The gene encodes an FtsH-homology protease domain in tandem with an AAA+ homology ATPase domain. The protein is believed to form a hexamer that uses ATPase-driven conformational changes in its AAA-domain to deliver substrate peptides to its protease domain. We present the crystal structure of the AAA-domain of human paraplegin bound to ADP at 2.2 Å. This enables assignment of the roles of specific side chains within the catalytic cycle, and provides the structural basis for understanding the mechanism of disease mutations.Paraplegin is an enhanced version in which the text of the article is integrated with interactive 3D representations and animated transitions. Please note that a web plugin is required to access this enhanced functionality. Instructions for the installation and use of the web plugin are available in This article can also be viewed as an  Molecular machines are protein assemblies, often multimeric complexes, that convert chemical energy into mechanical work by undergoing conformational changes in their protein subunits. An ATPase domain, alone or coupled in series, usually forms the basis for these machineries. The AAA+ domain (‘ATPases associated with diverse cellular activities’ and related proteins) is one such module; it is often found as a hexameric ring complex, and couples ATP hydrolysis to activities that involve protein remodeling (reviewed in Human paraplegin (SPG7) Paraplegin was originally discovered and named for its involvement in hereditary spastic paraplegias (HSP) Here, the crystal structure of the AAA-domain of human paraplegin is presented in complex with ADP. The overall fold and the nucleotide binding site are described. We outline the side chains that, by homology with FtsH, are implicated in hexamer formation, substrate binding, and chemomechanical coupling. Finally, we discuss the putative roles of disease-related residues.Escherichia coli strain BL21(DE3) gold pRARE2 was done in a LEX system (Harbinger Biotechnology and Engineering) using Terrific Broth medium supplemented with 8 g/l glycerol, 34 µg/ml chloramphenicol and 50 µg/ml kanamycin, induction with 0.5 mM IPTG, and over night culture at 18°C. Cell pellets were resuspended in 50 mM HEPES pH 7.8, 500 mM NaCl, 10 mM imidazole, 10% glycerol, 0.5 mM TCEP, and Complete EDTA-free Protease Inhibitor (Roche Biosciences). Cells were lysed by a freeze/thaw cycle followed by addition of benzonase (Novagen) and sonication (Sonics VibraCell). Lysates were centrifuged at 49,000 g for 20 min at 4°C. The supernatants were decanted and filtered.Human SPG7 cDNA was obtained from Deutsches Ressourcenzentrum für Genomforschung (accession no. BC036104). The sequence encoding paraplegin residues 305–565 was amplified by PCR and inserted into pNIC28-Bsa4 by ligation independent cloning. The expression construct included a TEV protease-cleavable N-terminal hexahistidine tag. Protein expression in 305–565 were loaded onto HiTrap Chelating HP columns  in buffer 1 . The columns were washed with buffer 1 containing 25 mM imidazole. Bound protein was eluted with buffer 1 containing 500 mM imidazole. The protein showed a strong tendency to precipitate in the presence of imidazole. The sample was filtered and loaded onto a HiLoad 16/60 Superdex-200 column  pre-equilibrated with buffer 2 . Fractions containing target protein were pooled, and the N-terminal hexahistidine tag was removed by incubation with His-tagged TEV protease (molar ratio 50∶1) over night at room temperature, followed by passage over a 1 ml HisTrap HP column in buffer 1 without imidazole  and reservoir solution containing 25% PEG-3350, 100 mM Bis-Tris pH 6.5, 200 mM ammonium acetate (T. maritima FtsH (PDB entry 2ce7) as a search model. The structure was refined with RefMac5 Crystals of paraplegin acetate . Crystalhttp://www.thesgc.com/SGC-WebPages/StructureDescription/MM-v2.php?pdb=2QZ4.For further details, please refer to www.rcsb.org (PDB entry code 2qz4).The atomic coordinates and structure factors have been deposited with the Protein Data Bank, We purified recombinant human paraplegin including residues 305–565  located to β-strand 3 is highly conserved among AAA+ proteins. The acidic side chains in this motif generally coordinate a magnesium ion that activates a water molecule for nucleophilic attack on the ATP γ-phosphate.Sequence Ser 454 – Asn 456 corresponds to the AAA+ Sensor 1 motif (STN). In FtsH, the asparagine is situated at the interface between the ATPase domain and the protease domain, interacting with the γ-phosphate of ADPNP The Sensor 2 motif Gly 518 - Asp 520 (GAD) couples the state of nucleotide hydrolysis to the oligomerization state. Sequence alignments suggest that the sensor 2 arginine is mutually exclusive with the second Arg near the arginine finger . Thus AAA+ domains contain either one Arg at the C-terminus of α5 and the sequence GARx at the N-terminal end of α8 , or the sequences RxxR and xGAD/E  Thermus thermophilus FtsH (1iy1) reveals a difference of up to 35 degrees between individual corresponding helices of the α-helical bundle. This reflects the mobility of the linker between these domains and shows their potential role in closure of the ATP binding cleft. Although there is no correlation between cleft closure and the state of ATP hydrolysis in the AAA+ domain structures solved to date, the binding of ATP and release of ADP most likely have an effect on the spatial arrangement of the bundle, which in turn could affect the protease domain.The carboxy-terminal α-helical bundle domain is present in essentially all known AAA+ ATPases Thermotoga maritima, 2cea, 2ce7; Helicobacter pylori, 2r65; T. thermophilus, 2dhr, 1iy0; Escherichia coli, 1lv7), p97/VCP , human fidgetin-like protein-1 (3d8b) and the human VPS4/SKD1 ATPase (1xwi). Pairwise superposition of the Cα-positions within the AAA+ domains of these structures gives rms differences between 1.7 and 2.8 Å. The structures of the closest paraplegin homologs are those of bacterial FtsH A structural homology search using the Dali server 5-screw axis FtsH, as paraplegin, forms hexamers. The FtsH AAA-domain has previously been determined as either a monomer  or a monomer arranged along a 6Given the high degree of similarity between paraplegin and FtsH, superposition of our paraplegin AAA-domain structure with the hexameric ring structures of FtsH  can reveal common mechanism as well as distinct properties of paraplegin. This analysis shows that in the paraplegin hexamer, the N-terminus and the C-terminal α-helical bundle are expected at high radius, while the segment between β2 and α3 are predicted to lie near the central pore . The α-hThe residues at the N-terminus of α-helix 3 form the so-called pore loop of the ATPase domain. In the hexamer structure this pore mediates access to the catalytic residues of the peptidase domain Most of the known disease mutations in the SPG7 gene lead to premature termination or map to the metallopeptidase domain. Some mutations, however, have been identified in the AAA+ domain, and surprisingly, they all map to the C-terminal α-helical bundle . The posA three-residue deletion (Δ485–487) in the N-terminal end of α-helix 5 The A510V mutation The F545L in vivo, ATPase-deficient paraplegin mutants are efficient enough to prevent the disease phenotype as long as the integrity of the pore loops and the conformational coupling between monomers and between the rings is maintained. Such mutants would be able to “ratchet” substrates through the pore and feed them to the peptidase with reduced ATPase activity, and thereby maintain the most important functions of paraplegin. However, disease mutations in the pore loops have not been identified to date. Paraplegin deficient mice develop progressive motor impairment, but are viable and live over 2 years In summary, the disease mutations point to the important role of the C–terminal α-helical bundle of the AAA-domain, which is implicated in coupling the state of ATP hydrolysis to both the neighboring monomer within the ring, and the protease domain in the underlying ring. It is surprising that no disease mutations in the sites that are essential for ATP binding and hydrolysis have been identified. AAA+ proteins have ATPase activities that are orders of magnitude lower than other NTPases that feature the same conserved motifs. Thus possibly Datapack S1http://www.molsoft.com/icm_browser.html.Standalone iSee datapack - contains the enhanced version of this article for use offline. This file can be opened using free software available for download at (ICB)Click here for additional data file.Text S1Instructions for installation and use of the required web plugin (to access the online enhanced version of this article).(PDF)Click here for additional data file.


