Molecular basis of HHQ biosynthesis: molecular dynamics simulations, enzyme kinetic and surface plasmon resonance studies
- Anke Steinbach†1,
- Christine K Maurer†1,
- Elisabeth Weidel3,
- Claudia Henn1, 4,
- Christian Brengel1,
- Rolf W Hartmann1, 2 and
- Matthias Negri1Email author
© Steinbach et al.; licensee BioMed Central Ltd. 2013
Received: 13 December 2012
Accepted: 25 July 2013
Published: 1 August 2013
PQS (Pseudomonas Quinolone Signal) and its precursor HHQ are signal molecules of the P. aeruginosa quorum sensing system. They explicate their role in mammalian pathogenicity by binding to the receptor PqsR that induces virulence factor production and biofilm formation. The enzyme PqsD catalyses the biosynthesis of HHQ.
Enzyme kinetic analysis and surface plasmon resonance (SPR) biosensor experiments were used to determine mechanism and substrate order of the biosynthesis. Comparative analysis led to the identification of domains involved in functionality of PqsD. A kinetic cycle was set up and molecular dynamics (MD) simulations were used to study the molecular bases of the kinetics of PqsD. Trajectory analysis, pocket volume measurements, binding energy estimations and decompositions ensured insights into the binding mode of the substrates anthraniloyl-CoA and β-ketodecanoic acid.
Enzyme kinetics and SPR experiments hint at a ping-pong mechanism for PqsD with ACoA as first substrate. Trajectory analysis of different PqsD complexes evidenced ligand-dependent induced-fit motions affecting the modified ACoA funnel access to the exposure of a secondary channel. A tunnel-network is formed in which Ser317 plays an important role by binding to both substrates. Mutagenesis experiments resulting in the inactive S317F mutant confirmed the importance of this residue. Two binding modes for β-ketodecanoic acid were identified with distinct catalytic mechanism preferences.
Quorum sensing (QS) is a chemical cell-to-cell communication system in bacteria ruled by small extracellular signal molecules. It coordinates the social life of bacteria by regulating many group-related behaviours, such as biofilm formation and virulence factor production [1–5]. Anti-QS has been recognized as an attractive strategy in the fight against bacteria  based on anti-virulence and anti-biofilm action and not on bacterial killing.
The opportunistic Gram-negative pathogen P. aeruginosa is a good model to study the complexity of QS systems [1, 4]. At least three distinct QS pathways are known which regulate in a hierarchical manner the QS-dependent target gene expression. The first two QS systems, las and rhl, utilize N-acyl homoserine lactones (C4- and C12-AHL) and the receptors LasR and RhlR . The third QS-system is 2-alkyl-4-hydroxyquinoline (HAQ)-dependent and specific for P. aeruginosa and some Burkholderia strains [10–12]. PQS ( P seudomonas Quinolone Signal) and to a lesser extent its precursor HHQ (2-heptyl-4-hydroxyquinoline) activate PqsR [13–15].
A key enzyme of the PQS biosynthesis pathway is PqsD (PQB biosynthetic 3-oxoacyl-[acyl-carrier-protein] [ACP] synthase III; EC 18.104.22.168), which catalyses the formation of HHQ by “head-to-head” decarboxylative condensation of anthranilate (as anthraniloyl-CoA; ACoA) and β-ketodecanoic acid (βK) [16, 17].
Several groups have proven that P. aeruginosa pqsD knock-out mutant as well as PQS-deficient P. aeruginosa strains have an attenuated pathogenicity in nematode and mouse models evidencing the significance of PQS signalling in mammalian pathogenesis . Increased PQS levels have been detected in lungs of cystic fibrosis patients supportive for an active role of QS in chronic lung infections [19–21]. These findings and in particular the recent identification of the first class of PqsD inhibitors that reduce biofilm and virulence factor formation in P. aeruginosa validates PqsD as a target for the development of anti-infectives .
PqsD is a homodimeric bi-substrate enzyme with high structural similarity to FabH and other β-ketoacyl-[ACP] synthases III (KAS III). They share a common thiolase fold (αβαβα), a long tunnel to the active site, and the same catalytic residues [23–25]. Three PDB structures of PqsD exist : as apoform (3H76), as Cys112-ligated anthranilate (CSJ) complex with ACoA molecules in the primary funnel (3H77) and as Cys112Ala mutant in complex with anthranilic acid (3H78) . In all three structures the catalytic centre is accessible by two channels in L-shape: the primary CoA/ACP-funnel, and the shorter secondary channel (Additional file 1: Figure. SI1). However, the molecular details of ACoA access and, in particular, the binding mode and the subsequent incorporation of βK are unknown.
Knowledge of the kinetics and of the conformational flexibility of an enzyme can significantly contribute to a successful rational drug design [27–29]. Herein we study the molecular basis of PqsD and the HHQ biosynthesis combining experimental and in silico methods. Enzyme kinetic analysis and surface plasmon resonance (SPR) biosensor experiments were used to determine the mechanism and the substrate order of the biosynthesis; comparative analysis of PqsD to homologous KAS-III enzymes was useful to identify domains specific for PqsD functionality. Molecular dynamics (MD) simulations were carried out to explore the binding modes of ACoA and βK as well as the conformational flexibility of PqsD.
Results and discussion
Knowledge of enzyme kinetics for multi-substrate reactions is helpful to set up and interpret MD simulations. We performed biochemical and biophysical studies to determine the underlying kinetic mechanism of PqsD.
