A Network of Conformational Transitions Revealed by Molecular

Nov 16, 2016 - A Network of Conformational Transitions Revealed by Molecular Dynamics Simulations of the Binary Complex of Escherichia coli ...
0 downloads 0 Views 4MB Size
Download PDF
Article pubs.acs.org/biochemistry

A Network of Conformational Transitions Revealed by Molecular Dynamics Simulations of the Binary Complex of Escherichia coli 6‑Hydroxymethyl-7,8-dihydropterin Pyrophosphokinase with MgATP Kaifu Gao,† Ya Jia,*,† and Minghui Yang*,‡ †

Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan 430079, P. R. China Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Centre for Magnetic Resonance, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, P. R. China

‡

S Supporting Information *

ABSTRACT: 6-Hydroxymethyl-7,8-dihydropterin pyrophosphokinase (HPPK) catalyzes the first reaction in the folate biosynthetic pathway. Comparison of its X-ray and nuclear magnetic resonance structures suggests that the enzyme undergoes significant conformational change upon binding to its substrates, especially in three catalytic loops. Experimental research has shown that, in its binary form, even bound by analogues of MgATP, loops 2 and 3 remain rather flexible; this raises questions about the putative large-scale induced-fit conformational change of the HPPK−MgATP binary complex. In this work, long-time all-atomic molecular dynamics simulations were conducted to investigate the loop dynamics in this complex. Our simulations show that, with loop 3 closed, multiple conformations of loop 2, including the open, semiopen, and closed forms, are all accessible to the binary complex. These results provide valuable structural insights into the details of conformational changes upon 6-hydroxymethyl-7,8-dihydropterin (HP) binding and biological activities of HPPK. Conformational network analysis and principal component analysis related to the loops are also discussed.

E

unknown whether one liganded form, such as binary or ternary substrate complexes, can assume multiple conformations that an enzyme must adopt throughout its catalytic cycle.11−19 6-Hydroxymethyl-7,8-dihydropterin pyrophosphokinase (HPPK) catalyzes the transfer of Mg2+-dependent pyrophosphoryl from ATP to 6-hydroxymethyl-7,8-dihydropterin (HP), leading to the biosynthesis of folate, which is essential for life and must be synthesized de novo for most microorganisms. Because of the importance of this reaction in microbial growth, HPPK can be an ideal target for the development of antibiotics.20,21 Moreover, as a small (158 residues, 18 kDa), stable, monomeric protein that undergoes dramatic conformational changes during its catalytic cycle and is amenable to a variety of biochemical and biophysical analyses, Escherichia coli HPPK has emerged as an excellent model system for studying the role of protein conformational dynamics in enzymatic catalysis.21−23 The HPPK-catalyzed reaction follows an ordered bi-bi mechanism in which the nucleotide substrate MgATP binds to the enzyme first. During its catalytic cycle, at least five liganded forms must be assumed: the apo-HPPK form (without either of the substrates), the binary substrate complex with

nzymes, or proteins generally, are not always static: X-ray crystallography and nuclear magnetic resonance (NMR) have revealed that multiple conformations can be adopted by different liganded states of the same enzyme or even by the multiple conformational equilibria of an enzyme in the same liganded state.1,2 Although the role of conformational dynamics in an enzymatic reaction is still under intense debate,3−8 it is clearly known that transitions between multiple conformations are critical for enzymatic catalysis because of the conflicting structural requirements for an enzyme through its catalytic cycle.9 An unbound enzyme must adopt an open conformation so that its substrate can enter its active site to form a Michaelis complex. Subsequently, to stabilize the transition state and prevent side reactions, the enzyme typically assumes a closed conformation with the substrate buried in its active site. After the chemical reaction, the enzyme must open its active site again to allow the product to exit.10 For a bisubstrate or multisubstrate enzyme, additional liganded forms, such as binary and ternary substrate complexes and product complexes, must be assumed, so as to undergo some additional conformational transitions between them. While ample evidence has indicated that, in a two-state equilibrium, an unbound enzyme can assume a bound as well as an unbound conformation, which is the current focus of experimental and computational studies; however, in a more complex situation for a bisubstrate or multisubstrate enzyme, it remains largely © 2016 American Chemical Society

Received: July 15, 2016 Revised: November 7, 2016 Published: November 16, 2016 6931