As a crosslinker in the stressed cytoskeleton, α-actinin can retain conformation, function, and strength. α-Actinin has an actin binding domain and a calmodulin homology domain separated by a long rod domain. Using molecular dynamics and normal mode analysis, we suggest that the α-actinin rod domain has flexible terminal regions which can twist and extend under mechanical stress, yet has a highly rigid interior region stabilized by aromatic packing within each spectrin repeat, by electrostatic interactions between the spectrin repeats, and by strong salt bridges between its two anti-parallel monomers. By exploring the natural vibrations of the α-actinin rod domain and by conducting bending molecular dynamics simulations we also predict that bending of the rod domain is possible with minimal force. We introduce computational methods for analyzing the torsional strain of molecules using rotating constraints. Molecular dynamics extension of the α-actinin rod is also performed, demonstrating transduction of the unfolding forces across salt bridges to the associated monomer of the α-actinin rod domain. The cell interacts with its environment in both biochemical and mechanical ways. In this study we explore one of the ways in which the cell interacts mechanically with its environment. α-Actinin is a cytoskeletal crosslinker: it functions to scaffold the cytoskeletal actin filaments that provide mechanical reinforcement to the cell. In its functional environment α-actinin is exposed to a multitude of mechanical stresses as it attaches itself to a dynamic network of actin filaments. The actin filaments extend, rotate, and bend the α-actinin crosslinkers. In this study we employ molecular dynamics techniques to understand the structural characteristics of α-actinin that underlie its ability to provide a scaffold in such a stressed environment. We analyzed the natural frequencies of α-actinin and simulated force-induced bending, extension, and twisting. Our results suggest that α-actinin has structural flexibility facilitating crosslinking in a dynamic environment and also structural rigidity stabilizing the linkage in the stressed environment. We have discovered novel natural bending movements of the rod domain that enhance its function as a crosslinker. We have also demonstrated the specific structural characteristics of α-actinin that give it the previously suggested property of having partial flexibility. Our results enhance the understanding of structural mechanics in the cytoskeletal molecules. Cytoskeletal microfilament networks contribute to the mechanical stability of the cell by dynamically arranging and rearranging actin filaments for reinforcement. The dynamic arrangement of actin filament requires actin filament crosslinking molecules such as α-actinin. α-Actinin is a 200 kDa homodimer with three major structural motifs: the actin binding domain (ABD), the calmodulin homology domain (Cam), and the central rod domain Structure of the α-actinin rod domain underlies the function of α-actinin as a partially flexible actin filament crosslinker. Each central rod domain monomer is 240 Å long and made up of 4 spectrin (R1–R4) repeats connected by helical linkers see  [11],[12The goal of this investigation is to understand the structural mechanisms of the partial flexibility of the α-actinin rod domain. The coiled-coil nature of the rod domain is an essential component of the rod domain structure. Coiled-coils are the dominant conformation for fibrous proteins Several studies have examined the mechanical properties of other molecules with rod-like coiled-coil conformations. These studies on DNA Studies of α-actinin and other spectrin repeat molecules have similarly demonstrated extension rigidity of the coiled-coil rod domain Using molecular dynamics and normal mode analysis this study investigates the mechanical partial flexibility of the α-actinin rod domain. Bending, torsion and extension simulations demonstrate that, as with other coiled-coil molecules, the α-actinin rod domain has bending and torsional flexibility and extensional rigidity. Normal mode analysis shows that the rod-like structure of α-actinin contributes towards its bending and torsional flexibility. Our simulations suggest that aromatic packing interactions determine the trajectory of torsion on the rod domain, and that electrostatic interaction between the monomers contributes extension rigidity to the rod domain.Structural properties of α-actinin can be inferred from its natural vibrations, therefore, to reveal the naturally rigid and flexible regions of the α-actinin rod domain, we carried out normal mode analysis (NMA). Results from NMA convey properties inherent in the structure of α-actinin regardless of what intermolecular interactions are present NMA from a single monomer of α-actinin suggested that the monomer has significant bending flexibility and some torsional flexibility. The six lowest frequencies of α-actinin are rotational and translational modes. Mode 7 and mode 8, the two lowest frequency vibrational modes, show bending movement mainly at the termini with a single hinge at the central linker . Of the NMA of the rod domain dimer suggested that the bending flexibility is retained in dimerization, while a new natural movement involving torsion and bending is present . The lowThe above results  and 3 inSimulation of bending of the monomer suggested bending flexibility. Interestingly, the bent monomer arranged itself into a coiled-coil structure. Total force as low as 24 pN was able to achieve complete bending of the α-actinin rod domain monomer . The benThe molecular dynamics simulations under bending forces with the α-actinin dimer showed more resistance to bending than the α-actinin rod domain monomer . The minUsing rotating constraints, we implemented a molecular dynamics study of torsion induced conformational changes in the α-actinin rod domain. Torque was applied first to a single monomer of the rod domain at both the N-terminus and C-terminus, and also to a single monomer in dimer conformation. For comparison between torsional simulations of the monomer and dimer we define terminus A as the terminus with residue 1 (N-terminus in the monomer) and terminus B as the terminus with residue 475 (C-terminus in the monomer) . Torque The trajectory of the α-actinin monomer under torsional simulation was largely determined by interaction between terminal aromatic residues near the torsional stress. Rotation at both terminus A and terminus B required different levels of torque for the clockwise and the counterclockwise rotation . ClockwiThe trajectory of the α-actinin rod domain dimer under torsional simulation was largely a result of steric interactions between the rod domain monomers. The topology of R1 near terminus A is different from the topology of R4 near terminus B, and the steric interactions with each of their respective complementary repeats after rotation is different . RotatioThe results from torsional molecular dynamics simulations suggest that the dimerization of the α-actinin rod domain prevents rotation by introducing steric interactions that increase the torque needed to achieve rotation. Simulations of rotation of the monomer needed maximum 330 pN*nm of torque to rotate, while simulation of rotation of the dimer needed maximum 450 pN*nm . Aside fUsing constant-force molecular dynamics we explored the mechanical properties of α-actinin. Constant-force molecular dynamics is simulation of the conformational changes in a molecule resulting from application of a constant external force to specific residues. In the α-actinin simulations here, we apply a constant force to the C-terminus (terminus B) of one monomer and hold the other terminus fixed. Specific conformational changes are indicative of structural properties. Our simulations suggest that specific electrostatic interactions within each monomer and between the monomers contribute significantly to the stability of the rod domain dimer under extensional simulation.The α-actinin rod domain showed significant extensional rigidity. Forces were applied ranging from 100 pN to 200 pN to a monomer alone and to a monomer in dimer conformation, and only the monomer conformation with 150 pN or more of force was completely extended after simulation . InteracDimerization increases the structural rigidity of α-actinin by introducing 22 specific charged interactions between the two monomers: E71-R937, R56-E929, R56-E922, E115-R925, R57-D897, E900-R925, R53-E908, K134-D745, K138-D743, K138-E741, R186-E741, E266-R661, E266-K613, D270-K613, D270-K609, E433-R528, E447-R531, E425-R450, E454-R531, E447-R450, D422-R532, and R462-E546. These interactions anchor one monomer under forced extension to the other monomer, increasing rigidity and reducing the length of extension. The extension profile of the α-actinin rod dimer  demonstrThe actin filament cytoskeletal network is highly dynamic and stressed. Actin filaments are in continuous movement and rearrangements, and are also continuously exposed to external stresses. As an actin filament crosslinker, it is therefore functionally necessary for α-actinin to be both rigid and be able to withstand the external stress exposure, and to flexibly and dynamically scaffold the actin filaments. The structural mechanisms determining the necessary partial flexibility of α-actinin are as yet not fully understood. By applying methods of computational simulation and normal mode analysis we suggest several possible explanations for the molecular basis of the partial flexibility. Our results suggest that the α-actinin rod domain is flexible and dynamic near its termini while its central helical linkers are rigid, and its dimerized surface is highly stable. Our results also suggest that α-actinin has bending flexibility. Our molecular dynamics simulations suggest aromatic packing interactions play a role in resisting torsion, while electrostatic interactions play a role in resisting extension.Our NMA study suggested that the most natural vibrational mode of the α-actinin rod domain, both for monomer and dimer conformations, is bending  and 3. TPrevious studies have shown through electron microscopy that α-actinin crosslinks actin filaments at numerous angles and lengths The NMA results as well as the torsional molecular dynamics simulations suggested that the torsion of the molecule during its natural vibrations occurred mostly near its termini . FlexibiAn interesting result from the bending simulations was the super coiling that occurred when bending a single