Biochemical and biophysical characterization hint at ping-pong kinetic mechanism of PqsD
The KM data (ACoA 0.875 ±0.140 μM, βK 1300 ±158 μM) correlate well with the KD values determined with SPR by our group (ACoA 1.08 μM, βK 2.95 mM) . Also, the kinetic parameters, derived mutually varying both substrates (Figure 1C) and fitting the data with the ping-pong Equation (1) are within the range of the apparent values determined by Pistorius et al. (KM app,ßK = 598.5 ± 106 μM; Vmax = 495.8 ± 37.5 fmol HHQ/min/pmol PqsD; kcat (PqsD as monomer) = 0.01 s-1). (1)  SPR biosensor assays were performed to assess the influence of substrate addition order on the HHQ product formation. Firstly, as recently reported , PqsD was immobilized to the SPR chip and ACoA injected; the increase in the response-line preserved also after washing was an indicator for the covalent linkage of anthraniloyl to Cys112 (Figure 1D and Additional file 1: SI1). The subsequent addition of βK displaced the anthraniloyl from Cys112 with HHQ formation as confirmed by mass spectrometry (see supporting information Text SI2). Strikingly, repeating the experiments with inverted substrate order (βK first, then ACoA) resulted in less than half of HHQ formation (Additional file 1: Figure SI2A) supportive for the preferential substrate order deducible from the kinetic analysis of the HHQ biosynthesis. However, the latter finding cannot be excluded to be at least in part due to substrate inhibition. The different plots of the kinetic data and the “inverted” SPR experiments sustain the idea that PqsD follows a ping-pong kinetic mechanism with ACoA as the first substrate (Figure 1E). Based on these results a putative kinetic cycle for HHQ biosynthesis was set-up and different PqsD-ligand complexes chosen to simulate the single steps (Additional file 1: Figure. SI2B).
As evident in PqsD and FabHs 3D-structures the sLs border the active site and contribute in the formation of a central cavity in the dimer structures, which is surmounted by the hL helixes H11 and which can be filled by water molecules (Figure 2 and Additional file 1: SI1B). Mutagenesis studies in FabH showed that residues placed within this sL play an exceptional role for the substrate specificity : bulky residues (Phe87; E. coli FabH - ecFabH) determine a clear preference for the short-chained acetyl-CoA, smaller residues (Thr87; M. tuberculosis FabH - mtFabH) account for a long-chained substrate preference, such as lauroyl-CoA [37–40]. PqsD and Burkholderia FabH2 both produce HHQ and both share the same palindromic sequence (−S82PDHHDPS89-) in the sL. According to the sequence alignment for PqsD and FabH2 we identified Asp87 as positional analogue of ecFabH Phe87 and mtFabH Thr87 (Figure 2); consequently, also, Asp87 might be involved in the substrate recognition process.
Mutagenesis studies on ecFabH showed that exchanging basic residues surrounding the primary funnel access to acid residues strongly affected CoA- and ACP-binding . As shown in Figure 2 in PqsD the corresponding residues are negatively charged or neutral (i.e. Glu227, Glu269, Gln270; violet triangle), which is reflected on a tertiary structure level in a modified electrostatic potential surface (Additional file 1: Figure SI5). The access to the primary funnel of PqsD is surrounded by basic residues, in part not present in other FabHs, forming a large “cationic belt” (i.e. Arg36, Arg151, Arg153, Arg221, Arg223, Arg262). This basic network forms part of the CoA and ACP binding site .
Molecular dynamics simulations (A-F) of the main steps of the kinetic cycle
Internal pockets volume variations
Primary funnel(chain A)
Primary funnel(chain B)
Secondary channel(chain A)
Secondary channel(chain B)
Estimated binding free energies (ΔG bind ) using MM-GB/PBSA methods
PqsD + ACoA ( chain B )
PqsD + ACoA ( chain A )
PqsD + ACoA ( chain B )
PqsD- CSJ + single β K, primary funnel ( chain A )
PqsD- CSJ + single β K, secondary channel ( chain A )
PqsD- CSJ + β K in secondary channel ( chain A )
PqsD- CSJ + β K in secondary channel ( chain B )
PqsD + HHQ ( chain B )
Accounting for all the MD simulations (B-F) a rather broad conformational ensemble was gathered. Thereby, most of the fluctuations were located in the upper third of PqsD (Additional file 1: Figure SI7B) in regions also found disordered in several FabH crystal structures. This implies that conformational rearrangements in any of them can significantly impact on the functioning of the enzymes. The most flexible domain in all the MD simulations is the dimer interface including hL and h8-9 (Additional file 1: Figure SI8).
In the apoform trajectory B the enzyme floats between different conformers. On contrary, in presence of the different substrates the equilibrium is selectively shifted toward one preferred structure. This is reflected in the volume variations of internal cavities and channels (Table 1 and Additional file 1: Figure SI10). In particular, tracking the central cavity volume of the MD simulations B-F resulted in a sinusoidal, “heart-beat”-like volume-profile (Table 1): in the apoform simulation (B) the volume is reduced; in the single-ACoA complex (C1) the cavity volume increases; with two ACoA molecules (C2) as well as for the CSJ-PqsD (D) complex the cavity volume decreases. Finally, in the single-βK E2a and, in particular, in the dual-βK simulations E2b the volume increased. The formation of channels from the central cavity into the oxyanion site (close to Asp87 and Ser317), sideways out to the surface, or up to the dimer interface suggests that water molecules might move along these channels depending on the catalytic needs.
