance overlap for different block sizes suggesting that an adequate sampling of the conformational space has been achieved (Table S1). AMP binding increases the compactness of the heterotrimeric AMPK complex



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Radius of gyration (Rg), the mass weighted scalar length of each atom from the center-of-mass (COM), is the most unambiguous measure to assess the compactness of a protein-ligand complex. Figure 2A shows the distribution of Rg values of the AMP and ATP bound heterotrimeric complex of AMPK. This clearly shows that the binding of ATP somewhat decreases the overall compactness of AMPK heterotrimer in the present simulations, and is in line with the findings of previous SAXS study.50 It should be noted that the change in experimental Rg value of full length AMPK (including the glycogen binding domain) was ~0.2 nm when saturated with AMP,50 and the single electron microscopy study also did not observe any appreciable overall structural changes in presence of 1 mM ATP or AMP.51 In this study we have perturbed only single nucleotide-binding site (site-3) which resulted in 3.14 ± 0.03 nm and 3.20 ± 0.03 nm average Rg values, respectively, for AMP- and ATP-bound AMPK complex (without the glycogen binding domain). Although one cannot compare these numbers directly with experimental data (where all sites were saturated with either nucleotide) but the change in our Rg values is significant considering the available experimental data. To find the origin of the observed variations in Rg values upon nucleotide binding, we also compared Rg values of KD as well as the γ-domain of AMP- and ATP-bound AMPK complex. However, we found that the corresponding distribution peaks at comparable Rg values in both AMP- and ATP-bound states (Figures 2B and S1). Further, we calculated the distance between the COM of KD and regulatory γ-subunit (Figure 2C) to assess the nucleotide-dependent (at site 3) conformational changes in AMPK. We found that AMP-bound state shows decrease in COM distance between KD and γsubunit, which is also consistent with a recent study23 in which AlphaScreen luminescence proximity assay have been used to show that KD and γ domain comes closer in the AMP bound state. The time series data of 1 µs simulated trajectories shows that the trend in change in Rg of


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heterotrimeric complex (Figure S2A) and the COM distances between γ-subunit and KD (Figure S2B) is very similar where the change in Rg values between AMP- and ATP-bound states are more than 0.1 nm, and the COM distance has increased more than 0.4 nm after 450 ns in ATPbound state compared with AMP-bound state. However, the Rg values of γ-subunit and KD are changing marginally from AMP- to ATP-bound states (Figure S2C and S2D).

Figure 2. Nucleotide-dependent conformational changes. (A) Distribution of radius of gyration (Rg) of the entire heterotrimeric complex in AMP- and ATP-bound states, (B) Rg of γ-subunit, (C) center of mass distance between kinase domain and γ-subunit in AMP- and ATP-bound states, and (D) Rg of γ-subunit plus the linker region.

AMP-bound complex shows highly correlated movements and restricted motions of AID− −α-linker region 13 ACS Paragon Plus Environment


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Previous experimental studies had shown that the linker region of the α-subunit play a central role in transmitting the nucleotide binding-induced allosteric signals through regulated interactions with the site 3 of γ-subunit.10, 52 We calculated the Rg of γ-subunit plus the linker region (334-372) and found that the linker sequence of α-subunit is more strongly associated with the γ-subunit of AMP-bound complex (Figure 2D). Complexity of biomolecular systems and large amount of simulated data makes it difficult to analyze the motions of interest, or to uncover the functional mechanisms. Principal component analysis (PCA) is a powerful method for dimensionality reduction and to separate large amplitude motions from irrelevant fluctuations along an MD trajectory.53, 54 We performed PCA for AMP and ATP-bound AMPK trajectories considering only Cα atoms of AID (residues 293-333) plus the linker region to identify functionally relevant collective motions with large amplitudes. Covariance matrix is constructed after fitting all conformations on γ-subunit, as our aim is to analyze the movement of α-linker and AID with respect to γ-subunit. Root-mean-square-fluctuations (RMSFs) along the top three PCs show that the linker loop region spanning residues 345-359 is quite flexible in both states (Figure 3). It should be noted that the residues 350-353 were missing in the crystal structure, perhaps due to the high flexibility of this region. The AID and the linker region that is close to γsubunit (i.e., residues 334-344 and 360-370) show less fluctuation in AMP-bound complex (Figure 3). These two regions of the linker sequence (residues 334-344 and 360-370) are known as α-regulatory subunit interacting motif 1 and 2 (αRIM1 and αRIM2), and in previous experiments, have been suggested to play crucial role in AMPK allosteric regulation.8, 15


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Figure 3. Fluctuations along PCs. Root-mean-square-fluctuations (RMSFs) of Cα atoms of AID plus α-linker region along the first (PC1), second (PC2), and third (PC3) principal components of AMP- and ATP-bound states.