α-actinin rod domain monomer see . The monThe torsional studies pointed out the role the aromatic residues in each spectrin repeat play in stabilizing the α-actinin molecule. The aromatic packing play a critical role in resisting torsion and more importantly in restricting rotation to regions near the termini while the molecule is subjected torsion. The importance of the aromatic residues as suggested by our results is consistent with a previous study which suggested that aromatic residues in the spectrin repeats are conserved Our extension results provide interesting insight into the mechanisms of dimer stabilization. The α-actinin rod domain was stabilized by both charged interaction within each monomer and by electrostatic interactions at the surface between the two monomers. Furthermore, the charged surface interactions relay extension on only one monomer into extension in the other monomer. Several previous studies are consistent with this phenomenon and our results strengthen these past studies. Law et al. The normal mode analysis results were determined using the WEBnm@ Results from our study suggest the α-actinin rod domain is stabilized by electrostatic interactions between the monomers, aromatic interactions within each monomer, and steric interactions between the monomers. The results also suggest that the rod domain has bending flexibility, torsional flexibility near its termini, and extensional rigidity. These mechanical properties could play a role in facilitating α-actinin's role as an actin filament crosslinker. Experimental investigations can further test some of the results presented here. Our results suggest that mutation of aromatic residues would reduce resistance to torsion, and mutation of electrostatic residues would reduce extensional rigidity. Mutagenesis of such residues, therefore, could reduce the mechanical stability of α-actinin and decrease its effectiveness as an actin filament crosslinker.Natural frequencies of both the monomer of the α-actinin rod and its dimer form were determined using WEBnm@ The crystal structure for the α-actinin rod domain (PDB ID = 1HCI) Molecular dynamics simulations were carried out using the commercially available software CHARMm Bending simulations were carried out on both dimer conformations (8 spectrin repeats) of α-actinin and monomer conformations (4 spectrin repeats). For the simulations on the monomer conformations, constraints were placed on the α-carbon of I320, S162, and L240, the three central residues suggested to be hinges in the NMA studies. Bending forces were applied to S1 and N85 at terminus A and to G398 and D475 at terminus B. Vector direction of the bending forces was taken directly from the bending normal modes. Total bending force applied to the α-actinin monomer ranged from 8 pN to 200 pN.Bending simulations on the dimer conformation were carried out similarly. Constraints were placed on the α-carbon of S161, L240, I320, S637, L715, and S798. Bending forces were applied to S1, N85, H873, and D950 at terminus A and G398, D475, S476, and N560 on terminus B. Total bending force applied to the α-actinin dimer ranged from 24 pN to 200 pN.Α-actinin crystal structure with PDB ID = 1HCI ν is the unit vector of the axis of rotation, M the rotation matrix, P the coordinates of the pivot point, R the coordinates of the reference atom, and 0R the original coordinates of the reference atom, then the location of the reference atom can be determined by R = M(0R−P )+P. If X is the location of the target atom, then the normal is defined as N = (P+((X−P) .ν)ν)−X. The force applied to the target atom by the spring attaching it to the rotating reference atom can be calculated as F = 2 K(R−X). The torque is then calculated as torque = F×N.Torsion was implemented using the rotating constraints function of NAMD using the CHARMM22 force field definition 2 was used. The reference atoms were rotated at a constant angular velocity of 0.005 degrees per timestep (0.5 degrees/ps) in the positive direction for counterclockwise simulations, and in the negative direction for clockwise simulations. Simulations were run for 400 ps to achieve near 180-degree rotation. To illustrate the effects of rotation speed, the C-terminus of the two central repeats of α-actinin were rotated clockwise at two different rotational speeds: 0.005 degrees per timestep (0.5 degrees/ps) and 0.0005 degrees per timestep (0.05 degrees/ps)  . QuantitExtension studies were carried out first in implicit solvent using CHARMm For accurate and efficient simulation the hydrogen atom bond length was constrained using SHAKE Figure S1Effects of dimerization on torque required for rotation of α-actinin. The α-actinin rod domain monomer and dimer were exposed to external torsional stress in both the clockwise and the counterclockwise directions. (A) Torsion applied at terminus A required torque of up to 350 pN*nm to rotate the monomer and torque of up to 400 pN*nm to rotate the dimer. Rotation beyond 140 degrees correlated with an increase in torque required for rotation of the dimer and the monomer in the clockwise direction, but not the monomer in the counterclockwise direction. Steric interactions between two monomers in the dimer conformation can account for the increase in torque in the dimer conformation. (B) Rotation at terminus B required torque of up to 350 pN*nm to rotate the monomer and torque of up to 450 pN*nm to rotate the dimer. The significant increase in rotation of the dimer conformation at terminus B can also be explained by steric interactions. In both plots, rotation of the dimer in the clockwise direction is shown in blue, dimer in the counterclockwise direction is shown in red, monomer in the clockwise direction is shown in green, monomer in the counterclockwise direction is shown in purple.(2.13 MB TIF)Click here for additional data file.Figure S2Effects of rotational velocity on the α-actinin rod domain central repeats. The two central repeats of the α-actinin rod domain monomer were rotated at the C-terminus in the clockwise direction at two different rotational velocities. The C-terminal residues of the rod domain were rotated at 0.5 degrees/ps (blue) and at 0.05 degrees/ps (red). Results show that rotation at the slower rotational velocity decreases the localization of rotation to the C-terminus. Residues further from the C-terminus undergo more rotation at the slower rotational velocity than at the faster rotational velocity. At both rotational velocities the residues near the N-terminus of the two central repeats under simulation are do not undergo appreciable rotation.(0.78 MB TIF)Click here for additional data file.Figure S3Equilibration of α-Actinin after rotation and propagation of torque. Rotation of the α-actinin rod domain dimer at terminus B was extended for a 100 ps equilibration. Static harmonic constraints were used in the place of the rotating harmonic constraints. The rotated conformation was then equilibrated for 100 ps. The rotation of each residue is plotted against the distance of the residue form terminus A at the end of the rotation simulation and after the 100 ps equilibration. Both before equilibration (blue curve) and after equilibration (green curve) show localization of rotation to residues near terminus B. Aromatic packing interactions prevent propagation of torque to further residues.(0.26 MB TIF)Click here for additional data file.Table S1Comparison of the vibrational normal modes in the α-actinin rod domain. The lowest frequency vibrational normal modes of the α-actinin rod domain monomer and dimer were calculated using WEBnm@ (5.82 MB TIF)Click here for additional data file.Video S1Torsional vibrational movement in the α-actinin rod domain monomer. Normal mode analysis of the α-actinin rod domain monomer showed torsional movement at some of the modal frequencies. Torsional movement occurs at the residues near the termini. Shown here is a video of the torsional movement at the termini during vibration in mode 11. Residues in the central repeats undergo little conformational change while residues near the termini experience the rotational conformation change.(0.22 MB MOV)Click here for additional data file.Video S2Torsional vibrational movement in the α-actinin rod domain dimer. Normal mode analysis of the α-actinin rod domain dimer conformation showed several torsional modes. The torsional modes in the dimer conformation occur near the terminal residues and little rotation occurs at the residues in the central repeats. This video shows that rotational movement occurs at the residues near the termini and not at the central repeat residues.(0.11 MB MOV)Click here for additional data file.Video S3Rotation of the α-actinin monomer with molecular dynamics. Residues at both terminus A and terminus B of the α-actinin rod domain monomer were rotated in the clockwise and the counterclockwise directions. Shown here is rotation of the rod domain monomer terminus B in the clockwise direction. Aromatic residues are shown in orange and other hydrophobic residues are shown in yellow. During rotation a specific aromatic packing interaction between aromatic residues W381 and Y417 is maintained until rotation passed about 140 degrees. Once terminus B is rotated beyond 140 degrees the aromatic packing interaction is broken. Breaking of the aromatic packing interaction corresponds to an increase in torque required for rotation beyond 140 degrees see .(5.67 MB MPG)Click here for additional data file.Video S4Rotation of the α-actinin rod domain dimer with molecular dynamics. One of the monomers in the α-actinin rod domain dimer was rotated using molecular dynamics. Rotation at both termini showed that the direction of rotation affects the torque required to achieve rotation . Differe(7.95 MB MPG)Click here for additional data file.Video S5Extension of the α-actinin rod domain dimer with molecular dynamics. External force of 150 pN was applied to terminus B of a monomer of the α-actinin rod domain dimer. Forced extension of a single monomer caused extension of its complementary monomer as well due to several salt bridges between the two monomers. Shown here is the extension of one monomer (pink) and the resulting extension of the other monomer (blue). Throughout the simulation, the electrostatic interactions between the monomers are resilient and remain intact. Because it is more energetically favorable, the complementary monomer undergoes extension and conformational change but the electrostatic interactions remain intact.(4.65 MB MOV)Click here for additional data file.