The fact that ligand-induced structural changes are observed in the very same regions in presence of diverse ligands makes us confident that a sufficiently large conformational ensemble has been gathered by the MD simulations to represent a good starting point for structure-based drug design and virtual screening. A good example is represented by the motions of Phe218, which is located on the C-terminal β-sheet of the hL of each monomer (Additional file 1: Figure SI11): in the MDs with ACoA C1 and C2, with CSJ D, with βK in the primary funnel E1, and with HHQ F (chain B) it stays turned towards the catalytic centre occluding the access from the catalytic site to secondary channel and dimer interface. On contrary, when βK is in the secondary channel (E2b and chain A of E2a) Phe218 rotated outwards increasing the distance between the centre of mass of Phe218 and the Cα of Cys112 (Additional file 1: Figure SI11); the secondary channel opened out to the dimer interface and the catalytic centre was enlarged. When no ligand is present in the catalytic centre (e.g. in the apoform MD B and in chain A of F), an intermediate position can be observed indicative for the conformational flexibility of this area.
ACoA progression into the catalytic site
Where and how does βK bind in PqsD?
Our SPR experiments showed that using free βK acid as second substrate yields higher HHQ formation than when it is added first. The active site of PqsD PDB structures seems inadequate to handle βK-(ACP) binding and βK-incorporation . However, in PqsD a secondary channel exists similar to that of mtFabH, which can host long-chained β-keto-acids (i.e. lauroyl-CoA) . The access to this channel is lined by polar residues (Arg145, Thr195, Ser317, and Asp87 of the second monomer), whereas its bottom part is rather hydrophobic (Leu81, Leu142, Leu155, Leu159, Leu193, Met194, Phe218 and Met220). Further, this secondary channel borders the central cavity and the dimer interface with the ion-pair Asp87-Arg145 (of the other chain) and Phe218 acting as barrier respectively. As PqsD is clearly capable of HHQ biosynthesis conformational changes are expected, in analogy to mtFabH, that allow HHQ formation and release.
We followed two approaches to determine the most plausible access path and binding mode of βK. In the first, we docked βK into the primary funnel of anthranilate-ligated PqsD. Two main orientations resulted: 1) with the polar head of βK pointing to the catalytic triad (Additional file 1: Figure SI14A); and 2) turned 180° with the carboxylic group interacting with the Arg of the cationic belt. The second binding mode was not supportive for any of the catalytic mechanisms [10, 17].
The second approach was based on the substrate size similarity and the structural homology between PqsD and mtFabH. We superimposed the Cys112-anthranilate PqsD (PDB-ID 3H77) and mtFabH co-crystallized with dodecyl-CoA (PDB-ID 1U6S)  and then transposed and modelled the 3-oxo-undecanoyl from the mtFabH structure into βK in the secondary channel of PqsD (Additional file 1: Figure SI14B). Active site refinement with the LigX module of MOE  with restrained βK resulted in an energy optimized complex with small Cα RMSD (~ 1 Å) compared to the starting complex. This second binding mode of βK looks similar to that obtained by Bera et al.  for decanethiol by superimposing PqsD with the mtFabH structure PDB-ID 2QO1 where the decanethiol is covalently attached to the catalytic Cys.
The MD simulations E1-E2b with βK either in the primary or in the secondary channels were supportive to understand the putative ping-pong kinetic cycle.
MD simulation CSJ-PqsD with βK in the primary funnel of chain B (E1; 34 ns): Only small conformational changes are observed in this MD simulation for βK, which points the 3-oxo-β-keto moiety towards the catalytic triad (Additional file 1: Figure SI15). Thereby, the carboxylate head is trapped in a hydrogen-bond network with His257 and Asn287 holding the β carbon of βK close to the CSJ-sulphur as shown by their favourable energy contributions (Additional file 1: Figure SI15B). Additionally, van der Waals interactions are formed between βK and Leu193, Met220, Met225, Phe226, Pro259, Ile263, and Tyr315 (Additional file 1: Figure SI15).
Catalytic mechanism and βK binding modes
In this study the free acid form of βK was used to test HHQ biosynthesis. Two aspects have thus to be considered: firstly, the free βK-acid is unlikely to exist in bacteria and its KD value in the millimolar range determined by SPR and enzyme kinetics does not fit with the substantial HHQ production in P. aeruginosa. Secondly, inverting the substrate order in either the SPR or the enzyme kinetic studies still yields HHQ formation, although substantially decreased (see Additional file 1: Figure SI2A).
Given the unlikeliness of free βK acid as “in vivo” substrate other events must occur in HHQ biosynthesis, such as involvement of PqsB and PqsC  or ACP-thioester binding, which could reduce the activation energy or favour the kinetics. Holding true the ACP-delivered βK, we notice that the binding site topology and final orientation of βK in the secondary channel (MDs E2a-E2b) does not disturb the access of ACoA (Figure 8C) nor it precludes thioester-delivered βK (Figure 8D). The interactions with CSJ112, Thr195, and Ser317 found for E2a-E2b (see energy contributions in Figure 6C) fit well with the imine/enamine mechanism proposed by Diggle et al.  (Figure 8F). An equal or even more favourable ΔGbind is estimated for the two βK in the dual-βK simulation E2b (−44 kcal/mol – chain A, -26 kcal/mol – chain B) as compared to ΔGbind of the single-βK (−27 kcal/mol in E1; -27 kcal/mol in E2a) (Table 2). This hints at a positive synergistic effect of the simultaneous presence of two βK molecules in the secondary channels, which might facilitate conformational changes necessary to accommodate βK in an energetically favoured pose. In this regard, it is helpful to remember that in the dual-βK simulation a dynamic channel-system is formed, which enlarges the catalytic centre (Additional file 1: Figure SI10), thus accounting for a putative intramolecular cyclization in HHQ formation. Although no conclusive data exists all the above aspects let us favour the secondary channel binding mode as the more probable.