DOI: 10.1021/acs.biochem.6b00720 Biochemistry 2016, 55, 6931−6939

Article

Biochemistry

these structures, HPPK has an αβα fold with a central sixstranded β-sheet sandwiched by two α-helices on either side. Of the loops connecting the β-strands and α-helices, three loops, β1−α1 (loop 1, residues 9−14), β2−β3 (loop 2, residues 43− 53), and β4−β2 (loop 3, residues 82−92), constitute the flexible parts of the active site. The dramatic conformational changes of the two long loops 2 and 3 occur during the catalytic cycle of the enzyme.20,22,26 The apo form of HPPK represents a semiopen conformation. The side chains of several important catalytic residues point away from the active site [PDB entry 1HKA (Figure 1a)]; the binding of MgAMPPCP, an analogue of MgATP, results in an open conformation, with both loops extended from the active site [PDB entry 1EQ0 (Figure 1b)]. The binding of MgAMPCPP, another analogue of MgATP, and HP leads to a closed conformation: both loops move in, and a fully assembled active site is formed with the two substrates buried in it [PDB entry 1Q0N (Figure 1c)]; after the chemical reaction, the loops re-open so that the products can exit from the active site [PDB entries 1RAO and 1RB0 (Figure 1d,e)]. More details of these five experimental structures are listed in Table 1. Our previous molecular dynamics (MD) simulations of apoHPPK9 revealed that the conformations of loops 2 and 3 that are detected in different phases of the catalytic cycle (see Figure 1) as well as other multiple conformations exist prior to MgATP binding. Of five reported experimental structures, MgATP can enter the active site of structures 1EQ0 and 1RAO but cannot gain entry into the other three structures, 1HKA, 1Q0N, and 1RB0 (see Table 1). Therefore, the conformational changes of apo-HPPK that occur upon MgATP binding can be better described by the conformational-selection model. Once MgATP binds to HPPK, the movement of loops 2 and 3 and the effect of MgATP in the active site on the motion of these loops remain unclear. The flexibility of the two catalytic loops in the HPPK−MgATP complex has been evidenced by the low intensities or disappearance of many loop residues in the 1H−15N heteronuclear single-quantum coherence spectrum27 and confirmed by the MD simulations with the locally enhanced sampling (LES) technique.24 However, even in the LES MD simulations, the motions of the loops with higher B factor values were not obviously observed. In this work, conventional MD simulations totaling 1 μs at room temperature (300 K), which represents a duration much longer than those of previous studies,24,28 were applied to investigate the conformational transitions of the HPPK−MgATP binary complex. Our simulations revealed that loop 3 of the HPPK−MgATP binary complex was stabilized in closed conformations; nevertheless, multiple conformations, including the open, semiopen, and closed forms, can be adopted by loop 2, providing additional insight into our understanding of the functional mechanism of the conformational change upon HP binding.23

MgATP, the ternary substrate complex (Michaelis complex) with MgATP and HP, the ternary product complex with AMP and 6-hydroxymethyl-7,8-dihydropterin pyrophosphate (HPPP), and the binary product complex with HPPP.21,24 Atomic structures of these five forms have been resolved by Xray crystallography and NMR using substrates [Protein Data Bank (PDB) structure 1RAO with AMP and HPPP and PDB structure 1RB0 with HPPP] or substrate analogues (PDB structure 1EQ0 with MgAMPPCP and PDB structure 1Q0N with MgAMPCPP and HP), and in particular, substrates or substrate analogues are not included in the NMR structures, such as PDB structure 1EQ0 (Figure 1).20−22,25 As revealed by

Figure 1. Snapshots of HPPK throughout the catalytic cycle. The steps of the catalytic cycle are indicated by the black arrows; the cycle begins with apo-HPPK, represented by structure 1HKA [here and below, structure designations are provided as Protein Data Bank (PDB) entries], and proceeds through the binary substrate complex represented by structure 1EQ0, the ternary substrate complex represented by structure 1Q0N, the ternary product complex represented by structure 1RAO, and the binary product complex represented by structure 1RB0. The protein molecule is drawn as a ribbon diagram; the ligands are drawn as stick models, and the two Mg2+ ions are drawn as spheres. The PDB entries are shown in parentheses. The structure of 1EQ0 was determined via NMR and therefore does not include the ligand, whereas the other structures were all determined via X-ray crystallography. The Cα atoms of Pro47, Ala86, and Phe101, used to monitor the conformational changes of the large loops, are marked with black dots.

■

COMPUTATIONAL METHOD Initial Structure. The initial coordinates of the binary complex of HPPK and MgATP were taken from the X-ray crystallographic structure of a ternary complex of HPPK with MgAMPCPP and HP [PDB entry 1Q0N (Figure 1c)];20 AMPCPP is an ATP analogue in which the O atom between βand γ-phosphates is replaced by a -CH2- group to prevent hydrolysis. We remove HP from the structure and mutate AMPCPP to ATP by replacing the C atom between β- and γphosphates of AMPCPP with an O atom, resulting in the final 6932

DOI: 10.1021/acs.biochem.6b00720 Biochemistry 2016, 55, 6931−6939

Article

Biochemistry

Table 1. PDB Entries, Ligands, and Conformational States of Loops 2 and 3 of the Five Experimental Structures Shown in Figure 1 PDB entry ligand(s) loop 2 loop 3

1HKA none semiopen semiopen

1EQ0 MgAMPPCP open open

1Q0N MgAMPCPP and HP closed closed

1RAO AMP and HPPP closed open

1RB0 HPPP closed semiopen

the MD trajectories, each cluster was treated as a node of a conformational network, with the node size being proportional to the number of snapshots in the cluster and the edge thickness being proportional to the number of direct transitions between two connected clusters. The visual representations of the conformational networks were created with Pajek software.41 Principal Component Analysis (PCA). Using the ptraj module of AmberTools,30 PCA was performed on the Cα atoms. The coordinates of each trajectory were superposed by means of a least-squares fit using the first snapshot as a reference. Then, a covariance matrix of atomic fluctuations, C, was calculated, with each element cij for a given pair of atoms defined as follows:

model of the HPPK−MgATP complex. The partial charge of ATP reported by Duan et al.24 is adopted. The binary complex was placed in an octahedral TIP3P water box,29 with the edges of the water box located at least 10 Å from the solute atoms. Upon solvation, the system consisted of 2585 solute atoms and 14277 solvent atoms, neutralized by five Na+ ions, under periodic boundary conditions. MD Simulations. The MD simulations were performed using the PMEMD simulation module for graphics processing units (pmemd.cuda.MPI) in AMBER1130−32 and the AMBER ff03 force field.33 To remove potential bad contacts, the initial model was subjected to two rounds of energy minimization: (1) 1000 steps of steepest descent followed by 1000 steps of a conjugate gradient with the protein atoms constrained by a harmonic potential with a force constant of 100 kcal/mol and (2) 2000 steps of steepest descent and 3000 steps of a conjugate gradient without any constraints. Five 200 ns MD trajectories at 300 K with different initial random velocities were obtained, resulting in a total simulation of 1 μs. The system was heated at a constant volume from 0 to 300 K over 60 ps. The MD simulations were then run at a constant temperature and a constant pressure with the temperature maintained at the temperatures set by Langevin’s thermostat,34 a collision frequency of 1.0 ps−1, and the pressure maintained at 1.0 atm using isotropic positional scaling with a pressure relaxation time of 2.0 ps. The particle mesh Ewald method35 was used to estimate the long-range electrostatic energies. The nonbonded cutoff was set to 10 Å. All bonds involving hydrogen atoms were fixed to their equilibrium values using the SHAKE algorithm36 to allow a 2.0 fs time step. The MD trajectories were recorded at 4 ps intervals. Clustering and Conformational Network Analysis. The MD snapshots were clustered on the basis of the distance root mean square (DRMS) using the leader algorithm implemented in WORDOM.37,38 The DRMS between two structures or snapshots (a and b) is defined as follows.

cij = (ri − ri )(rj − ⟨rj⟩) = ⟨rri j⟩−⟨ri⟩⟨rj⟩

(2)

where r1, r2, ..., and r3N are the Cartesian coordinates of the Cα atoms and the angular brackets denote an ensemble average calculated from all snapshots of the MD trajectory.42−44 Eigenvalues λi (the diagonal elements of the diagonalized matrix) and eigenvectors Li (the columns of the orthonormal transformation) were yielded by diagonalization of the covariance matrix. These eigenvectors represent the principal modes of motion, and the eigenvalues represent the meansquare fluctuations along the eigenvector coordinates. PCA is not restricted to harmonic motions; collective transitions between structures that differ greatly can be also described by it. The percentage vi of the total variance contained in a given eigenvector Li is given by vi =

λi 3N ∑k = 1 λk

(3)

where vi measures the contribution of a given PC to the overall protein fluctuations. The cumulative contribution from a set of PCs is n

n

DRMS =

⎛1⎞ a b 2 ⎜ ⎟ ∑ (d − d ) ij ij ⎝N⎠ i,j

Vn =

∑ vi , V3n = 1 i=1

(1)

(4)

Typically, the first few PCs capture most of the intramolecular fluctuation of a protein.44−51

■

where dij is the distance between atoms i and j and N is the total number of pairs of atoms. Thus, the DRMS for n atoms takes into account the fluctuations of N = n(n − 1)/2 distance pairs not only in one loop but also between two loops,39,40 which means the DRMS is sensitive to not only the conformations of individual loops but also the conformational coupling of the loops, whereas other methods such as root-mean-square deviation (RMSD) are simply based on coordinates of n individual atoms and lose the conformational coupling information. Therefore, the DRMS was chosen for the conformational clustering analysis. Backbone atoms Cα, C, and N of the three flexible loops (residues 8−15 in loop 1, residues 43−53 in loop 2, and residues 82−92 in loop 3) were used for the calculations. The DRMS cutoff for the conformational clustering was 3.5 Å. For

RESULTS Stability of Trajectories. Displayed in Figure 2a, the average RMSD value from the initial structure calculated using all Cα atoms is ∼1.5 Å for all the trajectories, which means the protein was stable during each of the 200 ns MD simulations. As shown by the B factor values calculated for the MD trajectories (Figure 2b), the conformational changes during the simulations were mostly localized in loops 2 and 3, consistent with NMR order parameters derived from NMR structures (note there are no crystal structures for the binary complexes). Similar to those found for apo-HPPK,9 Pro47 and Ala86 have the highest B factor values in loops 2 and 3, respectively, and, Phe101 is located at the bottom of the protein with little 6933