The crystal structure of a novel MACPF protein, which may play a role in the adaptation of commensal bacteria to host environments in the human gut, was determined and analyzed. Bacteroides species. The crystal structure of a bacterial MACPF-domain-containing protein BT_3439 (Bth-MACPF) from B. thetaiotaomicron, a predominant member of the mammalian intestinal microbiota, has been determined. Bth-MACPF contains a membrane-attack complex/perforin (MACPF) domain and two novel C-terminal domains that resemble ribonuclease H and interleukin 8, respectively. The entire protein adopts a flat crescent shape, characteristic of other MACPF proteins, that may be important for oligomerization. This Bth-MACPF structure provides new features and insights not observed in two previous MACPF structures. Genomic context analysis infers that Bth-MACPF may be involved in a novel protein-transport or nutrient-uptake system, suggesting an important role for these MACPF proteins, which were likely to have been inherited from eukaryotes via horizontal gene transfer, in the adaptation of commensal bacteria to the host environment.Membrane-attack complex/perforin (MACPF) proteins are transmembrane pore-forming proteins that are important in both human immunity and the virulence of pathogens. Bacterial MACPFs are found in diverse bacterial species, including most human gut-associated  They share a common domain (MACPF) that is also widely distributed in bacteria and protozoa, including many pathogens  revealed a fold similar to pili components of other bacteria despite no detectable sequential similarity  from B. vulgatus uncovered an unexpected similarity in fold to the β-lactamase inhibitor protein  at 2.46 Å resolution, which to our knowledge is the first structure of a potential CDC-like toxin from a gut symbiont.The Gram-negative anaerobic 2.2.1.et al., 2008B. thetaiotaomicron VPI-5482 genomic DNA using PfuTurbo DNA polymerase (Stratagene) and I-PIPE (Insert) primers  that included sequences for the predicted 5′ and 3′ ends. The expression vector pSpeedET, which encodes an amino-terminal tobacco etch virus (TEV) protease-cleavable expression and purification tag (MGSDKIHHHHHHENLYFQ/G), was PCR-amplified with V-PIPE (Vector) primers . The V-PIPE and I-PIPE PCR products were mixed to anneal the amplified DNA fragments together. Escherichia coli GeneHogs (Invitrogen) competent cells were transformed with the I-PIPE/V-PIPE mixture and dispensed onto selective LB–agar plates. The cloning junctions were confirmed by DNA sequencing. Using the PIPE method, the gene segment encoding residues Met1–Thr18 was excluded from the final construct as it was predicted to encode a signal peptide. Expression was performed in selenomethionine-containing medium at 310 K. Selenomethionine was incorporated via inhibition of methionine biosynthesis phosphine–HCl (TCEP)] and the lysate was clarified by centrifugation at 32 500g for 30 min. The soluble fraction was passed over nickel-chelating resin  pre-equilibrated with lysis buffer, the resin was washed with wash buffer  and the protein was eluted with elution buffer . The eluate was buffer-exchanged with TEV buffer  using a PD-10 column  and incubated with 1 mg TEV protease per 15 mg of eluted protein. The protease-treated eluate was run over nickel-chelating resin  pre-equilibrated with HEPES crystallization buffer  and the resin was washed with the same buffer. The flowthrough and wash fractions were combined and concentrated to 19.8 mg ml−1 by centrifugal ultrafiltration (Millipore) for crystallization trials. Bth-MACPF was crystallized by mixing 100 nl protein solution with 100 nl crystallization solution in a sitting drop over a 50 µl reservoir volume using the nanodroplet vapor-diffusion method  2-methyl-2,4-pentanediol, 12%(v/v) polyethylene glycol 6000, 0.1 M HEPES pH 6.7. A cube-shaped crystal of approximate dimensions 40 × 40 × 30 µm was harvested after 42 d at 277 K for data collection. Glycerol was diluted to 10%(v/v) using the reservoir solution and then added to the drop in a 1:1 ratio as a cryoprotectant prior to mounting. Initial screening for diffraction was carried out using the Stanford Automated Mounting system  cloning method  sodium azide. The molecular weight was calculated using ASTRA v.5.1.5 software (Wyatt Technology).The oligomeric state of Bth-MACPF in solution was determined using a 1 × 30 cm Superdex 200 size-exclusion column  coupled with miniDAWN static light-scattering (SEC/SLS) and Optilab differential refractive-index detectors (Wyatt Technology). The mobile phase consisted of 20 m2.2.1), high-energy remote (λ2) and inflection (λ3) wavelengths of a selenium MAD experiment . Sequence alignments were rendered using TEXshade  data were collected on beamline 9-2 at the SSRL at wavelengths corresponding to the peak , B. fragilis 3_1_12 (Bfra3_17507), B. plebeius DSM 17135 (BACPLE_01336), B. intestinalis DSM 17393 (BACINT_00423) and Porphyromonas endodontalis ATCC 35406 (POREN0001_1212) . MACPFs from Bacteroides are unique as most of them contain lipoprotein signal peptides  that are likely to form part of an operon (see more detailed discussion below), as well as a third more distant paralog (BT_3120) that consists of only an MACPF domain. B. fragilis YCH46  and B. intestinalis DSM 17393  each contain three MACPFs, with only one protein in each species having the same domain architecture as Bth-MACPF.Bacterial MACPFs are highly divergent in sequence and domain architecture. Homologs that have significant similarity over the entire sequence of Bth-MACPF are found mostly in other human-related Bacteroidetes, including unclassified 2) Fig. 1, but alset al., 2005B. vulgatus, the structure of which has also been determined by the JCSG (PDB code 3d33). It has an immunoglobulin-like fold that is common in cell-surface proteins such as fibronectin and complement C3. BT_3442 is a multi-domain protein containing TPR motifs, which often mediate protein interaction. Therefore, Bth-MACPF is associated with several pore-forming proteins, suggesting a possible role in a cross-membrane transport system. The association of Bacteroides MACPFs with lipoproteins and outer membrane porins is also observed in B. fragilis YCH46  that form a putative operon and which are located directly downstream of a well defined operon of cell-division and cell-wall biosynthesis proteins such as FtsZ, FtsA, FtsQ and MurC. This cluster, which appears to contain internal duplications resulting in three homologous pairs , is rich in potential pore-forming proteins . Most of the proteins in the cluster also contain similar lipoprotein signal peptides Fig. 2, suggest46 Fig. 2.PSORTb  matches the lipoprotein signal peptide pattern of Gram-negative bacteria, which usually consists of one or more positive charged residues followed by a stretch of hydrophobic residues and a lipobox motif L(A/S)(G/A)C  and a calculated isoelectric point of 5.5. We determined the structure using the high-throughput pipeline of the Joint Center for Structural Genomics . A selenomethionine derivative of Bth-MACPF was expressed in E. coli with an N-terminal TEV-cleavable His tag and was purified by metal-affinity chromatography. To improve the likelihood of obtaining crystals, the predicted N-terminal signal peptide (residues 1–18) was not included in the clone construct. The data were indexed in the orthorhombic space group P212121 and the structure was determined at 2.46 Å resolution with two molecules per asymmetric unit using the MAD method. The structure was refined to a final R factor of 20.9% and an R               free of 25.2%. The model of Bth-MACPF displays good geometry, with an all-atom clash score of 7.8, and the Ramachandran plot produced by MolProbity -2-methyl-2,4-pentanediol] molecule, one chloride ion and three ethylene glycol molecules. The residual residue (Gly0) from the cleaved N-terminal purification tag and segments A/B19–35, A/B57–65, A277–286, B272–286 and A482–483 were not included in the model owing to a lack of interpretable electron density. Additionally, side chains for 17 residues were only partially modeled owing to disorder. Data-collection, refinement and model statistics are summarized in Table 1The 3.3.α atoms) with larger deviations located at the two tips, mostly owing to a slight opening of the crescent in molecule B compared with molecule A. Bth-MACPF consists of three structured domains: an MACPF domain (residues 66–389) and two C-terminal domains, D2 (residues 390–492) and D3 (residues 493–558) . The interface contains 32 hydrogen bonds and helps to maintain overall structural integrity. This arrangement places the predicted N-terminal membrane-attachment site (Cys18) away from the MACPF domain. The remaining N-terminal residues that were included in the construct (residues 19–35 and 57–65) were not observed in the electron density and are most likely to be flexible in solution. Furthermore, Bth-MACPF is likely to be a monomer in solution, as supported by crystal-packing analysis and analytical size-exclusion chromatography (data not shown).Bth-MACPF Fig. 3 adopts aet al., 2007et al., 200710-helices that pack against the inner surface of the B sheet. TMH2 (residues 248–304) contains an antiparallel αβ–βα structure that sits on the outer surface of the B sheet. The two strands in TMH2 and another strand from the 4–4′ (B-sheet) insertion forms another β-sheet (C sheet) parallel to the B sheet. The MACPF motif Y/W-G-T/S-H-F/Y-X               6-GG . The corresponding Bth-MACPF region (225YGEFV               X               6GG237) is more divergent from the consensus, with nonconserved changes at positions 3–5. Two glycines from the MACPF motif (Gly236 and Gly237) and two additional nearby conserved glycines  in the central core, which is decorated by several helical insertions. The A sheet with its short strands (strand order 2134) and the B sheet with long strands (strand order 1234) assemble to form a twisted S shape. The B sheet itself is very distorted and bends fairly abruptly in the middle by ∼90°. This arrangement of central β-sheets with characteristic geometry is common to both MACPFs and CDCs and allowed the classification of MACPF and CDC into a single family (Rosado 2 (500 Å2 each). The interface is mostly hydrophilic. In particular, a buried Asp423 in D2 forms a bifurcated hydrogen bond to Arg375 of MACPF. D3 functions as a wedge between D2 and MACPF, with a similar interface area on either side . Leu558 is buried with its C-terminal carboxyl group forming a hydrogen-bond network involving the conserved residues Arg420 and Tyr530. Additionally, the interaction between domains is further stabilized by the N-terminal extended region (residues 36–56) described above. Gap-volume indices between these interacting components are less than 1.7, which is consistent with the expected average (1.8) for intrachain domain–domain interfaces  between β2B (β2 of the B sheet) and β3A. These helices form the interface between the MACPF and D2/D3 domains. Both D2 and D3 are layered structures with a central β-sheet protected by helices on two sides (see below). The D2 and MACPF interface involves interaction between αD and αI of MACPF and the β3–β4 and β5–β6 loops of D2 and buries a surface area of ∼1000 Å3.4.1mu2; Ren et al., 2002Z = 3.6; r.m.s.d. 3.3 Å for 68 aligned Cα atoms; sequence identity 6%), which is likely to have evolved from the ribonuclease H domain . However, the C-terminal portions of the two structures differ significantly. Domain D3 is similar, for instance, to a viral chemokine . Chemokines adopt a βββα interleukin 8-like structure stabilized by two conserved disulfide bonds. D3 lacks the long cysteine-containing N-terminal portion observed in chemokines. Instead, it contains an αβ C-terminal extension and forms a βββαααβ overall structure. The βββαβ motif is most likely to represent a repeated structural unit that can be found in nonhomologous proteins with different functions, thus limiting the interpretation of structural similarity in terms of common function.The MACPF domain is usually attached to other auxiliary domains that are expected to regulate the function of MACPF. As discussed earlier, both C-terminal domains of Bth-MACPF are only detected in its closest homologs in sequence-similarity searches Fig. 1. The D2 3.5.FFAS , can be superimposed onto Bth-MACPF with 218 aligned Cα atoms, an r.m.s.d. of 5.0 Å and 14% sequence identity (Z = 12.3). More distant similarity is also apparent between Bth-MACPF and CDCs, such as the thiol-activated cytolysin perfringolysin O . The structural similarity between MACPF domains and the CDC family of toxins has previously been noted, which led to the proposal that MACPF domains use a CDC-like mechanism for pore formation  that interact with the region containing the critical glycines that were discussed above. The β4B–β4B′ insertion in Bth-MACPF contains two additional short strands that augment the B sheet and the C sheet, respectively. As a result, this insertion in Bth-MACPF is more similar to PFO. The additional short β-strand in the B sheet of CDCs (β5B in Bth-MACPF) prevents premature oligomerization by blocking access to β4  rather than membrane attachment. The shape of Bth-MACPF appears to be self-complementary, which could facilitate ring-like self-assembly (Hadders et al., 2007via the C-terminal auxiliary domains. A model with 16 copies of Bth-MACPF forms a doughnut-shaped molecule with an inner radius of 110 Å, similar in pore size to the the C9 MACPF model (Hadders et al., 2007The helical insertion between β2B and β3A is involved in docking the D2 and D3 domains to the Bth-MACPF domain. These helices are also present in Plu-MACPF and C8α MACPF, but are currently not implicated in protein–protein interactions. Both Plu-MACPF and Bth-MACPF contain additional C-terminal domains. However, the locations of these domains are completely different. The C-terminal β-prism domain of Plu-MACPF is located on the opposite side of the central core (left corner of lower figure of Plu-MACPF in Fig. 5et al., 2007et al., 2003B. fragilis, are also opportunistic pathogens. It is well documented that many bacterial virulence-factor genes are located within genomic islands (Juhas et al., 2009B. thetaiotaomicron genome suggest that this region could be a pathogenicity island acquired through horizontal gene transfer, as predicted by a genome-wide genomic islands study (Ho Sui et al., 2009MACPFs are well known for killing cells by forming pores and thus are potential virulence factors. Here, we demonstrate the existence of a novel subfamily of secreted MACPF proteins in commensal bacteria. Unfortunately, the physiological functions of these proteins are currently unknown. The properties of the MACPF/CDC fold, such as structural flexibility and membrane penetration, may be utilized for nonlytic purposes (Rosado et al., 2010http://www.topsan.org/explore?PDBid=3kk7.Although the functions of the MACPFs represented by Bth-MACPF remain to be elucidated, our study provided clues that they are important targets for further exploration of how symbiotic microbes adapt to and influence their host environments. Additional information about the proteins described in this study is available from TOPSAN (Krishna MACPF family protein, 3kk7            PDB reference:


Conformational change induced by the binding of a substrate or coenzyme is a poorly understood stage in the process of enzyme catalysed reactions. For enzymes that exhibit a domain movement, the conformational change can be clearly characterized and therefore the opportunity exists to gain an understanding of the mechanisms involved. The development of the non-redundant database of protein domain movements contains examples of ligand-induced domain movements in enzymes, but this valuable data has remained unexploited.The domain movements in the non-redundant database of protein domain movements are those found by applying the DynDom program to pairs of crystallographic structures contained in Protein Data Bank files. For each pair of structures cross-checking ligands in their Protein Data Bank files with the KEGG-LIGAND database and using methods that search for ligands that contact the enzyme in one conformation but not the other, the non-redundant database of protein domain movements was refined down to a set of 203 enzymes where a domain movement is apparently triggered by the binding of a functional ligand. For these cases, ligand binding information, including hydrogen bonds and salt-bridges between the ligand and specific residues on the enzyme is presented in the context of dynamical information such as the regions that form the dynamic domains, the hinge bending residues, and the hinge axes.The presentation at a single website of data on interactions between a ligand and specific residues on the enzyme alongside data on the movement that these interactions induce, should lead to new insights into the mechanisms of these enzymes in particular, and help in trying to understand the general process of ligand-induced domain closure in enzymes. The website can be found at:  Enzymes are flexible molecules that change conformation upon ligand binding ,2. HowevIn enzymes with a domain movement the standard view is that the ligand binds to the open conformation and subsequently causes it to adopt a closed conformation where the ligand is surrounded by the enzyme in a highly specific environment. There are a number of different models of the kinetics of ligand binding and protein conformational change in domain proteins. One model, applicable beyond just domain proteins, is the "pre-existing equilibrium model". In thisIn this work a domain movement is defined by the DynDom program. The DynDom program takes two atomic structures and analyses the conformational difference between them in terms of a domain movement. It automatically determines domains, hinge axes, and hinge-bending residues. It does this based on movement, not on structure, and is soundly based in rigid-body kinematics. At its heart is the generation of short main-chain segments by use of a sliding window and the calculation of rotation vectors associated with the rotation of these segments between the two structures. By treating the components of these rotation vectors as coordinates in a "rotation space", segments that rotate together, perhaps comprising a rigid domain within the protein will have rotation points co-located. Effectively this means that domains can be identified as clusters of rotation points. The clusters are identified using the k-means clustering method and are modelled as 3-dimensional normal distributions. This allows one to define an "ellipsoid of significance" for each cluster. Rotation points that have the dual property of lying outside the ellipsoids and in moving along the protein chain are from segments that connect the domains, are assigned "bending" rotation points. The residues associated with the bending rotation points are assigned as bending residues. Further details can be found in the DynDom1.50 paper . An exhaThe database described here will be of particular use in understanding how the binding of a ligand can induce conformational change. Its key characteristic is the presentation of data related to the binding of the ligand in the context of dynamical features such as the dynamic domains, hinge axes, and the hinge-bending residues. It is the latter that are of particular interest as it is these that collectively control the domain movement  and in sHere the methods used to extract domain movements caused by the binding of a functional ligand to an enzyme are described. This involved the selection of enzymes from the non-redundant database of protein domain movements, the selection of those enzymes where a ligand is present in at least one of the structures, the verification of the ligand as a functional ligand, and the final selection of those cases where the ligand could have triggered the conformational change upon binding.The current DynDom database ,16 of prEach PDB file was scanned for EC numbers and protein chains were associated with one or more EC numbers. A domain movement was assigned to an enzyme if either or both of its two protein chains had been associated with at least one EC number. The domain movements not associated with any EC numbers or associated with incomplete EC numbers were excluded from the analysis. Out of the initial 2035 pairs, this procedure resulted