Viral Evolved Inhibition Mechanism of the RNA Dependent Protein Kinase PKR's Kinase Domain, a Structural Perspective

K. Hari Krishna; Yallamandayya Vadlamudi; Muthuvel Suresh Kumar

doi:10.1371/journal.pone.0153680

Abstract

The protein kinase PKR activated by viral dsRNA, phosphorylates the eIF2α, which inhibit the mechanism of translation initiation. Viral evolved proteins mimicking the eIF2α block its phosphorylation and help in the viral replication. To decipher the molecular basis for the PKR’s substrate and inhibitor interaction mechanisms, we carried the molecular dynamics studies on the catalytic domain of PKR in complex with substrate eIF2α, and inhibitors TAT and K3L. The studies conducted show the altered domain movements of N lobe, which confers open and close state to the substrate-binding cavity. In addition, PKR exhibits variations in the secondary structural transition of the activation loop residues, and inter molecular contacts with the substrate and the inhibitors. Phosphorylation of the P+1 loop at the Thr-451 increases the affinity of the binding proteins exhibiting its role in the phosphorylation events. The implications of structural mechanisms uncovered will help to understand the basis of the evolution of the host-viral and the viral replication mechanisms.

Citation: Krishna KH, Vadlamudi Y, Kumar MS (2016) Viral Evolved Inhibition Mechanism of the RNA Dependent Protein Kinase PKR's Kinase Domain, a Structural Perspective. PLoS ONE 11(4): e0153680. https://doi.org/10.1371/journal.pone.0153680

Editor: L. Michel Espinoza-Fonseca, University of Minnesota, UNITED STATES

Received: February 4, 2016; Accepted: April 1, 2016; Published: April 18, 2016

Copyright: © 2016 Krishna et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: The authors have no support or funding to report.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Protein Kinase R (PKR) or Eukaryotic translation initiation factor 2-alpha kinase 2 (EIF2AK2) is an interferon-induced protein, activated by the presence of double stranded RNA (dsRNA) plays a critical role in anti-viral and anti-proliferative defense mechanisms at the cellular levels [1]. PKR belongs to a group of kinases that block the protein synthesis in response to stress signals by the phosphorylation of the α-subunit of translation initiation factor eIF2 [2]. Presence of dsRNA amid viral invasion, cytokine and growth factor deprivation are the principal stress signals induced for the PKR activation [3, 4]. The α-subunit of the translation initiation factor eIF2, a GTP bound protein initiates the first step of the translation mechanism of transferring the methionyl-tRNA to the small ribosomal subunit [5]. PKR blocks protein synthesis by specific phosphorylation of eIF2α at Ser51 modulating the substrate to an inhibitor of its GDP-GTP exchange factor eIF2B [6].

PKR, a 551 amino acid protein consists of a N-terminal dsRNA binding regulatory domain (amino acids 1–170), a C-terminal kinase (amino acids 261–551) catalytic domain and a central region of incognito function. Like all eukaryotic protein kinases, PKR has a smaller, more dynamic amino-terminal lobe (N lobe) and a larger, stable, mostly helical carboxyl-terminal lobe (C lobe) [7]. The N lobe consists of a twisted five-strand antiparallel β sheet (denoted β1 to β5); two helices, α1 and α2 flank the grooves of the β sheet. The C lobe comprises of two paired antiparallel β strands (β7-β8 and β6-β9) and eight α helices (αD to αJ). The activation segment (residues 432–458) positioned between αE and αEF in the lower catalytic lobe serves the Phospho regulatory function. Three very stable helices (αE, αF, and αH) form the core of the C-lobe, whereas the αG-helix, in contrast, is more solvent exposed [8].

Virus, precisely pathogenic forms have evolved novel molecular mechanisms to impair the potent antiviral role of the PKR. Direct interaction with PKR, dsRNA sequestration, PKR pseudo activator and PKR pseudo substrate are the prominent molecular mechanisms of virus involved in countering the PKR role [9]. Proteins K3L of vaccinia virus [10, 11] and TAT of HIV [12, 13] competitively block eIF2α phosphorylation by mimicking the three-dimensional structure of eIF2α and its mode of interaction with PKR.

PKR employs a bipartite mechanism of substrate recognition in recognizing its substrate eIF2α. Phosphorylation sites at Thr-446 and Thr-451 [14] which lie within the activation loop between kinase sub domains VII and VIII play a vital role in the phosphorylation events. T451A mutation inactivates the kinase activity of PKR, while T446A substitution of PKR was partially functional which remains unexplained at the protein structure level [15]. The mechanism by which the viral inhibitors induce the conformational changes and inhibit the PKR interactions remains unclear.

The infections caused by Human Immunodeficiency Virus (HIV) and Hepatitis C Virus (HCV) rank as two of the most important public health problems worldwide [16, 17]. Hundreds of millions of people are infected with either HIV or HCV [18–20], and co-infection with both viruses represents a growing concern that dramatically complicates patient treatment and infection outcome. Understanding the nature and mechanisms of these host pathogen interactions has the potential to unveil novel targets for therapeutic intervention, as well as inform rational vaccine and adjuvant development aimed at protecting against infection by these viruses [21]. We employed Molecular Dynamics Studies (MDS) to understand the molecular interactions and mechanisms involved between PKR and substrate eIF2α including viral inhibitors TAT and K3L.

Materials and Methods

Protein Preparation and Docking Studies

The crystal structure of the PKR kinase domain of X-ray structure of the complex of PKR kinase domain-eIF2alpha (PDB entry 2A1A) [22] and the solution structure of the alpha subunit of human eIF2α (PDB entry 1Q8K) [23] were obtained from the PDB database. To model viral PKR inhibitory protein complexes, NMR structures HIV-1 TAT (PDB entry 1TBC) [24] and crystal structure of the K3L protein from Vaccinia virus (PDB entry 1LUZ) [25] were also retrieved from the PDB database. The missing residues of the proteins were filled using the Modeler Version 9.14 software [26, 27] and the resulting structures were minimized for their energy levels.

To investigate the interaction patterns of PKR with substrate eIF2α, inhibitors K3L and TAT we performed, data driven docking with the HADDOCK server [28, 29] which is ranked as best server in both prediction and scoring capabilities at the CAPRI meetings[30]. The interacting residues of the crystal structure 2A1A were used as the active site residues for PKR and the interacting residues of eIF2α Saccharomyces cerevisiae were mapped to Homo sapiens eIF structure using sequence homology. Similarly, the interacting sites of K3L [25] and TAT [31] were retrieved from literature study and docked with the above-mentioned active site of PKR as they inhibit by a competitive inhibition mechanism. The complete lists of the active residues of the proteins involved in the docking were shown in the S1 Table. Passive residues were selected automatically as the exposed surface neighbors of the active residues.

Guru Interface of HADDOCK 2.2 software was used to generate the 1000 models at the first iteration using docking rigid body EM. The best 200 structures (based on energy) were then allowed to perform a second iteration of three simulated annealing refinements. In first iteration, the proteins are considered as rigid bodies and their respective orientation is optimized. In the second simulated annealing, the side chains at the interface are allowed to move. In the third simulated annealing, both side chains and backbone at the interface are allowed to move for some conformational rearrangements. The HADDOCK server in the explicit solvent energy minimization refined the resultant 200 structures. The final stage consists of a gentle refinement by heating the system to 300 K and cooling the system to 100K. The final structures were clustered using the pairwise backbone RMSD at the interface. Upon cluster-structural analysis, 10 lowest energy models were selected, and among these, the best one was characterized based on the lowest HADDOCK score, electrostatic energy and Z-score.

Phosphorylation events of the protein complexes were exercised using the Vienna-PTM 2.0 web server (http://vienna-ptm.univie.ac.at) [32] which has automated the post-translational modifications of the pdb's. Mono-phosphorylation forms (PKR_p) of PKR complexes were generated by phosphorylating Thr on the 446^th residue to phosphothreonine. Double phosphorylation forms (PKR_pp) were generated by phophorylating Thr on 446^th and 451^th residues to phosphothreonine similar to the PKR_p. Six resultant PKR complexes of eIF2α and viral proteins were minimized for the structural variations due to the phosphorylation of the residues were used as the starting point for the MD simulations.

MD Simulations

MD simulations were performed using the 4.5.5 version of the GROMACS software (www.gromacs.org) [33] using the modified force field gromos54a7 [34] available at Vienna-PTM 2.0 web server (http://vienna-ptm.univie.ac.at). Protein complexes were solvated by SPC (Single Point Charge) [35] water molecules. Adding 4, 11, 2, 6 and 13 Cl^- ions respectively neutralized the non-neutral systems of PKR_pp-K3L, PKR_pp-TAT, PKR_p-EIF, PKR_p-K3L and PKR_p-TAT. Periodic boundary conditions were implemented in all directions, using a simulation cell with a distance minimum of 1.5 nm from the protein. The non-pair list was updated for every five steps of the md run. The grid neighbor searching method was applied in the simulation with a 1 nm cutoff distance for the short-range neighbor list. Electrostatic interactions were computed using the particle mesh Ewald (PME) [36] summation method with a 1nm short-range electrostatic cutoff. The short-range cutoff used for van der Waals interactions during the simulation was also 1 nm. For the temperature coupling, the solvated structures were divided into two groups (protein and non-protein) using velocity rescaling with a stochastic term. The isotropic pressure coupling was employed in the MD simulation by using the Parrinello–Rahman method [37] with a compressibility of 4.5 × 10⁻⁵ bar^-1. During the simulation, constraints were deployed in all bands using the LINC algorithm [38], with parameters LINC-order of 4. First, each system was energy-minimized for 5,000 steps each using steepest-decent and conjugate gradient algorithms; subsequently, the solvent, and ions were equilibrated for 100 ps in constant volume (NVT) and constant pressure (NPT) ensembles, respectively, while the heavy atoms of the protein were restrained harmonically using a force constant of 1000 kJ mol⁻¹nm⁻². Finally, six systems were performed for 50 ns MD simulations after removing all the restraints and all the trajectories were stored every 0.002 ps for further analysis.