Ser317Phe PqsD mutant
In the MD simulations Ser317 is involved in hydrogen bonds with the carbonyl-group of ACoA (C1 and C2) and with the carbonyl- or the carboxylic-moiety of βK (E2a and E2b). To verify its importance we exchanged Ser317 by site-directed mutagenesis into Phe and determined the catalytic activity by detection of the formed HHQ using UHPLC-MS/MS. Under assay conditions (0.1 μM enzyme, 5 μM ACoA, 70 μM βK) the mutant produced less factor 700 HHQ compared to the wild type. Tenfold higher enzyme concentration of the S317F did not result in an increase in HHQ production indicating a complete abolishment of the catalytic functionality.
In this work we elucidated some of the molecular bases of HHQ biosynthesis. A putative ping-pong kinetic mechanism was determined by enzyme kinetics experiments, which was substantiated by the preferred substrate order (ACoA prior to βK) assessed by SPR. These data were useful to set-up and analyse the MD simulations, which aimed to unmask dynamic motions governing PqsD functionality. MD simulations reveal a kinetic-step dependent adaptation of PqsD to the diverse ligands, a favoured binding mode for βK in the secondary channel, as well as an arginine-assisted progression of ACoA towards the catalytic site. Also, Ser317 was identified as an important binder for ACoA and βK, at least in part confirmed by the inactive S317F PqsD mutant.
In general, SPR studies with PqsD mutants will be helpful to determine the binding mode of inhibitors. Finally, the conformational ensemble retained from the MD simulations will serve as valuable starting point for structure-based design of PqsD inhibitors.
Production and purification of recombinant PqsD inE. coli
The overexpression and purification of PqsD as an N-terminal His6-tagged fusion protein in E. coli BL21 (ʎDE3) using the vector pET28b(+) -pqsD was performed as described by Pistorius et al. . To remove the His6-tag the protein was subjected to thrombin cleavage performed at 16°C for 16 h in a 50 mM Tris–HCl buffer, pH 8.0, containing 150 mM NaCl, 1 mM 2-ME, 2.5 mM CaCl2 and 1 unit thrombin per mg protein followed by a second passage through the His Trap HP 5 column. The protein was frozen in liquid nitrogen.
Preparation of S317F PqsD mutant
S317F PqsD mutant was generated using the QuikChange Site-Directed Mutagenesis Kit (Stratagene, La Jolla, CA) according to the manufacturer’s instructions using the pET28b(+)/pqsD plasmid as template and the mutagenesis primers 5′ GCTGGTCCTGACCTACGGTTTTGGCGCGACCTGGGGCG 3′ and 5′CGCCCCAGGTCGCGCCAAAACCGTAGGTCAGGACCAGC 3′. Plasmid DNA was purified using the GenEluteTM HP Plasmid Miniprep Kit (Sigma-Aldrich, St. Louis, MO) and sequenced to confirm the site-directed mutation.
Enzyme kinetic analysis
The PqsD catalysed formation of HHQ was analysed using a UHPLC-MS/MS based assay performed in 96-well microtiter plates (Greiner) using the method of Pistorius et al.  with some modifications. First, the purified enzyme PqsD (0.8 μM; in 50 mM MOPS, pH 7.0, 0.016% (v/v) Triton X-100) was preincubated without substrates for 5 min at 37°C. The reaction buffer (15 μL; 50 mM MOPS, pH 7.0) and the substrates ACoA (20 μL; concentrations: 2–40 μM) and ßK (20 μL; concentrations: 240–4000 μM) were added. The reaction was started by the addition of preincubated enzyme (25 μL; 0.8 μM) resulting in a total reaction volume of 80 μL with the following final concentrations: PqsD 0.25 μM, ACoA 0.5 - 10.0 μM and ßK 60–1000 μM, Triton X-100 0.005%, methanol 2%. The reaction was stopped after 4 min at 37°C by adding 80 μL of methanol containing 1 μM of the internal standard amitriptyline. For each sample, the reactions were performed in quadruplicate. HHQ-formation was detected using UHPLC-MS/MS according to method in supplementary information (Additional file 1: Text SI1). Data were plotted and analysed using GraphPad Prism 5 software.
Synthesis of Anthraniloyl-S-Coenzyme A thioester (ACoA). ACoA was synthesized from isatoic anhydride and coenzyme A (CoA) as described by Simon and Shemin .
Synthesis of ethyl 3-oxodecanoate, 3-oxodecanoic acid (β ketodecanoic acid), and of HHQ (2-heptylquinolin-4(1H)-one). Synthesis as described by Lu et al. .
Surface Plasmon resonance (SPR)
SPR binding studies were performed using a Reichert SR7000DC optical biosensors instrument (Reichert Technologie, Depew, NY 14043 USA). HC1000m sensor chips were purchased from Xantec Analytics (Düsseldorf, Germany).
Immobilization of H6PqsD or H6PqsD S317F
Overexpression and purification of PqsD or of the S317F PqsD mutant was performed as previously described . H 6 PqsD or H 6 PqsD S317F was immobilized on HC1000m sensor chip using standard amine coupling chemistry at 25°C analogous to the manufacturer’s instruction. H 6 PqsD or H 6 PqsD S317F was diluted into 10 mM sodium acetate (pH 4.5) to a concentration of 100 μg/mL and coupled to the surfaces with densities between 15000 and 20000 RU.