in 764 pairs being assigned to an enzyme.For each protein chain, there may be one or more ligands in its PDB file. Some of these ligands have the same chain ID as the protein chain. These ligands were associated directly to the protein chain. However, some ligands in the PDB file do not have a chain ID. In this case those ligands were provisionally associated to all the protein chains in the PDB file. For each protein chain this process resulted in a list of "PDB ligands". All domain movements were excluded from the dataset if both protein chains had an empty list. This list (one for each chain) is termed the "PDB-ligand list". This procedure reduced the dataset down to 693 pairs.The following procedure was carried out in order to ensure that the PDB ligand was a functional ligand for the enzyme, possibly able to induce a functional domain movement. In order to determine whether the PDB ligands associated with each protein chain were functional ligands, the Kyoto Encyclopaedia of Genes and Genomes (KEGG) LIGAND database for enzymes  was usedFor each protein chain all ligands in the functional-ligand list not in contact with the protein in either conformation were removed. Subsequently those protein pairs without any ligands were removed. The remaining pairs formed the "contact-ligand set" and the remaining ligands the "contact ligands". The contact-ligand set comprises all those protein pairs with at least one ligand contacting the protein in either conformation. Here and below "in contact" means that the ligand has a heavy atom within 4 Å of a heavy atom of the protein. Of the 312 domain movements from the previous stage, a further 14 were removed by this process leaving 298.The classic view for a ligand-induced domain closure in an enzyme is one where the ligand binds in the interdomain cleft and is surrounded by the protein. If a ligand is in contact with one domain as well as in contact with the other domain, or bending regions, or both, then the ligand will be termed a "spanning ligand". All other contact ligands are "non-spanning ligands".The basic concept of a "trigger ligand" is that it should be a contact ligand that is present in one conformation but not the other, i.e. it has caused the conformational change upon binding to the enzyme. However, if a ligand is in both conformations, but is spanning in one conformation but not the other, then it is also considered to be a trigger ligand. The procedure used, therefore, is one that gives priority to spanning ligands over non-spanning ligands. This is reasonable in the light of what is known about ligand-induced domain closure, i.e. the ligand is usually enclosed by an enzyme with a domain movement. The procedure to determine the "trigger-ligand set", illustrated in Figure The following section describes analyses performed on the 203 pairs in the trigger-ligand set where the domain movement is apparently triggered by the binding of a functional ligand.In the previous study , it was In order to determine residues making hydrogen bonds and salt bridges with the ligand, the program LIGPLOT was used . LIGPLOTGiven the analogy of ligand-induced domain closure with protein folding the radius of gyration of the ligand bound and ligand unbound conformations was calculated. The radius of gyration was calculated using backbone atoms with any insertions indicated by a pairwise sequence alignment excised from the structures.The results of this analysis occupy six tables in the DynDom database of protein domain motions. Figure . The protein structure is coloured according to domain (blue or red) and interdomain bending regions (green). The trigger-ligand is displayed in spacefilling model and the contacting residues are indicated in ball and stick model. Below this there are two Jmol windows displaying the domain movement in relation to the ligand. These two displays correspond to the two alternative scenarios of the sequential model [The web-interface to the database is implemented using JAVA Server Pages (JSP) and servlets. The database software itself is PostgreSQL. The front page lists the enzymes giving their names, EC numbers, PDB accession codes and chain identifiers for each protein pair. In addition the front page indicates whether the ligand is a spanning ligand or not, and whether the ligand has caused compaction of the proteins upon binding. For each pair there is a link to its main page. At the top of the main page the structure of the ligand bound state is displayed using the molecular graphics applet, Jmol al model : one wheal model . They shThe following section shows a sequence alignment of the two chains coloured according to domain and bending region. The sequences between the two chains need not be identical but according to the construction of the non-redundant database they will have a 90% or greater sequence identity with the representative of their family .Next is the main table shown in Figure Other sections of the main table list the residues that contact the ligand, residues in extended bending regions that make a hydrogen bond or salt-bridge with the ligand and residues in extended bending regions with a hydrogen bond between their main chain and the ligand. Using the sequence alignment one can identify the equivalent residues in the ligand unbound chain. Finally there is a link to a LIGPLOT, where a schematic diagram shows all the interactions between the ligand and individual residues.To our knowledge this is the only web-accessible database for enzymes that provides ligand-binding information in a dynamical context. Its primary aim is to help researchers in understanding ligand-induced conformational change in enzymes. The dataset accumulation was necessarily different from previous studies ,4 as it As mentioned in the Background section we have concentrated on domain movement as unlike other conformational changes in proteins, they can be characterised through the methods of rigid-body kinematics, e.g. the relative movement of the domains can be described by a screw movement according to Chasles' theorem . An unde but its main table is also shown in Figure Let us consider LADH as an example of how the data at the website might lead to an understanding of the relationship between the ligand-enzyme interactions and the domain movement. LADH is an enzyme that catalyses the oxidation of alcohol to aldehyde and is a homodimer, with each protomer comprising a coenzyme binding domain and a catalytic domain. The binding of NAD+ in the interdomain cleft induces domain closure preparing the enzyme for the binding of the alcohol substrate -28. The Following "the family" link on row 6 of the table one finds that there are a total of 73 structures in the non-redundant database belonging to the same LADH family. Through conformational clustering 1N8K(A) has been selected as the representative of one cluster (the closed structure) comprising 62 structures, and 1YE3(A) the representative of another cluster (the open structures) comprising 11 structures .Rows 11–14 detail specific residues contacting or making hydrogen bonds or salt-bridges with the NAD ligand. We find an appreciable number of residues in extended bending regions that make a hydrogen bond with the NAD ligand. In particular we find hydrogen bonds between the ligand and the main chain of extended bending residues Ala317, Phe319, Thr292 and Val294. The nicotinamide group of NAD forms hydrogen bonds with the Ala317 and Phe319 which are situated at the terminus of a β-sheet (this can be determined using the Jmol display at the top of the page) that would appear to mimic those found in a true β-sheet [This example illustrates that putting ligand binding information in a dynamical context can lead to the identification of key residues involved in inducing domain closure.Given that interdomain bending regions control the domain movement it is interesting to know in what proportion of cases there is a contact or specific interaction between the trigger-ligand and an extended bending region. It was found that out of the 298 domain pairs in the contact-ligand set, 59% of pairs have ligands contacting extended bending regions. For the 203 pairs in the trigger-ligand set, 66% of ligands contact an extended bending region, but for the spanning trigger-ligand set, the corresponding figure is 84% and for the non-spanning trigger-ligand set it is only 13%. Table If domain closure is like protein folding then one would expect the ligand bound conformation to be more compact than the ligand unbound. Although, anecdotally, one expects that the ligand bound conformation should be more compact than the ligand unbound, to our knowledge no study has demonstrated this. The radius of gyration was calculated for each pair in the spanning trigger-ligand set and the non-spanning trigger-ligand set. Figure For some examples in the non-spanning trigger-ligand set, the trigger ligand is bound to a region remote to the interdomain cleft. Although one can think of complex mechanisms that might explain how binding at these remote sites can initiate domain closure, in some of these cases the trigger ligand may not be the true initiator of domain closure. We therefore welcome comments from expert users on this issue. In the future, based on this expert knowledge, we plan to build a filtered version of the database.A new database has been described that presents ligand binding information in the context of dynamical information for enzymes that exhibit a domain movement upon ligand binding. The 203 domain movements in the database were derived using a careful data-filtering procedure applied to the 2035 domain movements that comprise the non-redundant database of protein domain movements. The database will be of particular use to experts interested in a particular enzyme present in the database. Alongside other studies it will also be of use in understanding how ligands induce conformational changes in enzymes in general, and which of the kinetic models of ligand-induced domain closure is most appropriate.GQ did the programming, data handling and constructed the website. SH and GQ both participated in the development of the methods that created the dataset and wrote the paper. SH had the original idea.