Principal Component Analysis

Principal component analysis (PCA) reveals the functionally important high-amplitude concerted motion of bio-molecules by applying the dimensional reduction method [39]. In MD simulations, the cross-correlation fluctuations between atoms can be described by the covariance matrix. The element C_ij in the matrix is defined as: (1) where ΔR_i, ΔR_j is the displacement vector corresponding to i^th, j^th atom of the systems and ⟨ ⟩ indicates an ensemble average. PCA diagonalizes the covariance matrix to obtain orthogonal eigenvectors and the corresponding eigenvalues. The eigenvectors, are the principal components (PCs) which represent the directions of the concerted motions. The eigenvalues indicate the magnitude of the motions along the direction. In MD simulations, the first few PCs usually describe the slow and functional motions of a bio-molecular system. In this work, g_covar and the g_anaeig utilities of GROMACS were used to perform PCA to explore and compare the motion of the different complex systems.

Free-Energy Landscape

The free-energy landscape of a protein obtained using MD simulation technique allows exploring the conformations of proteins in different states. In the FEL, the local basins usually represent the conformational ensemble in the stable states, and barriers indicate the transient states [40–42]. Based on the cosine content, the PCs are chosen as the reaction coordinates to construct the FEL. The relative free energy between two states is calculated by (2) where k is the Boltzmann constant, T is the absolute temperature, the reaction coordinate X is the PCs, and P(X) is the probability distribution of the system along the PCs. Considering two different PCs X₁ and X₂ the two-dimensional free-energy landscapes are obtained from the joint probability distributions P(X_1, X₂) of the system.

Binding Free Energies of Protein-Protein Complexes

Molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) computational approach was used to estimate binding free energies of protein-protein complexes, which quantifies the strength of a bio-molecular interaction, involved in recognition or catalysis. g_mmpbsa tool inheriting functions from GROMACS as well as Adaptive Poisson-Boltzmann Solver (APBS) was used to calculate energy components [43]. The binding free energy of the protein with protein in solvent can be expressed as (3) where G_complex is the total free energy of the protein−ligand complex and G_protein1 and G_prtoein2 are total free energies of the isolated proteins in solvent. The free energy for each individual entity can be given (4) where x is the protein complex. <E_MM> is the average molecular mechanics potential energy in a vacuum. TS refer to the entropy contribution to the free energy in a vacuum where T and S denote the temperature and entropy, respectively. The last term <G_solvation> is the free energy of solvation.

Results and Discussion

Protein-Protein Docking and Stability of MD trajectories

The missing residues S351-S354 and S542-A551 of the kinase domain of eIF2α were modeled using Modeler program. The resultant structures of PKR were docked with the eIF2α, K3L and TAT using the HADDOCK software with the mentioned docking procedures. The best structure representing the docked complex was retained based on the lowest HADDOCK score, electrostatic energy, and Z-score. Table 1 shows the HADDOCK scores and energy values of the selected complexes. The docked PKR-eIF2α shows an RMSD of 1.31 Å from the reference pdb structure 2A1A making the dockings of the Haddock server reliable. The selected structures were then subjected to post-translation modification of phosphorylation of T446 and T451 generating the six PKR_p and PKR_pp complexes. Further analysis was carried on the trajectory files generated from the simulation of the above complexes.

Download:

Table 1. Statistical analysis of HADDOCK complexes.

The statistical values of the selected PKR complexes were indicated along with their cluster numbers. The Z-score indicates the reliability of selected complexes.

https://doi.org/10.1371/journal.pone.0153680.t001

The resultant trajectories of the MDS studies were further analyzed using root mean square deviation (RMSD), root mean square fluctuation (RMSF), and radius of gyration (Rg) using g_rmsd, g_sas, and g_gyrate GROMACS utilities respectively, were shown in Fig 1. RMSD and Radius of Gyration (Rg) values of the trajectory files produced a stable plot after 10 ns of simulation with a fluctuation range lesser than 2Å, thus providing the suitability to carry out the further analysis. eIF2α bound PKR_p and PKR_pp molecules show rmsd plots with lesser fluctuations at 4 Å and 6 Å respectively. The rmsd plots of inhibitor bound complexes show a range of fluctuations ranging from 3 Å to 7 Å except the PKR_p-TAT form which shows a lesser fluctuation at the 5 Å range due to its smaller surface area. The rmsd plots provide insight how the substrate binding equilibrated the system than the inhibitor molecules.

Download:

Fig 1. Structral stability of the PKR complexes.

The time dependent variations of PKR forms in complex with the substrates and inhibitors. (a) shows the RMSD of the backbone atoms (b) RMSF fluctuation of the PKR residues (c) Radius of gyration of PKR forms in the complexes. Residues showing higher RMSF values are marked in the figure.

https://doi.org/10.1371/journal.pone.0153680.g001

The RMSF fluctuations calculated on the trajectory of the PKR molecules provide insight to the fluctuations induced by the bound proteins in the PKR molecules. All the complexes show a similar fluctuation pattern independent of the bound protein. The Rg plots of all the protein complexes show a lesser fluctuations ranging from 2.2 Å to 2.3 Å, which shows that the protein complexes are stable. PKR_pp-eIF2α complex tends to have an Rg value greater than 0.7 nm compared to other complexes showing that the eIF2α binding to PKR_pp induces less compactness in the protein complex. Thus, PKR_pp maintains short term contacts with the eIF2α substrate in the complex and it can dissociate easily compared to the other mentioned proteins.

Molecular Interactions of PKR with Substrates and Inhibitors

Hydrogen bonding numbers in protein—protein interactions confers rigidity to the protein structure and specificity of intermolecular interactions. Fig 2 shows the numbers of Hydrogen bonds formed between PKR and proteins in the complex. TAT complex shows an increased number of H-bonds with both forms of PKR. The substrate eIF2α shows a good tendency of H bonding patterns with PKR_pp and shows a rapid fall in numbers for PKR_p. Inhibitor K3L due to the substrate-mimicking feature exhibits an equal number of H-bonds. The TAT protein interacts with the N lobe terminal α-helices and C lobe αD helices showing an increase number of H bond formation than the other protein complexes. The αG lobe and the N lobe terminal α-helices of the PKR_pp protein shows an increased H bond formation than the PKR_p protein with the eIF2α which contributes to the increased H bonds numbers of PKR_pp-eIF2α complex. The PKR_pp-K3L protein complex exhibits H bond formations between phosphorylated Thr 446 and His 47 of the K3L protein. Other protein complexes lack interactions with Thr 446 of the PKR. The double phosphorylated PKR_pp-eIF2α, PKR_pp-K3L, PKR_pp-TAT forms show H-bond formations of phosphorylated Thr with the Arg 54, Arg 56, and Ser57 of eif2α, Lys 45 of K3L; and Lys 28 and Tyr32 of TAT. The phosphorylated Thr 446 and non-phosphorylated Thr 451 PKR_p do not show any interactions with the bound substrates or inhibitors at the residual level. Stable hydrogen bonds with greater than ten percent of existence were shown in the S2 Table.

Download:

Fig 2. Number of intermolecular hydrogen bonds formed by PKR.

The plot showing the number of hydrogen bonds between PKR and the interacting proteins in a time dependent manner.

https://doi.org/10.1371/journal.pone.0153680.g002

Salt bridges contribute to the protein structure and also to the specificity of protein-protein interactions, were retrieved using the VMD [44] salt bridge plug-in. The plug-in considers the oxygen atoms of acidic residues and the nitrogen atoms of basic residues within the cutoff distance of 3.2 Å in at least one frame for salt bridge formations. The PKR_pp forms an increased number of salt bridges with substrate eIF2α than the PKR_p involving the N lobe residues Glu-342 and Asp-345,347 residues of the N lobe of PKR. The five stranded β barrel sheet of K3L protein retains the interactions with the C lobe of PKR but fails to interact with the N lobe due to its structural similarity of eIF2α, which reduces the salt bridge numbers in PKR_pp and PKR_p form. The TAT forms an equal number of salt bridges with the PKR_p and PKR_pp forms as the substrate eIF2α. The terminal residues of αG helix (residues 497, 499, 500) coordinating with the five-strand β barrel of the interacting proteins shows decreased interactions with the inhibitors K3L and TAT. Glu-28 of eIF2α and Glu-37 of K3L form salt bridges with the activation loop residue Arg-453 of the PKR, in addition Asp-23 of K3L forms salt bridges with the Arg-447 of the PKR’s activation loop. TAT inhibitor lacking the β barrel structure, does not form any charged interactions with the active site residues. Table 2 provides the insight to the salt bridges exhibited by PKR with the interacting protein partners and their percentage of stability.

Download:

Table 2. Salt bridges formed by PKR with the interacting proteins.

Residues of PKR molecules involving in salt bridge formation with the interacting proteins during the course of the simulation. The salt bridges were tabulated along with the numerical values in brackets indicating the distance between N and O atom measured in Å units and percentage of the existence of the salt bridges.

https://doi.org/10.1371/journal.pone.0153680.t002

Aromatic interactions are important in the structural stabilization of proteins, in protein-protein recognition, and in ligand binding and protein folding. Pdb structure captured at every 5 ns of the simulation was used to predict the aromatic interactions using the PIC web server [45]. The six PKR complexes exhibit the following aromatic-aromatic and cation-pi interactions shown in Table 3. The residue Phe 489 of PKR plays a key role in making aromatic-aromatic interaction with the substrate eIF2α and the inhibitors K3L and TAT. The cation-pi interactions of the PKR-eIF2α complexes were dominated by the interactions with helix αG of the C lobe whereas the inhibitors exhibit a direct interaction with the activation loop residues. The double phosphorylation of PKR reduces the number of aromatic interactions with both the inhibitors but the substrate eIF2α exhibits increased number of contacts due to the conformational changes induced by the phosphorylation at T451.

Download:

Table 3. Aromatic interactions and Cation-pi interactions of PKR with the interacting proteins.