DOI: 10.1021/acs.biochem.6b00720 Biochemistry 2016, 55, 6931−6939

Article

Biochemistry

simulations, the initial structure of the simulations (PDB entry 1Q0N) is not included in the conformational space and the RMSDs with respect to 1Q0N are always >0.5 Å. However, as shown in Figure 3, the conformational space of our simulations nearly extends to the representative point of 1Q0N. Therefore, the conformational space of our simulations reveals that the conformations of loops 2 and 3 could transform among the five experimental structures (1Q0N, 1HKA, 1EQ0, 1RAO, and 1RB0) in the catalytic cycle (see Figure 1). In our simulations, a significant difference is found between loops 2 and 3. Loop 2 has a wide conformational space, which includes the closed (1RAO and 1RB0), semiopen (1HKA), and open (1EQ0) experimental structures shown in Figure 1 and Table 1 as well as some “superopen” conformations. However, the conformational space of loop 3 is very limited: it consists of only closed conformations with the distance between Ala86 and Phe101 being 50%) in each trajectory. The occurrence percentages of these clusters and conformational space network among them are shown in Figure 4c. Consistent with Figure 4b, cluster 2 has the largest percentage at 71.1%, implying it is the most stable. Cluster 1 with the closed conformation of loop 2 is metastable with the percentage being 24.1%. Moreover, the conformational space network indicates that cluster 2 is a key transition state as cluster 1 can connect to all the other clusters only via cluster 2. As loop 2 is semiopen in cluster 2, the closed to open transformation must pass through semiopen conformations. This point is more intuitively illustrated by Figure 3a: cluster 2 is “midway” between the closed and open conformations. Principal Component Analysis. To intuitively characterize the dynamics of the protein, PCA is performed. All Cα atoms were used to define the backbone conformation of HPPK for PCA, resulting in 474 principal components (PCs) from the 158 Cα atoms in the form of 474 eigenvectors and their associated eigenvalues. Figure S2 shows that, consistent with earlier research, 43 20 of the 474 PCs capture approximately 80% of the protein’s motion in all our trajectories. In particular, the first three PCs have eigenvalues much larger than the rest, and in every trajectory, they capture

Figure 2. (a) Time evolution of Cα RMSDs of the MD simulations. (b) B factor distributions of the MD simulations.

mobility. Therefore, the distance between Pro47 and Phe101 can be used to represent the motion of loop 2 such as its opening or closing. Similarly, the distance between Ala86 and Phe101 can be used to represent the motion of loop 3 (see Figure 1). Free Energy Landscape of Loops 2 and 3. To illustrate the conformational space of loops 2 and 3 in the MD simulations and to determine whether this space includes multiple conformations of loops 2 and 3 such as five experimental structures in Figure 1, the two-dimensional free energies for loops 2 and 3 were estimated as F(x) = −RT log P(x), where P(x) is the probability of point x calculated from the MD simulations, and then plotted in Figure 3. For loop 2 (Figure 3a), the Cα distance between Pro47 and Phe101 was chosen as the x-coordinate, whereas the RMSD between loop 2 and the initial structure (PDB entry 1Q0N) was chosen as the y-coordinate; for loop 3 (Figure 3b), the Cα distance between Pro86 and Phe101 and the RMSD between loop 3 and 1Q0N were chosen as the x-coordinate and y-coordinate, respectively. The reason for such choice is that the Cα distance between Pro47 and Phe101 (Pro86 and Phe101) can reasonably discriminate different conformations (open, closed, and semiopen) of loop 2 (loop 3), while the RMSD between loop 2 (loop 3) and the initial structure represents its conformational changes; more importantly, they are almost uncorrelated. The experimental structures 1Q0N, 1HKA, 1EQ0, 1RAO, and 1RB0 on the landscape are also shown in the graphs. Because the minimizing and heating were performed before the production 6934

DOI: 10.1021/acs.biochem.6b00720 Biochemistry 2016, 55, 6931−6939

Article

Biochemistry

Figure 3. Two-dimensional free energy for loops 2 and 3 in the MD simulations. For loop 2, the x-axis represents the Cα distance between Pro47 and Phe101 whereas the y-axis represents the RMSD between loop 2 and the initial structure (PDB entry 1Q0N); for loop 3, the x-axis represents the Cα distance between Pro86 and Phe101 whereas the y-axis represents the RMSD between loop 3 and 1Q0N. The representative points of the five experimental structures in Figure 1 as well as the centers of the clusters (see Clustering and Conformational Network Analysis) are also shown. (a) Loop 2 in all the simulations. (b) Loop 3 in all the simulations.

no less than 50% of the motion. Hence, the first three PCs are sufficient for the analysis. Figure 5 shows the vector field representations of the PCs obtained from the combined data of all trajectories. It clearly indicates that, quite consistent with the B factor graphs, loops 2 and 3 have the largest motions. In PCs 1 and 2 with the largest eigenvalues, the motion of loop 2 is moving up and down; however, for loop 3, there is no such motion. The motion of loop 3 is shearing (PC 1) and twisting (PC 2). Therefore, it is unsurprising that, even with high flexibility, loop 3 always remains in closed conformations in the HPPK−MgATP binary complex. Hydrogen Bonds Related to HP Binding. The crystal structure of the ternary complex (HPPK−AMPCPP−HP, PDB entry 1Q0N)20 reveals that HP is bound to HPPK through six hydrogen bonds with residues Thr42, Pro43, Leu45, and Asn55, which altogether form a strong hydrogen bond network

and stabilize the closed conformations of loop 2. However, because HP is removed in these simulations, the hydrogen bond network is lost and the closed conformations cannot be stabilized by the hydrogen bonds solely among these four residues, leading to a flexible loop 2 throughout our simulations. Therefore, only after HP binding can the hydrogen bond network stabilizing loop 2 be well formed; this is why loop 2 has large conformational changes in our simulations when HP is absent.