Catalytic activity of PqsD
ACoA was diluted into running buffer to 10 μM. β-ketodecanoate (βK) was dissolved in methanol to a 10 mM stock solution and diluted into running buffer to 20 μM. In the first experiment ACoA was injected for 5 min association and 10 min dissociation time, followed by a 20 min injection of βK. In the second experiment βK was added to the running buffer (100 μM in 50 mM Tris, pH 8.0, 150 mM NaCl, 0.1% Triton X-100). The ACoA-injection (10 μM for 5 min) followed when the binding site was saturated, indicated by a stable baseline. In all experiments the flow through was collected. After ACoA addition CoA emission was detected using UHPLC-MS/MS as reported elsewhere . After addition of the second substrate (experiment dependent) the flow through was extracted with 1 mL CHCl3, evaporated and diluted in 50 μL MeOH. HHQ formation was detected using UHPLC-MS/MS (see Additional file 1: Text SI1).
SPR study with S317F PqsD mutant
For comparison of ACoA binding to PqsD wild- type and to S317F PqsD mutant, the study was performed as in the first experiment described above.
Docking of βK and HHQ. βK and HHQ were docked each 50 times with GOLDv5.0  using the GOLDSCORE function . The docking site was defined including all residues within 9 Å of ACoA found in PDB structure 3H77. Covalent ligated PqsD and apoform PqsD were used to dock βK and HHQ, respectively. The default GOLD parameters were used.
System setup was performed as follows for all simulated systems. Atomic coordinates were taken from PDB-ID 3H76 for MD simulations A, B, C1, C2 and F, and from 3H77 for D, E1, E2a and E2b. Water molecules and ions present in the crystals were removed. The protonation states were determined at pH 7.4 with the Protonate3D module of MOE and the enzyme complexes then minimized using the MOE module ligX . The solvated systems were set up using the AMBER11  program xLeap with AMBER99SB force field . A 9 Å pad of TIP3P waters was added to solvate each system as octahedral box. Neutralizing counter ions were added to each system.
Parameters for ACoA, βK, HHQ and the Cys112-bound anthranilate (CSJ) were determined using the sqm routine of AmberTools1.5 . For each ligand AM1-BCC charges were computed. For ACoA and βK a net charge of -3 and -1 was set, respectively. For CSJ, which was taken from the PDB-ID 3H77, the Cys parameters were used as starting point followed by parameterization with the sqm routine.
MD simulations were performed with the parallelized PMEMD module of AMBER 11. The starting PqsD complexes were minimized and equilibrated with the backbone atoms restricted by harmonic restraints of initially 10 kcal mol −1 Å–2 and then progressively reduced to 5, 2, 1 and 0 kcal/mol. The systems were heated to 300 K in the canonical NVT ensemble (constant number of particles, N; constant volume, V; constant temperature, T) using a Langevin thermostat, with a collision frequency of 3.0 ps−1 Å−2. Production runs were then made for 30–37 ns duration in the NPT ensemble at 300 K. As with the heating, the temperature was controlled with a Langevin thermostat (but with a 1.0 ps−1 collision frequency). The time step used for all stages was 2 fs and all hydrogen atoms were constrained using the SHAKE algorithm . Long-range electrostatics were included on every step using the Particle Mesh Ewald algorithm with a 4th order B-spline interpolation .
B-factor, distances and RMSD time series were calculated using the cpptraj analysis tool of the AmberTools 1.5 package.
Structures were sampled at 20 ps intervals. RMSD values were calculated for the enzyme, but also for each of the following regions individually, facilitating the interpretation of the data: adenosine binding site (aBS), “substrate-loop” (sL), helix8-9 (h8-9), hairpin loop (hL), helix 12 (h12), oxyanion loop (oL).
Volume variations of internal cavities
The volume variations (Å) over the time (ns) for five cavities in PqsD were tracked for all PqsD dimer MD simulations using the software package fpocket2 . All MD snapshots were superimposed using the Cα of the apoform PDB-ID 3H76 as reference structure. Default parameters for the identification of small cavities and channels were used. All plots are added in supplementary materials.
Binding energies ΔGbind for ACoA, βK and HHQ were estimated by conventional MM-GBSA methods (Molecular Mechanic – General Born Surface Area)  using snapshots of the simulations sampled every 30 ps. Energy decomposition analyses with Generalized Born solvent were performed with per-residue decomposition and 1–4 interactions added to the electrostatic and Van der Waals terms (idecomp = 2). More details to the MM-GBSA method are in Additional file 1: Text_SI2.
A detailed description of the MD simulations A-F, cavity volume variation-versus-time profiles (Additional file 1: Figure SI7), time-dependent ΔGbind profiles (Additional file 1: Figure SI10), and RMSD plots (Additional file 1: Figure SI13-SI20) are added as additional data. The RMSD analyses of the different regions helped to visualize motions in the MD simulations not visible from the all-atom RMSD plots for the entire protein and complemented the amino acid fluctuation analyses shown in Figure 3.
Figures and plots
Pseudomonas Quinolone Signal
- MD(s) simulations:
Molecular Dynamics simulations
surface plasmon resonance
acyl carrier protein
E. coli FabH
M. tuberculosis FabH.
We thank Cenbin Lu for the synthesis of HHQ and ß-ketodecanoic acid, Simone Amann for her support in the kinetic experiments, Dr. Stefan Boettcher for support with analytics.