Aromatic interactions between the PKR and the interacting proteins formed during the course of simulation within 7 A°. The cation-pi interactions of PKR with the proteins formed during the course of simulation within 6 A°. The minimum distance between the residues is indicated in the brackets.

https://doi.org/10.1371/journal.pone.0153680.t003

Binding Energy

In order to illustrate the competitive inhibition mechanism at the molecular level the trajectory files were analyzed for the binding free energies using g_mmpbsa for a time step of 250 ps, thus selecting 200 frames from the trajectory files. The resultant binding energies of six complexes were summarized in the chart. To further analyze the contribution of the PKR molecules to the binding affinity the residual contribution of each PKR residue was summarized in the plots. Both the substrates and inhibitors exhibit higher binding energy with PKR_pp forms than the PKR_p in their complexes. The TAT form shows an increased binding energy than eIF2α and K3L in both the PKR_p and PKR_pp forms proving its high inhibition activity. K3L due to its similar structural interaction mechanism of eIF2α shows similar binding energy to PKR_p-EIF2Α complex.

To further analyze the role of phosphorylation events of PKR molecules we summarized the contribution to the binding affinity of activation loop residues of PKR to the free energy of binding in the Fig 3. Activation loop residues 432, 442, 446, 451 and 458 show a major contribution to the free energy of binding for the PKR and interacting proteins. TAT shows an increase in the binding energies for the major residues 432, 442, 446 and 458 in both the PKR_p and PKR_pp forms. Thr 451 phosphorylation shows an increased binding free energy contribution than the non-phosphorylation residues with the same protein complexes. Phosphorylation of Thr 451 residue increases the binding energy of both the substrates and the inhibitors with PKR_pp contributing to increased binding energy than the PKR_p proteins.

Download:

Fig 3. Binding energies calulations of PKR with the substrate and the inhibitors.

(a) PKR binding energies with the substrate and the inhibitors. The error bars are indicated by black lines. (b) Residue based decomposition of the binding energies for the activation segment of PKR in the PKR complexes.

https://doi.org/10.1371/journal.pone.0153680.g003

Secondary Structural Variations in Active Loop

The capacity of the activation loop to undergo large conformational changes aids the kinase protein to switch between inactive and active states. The activation loop of the PKR protein is highly unorganized with most of the residue residing in the coiled regions. The activation loop harbors the DFG motif, which interacts with the ATP and Mg ions, and P, P+1 phosphorylation sites which play a crucial role in the substrate phosphorylation mechanisms. The major secondary structural transitions visualized using DSSP [46, 47] accounted for the bend to turn transitions and coil to bend transitions to a lesser extent were shown in Fig 4. The DFG motif region of PKR_pp bounded to eIF2α and TAT proteins shows a transition from turns to the 3₁₀ helices. Other complexes show structural transitions from turns to bends in the DFG motif region, which becomes less compact than the 3₁₀ helices. The S3 Table shows the percentage of the secondary structures of both the activation loop and the PKR protein in the complexes. The six protein complexes retain the percentages of the secondary structures irrespective of the PKR’s phosphorylation or the interacting protein in the complexes. The PKR_pp-eIF2α and PKR_pp-TAT proteins show an increased percentage of the residues residing in the 3₁₀ helices showing the secondary structural alteration of this loop in the above complexes. The P+1 loop residues ranging from 452 to 455 shows structural variations from bend to turn except in PKR_p-eIF2α complexes where turns were retained. These structural transitions at the P+1 loop help in directing the Thr-451 as a part of the substrate binding groove, directly involved in substrate phosphorylation mechanisms.

Download:

Fig 4. Secondary structural variations of the aloop.

The secondary structral variation of the PKR’s aloop ranging from 438–458 residues. The plots are indicated by (a) PKR_pp-eIF2α, (b) PKR_pp-K3L, (c) PKR_pp-TAT, (d) PKR_p-eIF2α, (e) PKR_p-K3L and (f) PKR_p-TAT. The legend indicates the colors used to represent the secondary structural variations.

https://doi.org/10.1371/journal.pone.0153680.g004

The contact surface area between the PKR protein A loop and the binding proteins were calculated using g_sas. The resulting plot shows a steep increase in the residual surface area of the PKR_pp interacting with eIF2α from 20 ns, which shows matches with the secondary structural alterations of DFG site. These alterations of DFG site further induce the formation of a 3₁₀ helical conformation in the LRY (452–454) residues, which contributes to the increased contact surface area S1(A) Fig. The PDB files of PKR_pp-eIF2α were clustered based on the RMSD variations of the activation loop with the g_cluster program of GROMACS with a RMSD cutoff of 0.5 nm and using gromos method for cluster determination. These transformations induce Leu 452, Arg 453 and Tyr 454 residues of PKR_pp to form novel contacts with the Leu 28 and Arg 29 which are lacking at the start of the simulation as shown in S1(B) Fig. The other complexes retain the coil/bend configuration proving the necessity eIF2α binding and Thr 451 phosphorylation in inducing the secondary structural transitions in the activation loop. These DFG and substrate induced conformational changes of the activation loop promotes the phosphorylation of the substrates by making the P loop and P+1 loop phosphorylation sites accessible to the substrates [48–50].

PCA of the Protein Complexes

PCA performed on the simulated ensembles of the six systems as described in methods determines the concerted conformational motions relevant to protein function. The first few eigenvectors from the PCA of the trajectories captured the bulk of the protein dynamics. Fig 5(A) shows the cumulative percentages of the proportional variance contributed by the eigenvectors of the PKR complexes. The first three eigenvectors PKR_pp-eIF2α complex capture ninety percent of variance in the protein motions. The other protein complexes require greater number of eigenvectors to capture the similar variance in the PKR motions. This shows that the binding of the substrate eIF2α binding with the PKR_pp is more stable than the PKR_p form. The inhibitors binding with the PKR forms show a relatively lesser variation than the substrate binding, suggesting that the inhibitors form a stable complex with the PKR proteins.

Download:

Fig 5. Eigenvector calulations to predict the stability and structral variations of the simulated complexes.

(a) Figure showing the cumulative percentages of the proportional variance contributed by the eigenvectors of the PKR complexes. (b) 2D plot of the projections of the eigenvector 1 and eigenvector 2 showing the essential subspace of the complexes.

https://doi.org/10.1371/journal.pone.0153680.g005

The 2D PC graphs of eigenvector 1 and eigenvector 2 characterizes the essential subspace of the protein complexes, a characteristic of the different folded conformation of the protein [51, 52] were shown in Fig 5(B). The PKR_p in complex with the substrate and inhibitor exhibited lower values than the PKR_pp complex suggesting that the phosphorylation of the Thr 451 residue has a destabilizing effect on the protein complexes. TAT inhibitor bound to PKR_p shows minimal conformation variations due to the few conformational changes it induces in the PKR molecule. The TAT and K3L inhibitors bound PKR occupy a fewer essential subspace in the 2D PC plots than the substrate eIF2α with both the PKR_p and PKR_pp proteins demonstrating that the inhibitors form a stable complex with the protein PKR irrespective of the number of the phosphorylated sites. The eIF2α bound to PKR_pp occupies a larger conformational space than the PKR_p protein, which exemplified the structural variations induced by the phosphorylation of Thr 451 in the complexes.

Free Energy Landscape

The first two principal component vectors were considered for the construction of the free energy landscapes (FELs). Free energy landscapes of the PKR protein complexes generated were shown in Fig 6. The higher peak in the energy profile reflects the higher stable protein complexes in the simulation trajectory. The FELs were quite different among the six systems; in particular, their energy bins were located in different regions. The PKR_pp-eIF2α complex shows a two prominent energy bins on the left side and an energy bin with lesser depth energy bins on the right side. The PKR_p-eIF2α has a widespread energy bin on the left side and a discontinuous energy bin spanning on the right side. The PKR_pp-K3L has a smaller energy bin on the left side and two-energy bins one spanning lesser area and the other having a wider area with deeper energy bin was on the right side. The PKR_p-K3L energy bins on the right side have a smaller spanning area and on to the right side was a major spanning bin with discontinuous energy distributions. The TAT complexes with the PKR_pp and PKR_p have energy bins spanning from the left side to the left with discontinuous energy distributions. The two least energy conformations depicting the major conformational states of the complexes were retrieved from the trajectory file and used for further analysis.

Download:

Fig 6. Free energy landscapes of the PKR protein complexes.

The plots are indicated by (a) PKR_pp-eIF2α, (b) PKR_pp-K3L, (c) PKR_pp-TAT, (d) PKR_p- eIF2α, (e) PKR_p-K3L, (f) PKR_p-TAT. Conformations having lower energies retrieved from the Free energy landscapes are marked with a dot in the square. The energy bars indicate binding free energy in KJ/mol.

https://doi.org/10.1371/journal.pone.0153680.g006

Electrostatic Interactions of the Binding Surface

The electrostatic potential maps of the binding surface area along with the aloop of PKR protein for the above selected structures from free energy landscape plot were generated using the Delphi [53] plugin of UCSF Chimera [54] program. The maps show an altered potentials for the same protein derived from the varied energy bins. The electrostatic potential maps shown in the Fig 7 provide insight to the distribution of the charge potentials on the binding surface. The PKR_pp forms show an increased electropositive potential in the PKR interaction surface along with its activation loop than that of the PKR_p forms. The eIF2α substrate shows a high-density electronegative region in both the PKR forms, which aids in the retention of the binding interaction with the PKR forms. The inhibitors proteins show a minor electronegativity patch with a lesser variations on the electron density scales. In addition the inhibitors are dominated by a predominant electropositive surface area similar to the PKR forms which inhibits the transfer of a charge moiety. On the other hand, the substrate eIF2α maintains good electro potential balance in both the PKR complexes. The PKR_pp form comparable to PKR_p form shows an increased electropositive nature due to the presence of additional phosphate group which helps in the phosphorylation of the eIF2α.

Download:

Fig 7. Electrostatic potential maps of the binding surfaces of PKR complexes.