In proteins, when motions of two sites are correlated then perturbation at one site can affect the motions at the other site. The closely spaced residues and the residues of secondary structure elements show more correlation, and the correlation generally decreases as the distance between two sites in protein increases. Cross-correlations between all Cα atoms of the AMP- and ATPbound states were analyzed to identify the correlated motions in AMPK complex using generalized cross-correlation method.55 Figure 4 shows mutual information based crosscorrelations calculated considering all the trajectories of AMP- and ATP-bound complex. AMP15 ACS Paragon Plus Environment


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bound state show more correlated motions throughout the entire protein as compared to the ATPbound state. In particular, we observed increased intra-domain correlations in KD as well as inter-domain correlations between KD and the regulatory γ-subunit (>0.6) in AMP-bound state. This suggests that the binding of AMP at site 3 of the γ-subunit allosterically modulates the functional motions by strengthening the inter- and intra-subunit coupling. On the other hand, the binding of ATP at site 3 not only destabilizes the linker region (Figure 3) but also affects the inter- and intra-correlated motion of γ-subunit and KD domain (Figure 4).

Figure 4. Generalized cross-correlation analysis. Cross-correlation matrix of the atomic fluctuations of Cα atoms calculated from all of the simulated trajectories of AMP-bound (upper triangle) and ATP-bound (lower triangle) heterotrimeric AMPK complex. Rectangular boxes represent the intra-correlations in KD, and inter-correlations between KD and γ-subunit. Correlation increases from black to red color.

ATP binding affects the stability and functional motions of catalytically important residues of Kinase Domain


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Binding of a small molecule or different ligands at the same binding site can produce different force distributions or mechanical stress in different parts of the protein, and these allosteric signals can be delineated by comparing the forces of different states.39, 40, 56 Force distribution analysis (FDA) was used to identify residues involve in allosteric communications due to nucleotide binding. In FDA, scalar pair-wise forces Fij were calculated between each pair of atoms, i and j from all the simulated trajectories of each state. The force computation was performed considering all interaction types (bonded and non-bonded components). In protein kinases there are two well-known regulatory modules that control their activity: an αC helix (residues 59–72 in AMPK) and a peptide spanning the activation loop, which includes the threonine residue (Thr172/Thr174 in AMPK) important for kinase activation. First we tried to understand how phosphorylation at Thr174 changes the force distribution in isolated KD. For this purpose, we performed 500 ns MD simulation of only KD with/without phosphorylation at Thr174 and compared the forces. The αC helix of KD contains Glu66, a highly conserved negatively charged residue, which interact with Lys47, a positively charged conserved residue in β strand. This ion pair is very important for an active kinase, without which the kinase cannot support efficient steady state catalysis.57, 58 We found that residues affected by phosphorylation of Thr174 are Lys47, Arg65, Glu66, Arg140, and Asp159 (part of DFG motif) (Figures 5A and S3). It should be noted that the ionic interactions of Arg65 (part of αC-helix) and Arg140 (part of HRD motif) with phosphorylated Thr174 are crucial to achieve the correct closure of N- and Clobes of KD, and for proper orientation and positioning of catalytic base Asp141 and the DFG motif for efficient catalysis.59



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Figure 5. Force distributions in kinase domain. The absolute difference reflecting the change in force sensed by a single residue in kinase domain (A) with or without phosphorylated kinase domain simulations, and (B) in AMPand ATP-bound states of AMPK complex mapped onto a cartoon (sausage) representation. Color range magenta to red represents low to high mechanical stress; values >400 and >260 were reassigned to the values 400 and 260 in (A) and (B) respectively.