- Schuster M, Greenberg EP: A network of networks: Quorum-sensing gene regulation in Pseudomonas aeruginosa. Int J Med Microbiol. 2006, 296: 73-81. 10.1016/j.ijmm.2006.01.036.View ArticleGoogle Scholar
- Williams P, Cámara M: Quorum sensing and environmental adaptation in Pseudomonas aeruginosa: a tale of regulatory networks and multifunctional signal molecules. Curr Opin Microbiol. 2009, 12: 182-191. 10.1016/j.mib.2009.01.005.View ArticleGoogle Scholar
- Bjarnsholt T, Givskov M: The role of quorum sensing in the pathogenicity of the cunning aggressor Pseudomonas aeruginosa. Anal Bioanal Chem. 2007, 387: 409-414. 10.1007/s00216-006-0774-x.View ArticleGoogle Scholar
- Jimenez PN, Koch G, Thompsona JA, Xaviera KB, Coolb RH, Quaxb WJ: The Multiple Signaling Systems Regulating Virulence in Pseudomonas aeruginosa. Microbiol Mol Biol Rev. 2012, 76: 46-65. 10.1128/MMBR.05007-11.View ArticleGoogle Scholar
- Reen FJ, Mooij MJ, Holcombe LJ, McSweeney CM, McGlacken GP, Morrissey JP, O’Gara F: The Pseudomonas quinolone signal (PQS), and its precursor HHQ, modulate interspecies and interkingdom behaviour. FEMS Microbiol Ecol. 2011, 77: 413-428. 10.1111/j.1574-6941.2011.01121.x.View ArticleGoogle Scholar
- Lesic B, Lépine F, Déziel E, Zhang J, Zhang Q, Padfield K, Castonguay M, Milot S, Stachel S, Tzika AA, Tompkins RG, Rahme LG: Inhibitors of Pathogen Intercellular Signals as Selective Anti-Infective Compounds. PLoS Pathog. 2007, 3: e126-10.1371/journal.ppat.0030126.View ArticleGoogle Scholar
- Gambello MJ, Iglewski BH: Cloning and characterization of the Pseudomonas aeruginosa lasR gene, a transcriptional activator of elastase expression. J Bacteriol. 1991, 173: 3000-3009.Google Scholar
- Ochsner UA, Reiser J: Autoinducer-mediated regulation of rhamnolipid biosurfactant synthesis in Pseudomonas aeruginosa. Proc Natl Acad Sci USA. 1995, 92: 6424-6428. 10.1073/pnas.92.14.6424.View ArticleGoogle Scholar
- Shiner EK, Rumbaugh KP, Williams SC: Inter-kingdom signaling: deciphering the language of acyl homoserine lactones. FEMS Microbiol Rev. 2005, 29: 935-947. 10.1016/j.femsre.2005.03.001.View ArticleGoogle Scholar
- Diggle SP, Cornelis P, Williams P, Cámara M: 4-Quinolone signaling in Pseudomonas aeruginosa: Old molecules, new perspectives. Int J Med Microbiol. 2006, 296: 83-91. 10.1016/j.ijmm.2006.01.038.View ArticleGoogle Scholar
- Dubern JF, Diggle SP: Quorum sensing by 2-alkyl-4-quinolones in Pseudomonas aeruginosa and other bacterial species. Mol Biosyst. 2008, 4: 882-888. 10.1039/b803796p.View ArticleGoogle Scholar
- Vial L, Lépine F, Milot S, Groleau MC, Dekimpe V, Woods DE, Déziel E: Burkholderia pseudomallei, B. thailandensis, and B. ambifaria produce 4-hydroxy-2-alkylquinoline analogues with a methyl group at the 3 position that is required for quorum-sensing regulation. J Bacteriol. 2008, 190: 5339-5352. 10.1128/JB.00400-08.View ArticleGoogle Scholar
- Cao H, Krishnan G, Goumnerov B, Tsongalis J, Tompkins R, Rahme LG: A quorum sensing-associated virulence gene of Pseudomonas aeruginosa encodes a LysR-like transcription regulator with a unique self-regulatory mechanism. Proc Natl Acad Sci USA. 2001, 98: 14613-14618. 10.1073/pnas.251465298.View ArticleGoogle Scholar
- Xiao G, Déziel E, He J, Lépine F, Lesic B, Castonguay MH, Milot S, Tampakaki AP, Stachel SE, Rahme LG: MvfR, a key Pseudomonas aeruginosa pathogenicity LTTR-class regulatory protein, has dual ligands. Mol Microbiol. 2006, 62: 1689-1699. 10.1111/j.1365-2958.2006.05462.x.View ArticleGoogle Scholar
- Gallagher LA, McKnight SL, Kuznetsova MS, Pesci EC, Manoil C: Functions required for extracellular quinolone signaling by Pseudomonas aeruginosa. J Bacteriol. 2002, 184: 6472-6480. 10.1128/JB.184.23.6472-6480.2002.View ArticleGoogle Scholar
- Bredenbruch F, Nimtz M, Wray V, Morr M, Müller R, Häussler S: Biosynthetic pathway of Pseudomonas aeruginosa 4-hydroxy-2-alkylquinolines. J Bacteriol. 2005, 187: 3630-3635. 10.1128/JB.187.11.3630-3635.2005.View ArticleGoogle Scholar
- Pistorius D, Ullrich A, Lucas S, Hartmann RW, Kazmaier U, Müller R: Biosynthesis of 2-Alkyl-4(1H)-quinolones in Pseudomonas aeruginosa: potential for therapeutic interference with pathogenicity. Chembiochem. 2011, 12: 850-853. 10.1002/cbic.201100014.View ArticleGoogle Scholar
- Déziel E, Gopalan S, Tampakaki AP, Lépine F, Padfield KE, Saucier M, Xiao G, Rahme LG: The contribution of MvfR to Pseudomonas aeruginosa pathogenesis and quorum sensing circuitry regulation: multiple quorum sensing-regulated genes are modulated without affecting lasRI, rhlRI or the production of N-acyl-L-homoserine lactones. Mol Microbiol. 2005, 55: 998-1014.View ArticleGoogle Scholar