Proteins often consist of one or more spatially compact structural regions (domains) which may fold autonomously and which, singly or in combination, confer the protein's specific functions. As LGT is frequent in strongly selective environments and natural selection is based on function, we hypothesized that domains might also serve as modules of genetic transfer, i.e. that regions of DNA that are transferred and recombined between lineages might encode intact structural domains of proteins.In prokaryotes and some eukaryotes, genetic material can be transferred laterally among unrelated lineages and recombined into new host genomes, providing metabolic and physiological novelty. Although the process is usually framed in terms of gene sharing (domons), except where these regions constitute most of the sequence length. Recombination breakpoints that fall within longer domons are distributed uniformly at random, but those that fall within shorter domons may show a slight tendency to avoid the domon midpoint. As we find no evidence for differential selection against nucleotide substitutions following the recombination event, any bias against disruption of domains must be a consequence of the recombination event per se.We selected 1,462 orthologous gene sets representing 144 prokaryotic genomes, and applied a rigorous two-stage approach to identify recombination breakpoints within these sequences. Recombination breakpoints are very significantly over-represented in gene sets within which protein domain-encoding regions have been annotated. Within these gene sets, breakpoints significantly avoid the domain-encoding regions (This is the first systematic study relating the units of LGT to structural features at the protein level. Many genes have been interrupted by recombination following inter-lineage genetic transfer, during which the regions within these genes that encode protein domains have not been preferentially preserved intact. Protein domains are units of function, but domons are not modules of transfer and recombination. Our results demonstrate that LGT can remodel even the most functionally conservative modules within genomes. Origin of Species, vertical (parent-to-offspring) descent within a lineage has been considered to be the main mode of genetic transmission. More recently the role of lateral genetic transfer (LGT) has been emphasized, particularly among prokaryotes Genomes are shaped by processes that direct the acquisition and inheritance of genetic material. Since Darwin's domains which may fold autonomously and which, singly or in combination, confer the protein's specific functions i.e. that regions of DNA that are transferred and recombined might encode intact structural domains of proteins. In support of this view, recombination events that disrupt the folds of bacterial β-lactamases Genomes of prokaryotes consist largely of protein-coding sequences separated by short intergenic regions. The corresponding proteins often consist of one or more spatially compact structural units known as The dataset developed for that study provides a unique platform to examine the distribution of recombination breakpoints that occur within protein-coding sequences, and the extent to which domain-encoding sequences have been disrupted by LGT. Our three null hypotheses are that (a) recombination breakpoints are uniformly distributed among protein-coding sequences, such that across putatively orthologous gene sets, no correlation exists between the occurrence of recombination breakpoints and the presence of protein structural domains; (b) within gene sets that have annotated domains, recombination breakpoints are uniformly distributed, such that no correlation exists between their location and domain-encoding regions; and (c) breakpoints that fall within domain-encoding regions do not preferentially associate with any particular feature of that region, for example its midpoint or boundaries. Translated to the protein level, the last hypothesis, if true, would mean that protein domains tend neither to be preserved as intact units of genetic transfer, nor to suffer preferential disruption within their core structural region.domon, a gene (exon) region that encodes a protein domain, and nomon, a gene (exon) region that encodes a part of a protein not recognized as a domain. Domon boundaries in DNA thus correspond to domain boundaries in the protein product.To facilitate our presentation of these hypotheses and description of test results, we introduce two new terms: i.e. putatively orthologous gene sets); the number of sequences in each set ranged from 4 to 52. We implemented a two-phase strategy To minimize potential confounding effects of duplicated genes, we extracted the 1,462 aligned sequence sets for which no gene is duplicated within the corresponding genome  database x = 48, n = 81, p = 286/1462) strongly rejects chance (p<10−14) as an explanation for the over-representation of these breakpoints in gene sets with annotated domains. Thus breakpoints are not uniformly distributed throughout protein-coding sequences, and our first null hypothesis can be rejected.Of the 1,462 gene sets, 286 (19.6%) exhibit clear evidence of recombination (Classes A–C). In all, 820 recombination breakpoints were identified within these sets, yielding an average of 2.87 breakpoints per gene set. Of the 1,462 gene sets, 81 (5.5%) have annotated domain information (one or more domains annotated in one or more protein sequences from each set) and 48 of these  were inferred to have at least one recombination breakpoint. These 48 contain 166 breakpoints, 20.2% of the total 820. Thus recombination breakpoints are 3.27-fold over-represented in gene sets with annotated domains (and domains are similarly over-represented in gene sets with breakpoints). Even if the sequences examined in this study are under-annotated with SCOP domains, the over-representation of recombination breakpoints in gene sets with domain annotations suggests that breakpoints do not occur uniformly at random in genes, but instead are preferentially associated with genes that encode protein domains (or with genomic regions close to encoded protein domains). Indeed, a one-sided binomial test  annotated in at least one of the aligned sequences. Where the associated domon varies in length within an alignment (e.g. due to insertion or deletion of codons), we nonetheless compute a single ρ value for that breakpoint, with its value calculated as the average of the individual breakpoint-to-midpoint values for each sequence. A large ρ value (ρ≈1) indicates that the associated breakpoint is located far from a domon midpoint (i.e. close to or beyond the domon boundary), and thus that the protein domain has remained structurally intact, or mostly so, during recombination. In contrast, a small ρ value (ρ≈0) indicates that the associated breakpoint is positioned close to a domon midpoint, and thus that the core of the corresponding protein domain is likely to have been disrupted by recombination.Our second null hypothesis states that recombination breakpoints are uniformly distributed within domon-containing gene sets, such that no correlation exists between breakpoints and the locations of domons. To test this, we focus on the 48 gene sets that contain at least one clear recombination breakpoint (Class A–C), and also encode at least one annotated SCOP domain. First we associate each breakpoint uniquely with a domon boundary: if the breakpoint falls within a domon, we associate it with the closer boundary of that domon; and if it falls outside any domon, we associate it with the closest domon boundary in that aligned gene set. We introduce the normalized breakpoint-to-midpoint distance statistic g domain ; where tρ and the length of the corresponding domons, including breakpoints outside domons, as shown in ρ values inferred in the dataset, many  associate with a protein domain of length 105–142 residues. One striking observation from ρ≈1 in cases where the corresponding domain region length is ≤239 residues; this is not observed where the corresponding domain region length is >239 residues. To illustrate this more clearly, ρ for these two instances. A Kolmogorov-Smirnov test between the two distributions yielded D = 0.38 and p<10−7, strongly suggesting that they differ significantly from each other. There is thus a strong bias for breakpoints associated with shorter domons to be located relatively far from the domon midpoint or outside the domon, i.e. small protein domains tend to be largely or wholly conserved in recombination. In contrast, breakpoints associated with longer domons are not similarly biased to avoid the midpoint, i.e. larger protein domains tend to be disrupted by recombination. This bias could result, at least in part, from the interplay between the lengths of domons and non-domon regions (nomons), as shown in i.e. inside nomons), hence harder for ρ to attain its maximum value (bounded by definition at 1). When the proportion of nucleotides in domons is small (<80%: ρ≈1 (non-uniform distribution at p<0.05), suggesting that breakpoints are avoiding these domons. An alternative, if at this point speculative, explanation might be that large domains tend to consist of smaller structural features (sub-domains) of functional significance and therefore selective value, but these sub-domains are distributed irregularly within large domains. Devising a test to distinguish between these two alternative explanations poses an interesting challenge in computational structural biology.We examined the relationship between ll <80%: , there iρ values to the null (uniform) distribution on  , we sub-sampled the dataset randomly  and compared each sub-sample to a uniform distribution on  using a Kolmogorov-Smirnov test, yielding distributions of the 10,000 D test statistics and p values. The D statistic represents the magnitude of difference between the two distributions, and p indicates the significance of this observed difference; both range between 0 and 1. We observed small values of D (mean 0.24), indicating that the deviation of each subsample from the uniform distribution is small, and moderate values of p , suggesting that this deviation from uniformity is at best only marginally significant  are expected to be distributed uniformly-at-random therein. If so, recombination would neither preferentially preserve, nor avoid disruption of, core