The structure of the heterotrimeric complex of human AMPK23 considered in this study represents an active state of AMPK where the Thr174 of the activation loop is phosphorylated in both AMP-bound and ATP-bound states. FDA reveals that binding of AMP or ATP at site 3 of regulatory subunit induces different stress in KD. The difference in forces at N-lobe of KD are less pronounced compared to a high degree of mechanical stress observed at C-lobe of KD, AID, the α-linker region, and the neighboring residues of site 3 nucleotide of γ-subunit (Figures S4 and 5B). The regulatory modules of kinase domain (αC helix and the activation loop) senses difference in mechanical stress due to nucleotide binding at site 3 (Figures 5B and S5). The RMSF around the average structure of KD also confirms that ATP binding at site 3 destabilizes the activation loop (Figure 6). Residues Arg65, Glu66, Asp141 (conserved residue of HRD motif), and Arg173 (neighboring residue of phosphorylated Thr174) sensing different stress are catalytically important, and could be responsible for regulating the allosteric activity of this 18 ACS Paragon Plus Environment

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enzyme (Figure 5B). Interestingly, we have observed a similar force distribution pattern with/without phosphorylated Thr174 of KD in absence of regulatory γ-subunit (Figure 5A).

Figure 6. Root-mean-square-fluctuation (RMSF) of Cα atoms of kinase domain from its average structure (shown in supplementary Figure S6). Big dotted circle represents the activation loop region and small dotted circle represents the αC-helix C-terminal part region including the residues 71-73.

Allosteric communications and rearrangements of community network in different nucleotide bound states Dynamical network for AMP- and ATP-bound complex of AMPK are examined through community network analysis. Average non-bonded interaction energies between residue pairs obtained from MD simulations were used to create the weighted network as defined in method section. Figures 7A and 7C show, respectively, graph network representation of the optimum structure of communities obtained for AMP- and ATP-bound complex of AMPK, where node represents a community and edge represents inter-community information flow i.e. total betweenness centrality. These communities are also mapped on 3D structure of AMPK and



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shown in Figures 7B and 7D. A community is a group of nodes that are strongly connected internally and weakly connected to other nodes of the network. The Girvan-Newman algorithm splits the weighted network of AMP-bound complex into 11 communities and ATP-bound complex into 12 communities. For each nucleotide-bound state, we have also created community network separately for two randomly selected data sets, each comprising half the length of the entire simulation data (1.9 µs). The corresponding Rand index values (Table S2) suggest that they are very similar to the network derived from the entire data, which again emphasizes the convergence of our simulations.

Figure 7. Community network analysis. Coarse-grained community network structure of the (A) AMP-bound and (C) ATP-bound heterotrimeric complex of AMPK, where the node size represents the number of residues in a


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community and edge thickness represents the communication strength (i.e., total betweenness). Community structures of the (B) AMP-bound and (D) ATP-bound complex are also mapped onto 3D structure of AMPK. Color representations are same in both 2D and 3D community structures.

Major rearrangements in communities are observed in KD and near α-linker region. It is well known that the stability of αC-helix of KD is crucial for catalytic activity.60 The αC-helix contains the regulatory spine (R-spine) residue RS3 (Leu70) and the position of this helix between N-lobe and C-lobe makes it a very important structural motif to control the dynamic behavior of the entire kinase core. It also contains a conserved residue Glu66, which forms an ion pair with Lys47 that coordinates α and β phosphates of ATP molecule in the kinase core. Any alteration in αC-helix position affects the integrity of these interactions and leads to a change in kinase activity.61, 62 The αC-helix shows weak interactions with other parts of KD in the ATP-bound AMPK complex, and one new community A' consisting of αC-helix has emerged in addition to the communities A and B of KD (Figures 7 and 8). The community C (yellow color) represents the AID in both the nucleotide bound states (Figure 7). The community D represents the α-linker region in ATP-bound state, whereas in the AMP-bound state, the α-RIM2 helix of α-linker region merged with the community G as it strongly interacts with the regulatory γ-subunit (Figures 7 and 8). Community structure in γ-subunit of AMP- and ATP-bound complex of AMPK is consistent with its modular nature where four CBS (CBS1-4) motifs of γ-subunit forming four communities (Figures S7A and S7B). However, the modular nature of CBS motifs of γ-subunit is more evident in the ATP-bound state (Figure S7B). This suggests that AMP binding at site 3 helps in establishing strong inter-CBS communications. The scaffolding β-subunit, which interacts with KD, α-linker, and the γ-subunit, is shared by the communities E, I, M and D (Figure 7). 21 ACS Paragon Plus Environment