■

DISCUSSION Dynamics of Loops 2 and 3 of the Binary Complex. The free energy and clustering analysis indicate that, even beginning from the initial crystal structure of the binary complex (PDB entry 1Q0N) with loop 2 closed, all the conformations of loop 2 in the five experimental structures 6935

DOI: 10.1021/acs.biochem.6b00720 Biochemistry 2016, 55, 6931−6939

Article

Biochemistry

Figure 4. DRMS-based clustering and network analysis of the MD simulations: (a) the center structures of each cluster, (b) the time evolution of the clusters, and (c) the distribution and interconversion network of the clusters. In panel b, the different clusters are represented by different colors. In panel c, the relative populations of the clusters are indicated by percentage values and the thickness of each connecting line is proportional to the corresponding frequency of interconversion.

(1Q0N, 1HKA, 1EQ0, 1RAO, and 1RB0) depicted in Figure 1 and Table 1 as well as some superopen conformations can be observed in our simulations, and loop 3 is stabilized in closed conformations by MgATP, consistent with the PCA. Moreover, the clustering analysis shows that one particular semiopen conformation of loop 2 (cluster 2) is a key transition state as

every transformation between open and closed conformations of loop 2 must pass through it (Figures 3a and 4c). “Induced Fit” versus “Conformational Selection”. Until now, two different models, which are generally termed “induced fit” and “conformational selection”, have been proposed to describe the conformational changes that occur upon ligand binding.52−54 The induced-fit model presumes that 6936

DOI: 10.1021/acs.biochem.6b00720 Biochemistry 2016, 55, 6931−6939

Article

Biochemistry

MD trajectories (the open conformations of loop 2 last ∼100 ns in our 1000 ns simulations), they present only the possibilities that the HP can enter the binding pocket at these conformations. Previous research has indicated that gating by the flexible loops has little effect on the rate of substrate binding: opening and closing of the gate is always much faster than the characteristic diffusion time required for the substrate to explore the region around the enzyme.63,64 Moreover, the further process of HP binding at the pocket is expected to be even slower as specific hydrogen bonds between the HPPK−MgATP complex and HP20 have to form until the equilibrium is reached. HPPK−MgATP Complex versus Apo-HPPK. Our previous study9 reveals that apo-HPPK can freely access multiple open, semiopen, and closed conformations of loops 2 and 3. A different mechanism emerges for the HPPK−MgATP binary complex as described here. As only closed conformations are found for loop 3 from the simulations, loop 3 is unlikely to be opened due to the blockage caused by the binding of ATP.

Figure 5. Vector field representations and corresponding eigenvalues of the first three PCs obtained from the combined data of the MD trajectories.

■

the binding of the ligand may induce a conformational change in the enzyme, resulting in an optimized geometry that exists only in the complex state.55 In this scenario, the protein energy landscape changes upon the binding of the ligand. By contrast, the conformational-selection model presumes that more than one structural conformation preexists in the conformational equilibrium before ligand binding.56−58 This model involves the stabilization of an accessible conformation, and the ligand binding causes a shift in the preexisting conformational equilibrium. It is important to note that these two models are not mutually exclusive: a survey of the recent literature indicates that many processes can simultaneously include certain elements of both the induced-fit and conformationalselection models.53,54,59−61 Our MD simulations show that, even after the binding of MgATP, loop 2 still undergoes dramatic conformational changes: the five experimental structures in the catalytic cycle (see Figure 1 and Table 1) as well as multiple other conformations of loop 2 coexist. Of these five structures, structures 1HKA and 1EQ0 have large entrance channels for HP binding25 (the channels are at least 18 Å long, 8 Å wide, and 10 Å deep, while HP is only ∼8 Å × ∼2 Å × ∼5 Å). However, the situation is quite different for 1Q0N, 1RAO, and 1RB0: the channels are blocked by the side chains of loop 2,20,21 so that HP can enter the binding pocket of only structures 1HKA and 1EQ0 but cannot gain entry into the other three structures (1Q0N, 1RAO, and 1RB0). Our simulations also include multiple other conformations of loop 2, such as “superopen”, etc., indicating that such transformations between active and inactive structures of HP binding are evident. In other words, the conformational changes of the HPPK−MgATP complex upon HP binding observed from our simulations favor the conformationalselection model as the conformation of loop 2 is variable, and its open conformations can exist prior to HP binding. When loop 2 is open, HP enters and binds to the HPPK− MgATP binary complex. With a measure of the association rate constant of 11 ± 0.03 μM−1 s−1,62 kinetic studies have demonstrated that the addition of the second substrate (HP) to bind is much faster (40 times) than the binding of MgATP, but still much slower than nanosecond scale, the time frame in which the HP-binding conformations are formed in our MD simulations. As the HPbinding conformations are only transition state-like from the

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.biochem.6b00720. Variations in the Cα distances between Pro47 and Phe101 and between Ala86 and Phe101 in the MD trajectories (Figure S1) and magnitudes and cumulative percentages of the first 20 PC eigenvectors of the MD trajectories and a typical trajectory (Figure S2) (PDF)

■

AUTHOR INFORMATION

Corresponding Authors

*Telephone: +86-027-67867675. E-mail: [email protected]. cn. *Telephone: +86-027-87197783. E-mail: [email protected]. ORCID

Kaifu Gao: 0000-0001-7574-4870 Minghui Yang: 0000-0003-1067-2217 Funding

This work was supported by the National Natural Science Foundation of China (Projects 21221064, 21373266, 11175068, and 11474117). Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS We greatly appreciate the idea, suggestion, and comment from Prof. Honggao Yan (Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI) as well as Dr. Jun Zeng at MedChemSoft Solutions (Scoresby, Australia).