The electrostatic potential maps of the binding surfaces along with the activation loop of PKR are shown for the selected stuctres from the free energy landscape plot. Figs 1, 3 and 5 indicate PKRpp bound to eIF2α, K3L and TAT. Figs 2, 4 and 6 indicate PKRp bound to eIF2α, K3L and TAT. The region in red color indicate high electronegative regions and the regions in blue color indicate electropositive regions. The color bars are calibrated in kcal/mol/e.

https://doi.org/10.1371/journal.pone.0153680.g007

Domain Motion Analysis

The substrates and the inhibitors bound to the protein in the complex induce the conformational transitions hinge bending which involves the movement of relatively rigid parts of a protein about flexible joints. Hingefind [55] an algorithm partitions a protein into domains of preserved geometry and subsequently characterizes the relative movements of the found domains by effective rotation axes (hinges). The residues involved in the domain organization are the axis of rotation of the Domain II and I pass through the N lobe of the PKR molecule, which exemplifies that the conformational change of domain opening in the PKR protein was induced, by the flexible N lobes. Fig 8 shows the domain motion of the PKR protein along with their rotational directions.

Download:

Fig 8. Domain movements of PKR in the protein protein complexes.

Effective rotation axes and perpendicular centroid-connecting lines are rendered as tubes in the color of the corresponding domain. The arrows indicate a left-hand rotation, indicating a shift in the center of mass of the domain from the first structure to the second structure. The plots are indicated by (a) PKR_pp-eIF2α, (b) PKR_pp-K3L, (c) PKR_pp-TAT, (d) PKR_p-eIF2α, (e) PKR_p-K3L, (f) PKR_p-TAT. Reference domain, Domain 1 and Domain 2 are indicated by Blue, Red and Black respectively. The arrows drawn indicate the direction of the domain motion.

https://doi.org/10.1371/journal.pone.0153680.g008

The four protein complexes PKR_pp-eIF2α, PKR_p-eIF2α, PKR_p-K3L and PKR_p-TAT organized into Domain 1 and Domain 2, whereas the PKR_pp inhibitor complexes with K3L and TAT have a single Domain. The N lobe residues induce a major conformational variation of the PKR in the protein complexes and contribute to a major portion Domain I and Domain II, whereas in PKR_p-K3L and PKR_p-TAT the C lobe contributes to the domain organization. The domain motion of PKR in PKR_pp-eIF2α and PKR_p-eIF2α complexes were directed away from C lobe with an angular twist of 12.0° and 17.0° respectively inducing an open conformation to the PKR molecule irrespective of the phosphorylation sites. The Domain I of the four-inhibitor complexes of PKR shows an inward movement leading to the closure of PKR binding cavity and inducing a conformational change [56, 57]. The coil regions bridging the β sheets in the N lobe organize to form Domain 2 showing a higher effective angular rotation, which induces the conformational changes in N lobe region. The residues involved in the domain organization and the angular rotation of the domain were mentioned in the S4 Table.

Activation loop of the PKR plays a key role in the molecular phosphorylation events by interacting with the Ser51 of eIF2α. The inhibitors bound to the PKR forms induce an inward closure domain motion, which pushes the activation loop inside, making it inaccessible to the substrates further binding with the PKR forms. The substrate binding induces an open conformation in the PKR form which pushes the activation loop towards the interacting substrate protein, which exposes the activation loop phosphor donor site to the Ser 51 of eIF2α aiding in the phosphorylation. The Thr 551 additional phosphorylation induces an increased conformational variation of 5° than the mono phosphorylation Thr 446 making it a favorable substrate phosphorylation site.

To further analyze the altered domain motion of the PKR protein’s N and C-lobe upon interacting with the substrate or inhibitor, a cross-correlation heat map was generated based on the PCA vectors using ProDy python package [58]. Comparing the cross-correlation matrices of different simulation trajectories reveal the similar features shown by the relative motions. Fig 9 shows the cross-correlation matrices of the six protein complexes generated using the PCA eigenvectors. Many regions of the protein, especially the regions of secondary structure, move in a correlated manner [59, 60]. A higher degree of cross correlation among the residues was observed in the PKR_p-eIF2α complex followed by the PKR_p-K3L complex.

Download:

Fig 9. Cross-correlation heat maps of the PKR protein.

Cross-correlation heat map generated using the PCA vectors showing the correlated and anti-correlated regions in the protein structure. The plots are indicated by (a) PKR_pp-eIF2α, (b) PKR_pp-K3L, (c) PKR_pp-TAT, (d) PKR_p-eIF2α, (e) PKR_p-K3L, (f) PKR_p-TAT. The P, N lobes and the activation segment are demarked by a boxed structure.

https://doi.org/10.1371/journal.pone.0153680.g009

The N-lobe of all the protein complexes shows a similar inter-residual correlation whereas the C-lobe shows higher variations in inter-residual correlations due to its interactions with the substrate or the inhibitor protein. The inhibitors’ binding induces a higher degree of negative correlation in the internal domain motions of the PKR’s C-terminal residues; in comparison, the substrate binding shows a higher degree of positive correlation in comparison to the inhibitor bound complexes. A similar induced negative correlation motion was observed in the activation segment residues in the inhibitor complexes with the C-lobe region and a lesser extent with the N-lobe region. The αG which interacts with the protein partners shows a positive correlation with the activation loop residues only in the PKR_pp-eIF2α complex and all the other protein complexes show the negative correlation.

The correlated domain motions of the PKR protein provide insight to alter domain motions of PKR in relation to the secondary structural features. The substrate binding and the phosphorylation events of the Thr-451 decreases the negative correlation between the secondary structure elements, whereas the inhibitors binding and non-phosphorylation of Thr-451 relatively induces the negatively correlated movements in the secondary structure elements of the PKR. These positively correlated movements of the PKR elements induced by the eIF2α binding play a key role in directing the N-lobe and C-lobe an outward motion aiding in the projection of the activation segment.

Conclusion

Auto phosphorylation of the PKR is a post-translational modification, which activates the protein through multiple phosphorylations of Ser and Thr residues by autocatalysis. Multiple auto phosphorylation events appear to be an integral part of eIF-2AK family protein kinase function. Contemplating the potential role of the phosphorylation sites in the functional mechanism of the kinases will help in understanding the activation and inhibition mechanisms of eIF2-AK family kinase proteins. Thr 551 residue in addition to Thr 446 residue phosphorylation increase the interactions of PKR with eIF2α in numbers of the H bond, salt bridge and aromatic interaction formed by C-lobe and activation loop of PKR with the five stranded β barrel.

The inhibitor interacted with the PKR molecules by forming stable salt bridges, H-bonds and aromatic interactions comparable to the substrate eIF2α. Five stranded β barrel structure of K3L forms stable interactions with the αG loop of the PKR molecule. TAT protein inhibits in a distinct mechanism of interactions with the PKR spanning from N-lobe to C-lobe. Distortions induced by phosphorylation of Thr-451 in the αG helix of PKR_pp forms alters its interaction pattern with the viral inhibitors by decreasing its interactions with the distorted αG helix and the activation loop residues.

The inhibitor interaction with the varied phosphorylation PKR forms shows much stability with a fewer conformational alteration whereas the substrate eIF2α shows a much varied conformational changes in the PCA plots. Though the inhibitors show lesser interactions than the substrate, the inhibitor protein complexes were stabilized with higher binding energies from the PKR protein, which involves large amounts of interaction energy contributed by the non-bonded interactions of the activation segment residues significantly by the phosphorylation of Thr 451 residue.

PC analysis of the PKR complexes gives insight to the higher conformational flexibility induced in the substrate bound complexes than the inhibitors, which prefer a stable configuration with limited conformational changes. The substrates and the inhibitors induce an altered opening and closing domain motion by inducing changes in the C-lobe of PKR protein. The inhibitor characteristic properties of inducing the conformational stability and closing domain motion offer an insight to the competitive inhibition of PKR by viral proteins and its structural alteration. The PKR_pp complexes with eIF2α and TAT molecules show a functional transition from turns to 3₁₀ helices in the DFG motif region, whereas a similar transition was seen in the P+1 loop which interacts directly with the substrate was viewed in PKR_pp-eIF2α but absent in other substrates.

The present works emphasizes on the loss or gain of contacts due to the altered phosphorylation of Thr 446 and Thr 451 residues of PKR and predicts domain motions based on the resultant structural variations. To understand the exhaustive phosphorylation and inhibition events by the substrates or the inhibitors an extensive simulation at a millisecond scale and quantum chemical calculations have to be applied on the protein complexes. The events involved in phosphorylation of eIF2α by PKR and its inhibition of cellular translation mechanism remain vague. An insight into the inhibition mechanisms evolved by the virus to control the above process will help to regulate the viral replication mechanisms in host tissues. The molecular dynamics studies provide insight into the role of auto phosphorylation of Thr 451 in substrate and inhibitor recognition mechanisms. Varied interaction patterns of viral inhibitors with PKR_p and PKR_pp forms discussed will help us to understand the host-viral interaction mechanisms.

Supporting Information

S1 Fig. Contact surface calulations between PKR and the interacting proteins.

(a) The time course vaiation ofthe surface area in contact between the PKR and the interacting prtoeins. (b) The newly formaed contacts between the PKR and eIF2α due to the indcution of 3₁₀ helix in the DFG loop

https://doi.org/10.1371/journal.pone.0153680.s001

(TIF)

S1 Table. Active residues of proteins involved in docking.

The residues of the proteins tabled below are defined as the active site residues for protein-protein docking in the HADDOCK server.

https://doi.org/10.1371/journal.pone.0153680.s002

(DOCX)

S2 Table. Hydrogen bonds formed by PKR protein in the six complexes.

Hydrogen bonds formed between the PKR protein and interacting proteins having more than ten percentage of existence during the course of the simulation. The values indicate the percentage of existence of the Hydrogen bonds.

https://doi.org/10.1371/journal.pone.0153680.s003

(DOCX)

S3 Table. Secondary structural variations of PKR protein.

The percentages of PKR residues and the Aloop residues in different secondary structural regions in the six protein complexes. A major change can be observed in the 3₁₀ helix formations of the Aloop.

https://doi.org/10.1371/journal.pone.0153680.s004

(DOCX)

S4 Table. Domain motion contributing residues.

Residues involved in the domain formation, which helps the PKR protein to rotate, and their effective rotational angles are indicated.

https://doi.org/10.1371/journal.pone.0153680.s005

(DOCX)

Acknowledgments

We are very thankful to Bioinformatics Resources & Applications Facility (BRAF) at C-DAC, Pune for providing high-end supercomputing facility for carrying out the simulations.