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Importantly, the β-loop residues of β-subunit are members of two communities D (α-linker) and G (CBS1) in AMP-bound state, whereas they solely belong to community D in ATP-bound state (Figure S7C and S7D). This clearly indicates that the β-loop strongly communicates with αlinker region as well as the γ-subunit in AMP-bound state. The flow of information (thickness of lines) from regulatory γ-subunit to the active site (KD) through communities shared by the residues of β-subunit and α-CTD (E and J in case of AMP and only E in case of ATP) is more in case of AMP-bound state (Figure 7). The community C, representing the AID, shares seven residues (334-340) of α-RIM1 in both the nucleotide bound states. In AMP-bound state, the communities I, D, and H communicates with C via residue pairs Glu293(α)-Lys261(β) (communities C and I), Lys340(α)-Asp341(α) (communities C and D), and Ala339(α)-Leu178(γ) (communities C and H); whereas, in the ATP-bound state, though the community D interact to C via the same residue pair and I via Lys340(α)-Tyr343(α), the communications between C and H is completely lost. This suggests that the γ-subunit has less control over AID, thereby increasing its ability to interact with KD (Figures 7, S7E and S7F). The community map analysis therefore clearly shows that weakening of the communication of α-linker region with γ-subunit helps the AID to move closer to KD, and further, KD has lost its modular structure with introduction of a new community A' in the ATP-bound state.


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Figure 8. Community structure displayed as 3D cartoon representation. (A) and (B) near nucleotide binding site 3 and (C) and (D) in kinase domain. (A) and (C) are for AMP-bound complex and (B) and (D) are for ATP-bound AMPK complex. Colors of communities are same as in Figure 7.

Intercommunity information flow depends on the nodes (critical residues) that connect them and mutations in these sites can disrupt communications between distant communities.46, 63-67 Table S3 shows the critical residues located at the interface of neighboring communities for both the nucleotide bound states. The α-linker regions (α-RIM1 and α-RIM2) play important role in 23 ACS Paragon Plus Environment


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allosteric regulation; α-RIM1 interacts with CBS2 of γ-subunit, whereas α-RIM2 interacts with adenine nucleotide binding site 3 (CBS3). Both community network and force distribution analysis shows that the conserved residues Glu-364(α-RIM2) and Arg-70(γ) are important for regulation of AMPK activity (Tables S3 and S4). These charged residues, making salt-bridge interaction with each other, are close enough to adenine binding site 3 to distinguish between nucleotides AMP and ATP (Figures 9A and S8). Further, they communicate with closely placed negatively charged residue Asp-226(β-loop) of β-subunit, which is also found to be a critical residue in community network analysis (Figures 9A, S8, and Table S3). The ionic interaction between Glu-364(α-RIM2) and Arg-70(γ) establishes a strong association of α-RIM2 with CBS3 of γsubunit. In fact, they were members of the same community in AMP-bound state but in ATPbound state, they belong to two different communities (Figure 8). The additional negative charges of ATP molecule destabilize the negatively charged residue Glu-364(α-RIM2), which is evident in the increased distance between the Cα atom of Glu-364(α-RIM2) and Pα atom of adenosine nucleotides (Figures 9B and S9A). The β-loop, which is in close proximity of both αRIM1 and α-RIM2, also plays an important role in AMPK allosteric regulation. Notably, the detachment of α-RIM2 from CBS-3 in ATP bound state pushes the β-loop upward (Figures 9C and S9B), and the concerted movement of α-RIM1 and the nearby segment of β-loop in turn helps in releasing the α-RIM1 from the γ-subunit (Figure 9D).


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Figure 9. (A) Position of residues Glu364(α-RIM2), Asp226(β-loop), and Arg70(γ) with respect to site-3 AMP nucleotide in crystal structure (PDB 4RER); distances in 3D structure are shown in Å. (B) Distribution of distance between Cα atom of Glu364(α-RIM2) and Pα atom of AMP or ATP, (C) distribution of distance between Cα atom of Asp226(β-loop) and Pα atom of AMP or ATP, (D) distribution of distance between the center of mass of α-RIM1 residues (341-345) and β-loop residues (224-227).

Global conformational motions of Kinase Domain The PCA was carried out for both the nucleotide bound states to identify the differences in collective motions in KD. The first two PCs show significant contributions to the total variance observed in the simulation data. In both AMP- and ATP-bound state, the first dominant PC1 captures the “breathing” motions responsible for opening and closing of the N- and C-lobes (Figure 10A and B). The inter-lobe movements due to the ‘breathing’ motions were also observed for other active kinases.68, 69 Whereas, the second dominant PC2 represents the twisting 25 ACS Paragon Plus Environment


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motion of KD. Interestingly, the PC2 shows large movements near the catalytic site and in αC helix region in the ATP-bound state (Figure 10C and D). These motions could be responsible for differences in catalytic efficiency of the two states. In fact, the PCA results corroborate with the findings of force distribution and community network analysis.