■

REFERENCES

(1) Gerstein, M., and Krebs, W. (1998) A database of macromolecular motions. Nucleic Acids Res. 26, 4280−4290. (2) Goh, C. S., Milburn, D., and Gerstein, M. (2004) Conformational changes associated with protein-protein interactions. Curr. Opin. Struct. Biol. 14, 104−109. (3) Pisliakov, A. V., Cao, J., Kamerlin, S. C. L., and Warshel, A. (2009) Enzyme millisecond conformational dynamics do not catalyze the chemical step. Proc. Natl. Acad. Sci. U. S. A. 106, 17359−17364.

6937

DOI: 10.1021/acs.biochem.6b00720 Biochemistry 2016, 55, 6931−6939

Article

Biochemistry (4) Karplus, M. (2010) Role of conformation transitions in adenylate kinase. Proc. Natl. Acad. Sci. U. S. A. 107, E71 , author reply E72.. (5) Kamerlin, S. C. L., and Warshel, A. (2010) Reply to Karplus: Conformational dynamics have no role in the chemical step. Proc. Natl. Acad. Sci. U. S. A. 107, E72. (6) Bhabha, G., Lee, J., Ekiert, D. C., Gam, J., Wilson, I. A., Dyson, H. J., Benkovic, S. J., and Wright, P. E. (2011) A dynamic knockout reveals that conformational fluctuations influence the chemical step of enzyme catalysis. Science (Washington, DC, U. S.) 332, 234−238. (7) Doshi, U., McGowan, L. C., Ladani, S. T., and Hamelberg, D. (2012) Resolving the complex role of enzyme conformational dynamics in catalytic function. Proc. Natl. Acad. Sci. U. S. A. 109, 5699−5704. (8) Warshel, A., and Bora, R. P. (2016) Perspective: Defining and quantifying the role of dynamics in enzyme catalysis. J. Chem. Phys. 144, 180901. (9) Gao, K., He, H., Yang, M., and Yan, H. (2015) Molecular Dynamics Simulations of the Escherichia coli HPPK Apo-enzyme Reveal a Network of Conformational Transitions. Biochemistry 54, 6734−6742. (10) Gerstein, M., Lesk, A. M., and Chothia, C. (1994) Structural mechanisms for domain movements in proteins. Biochemistry 33, 6739−6749. (11) Mccammon, J. A. (1984) Protein dynamics. Rep. Prog. Phys. 47, 1−46. (12) Jardetzky, O. (1996) Protein dynamics and conformational transitions in allosteric proteins. Prog. Biophys. Mol. Biol. 65, 171−219. (13) Jardetzky, O., and Lefevre, J. F. (1994) Protein dynamics. FEBS Lett. 338, 246−250. (14) Ma, L., and Cui, Q. (2007) Activation mechanism of a signaling protein at atomic resolution from advanced computations. J. Am. Chem. Soc. 129, 10261−10268. (15) Formaneck, M. S., Ma, L., and Cui, Q. (2006) Reconciling the “old” and “new” views of protein allostery: A molecular simulation study of chemotaxis Y protein (CheY). Proteins: Struct., Funct., Genet. 63, 846−867. (16) Ma, J., and Karplus, M. (1997) Molecular switch in signal transduction: reaction paths of the conformational changes in ras p21. Proc. Natl. Acad. Sci. U. S. A. 94, 11905−11910. (17) Ma, J., Sigler, P. B., Xu, Z., and Karplus, M. (2000) A dynamic model for the allosteric mechanism of GroEL. J. Mol. Biol. 302, 303− 313. (18) Maragakis, P., Lindorff-Larsen, K., Eastwood, M. P., Dror, R. O., Klepeis, J. L., Arkin, I. T., Jensen, M., Xu, H., Trbovic, N., Friesner, R. A., Palmer, A. G., and Shaw, D. E. (2008) Microsecond molecular dynamics simulation shows effect of slow loop dynamics on backbone amide order parameters of proteins. J. Phys. Chem. B 112, 6155−6158. (19) D’Abramo, M., Rabal, O., Oyarzabal, J., and Gervasio, F. L. (2012) Conformational selection versus induced fit in kinases: The case of PI3K-I. Angew. Chem., Int. Ed. 51, 642−646. (20) Blaszczyk, J., Shi, G., Yan, H., and Ji, X. (2000) Catalytic center assembly of HPPK as revealed by the crystal structure of a ternary complex at 1.25 Å resolution. Structure 8, 1049−1058. (21) Blaszczyk, J., Shi, G., Li, Y., Yan, H., and Ji, X. (2004) Reaction trajectory of pyrophosphoryl transfer catalyzed by 6-hydroxymethyl7,8-dihydropterin pyrophosphokinase. Structure 12, 467−475. (22) Xiao, B., Shi, G., Gao, J., Blaszczyk, J., Liu, Q., Ji, X., and Yan, H. (2001) Unusual Conformational Changes in 6-Hydroxymethyl-7,8dihydropterin Pyrophosphokinase as Revealed by X-ray Crystallography and NMR. J. Biol. Chem. 276, 40274−40281. (23) Yan, H., and Ji, X. (2011) Role of protein conformational dynamics in the catalysis by 6-hydroxymethyl-7,8-dihydropterin pyrophosphokinase. Protein Pept. Lett. 18, 328−335. (24) Yang, R., Lee, M. C., Yan, H., and Duan, Y. (2005) Loop conformation and dynamics of the Escherichia coli HPPK apo-enzyme and its binary complex with MgATP. Biophys. J. 89, 95−106. (25) Xiao, B., Shi, G., Chen, X., Yan, H., and Ji, X. (1999) Crystal structure of 6-hydroxymethyl-7,8-dihydropterin pyrophosphokinase, a