- Collier DN, Anderson L, McKnight SL, Noah TL, Knowles M, Boucher R, Schwab U, Gilligan P, Pesci EC: A bacterial cell to cell signal in the lungs of cystic fibrosis patients. FEMS Microbiol Lett. 2002, 21: 41-46.View ArticleGoogle Scholar
- Willcox MDP, Zhu H, Conibear TCR, Hume EBH, Givskov M, Kjelleberg S, Rice SA: Role of quorum sensing by Pseudomonas aeruginosa in microbial keratitis and cystic fibrosis. Microbiology. 2008, 154: 2184-2194. 10.1099/mic.0.2008/019281-0.View ArticleGoogle Scholar
- Bjarnsholt T, Jensen PØ, Jakobsen TH, Phipps R, Nielsen AK, Rybtke MT, Tolker-Nielsen T, Givskov M, Høiby N, Ciofu O, the Scandinavian Cystic Fibrosis Study Consortium: Quorum Sensing and Virulence of Pseudomonas aeruginosa during Lung Infection of Cystic Fibrosis Patients. PLoS ONE. 2010, 5: e10115-10.1371/journal.pone.0010115.View ArticleGoogle Scholar
- Storz MP, Maurer CK, Zimmer C, Wagner N, Brengel C, De Jong JC, Lucas S, Müsken M, Häussler S, Steinbach A, Hartmann RW: Validation of PqsD as an anti-biofilm target in Pseudomonas aeruginosa by development of small-molecule inhibitors. J Am Chem Soc. 2012, 134: 16143-16146. 10.1021/ja3072397.View ArticleGoogle Scholar
- Bera AK, Atanasova V, Robinson H, Eisenstein E, Coleman JP, Pesci EC, Parsons JF: Structure of PqsD, a Pseudomonas quinolone signal biosynthetic enzyme, in complex with anthranilate. Biochemistry. 2009, 48: 8644-8655. 10.1021/bi9009055.View ArticleGoogle Scholar
- Heath RJ, Rock CO: The Claisen condensation in biology. Nat Prod Rep. 2002, 19: 581-596. 10.1039/b110221b.View ArticleGoogle Scholar
- Haapalainen AM, Meriläinen G, Wierenga RK: The thiolase superfamily: condensing enzymes with diverse reaction specificities. Trends Biochem Sci. 2006, 31: 64-71. 10.1016/j.tibs.2005.11.011.View ArticleGoogle Scholar
- Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE: The Protein Data Bank. Nucleic Acids Res. 2000, 28: 235-242. 10.1093/nar/28.1.235.View ArticleGoogle Scholar
- Negri M, Recanatini M, Hartmann RW: Insights in 17β-HSD1 enzyme kinetics and ligand binding by dynamic motion investigation. PLoS One. 2010, 5 (8): e12026-10.1371/journal.pone.0012026.View ArticleGoogle Scholar
- Negri M, Recanatini M, Hartmann RW: Computational investigation of the binding mode of bis(hydroxylphenyl)arenes in 17β-HSD1: molecular dynamics simulations, MM-PBSA free energy calculations, and molecular electrostatic potential maps. J Comput Aided Mol Des. 2011, 25: 795-811. 10.1007/s10822-011-9464-7.View ArticleGoogle Scholar
- Pérez-Castillo Y, Froeyen M, Cabrera-Pérez MÁ, Nowé A: Molecular dynamics and docking simulations as a proof of high flexibility in E. coli FabH and its relevance for accurate inhibitor modeling. J Comput Aided Mol Des. 2011, 25: 371-393. 10.1007/s10822-011-9427-z.View ArticleGoogle Scholar
- Davies C, Heath RJ, White SW, Rock CO: The 1.8 A crystal structure and active-site architecture of beta-ketoacyl-acyl carrier protein synthase III (FabH) from escherichia coli. Structure Fold.Des. 2000, 8: 185-10.1016/S0969-2126(00)00094-0. 195View ArticleGoogle Scholar
- Zhang YM, Frank MW, Zhu K, Mayasundari A, Rock CO: PqsD is responsible for the synthesis of 2,4-dihydroxyquinoline, an extracellular metabolite produced by Pseudomonas aeruginosa. J Biol Chem. 2008, 283: 28788-28794. 10.1074/jbc.M804555200.View ArticleGoogle Scholar
- Henn C, Boettcher B, Steinbach A, Hartmann RW: Catalytic Enzyme Activity on a Biosensor Chip: Combination of Surface Plasmon Resonance and Mass Spectrometry. Anal Biochem. 2012, 428: 28-30. 10.1016/j.ab.2012.05.024.View ArticleGoogle Scholar
- Segel IH: Equation IX-143. in Enzyme Kinetics: Behavior and Analysis of Rapid Equilibrium and Steady-State Enzyme Systems. 1975, New York: John Wiley and Sons,Google Scholar
- Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local alignment search tool. J Mol Biol. 1990, 215: 403-410.View ArticleGoogle Scholar
- Do CB, Mahabhashyam MSP, Brudno M, Batzoglou S: PROBCONS: Probabilistic Consistency-based Multiple Sequence Alignment. Genome Res. 2005, 15: 330-340. 10.1101/gr.2821705.View ArticleGoogle Scholar
- Waterhouse AM, Procter JB, Martin DM, Clamp M, Barton GJ: Jalview Version 2 - a multiple sequence alignment editor and analysis workbench. Bioinformatics. 2009, 25: 1189-1191. 10.1093/bioinformatics/btp033.View ArticleGoogle Scholar