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Figure 10. PCA results of KD. Motions (porcupine plots) along the first principal component (PC1) calculated for kinase domain from all of the simulated trajectories of (A) AMP-bound and (B) ATP-bound AMPK complex; motions along the second principal component (PC2) calculated for kinase domain from all of the simulated trajectories of (C) AMP-bound and (D) ATP-bound AMPK complex. Length and direction of porcupine plot show the deviation of each Cα atom from one extreme conformation to another extreme conformation along PC1 and PC2.

DISCUSSION Activation of AMPK heterotrimeric complex depends on two factors: phosphorylation of Thr172/Thr174 by upstream kinases and the binding of AMP at regulatory γ-subunit (i.e., allosteric activation).1, 15 The γ-subunit has two nucleotide exchangeable sites: site 1 and site 3. Recently, it has been shown that the AMP, ADP, and ATP can bind competitively at both sites 1 and 3 with similar affinity, although the site 3, responsible for the allosteric activation, shows at least 30- to 40-fold lower affinity than the site 1 for all three nucleotides.10, 22 Pervious experimental data suggested that that the γ-subunit can have a maximum of two ATP molecules at a time. AMPK co-crystalized with AMP was able to exchange AMP with ATP at site 1 and site 3 when soaked in ATP solution and site 4 remained non-exchangeable, while site 1 and site 4 of the γ-subunit were occupied by ATP and site 3 was empty when AMPK was co-crystalized with ATP.12 In this study, we have presented molecular insights into allosteric communications between KD and γ-subunit due to different nucleotide binding at site 3 with the help of extensive molecular dynamics simulations. Our results are in line with available experimental data and provide a more detailed atomistic picture of allosteric regulation of AMPK. Previous models based on experimental studies suggested that the binding of ATP to γ-subunit of the AMPK keeps α-catalytic subunit in its autoinhibited inactive conformation via AID



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interactions, and binding of AMP to γ-subunit triggers AMPK activation by releasing AID from the KD.50, 70 Recent crystal structures of AMP-bound activated AMPK and mutational studies provided valuable information about the activation of the heterotrimeric complex of AMPK, and suggested that the regulatory region of α-subunit (α-RIM2/α-hook) and β-loop sensed the different nucleotide bound states, and disengages the AID from the KD after binding of AMP.11, 13, 52

A recent study used tagged AMPK for AlphaScreen luminescence assay, which can sense

the proximity of one domain to another, to examine the inactive to active transition of the enzyme, and observed that the KD and the γ-subunit moves closer to each other forming a more compact structure in presence of AMP.23 Our results also show that the AMP-bound complex is more compact in comparison to ATP-bound complex, and the COM distance between KD and γsubunit has increased in ATP-bound state (Figures 2 and S2). Crystal structure of human KDAID fragment has only been solved very recently after the introduction of entropy-reducing mutation K43A that does not affect kinase activity.23 Previous studies suggested that AID play a central role in mammalian AMPK regulation.52, 70, 71 The results of the present study also show that binding of AMP stabilizes the regulatory region of α-subunit (α-RIM1 and α-RIM2) to keep AID away from KD while binding of ATP destabilizes the α-linker region, which could help the AID to interact with the KD. Our simulation results also show the enhanced flexibility of activation loop in ATP-bound state (Figure 6), which suggests that the presence of ATP could trigger motions from an open-active loop state in the KD to a closed-inactive loop state. After visual inspection of the extended trajectory of each state, we found that this enhanced flexibility is due to increase in flexibility of the KD and AID connecting loop (residues 280-290) (Figure S10A) and N-terminal residues 206-214 of β-loop (Figure S10B). The ATP binding at site-3 affects the stability of α-linker 28 ACS Paragon Plus Environment