potential target for the development of novel antimicrobial agents. Structure 7, 489−496. (26) Li, G., Felczak, K., Shi, G., and Yan, H. (2006) Mechanism of the conformational transitions in 6-hydroxymethyl-7,8-dihydropterin pyrophosphokinase as revealed by NMR spectroscopy. Biochemistry 45, 12573−12581. (27) Lescop, E., Lu, Z., Liu, Q., Xu, H., Li, G., Xia, B., Yan, H., and Jin, C. (2009) Dynamics of the conformational transitions in the assembling of the michaelis complex of a bisubstrate enzyme: A 15N relaxation study of Escherichia coli 6-hydroxymethyl-7,8-dihydropterin pyrophosphokinase. Biochemistry 48, 302−312. (28) Su, L., and Cukier, R. I. (2009) An enhanced molecular dynamics study of HPPK-ATP conformation space exploration and ATP binding to HPPK. J. Phys. Chem. A 113, 2025−2035. (29) Pomelli, C. S., Tomasi, J., and Barone, V. (2001) An improved iterative solution to solve the electrostatic problem in the polarizable continuum model. Theor. Chem. Acc. 105, 446−451. (30) Case, D. A., Darden, T. A., Cheatham, T. E., III, Simmerling, C. L., Wang, J., Duke, R. E., Luo, R., Walker, R. C., Zhang, W., Merz, K. M., Roberts, B., Hayik, S., Roitberg, A., Seabra, G., Swails, J., Goetz, A. W., Kolossváry, I., Wong, K. F., Paesani, F., Vanicek, J., Wolf, R. M., Li, J., and Kollman, P. A. (2011) AMBER 11, University of California, San Francisco. (31) Case, D. A., Cheatham, T. E., Darden, T., Gohlke, H., Luo, R., Merz, K. M., Onufriev, A., Simmerling, C., Wang, B., and Woods, R. J. (2005) The Amber biomolecular simulation programs. J. Comput. Chem. 26, 1668−1688. (32) Götz, A. W., Williamson, M. J., Xu, D., Poole, D., Le Grand, S., and Walker, R. C. (2012) Routine microsecond molecular dynamics simulations with AMBER on GPUs. 1. generalized born. J. Chem. Theory Comput. 8, 1542−1555. (33) Duan, Y., Wu, C., Chowdhury, S., Lee, M. C., Xiong, G., Zhang, W., Yang, R., Cieplak, P., Luo, R., Lee, T., Caldwell, J., Wang, J., and Kollman, P. (2003) A Point-Charge Force Field for Molecular Mechanics Simulations of Proteins Based on Condensed-Phase Quantum Mechanical Calculations. J. Comput. Chem. 24, 1999−2012. (34) Loncharich, R. J., Brooks, B. R., and Pastor, R. W. (1992) Langevin dynamics of peptides: The frictional dependence of isomerization rates of N-acetylalanyl-N’-methylamide. Biopolymers 32, 523−535. (35) Darden, T., York, D., and Pedersen, L. (1993) Particle mesh Ewald: An N log(N) method for Ewald sums in large systems. J. Chem. Phys. 98, 10089−10092. (36) Ryckaert, J. P., Ciccotti, G., and Berendsen, H. J. C. (1977) Numerical-Integration of Cartesian Equations of Motion of a System with Constraints - Molecular-Dynamics of N-Alkanes. J. Comput. Phys. 23, 327−341. (37) Seeber, M., Cecchini, M., Rao, F., Settanni, G., and Caflisch, A. (2007) Wordom: A program for efficient analysis of molecular dynamics simulations. Bioinformatics 23, 2625−2627. (38) Seeber, M., Felline, A., Raimondi, F., Muff, S., Friedman, R., Rao, F., Caflisch, A., and Fanelli, F. (2011) Wordom: a user-friendly program for the analysis of molecular structures, trajectories, and free energy surfaces. J. Comput. Chem. 32, 1183−1194. (39) Banerjee, R., Yan, H., and Cukier, R. I. (2014) Conformational transition of response regulator rr468 in a two-component system signal transduction process. J. Phys. Chem. B 118, 4727−4742. (40) Holm, L., and Sander, C. (1993) Protein structure comparison by alignment of distance matrices. J. Mol. Biol. 233, 123−38. (41) Batagelj, V., and Mrvar, A. (1998) Pajek − program for large network analysis. Connections 21, 47−57. (42) Mesentean, S., Koppole, S., Smith, J. C., and Fischer, S. (2007) The Principal Motions Involved in the Coupling Mechanism of the Recovery Stroke of the Myosin Motor. J. Mol. Biol. 367, 591−602. (43) Skjaerven, L., Martinez, A., and Reuter, N. (2011) Principal component and normal mode analysis of proteins; a quantitative comparison using the GroEL subunit. Proteins: Struct., Funct., Genet. 79, 232−243. 6938