Author Contributions

Conceived and designed the experiments: KHK MSK. Performed the experiments: KHK MSK. Analyzed the data: KHK MSK. Contributed reagents/materials/analysis tools: KHK MSK. Wrote the paper: KHK YV MSK.

References

1. Meurs E, Chong K, Galabru J, Thomas NS, Kerr IM, Williams BR, et al. Molecular cloning and characterization of the human double-stranded RNA-activated protein kinase induced by interferon. Cell. 1990;62(2):379–90. pmid:1695551.
- View Article
- PubMed/NCBI
- Google Scholar
2. Proud CG. eIF2 and the control of cell physiology. Seminars in cell & developmental biology. 2005;16(1):3–12. pmid:15659334.
- View Article
- PubMed/NCBI
- Google Scholar
3. Cheshire JL, Williams BR, Baldwin AS Jr. Involvement of double-stranded RNA-activated protein kinase in the synergistic activation of nuclear factor-kappaB by tumor necrosis factor-alpha and gamma-interferon in preneuronal cells. The Journal of biological chemistry. 1999;274(8):4801–6. pmid:9988719.
- View Article
- PubMed/NCBI
- Google Scholar
4. Goh KC, deVeer MJ, Williams BR. The protein kinase PKR is required for p38 MAPK activation and the innate immune response to bacterial endotoxin. The EMBO journal. 2000;19(16):4292–7. pmid:10944112; PubMed Central PMCID: PMC302024.
- View Article
- PubMed/NCBI
- Google Scholar
5. Dever TE, Feng L, Wek RC, Cigan AM, Donahue TF, Hinnebusch AG. Phosphorylation of initiation factor 2 alpha by protein kinase GCN2 mediates gene-specific translational control of GCN4 in yeast. Cell. 1992;68(3):585–96. pmid:1739968.
- View Article
- PubMed/NCBI
- Google Scholar
6. Hinnebusch AG. eIF2alpha kinases provide a new solution to the puzzle of substrate specificity. Nature structural & molecular biology. 2005;12(10):835–8. pmid:16205706.
- View Article
- PubMed/NCBI
- Google Scholar
7. Taylor SS, Haste NM, Ghosh G. PKR and eIF2alpha: integration of kinase dimerization, activation, and substrate docking. Cell. 2005;122(6):823–5. pmid:16179248.
- View Article
- PubMed/NCBI
- Google Scholar
8. Yang J, Garrod SM, Deal MS, Anand GS, Woods VL Jr., Taylor S. Allosteric network of cAMP-dependent protein kinase revealed by mutation of Tyr204 in the P+1 loop. Journal of molecular biology. 2005;346(1):191–201. pmid:15663937.
- View Article
- PubMed/NCBI
- Google Scholar
9. Clemens MJ. Targets and mechanisms for the regulation of translation in malignant transformation. Oncogene. 2004;23(18):3180–8. pmid:15094767.
- View Article
- PubMed/NCBI
- Google Scholar
10. Kawagishi-Kobayashi M, Silverman JB, Ung TL, Dever TE. Regulation of the protein kinase PKR by the vaccinia virus pseudosubstrate inhibitor K3L is dependent on residues conserved between the K3L protein and the PKR substrate eIF2alpha. Molecular and cellular biology. 1997;17(7):4146–58. pmid:9199350; PubMed Central PMCID: PMC232268.
- View Article
- PubMed/NCBI
- Google Scholar
11. Sharp TV, Witzel JE, Jagus R. Homologous regions of the alpha subunit of eukaryotic translational initiation factor 2 (eIF2alpha) and the vaccinia virus K3L gene product interact with the same domain within the dsRNA-activated protein kinase (PKR). European journal of biochemistry / FEBS. 1997;250(1):85–91. pmid:9431994.
- View Article
- PubMed/NCBI
- Google Scholar
12. McMillan NA, Chun RF, Siderovski DP, Galabru J, Toone WM, Samuel CE, et al. HIV-1 Tat directly interacts with the interferon-induced, double-stranded RNA-dependent kinase, PKR. Virology. 1995;213(2):413–24. pmid:7491766.
- View Article
- PubMed/NCBI
- Google Scholar
13. Brand SR, Kobayashi R, Mathews MB. The Tat Protein of Human Immunodeficiency Virus Type 1 Is a Substrate and Inhibitor of the Interferon-induced, Virally Activated Protein Kinase, PKR. Journal of Biological Chemistry. 1997;272(13):8388–95. pmid:9079663
- View Article
- PubMed/NCBI
- Google Scholar
14. Romano PR, Garcia-Barrio MT, Zhang X, Wang Q, Taylor DR, Zhang F, et al. Autophosphorylation in the activation loop is required for full kinase activity in vivo of human and yeast eukaryotic initiation factor 2alpha kinases PKR and GCN2. Molecular and cellular biology. 1998;18(4):2282–97. pmid:9528799; PubMed Central PMCID: PMC121479.
- View Article
- PubMed/NCBI
- Google Scholar
15. Zhang F, Romano PR, Nagamura-Inoue T, Tian B, Dever TE, Mathews MB, et al. Binding of double-stranded RNA to protein kinase PKR is required for dimerization and promotes critical autophosphorylation events in the activation loop. The Journal of biological chemistry. 2001;276(27):24946–58. pmid:11337501.
- View Article
- PubMed/NCBI
- Google Scholar
16. Esterhuyse T, Doyle P. AIDS update: the HIV epidemic: what lies ahead? Nursing RSA = Verpleging RSA. 1993;8(10):14–5. pmid:8264775.
- View Article
- PubMed/NCBI
- Google Scholar
17. Muhlberger N, Schwarzer R, Lettmeier B, Sroczynski G, Zeuzem S, Siebert U. HCV-related burden of disease in Europe: a systematic assessment of incidence, prevalence, morbidity, and mortality. BMC public health. 2009;9:34. pmid:19161623; PubMed Central PMCID: PMC2656539.
- View Article
- PubMed/NCBI
- Google Scholar
18. Thein HH, Yi Q, Dore GJ, Krahn MD. Natural history of hepatitis C virus infection in HIV-infected individuals and the impact of HIV in the era of highly active antiretroviral therapy: a meta-analysis. Aids. 2008;22(15):1979–91. pmid:18784461.
- View Article
- PubMed/NCBI
- Google Scholar
19. Weber R, Sabin CA, Friis-Moller N, Reiss P, El-Sadr WM, Kirk O, et al. Liver-related deaths in persons infected with the human immunodeficiency virus: the D:A:D study. Archives of internal medicine. 2006;166(15):1632–41. pmid:16908797.
- View Article
- PubMed/NCBI
- Google Scholar
20. Greub G, Ledergerber B, Battegay M, Grob P, Perrin L, Furrer H, et al. Clinical progression, survival, and immune recovery during antiretroviral therapy in patients with HIV-1 and hepatitis C virus coinfection: the Swiss HIV Cohort Study. Lancet. 2000;356(9244):1800–5. pmid:11117912.
- View Article
- PubMed/NCBI
- Google Scholar
21. Loo YM, Gale M Jr. Viral regulation and evasion of the host response. Current topics in microbiology and immunology. 2007;316:295–313. pmid:17969453.
- View Article
- PubMed/NCBI
- Google Scholar
22. Dar AC, Dever TE, Sicheri F. Higher-order substrate recognition of eIF2alpha by the RNA-dependent protein kinase PKR. Cell. 2005;122(6):887–900. pmid:16179258.
- View Article
- PubMed/NCBI
- Google Scholar
23. Ito T, Marintchev A, Wagner G. Solution structure of human initiation factor eIF2alpha reveals homology to the elongation factor eEF1B. Structure. 2004;12(9):1693–704. pmid:15341733.
- View Article
- PubMed/NCBI
- Google Scholar
24. Boehm M, Sticht H, Seidel G, Roesch P. Solution Structure of HIV-1 Tat Protein. To be Published. 1998:null-null.
- View Article
- Google Scholar
25. Dar AC, Sicheri F. X-ray crystal structure and functional analysis of vaccinia virus K3L reveals molecular determinants for PKR subversion and substrate recognition. Molecular cell. 2002;10(2):295–305. pmid:12191475.
- View Article
- PubMed/NCBI
- Google Scholar
26. Eswar N, Webb B, Marti-Renom MA, Madhusudhan MS, Eramian D, Shen MY, et al. Comparative protein structure modeling using MODELLER. Current protocols in protein science / editorial board, Coligan John E [et al]. 2007;Chapter 2:Unit 2 9. pmid:18429317.
- View Article
- PubMed/NCBI
- Google Scholar
27. Marti-Renom MA, Stuart AC, Fiser A, Sanchez R, Melo F, Sali A. Comparative protein structure modeling of genes and genomes. Annual review of biophysics and biomolecular structure. 2000;29:291–325. pmid:10940251.
- View Article
- PubMed/NCBI
- Google Scholar
28. de Vries SJ, van Dijk M, Bonvin AM. The HADDOCK web server for data-driven biomolecular docking. Nature protocols. 2010;5(5):883–97. pmid:20431534.
- View Article
- PubMed/NCBI
- Google Scholar
29. Dominguez C, Boelens R, Bonvin AM. HADDOCK: a protein-protein docking approach based on biochemical or biophysical information. Journal of the American Chemical Society. 2003;125(7):1731–7. pmid:12580598.
- View Article
- PubMed/NCBI
- Google Scholar
30. de Vries SJ, van Dijk AD, Krzeminski M, van Dijk M, Thureau A, Hsu V, et al. HADDOCK versus HADDOCK: new features and performance of HADDOCK2.0 on the CAPRI targets. Proteins. 2007;69(4):726–33. pmid:17803234.
- View Article
- PubMed/NCBI
- Google Scholar
31. Cai R, Carpick B, Chun RF, Jeang KT, Williams BR. HIV-I TAT inhibits PKR activity by both RNA-dependent and RNA-independent mechanisms. Archives of biochemistry and biophysics. 2000;373(2):361–7. pmid:10620360.
- View Article
- PubMed/NCBI
- Google Scholar
32. Margreitter C, Petrov D, Zagrovic B. Vienna-PTM web server: a toolkit for MD simulations of protein post-translational modifications. Nucleic acids research. 2013;41(Web Server issue):W422–6. pmid:23703210; PubMed Central PMCID: PMC3692090.
- View Article
- PubMed/NCBI
- Google Scholar
33. Pronk S, Pall S, Schulz R, Larsson P, Bjelkmar P, Apostolov R, et al. GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics. 2013;29(7):845–54. pmid:23407358; PubMed Central PMCID: PMC3605599.
- View Article
- PubMed/NCBI
- Google Scholar
34. Petrov D, Margreitter C, Grandits M, Oostenbrink C, Zagrovic B. A systematic framework for molecular dynamics simulations of protein post-translational modifications. PLoS computational biology. 2013;9(7):e1003154. pmid:23874192; PubMed Central PMCID: PMC3715417.
- View Article
- PubMed/NCBI
- Google Scholar
35. Berendsen HJC, Postma JPM, Gunsteren WFv, Hermans J. Interaction Models for Water in Relation to Protein Hydration. In: Pullman B, editor. Intermolecular Forces. The Jerusalem Symposia on Quantum Chemistry and Biochemistry: Springer Netherlands; 1981. p. 331–42.
36. Darden T, York D, Pedersen L. Particle Mesh Ewald—an N.Log(N) Method for Ewald Sums in Large Systems. J Chem Phys. 1993;98(12):10089–92. pmid:WOS:A1993LG10100091.
- View Article
- PubMed/NCBI
- Google Scholar
37. Parrinello M. Polymorphic transitions in single crystals: A new molecular dynamics method. Journal of Applied Physics. 1981;52(12):7182.
- View Article
- Google Scholar
38. Hess B, Bekker H, Berendsen HJC, Fraaije JGEM. LINCS: A linear constraint solver for molecular simulations. Journal of Computational Chemistry. 1997;18(12):1463–72.
- View Article
- Google Scholar
39. Amadei A, Linssen ABM, Berendsen HJC. Essential Dynamics of Proteins. Proteins. 1993;17(4):412–25. pmid:WOS:A1993MJ75700007.
- View Article
- PubMed/NCBI
- Google Scholar
40. Frauenfelder H, Sligar SG, Wolynes PG. The energy landscapes and motions of proteins. Science. 1991;254(5038):1598–603. pmid:1749933.
- View Article
- PubMed/NCBI
- Google Scholar
41. Tavernelli I, Cotesta S, Di Iorio EE. Protein dynamics, thermal stability, and free-energy landscapes: a molecular dynamics investigation. Biophysical journal. 2003;85(4):2641–9. pmid:14507727; PubMed Central PMCID: PMC1303488.
- View Article
- PubMed/NCBI
- Google Scholar
42. Wales DJ. Energy landscapes and properties of biomolecules. Physical biology. 2005;2(4):S86–93. pmid:16280625.
- View Article
- PubMed/NCBI
- Google Scholar
43. Kumari R, Kumar R, Open Source Drug Discovery C, Lynn A. g_mmpbsa—a GROMACS tool for high-throughput MM-PBSA calculations. Journal of chemical information and modeling. 2014;54(7):1951–62. pmid:24850022.
- View Article
- PubMed/NCBI
- Google Scholar
44. Humphrey W, Dalke A, Schulten K. VMD: visual molecular dynamics. Journal of molecular graphics. 1996;14(1):33–8, 27–8. pmid:8744570.
- View Article
- PubMed/NCBI
- Google Scholar
45. Tina KG, Bhadra R, Srinivasan N. PIC: Protein Interactions Calculator. Nucleic acids research. 2007;35(Web Server issue):W473–6. pmid:17584791; PubMed Central PMCID: PMC1933215.
- View Article
- PubMed/NCBI
- Google Scholar
46. Joosten RP, te Beek TA, Krieger E, Hekkelman ML, Hooft RW, Schneider R, et al. A series of PDB related databases for everyday needs. Nucleic acids research. 2011;39(Database issue):D411–9. pmid:21071423; PubMed Central PMCID: PMC3013697.
- View Article
- PubMed/NCBI
- Google Scholar
47. Kabsch W, Sander C. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers. 1983;22(12):2577–637. pmid:6667333.
- View Article
- PubMed/NCBI
- Google Scholar
48. Johnson LN, Lowe ED, Noble ME, Owen DJ. The Eleventh Datta Lecture. The structural basis for substrate recognition and control by protein kinases. FEBS letters. 1998;430(1–2):1–11. pmid:9678585.
- View Article
- PubMed/NCBI
- Google Scholar
49. Huse M, Kuriyan J. The conformational plasticity of protein kinases. Cell. 2002;109(3):275–82. pmid:12015977.
- View Article
- PubMed/NCBI
- Google Scholar
50. Nolen B, Taylor S, Ghosh G. Regulation of protein kinases: Controlling activity through activation segment conformation. Molecular cell. 2004;15(5):661–75. pmid:WOS:000223856700001.
- View Article
- PubMed/NCBI
- Google Scholar
51. Hess B. Similarities between principal components of protein dynamics and random diffusion. Physical review E, Statistical physics, plasmas, fluids, and related interdisciplinary topics. 2000;62(6 Pt B):8438–48. pmid:11138145.
- View Article
- PubMed/NCBI
- Google Scholar
52. Alakent B, Doruker P, Camurdan MC. Application of time series analysis on molecular dynamics simulations of proteins: a study of different conformational spaces by principal component analysis. J Chem Phys. 2004;121(10):4759–69. pmid:15332910.
- View Article
- PubMed/NCBI
- Google Scholar
53. Li L, Li C, Sarkar S, Zhang J, Witham S, Zhang Z, et al. DelPhi: a comprehensive suite for DelPhi software and associated resources. BMC biophysics. 2012;5:9. pmid:22583952; PubMed Central PMCID: PMC3463482.
- View Article
- PubMed/NCBI
- Google Scholar
54. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, et al. UCSF Chimera—a visualization system for exploratory research and analysis. J Comput Chem. 2004;25(13):1605–12. pmid:15264254.
- View Article
- PubMed/NCBI
- Google Scholar
55. Wriggers W, Schulten K. Protein domain movements: Detection of rigid domains and visualization of hinges in comparisons of atomic coordinates. Proteins. 1997;29(1):1–14. pmid:WOS:A1997XU73100001.
- View Article
- PubMed/NCBI
- Google Scholar
56. Cole JL. Activation of PKR: an open and shut case? Trends in biochemical sciences. 2007;32(2):57–62. pmid:17196820; PubMed Central PMCID: PMC2703476.
- View Article
- PubMed/NCBI
- Google Scholar
57. Cox S, Radzioandzelm E, Taylor SS. Domain Movements in Protein-Kinases. Curr Opin Struc Biol. 1994;4(6):893–901. pmid:WOS:A1994QB63000015.
- View Article
- PubMed/NCBI
- Google Scholar
58. Bakan A, Meireles LM, Bahar I. ProDy: protein dynamics inferred from theory and experiments. Bioinformatics. 2011;27(11):1575–7. pmid:21471012; PubMed Central PMCID: PMC3102222.
- View Article
- PubMed/NCBI
- Google Scholar
59. Ichiye T, Karplus M. Collective motions in proteins: a covariance analysis of atomic fluctuations in molecular dynamics and normal mode simulations. Proteins. 1991;11(3):205–17. pmid:1749773.
- View Article
- PubMed/NCBI
- Google Scholar
60. Hunenberger PH, Mark AE, van Gunsteren WF. Fluctuation and cross-correlation analysis of protein motions observed in nanosecond molecular dynamics simulations. Journal of molecular biology. 1995;252(4):492–503. pmid:7563068.
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Meurs E, Chong K, Galabru J, Thomas NS, Kerr IM, Williams BR, et al. Molecular cloning and characterization of the human double-stranded RNA-activated protein kinase induced by interferon. Cell. 1990;62(2):379–90. pmid:1695551.
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Proud CG. eIF2 and the control of cell physiology. Seminars in cell & developmental biology. 2005;16(1):3–12. pmid:15659334.
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Cheshire JL, Williams BR, Baldwin AS Jr. Involvement of double-stranded RNA-activated protein kinase in the synergistic activation of nuclear factor-kappaB by tumor necrosis factor-alpha and gamma-interferon in preneuronal cells. The Journal of biological chemistry. 1999;274(8):4801–6. pmid:9988719.
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref4] 4. Goh KC, deVeer MJ, Williams BR. The protein kinase PKR is required for p38 MAPK activation and the innate immune response to bacterial endotoxin. The EMBO journal. 2000;19(16):4292–7. pmid:10944112; PubMed Central PMCID: PMC302024.
View Article
PubMed/NCBI
Google Scholar