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region as well as the β-loop (Figure 9). The higher fluctuations of α-linker and β-loop caused due to binding of ATP at site 3 propagate to the β-loop region (residues 206-214), which is very close to the activation loop and C-lobe residues 190-200 of the KD. These high fluctuations in βloop region (residues 206-214) further propagate to the activation loop and C-lobe residues 190200 of KD (Figure S11A-C). Moreover, the higher fluctuations of α-linker region due to ATP binding also propagate to the C-terminal of KD via AID and can be seen as enhanced RMSD of the KD and AID connecting region (Figure S11D). This large motion at the C-terminal end of KD due to ATP binding helps in increasing the distance between KD and γ-subunit, and also affects the flexibility of the activation loop (Figure S11B and S11D). Thus, AMP binding enhances the stability of regulatory motifs present in different subunits, thereby making the whole complex more compact, whereas destabilization by ATP binding reflected in decrease of compactness of the whole complex. The catalytic core of all eukaryotic kinase enzymes is structurally conserved with bilobal structure and their catalytic activity is regulated by the flexibility of the structural motifs, the αChelix, the activation loop, and the movement of the N-lobe relative to the C-lobe.58, 72 The critical residues identified in FDA and network analysis of MD trajectories are found to be evolutionarily conserved, and are part of these functional motifs. Community analysis of energybased network also shows that the pair-wise interaction energy pattern for residues of these motifs (αC-helix and the activation loop) is different in AMP- and ATP-bound states, which leads to rearrangement of community structure in kinase core (Figure 8). The crystal structure of human AMPK with unphosphorylated Thr174 in the activation loop (PDB ID 4REW) was recently solved in which the overall structure of kinase core is very similar to the phosphorylated human AMPK.23 This indicates that a significant change in conformation of the kinase core may 29 ACS Paragon Plus Environment


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not be required for regulation of the kinase activity of AMPK. It is noteworthy that previous study on cAMP-dependent protein kinase observed a 30-fold decrease in activity due to Y204A

mutation; however, a Cα root mean-square deviation of only 0.4 Å was observed between the wild type and mutant structures of the catalytic subunit.73 In another study, a very similar

structure of active and inactive state ERK2 enzyme was also observed.57,

74

These studies

therefore suggest that small local changes in structure can cause significant changes in kinase activity. In fact, we observed increased fluctuations in the functionally important regions, αChelix that includes the RS3 residue of R-spine and the activation loop of KD in ATP-bound state. Our results demonstrated that, although the average structure of kinase core is very similar in both AMP- and ATP-bound state but the difference in residue-residue pairwise interaction energy pattern and high flexibility of important structural motifs could be responsible for the kinase activity regulation. To respond quickly to the change in the cellular energy status, AMPK requires long-range allosteric communications between the KD and γ-subunit. Our results suggest that AMP binding at site 3 of γ-subunit caused increased intra- as well as inter-subunit correlated movements and brings the KD and γ-subunit closer to increase the overall compactness of AMPK complex. The ionic interaction network formed by the residues of site 3, α-RIM2, and β-loop senses different nucleotide binding states and allosterically modulates the kinase activity by affecting the stability of AID, αC-helix, and the activation loop of α-subunit. Taken together, the results presented here are in good agreement with experimental findings and shed important insights on the molecular mechanism of allosteric regulation in AMPK.


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CONCLUSION In the present study, we have investigated computationally the canonical allosteric activation mechanism of human AMPK in which nucleotide binding at regulatory γ-subunit affects the activity of the kinase core of α-subunit. We have shown how different nucleotide binding at site 3 of γ-subunit modulates the local and global motions at intra- and inter-subunit level to regulate the kinase activity of this enzyme. The binding of AMP increases the compactness of the whole AMPK-complex by enhancing the stability of regulatory motifs present in different subunits, whereas destabilization of regulatory motifs by ATP binding reflected in decrease of compactness of the whole complex. Moreover, our results suggest that the enhanced fluctuations observed near the catalytic region in ATP-bound state may lead to a difference in catalytic activity. Overall, our results are consistent with available experimental data and could be useful for the design of allosteric AMPK activators.

SUPPORTING INFORMATION Figures S1-S11 and Tables S1-S4, providing additional details concerning the results obtained in this study. This material is available free of charge via the Internet at http://pubs.acs.org.

Author Information *

Corresponding Author

E-mail: [email protected] Phone: +91-755-669-1319

Notes The authors declare no competing financial interest.



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ACKNOWLEDGMENTS This work was performed using the high performance computing resources of IISER Bhopal. N.A. was supported by the research fellowship provided by IISER Bhopal.

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TOC Graphic


Molecular Mechanism of Nucleotide-Dependent Allosteric Regulation

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