- Gajiwala KS, Margosiak M, Lu J, Cortez J, Su Y, Nie Z, Appelt K: Crystal structures of bacterial FabH suggest a molecular basis for the substrate specificity of the enzyme. FEBS Lett. 2009, 583: 2939-2946. 10.1016/j.febslet.2009.08.001.View ArticleGoogle Scholar
- Qiu X, Choudhry AE, Janson CA, Grooms M, Daines RA, Lonsdale JT, Khandekar SS: Crystal structure and substrate specificity of the β-ketoacyl-acyl carrier protein synthase III (FabH) from Staphylococcus aureus. Protein Sci. 2005, 14: 2087-2094. 10.1110/ps.051501605.View ArticleGoogle Scholar
- Choi KH, Kremer L, Besra GS, Rock CO: Identification and substrate specificity of beta-ketoacyl (acyl carrier protein) synthase III (mtFabH) from Mycobacterium tuberculosis. J Biol Chem. 2000, 275: 28201-28207.Google Scholar
- Musayev F, Sachdeva S, Scarsdale JN, Reynolds KA, Wright HT: Crystal structure of a substrate complex of Mycobacterium tuberculosis beta-ketoacyl-acyl carrier protein synthase III (FabH) with lauroyl-coenzyme A. J Mol Biol. 2005, 346: 1313-1321. 10.1016/j.jmb.2004.12.044.View ArticleGoogle Scholar
- Zhang Y-M, Rao MS, Heath RJ, Price AC, Olson AJ, Rock CO, White SW: Identification and analysis of the acyl carrier protein (ACP) docking site on β-ketoacyl-ACP synthase III. J Biol Chem. 2001, 276: 8231-8238. 10.1074/jbc.M008042200.View ArticleGoogle Scholar
- Schmidtke P, Bidon-Chanal A, Luque FJ, Barril X: MDpocket: open-source cavity detection and characterization on molecular dynamics trajectories. Bioinformatics. 2011, 27: 3276-3285. 10.1093/bioinformatics/btr550.View ArticleGoogle Scholar
- Emekli U, Schneidman-Duhovny D, Wolfson HJ, Nussinov R, Haliloglu T: HingeProt: automated prediction of hinges in protein structures. Proteins. 2008, 70: 1219-1227.View ArticleGoogle Scholar
- Flores SC, Keating KS, Painter J, Morcos F, Nguyen K, Merritt EA, Kuhn LA, Gerstein MB: HingeMaster: normal mode hinge prediction approach and integration of complementary predictors. Proteins. 2008, 73: 299-319. 10.1002/prot.22060.View ArticleGoogle Scholar
- Sachdeva S, Musayev FN, Alhamadsheh MM, Scarsdale JN, Wright HT, Reynolds KA: Separate entrance and exit portals for ligand traffic in Mycobacterium tuberculosis FabH. Chem Biol. 2008, 15: 402-412. 10.1016/j.chembiol.2008.03.007.View ArticleGoogle Scholar
- Molecular Operating Environment (MOE): Chemical Computing Group Inc. 2010, Montreal, QC, Canada: 1010 Sherbooke St. West, Suite #910Google Scholar
- Simon EJ, Shemin D: The Preparation of S-Succinyl Coenzyme A. J Am Chem Soc. 1953, 75: 2520-2522.View ArticleGoogle Scholar
- Lu C, Kirsch B, Zimmer C, De Jong JC, Henn C, Maurer CK, Müsken M, Häussler S, Steinbach A, Hartmann RW: Discovery of antagonists of PqsR, a key player in 2-alkyl-4-quinolone-dependent quorum sensing in Pseudomonas aeruginosa. Chem Biol. 2010, 19: 381-390.View ArticleGoogle Scholar
- Jones G, Willett P, Glen RC, Leach AR, Taylor R: Development and validation of a genetic algorithm for flexible docking. J Mol Biol. 1997, 267: 727-748. 10.1006/jmbi.1996.0897.View ArticleGoogle Scholar
- Verdonk ML, Cole JC, Hartshorn MJ, Murray CW, Taylor RD: Improved protein-ligand docking using GOLD. Proteins. 2003, 52: 609-623. 10.1002/prot.10465.View ArticleGoogle Scholar
- Case DA, Darden TA, Cheatham TE, Simmerling CL, Wang J, Duke RE, Luo R, Walker RC, Zhang W, Merz KM, Roberts B, Wang B, Hayik S, Roitberg A, Seabra G, Kolossváry I, Wong KF, Paesani F, Vanicek J, Liu J, Wu X, Brozell SR, Steinbrecher T, Gohlke H, Cai Q, Ye X, Wang J, Hsieh M-J, Cui G, Roe DR, Mathews DH, Seetin MG, Sagui C, Babin V, Luchko T, Gusarov S, Kovalenko A, Kollman PA: AMBER 11. 2010, San Francisco: University of CaliforniaGoogle Scholar
- Hornak V, Abel R, Okur A, Strockbine B, Roitberg A, Simmerling C: Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins. 2006, 65: 712-725. 10.1002/prot.21123.View ArticleGoogle Scholar
- Andersen HC: Rattle: a ‘velocity’ version of the Shake algorithm for molecular dynamics calculations. J Comput Phy. 1983, 52: 24-34. 10.1016/0021-9991(83)90014-1.MATHView ArticleGoogle Scholar
- 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: 10089-10092. 10.1063/1.464397.View ArticleGoogle Scholar
- Kollman PA, Massova I, Reyes C, Kuhn B, Huo S, Chong L, Lee M, Lee T, Duan Y, Wang W, Donini O, Cieplak P, Srinivasan J, Case DA, Cheatham TE: Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Acc Chem Res. 2000, 33: 889-897. 10.1021/ar000033j.View ArticleGoogle Scholar
- The PyMOL Molecular Graphics System, 1.3. Schrödinger, LLC
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.