[14] View Article

[15] PubMed/NCBI

[16] Google Scholar

[ref5] 5. Dever TE, Feng L, Wek RC, Cigan AM, Donahue TF, Hinnebusch AG. Phosphorylation of initiation factor 2 alpha by protein kinase GCN2 mediates gene-specific translational control of GCN4 in yeast. Cell. 1992;68(3):585–96. pmid:1739968.
View Article
PubMed/NCBI
Google Scholar

[18] View Article

[19] PubMed/NCBI

[20] Google Scholar

[ref6] 6. Hinnebusch AG. eIF2alpha kinases provide a new solution to the puzzle of substrate specificity. Nature structural & molecular biology. 2005;12(10):835–8. pmid:16205706.
View Article
PubMed/NCBI
Google Scholar

[22] View Article

[23] PubMed/NCBI

[24] Google Scholar

[ref7] 7. Taylor SS, Haste NM, Ghosh G. PKR and eIF2alpha: integration of kinase dimerization, activation, and substrate docking. Cell. 2005;122(6):823–5. pmid:16179248.
View Article
PubMed/NCBI
Google Scholar

[26] View Article

[27] PubMed/NCBI

[28] Google Scholar

[ref8] 8. Yang J, Garrod SM, Deal MS, Anand GS, Woods VL Jr., Taylor S. Allosteric network of cAMP-dependent protein kinase revealed by mutation of Tyr204 in the P+1 loop. Journal of molecular biology. 2005;346(1):191–201. pmid:15663937.
View Article
PubMed/NCBI
Google Scholar