DOI: 10.1021/acs.biochem.6b00720 Biochemistry 2016, 55, 6931−6939

Article

Biochemistry (44) Amadei, A., Linssen, A. B. M., and Berendsen, H. J. C. (1993) Essential dynamics of proteins. Proteins: Struct., Funct., Genet. 17, 412− 425. (45) Horiuchi, T., and Go, N. (1991) Projection of Monte Carlo and molecular dynamics trajectories onto the normal mode axes: human lysozyme. Proteins: Struct., Funct., Genet. 10, 106−116. (46) García, A. (1992) Large-amplitude nonlinear motions in proteins. Phys. Rev. Lett. 68, 2696−2699. (47) Van Aalten, D. M. F., Amadei, A., Linssen, A. B. M., Eijsink, V. G. H., Vriend, G., and Berendsen, H. J. C. (1995) The essential dynamics of thermolysin: Confirmation of the hinge-bending motion and comparison of simulations in vacuum and water. Proteins: Struct., Funct., Genet. 22, 45−54. (48) Mesentean, S., Fischer, S., and Smith, J. C. (2006) Analyzing large-scale structural change in proteins: Comparison of principal component projection and Sammon mapping. Proteins: Struct., Funct., Genet. 64, 210−218. (49) Lou, H., and Cukier, R. I. (2006) Molecular dynamics of apoadenylate kinase: A distance replica exchange method for the free energy of conformational fluctuations. J. Phys. Chem. B 110, 24121− 24137. (50) Tournier, A. L., and Smith, J. C. (2003) Principal components of the protein dynamical transition. Phys. Rev. Lett. 91, 208106. (51) Kitao, A., and Go, N. (1991) Conformational dynamics of polypeptides and proteins in the dihedral angle space and in the cartesian coordinate space: Normal mode analysis of deca-alanine. J. Comput. Chem. 12, 359−368. (52) Weikl, T. R., and Von Deuster, C. (2009) Selected-fit versus induced-fit protein binding: Kinetic differences and mutational analysis. Proteins: Struct., Funct., Genet. 75, 104−110. (53) Csermely, P., Palotai, R., and Nussinov, R. (2010) Induced fit, conformational selection and independent dynamic segments: An extended view of binding events. Trends Biochem. Sci. 35, 539−546. (54) Bucher, D., Grant, B. J., and McCammon, J. A. (2011) Induced fit or conformational selection? the role of the semi-closed state in the maltose binding protein. Biochemistry 50, 10530−10539. (55) Koshland, D. E. (1958) Application of a Theory of Enzyme Specificity to Protein Synthesis. Proc. Natl. Acad. Sci. U. S. A. 44, 98− 104. (56) Ma, B., Kumar, S., Tsai, C. J., and Nussinov, R. (1999) Folding funnels and binding mechanisms. Protein Eng., Des. Sel. 12, 713−720. (57) Tsai, C. J., Ma, B., Sham, Y. Y., Kumar, S., and Nussinov, R. (2001) Structured disorder and conformational selection. Proteins: Struct., Funct., Genet. 44, 418−427. (58) Ma, B., and Nussinov, R. (2010) Enzyme dynamics point to stepwise conformational selection in catalysis. Curr. Opin. Chem. Biol. 14, 652−659. (59) Boehr, D. D., Nussinov, R., and Wright, P. E. (2009) The role of dynamic conformational ensembles in biomolecular recognition. Nat. Chem. Biol. 5, 789−796. (60) Okazaki, K.-I., and Takada, S. (2008) Dynamic energy landscape view of coupled binding and protein conformational change: inducedfit versus population-shift mechanisms. Proc. Natl. Acad. Sci. U. S. A. 105, 11182−11187. (61) Grant, B. J., Gorfe, A. A., and McCammon, J. A. (2010) Large conformational changes in proteins: Signaling and other functions. Curr. Opin. Struct. Biol. 20, 142−147. (62) Li, Y., Gong, Y., Shi, G., Blaszczyk, J., Ji, X., and Yan, H. (2002) Chemical Transformation Is Not Rate-Limiting in the Reaction Catalyzed by Escherichia coli 6-Hydroxymethyl-7,8- dihydropterin Pyrophosphokinase. Biochemistry 41, 8777−8783. (63) Wade, R. C., Luty, B. a, Demchuk, E., Madura, J. D., Davis, M. E., Briggs, J. M., and McCammon, J. a. (1994) Simulation of enzymesubstrate encounter with gated active sites. Nat. Struct. Biol. 1, 65−69. (64) McCammon, J. A. (2011) Gated Diffusion-controlled Reactions. BMC Biophys. 4, 4.

6939

DOI: 10.1021/acs.biochem.6b00720 Biochemistry 2016, 55, 6931−6939