[30] View Article

[31] PubMed/NCBI

[32] Google Scholar

[ref9] 9. Clemens MJ. Targets and mechanisms for the regulation of translation in malignant transformation. Oncogene. 2004;23(18):3180–8. pmid:15094767.
View Article
PubMed/NCBI
Google Scholar

[34] View Article

[35] PubMed/NCBI

[36] Google Scholar

[ref10] 10. Kawagishi-Kobayashi M, Silverman JB, Ung TL, Dever TE. Regulation of the protein kinase PKR by the vaccinia virus pseudosubstrate inhibitor K3L is dependent on residues conserved between the K3L protein and the PKR substrate eIF2alpha. Molecular and cellular biology. 1997;17(7):4146–58. pmid:9199350; PubMed Central PMCID: PMC232268.
View Article
PubMed/NCBI
Google Scholar

[38] View Article

[39] PubMed/NCBI

[40] Google Scholar

[ref11] 11. Sharp TV, Witzel JE, Jagus R. Homologous regions of the alpha subunit of eukaryotic translational initiation factor 2 (eIF2alpha) and the vaccinia virus K3L gene product interact with the same domain within the dsRNA-activated protein kinase (PKR). European journal of biochemistry / FEBS. 1997;250(1):85–91. pmid:9431994.
View Article
PubMed/NCBI
Google Scholar

[42] View Article

[43] PubMed/NCBI

[44] Google Scholar

[ref12] 12. McMillan NA, Chun RF, Siderovski DP, Galabru J, Toone WM, Samuel CE, et al. HIV-1 Tat directly interacts with the interferon-induced, double-stranded RNA-dependent kinase, PKR. Virology. 1995;213(2):413–24. pmid:7491766.
View Article
PubMed/NCBI
Google Scholar

[46] View Article

[47] PubMed/NCBI

[48] Google Scholar

[ref13] 13. Brand SR, Kobayashi R, Mathews MB. The Tat Protein of Human Immunodeficiency Virus Type 1 Is a Substrate and Inhibitor of the Interferon-induced, Virally Activated Protein Kinase, PKR. Journal of Biological Chemistry. 1997;272(13):8388–95. pmid:9079663
View Article
PubMed/NCBI
Google Scholar

[50] View Article

[51] PubMed/NCBI

[52] Google Scholar

[ref14] 14. Romano PR, Garcia-Barrio MT, Zhang X, Wang Q, Taylor DR, Zhang F, et al. Autophosphorylation in the activation loop is required for full kinase activity in vivo of human and yeast eukaryotic initiation factor 2alpha kinases PKR and GCN2. Molecular and cellular biology. 1998;18(4):2282–97. pmid:9528799; PubMed Central PMCID: PMC121479.
View Article
PubMed/NCBI
Google Scholar

[54] View Article

[55] PubMed/NCBI

[56] Google Scholar

[ref15] 15. Zhang F, Romano PR, Nagamura-Inoue T, Tian B, Dever TE, Mathews MB, et al. Binding of double-stranded RNA to protein kinase PKR is required for dimerization and promotes critical autophosphorylation events in the activation loop. The Journal of biological chemistry. 2001;276(27):24946–58. pmid:11337501.
View Article
PubMed/NCBI
Google Scholar

[58] View Article

[59] PubMed/NCBI

[60] Google Scholar

[ref16] 16. Esterhuyse T, Doyle P. AIDS update: the HIV epidemic: what lies ahead? Nursing RSA = Verpleging RSA. 1993;8(10):14–5. pmid:8264775.
View Article
PubMed/NCBI
Google Scholar

[62] View Article

[63] PubMed/NCBI

[64] Google Scholar

[ref17] 17. Muhlberger N, Schwarzer R, Lettmeier B, Sroczynski G, Zeuzem S, Siebert U. HCV-related burden of disease in Europe: a systematic assessment of incidence, prevalence, morbidity, and mortality. BMC public health. 2009;9:34. pmid:19161623; PubMed Central PMCID: PMC2656539.
View Article
PubMed/NCBI
Google Scholar

[66] View Article

[67] PubMed/NCBI

[68] Google Scholar

[ref18] 18. Thein HH, Yi Q, Dore GJ, Krahn MD. Natural history of hepatitis C virus infection in HIV-infected individuals and the impact of HIV in the era of highly active antiretroviral therapy: a meta-analysis. Aids. 2008;22(15):1979–91. pmid:18784461.
View Article
PubMed/NCBI
Google Scholar

[70] View Article

[71] PubMed/NCBI

[72] Google Scholar

[ref19] 19. Weber R, Sabin CA, Friis-Moller N, Reiss P, El-Sadr WM, Kirk O, et al. Liver-related deaths in persons infected with the human immunodeficiency virus: the D:A:D study. Archives of internal medicine. 2006;166(15):1632–41. pmid:16908797.
View Article
PubMed/NCBI
Google Scholar

[74] View Article

[75] PubMed/NCBI

[76] Google Scholar

[ref20] 20. Greub G, Ledergerber B, Battegay M, Grob P, Perrin L, Furrer H, et al. Clinical progression, survival, and immune recovery during antiretroviral therapy in patients with HIV-1 and hepatitis C virus coinfection: the Swiss HIV Cohort Study. Lancet. 2000;356(9244):1800–5. pmid:11117912.
View Article
PubMed/NCBI
Google Scholar

[78] View Article

[79] PubMed/NCBI

[80] Google Scholar

[ref21] 21. Loo YM, Gale M Jr. Viral regulation and evasion of the host response. Current topics in microbiology and immunology. 2007;316:295–313. pmid:17969453.
View Article
PubMed/NCBI
Google Scholar

[82] View Article

[83] PubMed/NCBI

[84] Google Scholar

[ref22] 22. Dar AC, Dever TE, Sicheri F. Higher-order substrate recognition of eIF2alpha by the RNA-dependent protein kinase PKR. Cell. 2005;122(6):887–900. pmid:16179258.
View Article
PubMed/NCBI
Google Scholar

[86] View Article

[87] PubMed/NCBI

[88] Google Scholar

[ref23] 23. Ito T, Marintchev A, Wagner G. Solution structure of human initiation factor eIF2alpha reveals homology to the elongation factor eEF1B. Structure. 2004;12(9):1693–704. pmid:15341733.
View Article
PubMed/NCBI
Google Scholar

[90] View Article

[91] PubMed/NCBI

[92] Google Scholar

[ref24] 24. Boehm M, Sticht H, Seidel G, Roesch P. Solution Structure of HIV-1 Tat Protein. To be Published. 1998:null-null.
View Article
Google Scholar

[94] View Article

[95] Google Scholar

[ref25] 25. Dar AC, Sicheri F. X-ray crystal structure and functional analysis of vaccinia virus K3L reveals molecular determinants for PKR subversion and substrate recognition. Molecular cell. 2002;10(2):295–305. pmid:12191475.
View Article
PubMed/NCBI
Google Scholar

[97] View Article

[98] PubMed/NCBI

[99] Google Scholar

[ref26] 26. Eswar N, Webb B, Marti-Renom MA, Madhusudhan MS, Eramian D, Shen MY, et al. Comparative protein structure modeling using MODELLER. Current protocols in protein science / editorial board, Coligan John E [et al]. 2007;Chapter 2:Unit 2 9. pmid:18429317.
View Article
PubMed/NCBI
Google Scholar

[101] View Article

[102] PubMed/NCBI

[103] Google Scholar

[ref27] 27. Marti-Renom MA, Stuart AC, Fiser A, Sanchez R, Melo F, Sali A. Comparative protein structure modeling of genes and genomes. Annual review of biophysics and biomolecular structure. 2000;29:291–325. pmid:10940251.
View Article
PubMed/NCBI
Google Scholar

[105] View Article

[106] PubMed/NCBI

[107] Google Scholar

[ref28] 28. de Vries SJ, van Dijk M, Bonvin AM. The HADDOCK web server for data-driven biomolecular docking. Nature protocols. 2010;5(5):883–97. pmid:20431534.
View Article
PubMed/NCBI
Google Scholar

[109] View Article

[110] PubMed/NCBI

[111] Google Scholar

[ref29] 29. Dominguez C, Boelens R, Bonvin AM. HADDOCK: a protein-protein docking approach based on biochemical or biophysical information. Journal of the American Chemical Society. 2003;125(7):1731–7. pmid:12580598.
View Article
PubMed/NCBI
Google Scholar

[113] View Article

[114] PubMed/NCBI

[115] Google Scholar

[ref30] 30. de Vries SJ, van Dijk AD, Krzeminski M, van Dijk M, Thureau A, Hsu V, et al. HADDOCK versus HADDOCK: new features and performance of HADDOCK2.0 on the CAPRI targets. Proteins. 2007;69(4):726–33. pmid:17803234.
View Article
PubMed/NCBI
Google Scholar

[117] View Article

[118] PubMed/NCBI

[119] Google Scholar

[ref31] 31. Cai R, Carpick B, Chun RF, Jeang KT, Williams BR. HIV-I TAT inhibits PKR activity by both RNA-dependent and RNA-independent mechanisms. Archives of biochemistry and biophysics. 2000;373(2):361–7. pmid:10620360.
View Article
PubMed/NCBI
Google Scholar

[121] View Article

[122] PubMed/NCBI

[123] Google Scholar

[ref32] 32. Margreitter C, Petrov D, Zagrovic B. Vienna-PTM web server: a toolkit for MD simulations of protein post-translational modifications. Nucleic acids research. 2013;41(Web Server issue):W422–6. pmid:23703210; PubMed Central PMCID: PMC3692090.
View Article
PubMed/NCBI
Google Scholar

[125] View Article

[126] PubMed/NCBI

[127] Google Scholar

[ref33] 33. Pronk S, Pall S, Schulz R, Larsson P, Bjelkmar P, Apostolov R, et al. GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics. 2013;29(7):845–54. pmid:23407358; PubMed Central PMCID: PMC3605599.
View Article
PubMed/NCBI
Google Scholar

[129] View Article

[130] PubMed/NCBI

[131] Google Scholar

[ref34] 34. Petrov D, Margreitter C, Grandits M, Oostenbrink C, Zagrovic B. A systematic framework for molecular dynamics simulations of protein post-translational modifications. PLoS computational biology. 2013;9(7):e1003154. pmid:23874192; PubMed Central PMCID: PMC3715417.
View Article
PubMed/NCBI
Google Scholar

[133] View Article

[134] PubMed/NCBI

[135] Google Scholar

[ref35] 35. Berendsen HJC, Postma JPM, Gunsteren WFv, Hermans J. Interaction Models for Water in Relation to Protein Hydration. In: Pullman B, editor. Intermolecular Forces. The Jerusalem Symposia on Quantum Chemistry and Biochemistry: Springer Netherlands; 1981. p. 331–42.

[ref36] 36. Darden T, York D, Pedersen L. Particle Mesh Ewald—an N.Log(N) Method for Ewald Sums in Large Systems. J Chem Phys. 1993;98(12):10089–92. pmid:WOS:A1993LG10100091.
View Article
PubMed/NCBI
Google Scholar

[138] View Article

[139] PubMed/NCBI

[140] Google Scholar

[ref37] 37. Parrinello M. Polymorphic transitions in single crystals: A new molecular dynamics method. Journal of Applied Physics. 1981;52(12):7182.
View Article
Google Scholar

[142] View Article

[143] Google Scholar

[ref38] 38. Hess B, Bekker H, Berendsen HJC, Fraaije JGEM. LINCS: A linear constraint solver for molecular simulations. Journal of Computational Chemistry. 1997;18(12):1463–72.
View Article
Google Scholar

[145] View Article

[146] Google Scholar

[ref39] 39. Amadei A, Linssen ABM, Berendsen HJC. Essential Dynamics of Proteins. Proteins. 1993;17(4):412–25. pmid:WOS:A1993MJ75700007.
View Article
PubMed/NCBI
Google Scholar

[148] View Article

[149] PubMed/NCBI

[150] Google Scholar

[ref40] 40. Frauenfelder H, Sligar SG, Wolynes PG. The energy landscapes and motions of proteins. Science. 1991;254(5038):1598–603. pmid:1749933.
View Article
PubMed/NCBI
Google Scholar

[152] View Article

[153] PubMed/NCBI

[154] Google Scholar

[ref41] 41. Tavernelli I, Cotesta S, Di Iorio EE. Protein dynamics, thermal stability, and free-energy landscapes: a molecular dynamics investigation. Biophysical journal. 2003;85(4):2641–9. pmid:14507727; PubMed Central PMCID: PMC1303488.
View Article
PubMed/NCBI
Google Scholar

[156] View Article

[157] PubMed/NCBI

[158] Google Scholar

[ref42] 42. Wales DJ. Energy landscapes and properties of biomolecules. Physical biology. 2005;2(4):S86–93. pmid:16280625.
View Article
PubMed/NCBI
Google Scholar

[160] View Article

[161] PubMed/NCBI

[162] Google Scholar

[ref43] 43. Kumari R, Kumar R, Open Source Drug Discovery C, Lynn A. g_mmpbsa—a GROMACS tool for high-throughput MM-PBSA calculations. Journal of chemical information and modeling. 2014;54(7):1951–62. pmid:24850022.
View Article
PubMed/NCBI
Google Scholar

[164] View Article

[165] PubMed/NCBI

[166] Google Scholar

[ref44] 44. Humphrey W, Dalke A, Schulten K. VMD: visual molecular dynamics. Journal of molecular graphics. 1996;14(1):33–8, 27–8. pmid:8744570.
View Article
PubMed/NCBI
Google Scholar

[168] View Article

[169] PubMed/NCBI

[170] Google Scholar

[ref45] 45. Tina KG, Bhadra R, Srinivasan N. PIC: Protein Interactions Calculator. Nucleic acids research. 2007;35(Web Server issue):W473–6. pmid:17584791; PubMed Central PMCID: PMC1933215.
View Article
PubMed/NCBI
Google Scholar

[172] View Article

[173] PubMed/NCBI

[174] Google Scholar

[ref46] 46. Joosten RP, te Beek TA, Krieger E, Hekkelman ML, Hooft RW, Schneider R, et al. A series of PDB related databases for everyday needs. Nucleic acids research. 2011;39(Database issue):D411–9. pmid:21071423; PubMed Central PMCID: PMC3013697.
View Article
PubMed/NCBI
Google Scholar

[176] View Article

[177] PubMed/NCBI

[178] Google Scholar

[ref47] 47. Kabsch W, Sander C. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers. 1983;22(12):2577–637. pmid:6667333.
View Article
PubMed/NCBI
Google Scholar

[180] View Article

[181] PubMed/NCBI

[182] Google Scholar

[ref48] 48. Johnson LN, Lowe ED, Noble ME, Owen DJ. The Eleventh Datta Lecture. The structural basis for substrate recognition and control by protein kinases. FEBS letters. 1998;430(1–2):1–11. pmid:9678585.
View Article
PubMed/NCBI
Google Scholar

[184] View Article

[185] PubMed/NCBI

[186] Google Scholar

[ref49] 49. Huse M, Kuriyan J. The conformational plasticity of protein kinases. Cell. 2002;109(3):275–82. pmid:12015977.
View Article
PubMed/NCBI
Google Scholar

[188] View Article

[189] PubMed/NCBI

[190] Google Scholar

[ref50] 50. Nolen B, Taylor S, Ghosh G. Regulation of protein kinases: Controlling activity through activation segment conformation. Molecular cell. 2004;15(5):661–75. pmid:WOS:000223856700001.
View Article
PubMed/NCBI
Google Scholar

[192] View Article

[193] PubMed/NCBI

[194] Google Scholar

[ref51] 51. Hess B. Similarities between principal components of protein dynamics and random diffusion. Physical review E, Statistical physics, plasmas, fluids, and related interdisciplinary topics. 2000;62(6 Pt B):8438–48. pmid:11138145.
View Article
PubMed/NCBI
Google Scholar

[196] View Article

[197] PubMed/NCBI

[198] Google Scholar

[ref52] 52. Alakent B, Doruker P, Camurdan MC. Application of time series analysis on molecular dynamics simulations of proteins: a study of different conformational spaces by principal component analysis. J Chem Phys. 2004;121(10):4759–69. pmid:15332910.
View Article
PubMed/NCBI
Google Scholar

[200] View Article

[201] PubMed/NCBI

[202] Google Scholar

[ref53] 53. Li L, Li C, Sarkar S, Zhang J, Witham S, Zhang Z, et al. DelPhi: a comprehensive suite for DelPhi software and associated resources. BMC biophysics. 2012;5:9. pmid:22583952; PubMed Central PMCID: PMC3463482.
View Article
PubMed/NCBI
Google Scholar

[204] View Article

[205] PubMed/NCBI

[206] Google Scholar

[ref54] 54. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, et al. UCSF Chimera—a visualization system for exploratory research and analysis. J Comput Chem. 2004;25(13):1605–12. pmid:15264254.
View Article
PubMed/NCBI
Google Scholar

[208] View Article

[209] PubMed/NCBI

[210] Google Scholar

[ref55] 55. Wriggers W, Schulten K. Protein domain movements: Detection of rigid domains and visualization of hinges in comparisons of atomic coordinates. Proteins. 1997;29(1):1–14. pmid:WOS:A1997XU73100001.
View Article
PubMed/NCBI
Google Scholar

[212] View Article

[213] PubMed/NCBI

[214] Google Scholar

[ref56] 56. Cole JL. Activation of PKR: an open and shut case? Trends in biochemical sciences. 2007;32(2):57–62. pmid:17196820; PubMed Central PMCID: PMC2703476.
View Article
PubMed/NCBI
Google Scholar

[216] View Article

[217] PubMed/NCBI

[218] Google Scholar

[ref57] 57. Cox S, Radzioandzelm E, Taylor SS. Domain Movements in Protein-Kinases. Curr Opin Struc Biol. 1994;4(6):893–901. pmid:WOS:A1994QB63000015.
View Article
PubMed/NCBI
Google Scholar

[220] View Article

[221] PubMed/NCBI

[222] Google Scholar

[ref58] 58. Bakan A, Meireles LM, Bahar I. ProDy: protein dynamics inferred from theory and experiments. Bioinformatics. 2011;27(11):1575–7. pmid:21471012; PubMed Central PMCID: PMC3102222.
View Article
PubMed/NCBI
Google Scholar

[224] View Article

[225] PubMed/NCBI

[226] Google Scholar

[ref59] 59. Ichiye T, Karplus M. Collective motions in proteins: a covariance analysis of atomic fluctuations in molecular dynamics and normal mode simulations. Proteins. 1991;11(3):205–17. pmid:1749773.
View Article
PubMed/NCBI
Google Scholar

[228] View Article

[229] PubMed/NCBI

[230] Google Scholar

[ref60] 60. Hunenberger PH, Mark AE, van Gunsteren WF. Fluctuation and cross-correlation analysis of protein motions observed in nanosecond molecular dynamics simulations. Journal of molecular biology. 1995;252(4):492–503. pmid:7563068.
View Article
PubMed/NCBI
Google Scholar

[232] View Article

[233] PubMed/NCBI

[234] Google Scholar

Figures

Abstract

Introduction

Materials and Methods

Protein Preparation and Docking Studies

MD Simulations

Principal Component Analysis

Free-Energy Landscape

Binding Free Energies of Protein-Protein Complexes

Results and Discussion

Protein-Protein Docking and Stability of MD trajectories

Molecular Interactions of PKR with Substrates and Inhibitors

Binding Energy

Secondary Structural Variations in Active Loop

PCA of the Protein Complexes

Free Energy Landscape

Electrostatic Interactions of the Binding Surface

Domain Motion Analysis

Conclusion

Supporting Information

S1 Fig. Contact surface calulations between PKR and the interacting proteins.

S1 Table. Active residues of proteins involved in docking.

S2 Table. Hydrogen bonds formed by PKR protein in the six complexes.

S3 Table. Secondary structural variations of PKR protein.

S4 Table. Domain motion contributing residues.

Acknowledgments

Author Contributions

References