domain structure, for which we use the domon midpoint as proxy. The analysis described in the previous section was designed within the context of whole gene sequences; here, we focus on domons themselves. To test this hypothesis, we constructed a quantile-quantile plot to compare the observed distribution of on  . Since ategy see , we omitnificant . Therefoρ value. We pooled these ρ values based on the individual domain description in SCOP (irrespective of length variations) to examine whether certain types of protein domains tend to be conserved or disrupted in the event of recombination. These protein domains and their respective average ρ values are listed in ρ between 0.9 and 1.0, and for 13 (26%) of these ρ = 1. The ρ values are not uniformly distributed . While our observations suggest that certain domain types tend to be conserved or disrupted in the event of recombination and that this observation is unlikely to be explained by chance, a greater number of domain types will need to be sampled for this hypothesis to be tested with the necessary rigor.We found boundaries corresponding to a total of 50 distinct types of protein domains to have an associated per se. The cases of recent transfer into the gene (Class A breakpoints) cannot be explained by selection: the abrupt change in BPP support for alternative topologies in the breakpoint region indicates a recent event, leaving insufficient time for substitutions to accumulate. For cases of less-recent transfer (Classes B and C), invoking selection on substitution processes to explain the more-gradual change of BPP would imply the existence of substantially different substitution rates between introgressed and background sequences; this is not the case, as using a Bayesian approach p-value = 0.48) in the presence of recombination (182 amino acid residues) compared to its absence (173 amino acid residues).Our observations raise the question of whether the bias in breakpoint location results from selection against nucleotide substitution on one side of the domon boundary and accumulation of substitutions on the other, or is alternatively a consequence of the recombination event Genomes evolve in modular fashion, with different evolutionary histories for different regions N≥4 using a hybrid clustering approach http://www.ncbi.nlm.nih.gov/), with the arrangement of the nucleotide triplets reflecting the protein alignment in each case (gene set). To minimize erroneous inference arising from the presence of paralogous sequences within these sets, we further restricted our dataset to those 1,462 sequence sets for which each member represents a different genome. These sets of single-copy genes range in size from 4 to 52 members each, and total 11,128 sequences. The pairwise nucleotide identity across all sequences in each set is roughly normally distributed around mean 52.8% .From 144 completely sequenced prokaryote genomes in a previous work We adopted a two-phase strategy for detecting recombination in nucleotide sequences e-value cut-off of 10−3.Protein domain and boundary information for each of the 11,128 proteins in the dataset was determined by sequence similarity search against domain entries in Structural Classifications of Proteins (SCOP) version 1.69 Methods S1Methods in detail.(0.15 MB PDF)Click here for additional data file.Table S1Number of gene sets , sequences, annotated domains and the inferred recombination breakpoints in this study. N/A denotes not applicable.(0.01 MB PDF)Click here for additional data file.Table S2ρ value of its associated breakpoints inferred in this study. Large associated ρ values (ρ≈1) indicate these domains tend to be conserved, whereas small associated ρ values (ρ≈0) indicate that these domains tend to be disrupted in the event of recombination.List of distinctive protein domains and each respective average (0.10 MB PDF)Click here for additional data file.Figure S1Identification of a recombination breakpoint based on change-of-profile (COP) profile plot from DualBrothers. The Y-axis represents the marginal posterior probability of the position in the alignment being a COP, while the X-axis represents the positions in the sequence alignment. The breakpoint was defined as the median of the sample distribution. The shaded area represents the area bounded within the 95% Bayesian Confidence Interval, as identified between quantiles 0.025 and 0.975.(0.15 MB TIF)Click here for additional data file.Figure S2D value 0.16 in Kolmogorov-Smirnov test) although the difference may be statistically significant . (B) Distribution of the ratio of percent identity within domon and within nomon regions (D/N ratio) in the dataset.Sequence identity within the dataset. (A) Sequence identity across the whole dataset, within domon (X-axis) and within nomon (Y-axis) regions, based on SCOP annotations. The trend-line describing the linear relationship between the two axes is shown. The two distributions differ very little from each other ((0.25 MB TIF)Click here for additional data file.Figure S3Distribution of the lengths of domain regions in the dataset.(0.14 MB TIF)Click here for additional data file.Figure S4Relationship of the length of domain region (X-axis) and that of inter-domain region (Y-axis), shown for sequences in which recombination is inferred. The relationship is shown as (A) a heat map and (B) a dot plot. In the heat map, blue cells contain the least number of data points (minimum 0), while the bright red contain the most number of data points (maximum 13). Both X and Y axes are shown in natural logarithmic scale.(0.46 MB TIF)Click here for additional data file.


The between- and within-herd variability of porcine reproductive and respiratory syndrome virus (PRRSV) antibodies were investigated in a cross-sectional study of 103 British pig herds conducted 2003–2004. Fifty pigs from each farm were tested for anti-PRRSV antibodies using ELISA. A binomial logistic model was used to investigate management risks for farms with and without pigs with PRRSV antibodies and multilevel statistical models were used to investigate variability in pigs' log ELISA IRPC (relative index × 100) in positive herds.Thirty-five herds (34.0%) were seronegative, 41 (39.8%) were seropositive and 27 (26.2%) were vaccinated. Herds were more likely to be seronegative if they had < 250 sows ) and if the nearest pig herd was ≥ 2 miles away ). The mean log IRPC in seropositive herds was 3.02 . Sixteen seropositive herds had only seropositive adult pigs. In these herds, pigs had -0.06  lower log IRPC for every mile increase in distance to the nearest pig unit, and -0.56  lower log IRPC when quarantine facilities were present. For 25 herds with seropositive young stock and adults, lower log IRPC were associated with isolating purchased stock for ≥ 6 days , requesting ≥ 48 hours 'pig-free time' from humans  and purchasing gilts .These patterns are consistent with PRRSV failing to persist indefinitely on some infected farms, with fadeout more likely in smaller herds with little/no reintroduction of infectious stock. Persistence of infection may be associated with large herds in pig-dense regions with repeated reintroduction. Porcine Reproductive and Respiratory Syndrome (PRRS) caused by PRRS virus (PRRSV), was first reported in North America in 1987 and in the United Kingdom in 1991 .in utero [The clinical signs of PRRSV are reproductive loss in sows including return to oestrus, abortion, premature farrowing, mummified foetuses and stillbirths ,4. PRRSVin utero  and immuin utero . The cliin utero ,8, otherin utero  suggestiin utero  and somein utero ,9,13.inter alia, transmissibility of the virus, infectious period and existence of alternative hosts or environment contamination [The role of fadeout and persistence in determining viral transmission dynamics has been recognised for some time, especially in the context of measles and other childhood infections ,15. Perimination -18. ThusAnti-PRRSV antibodies (detectable by ELISA) arise approximately 9 – 13 days after infection  and decaIn this paper we present the farm and pig characteristics associated with herd seropositivity and pig heterogeneity in seroprevalence to PRRSV on 103 GB pig herds using ELISA antibodies as a marker for previous exposure to PRRSV and hypothesize on patterns of fadeout and persistence.Data used in this study came from a cross-sectional study of 103 pig herds in England, Wales and Scotland. Data were collected from June 2003 to August 2004 as part of a study of post weaning multisystemic wasting syndrome . From eaDuring the farm visit, the farmer was interviewed and management variables relating to the unit were recorded. Variables that were selected for use in the current analysis were plausibly associated with infectious disease transmission. These included the size and purpose of the herd, purchase of stock, quarantine facilities, biosecurity within the herd, and characteristics of the nearest pig unit Table . In addiA pig was defined as seropositive if the IRPC of the sample was ≥ 20 units (according to manufacturer's instructions).A herd was defined as seropositive if at least one pig in the herd was seropositive. Given the specificity of the ELISA (98.3%), a sample size of 50 pigs from a disease free population would result in less than one false positive pig being detected (mean 0.85). This definition minimises the false negative errors.A herd that, according to the veterinarian, was vaccinated. If the veterinarian did not give a response regarding vaccination, it was assumed that vaccination was not used. was used to calculate the minimum expected seroprevalence on a farm and by age when no seropositive animals were detected, adjusted by the sensitivity and specificity of the ELISA.FreeCalc (Version 2) The total proportion of pigs seropositive per farm and for each age category was calculated and vaccinated and unvaccinated seropositive herds were compared. Seropositive herds were categorised according to whether there were any seropositive eight and 14 week old pigs (young stock) on the unit or not. The veterinarian questionnaire results were used to investigate the history of PRRS on all herds.Three models were built.Model 1: A binomial logistic regression model was used to determine associations between farm characteristics and the probability that a herd was seronegative for PRRSV antibodies. All veterinarians of vaccinated herds stated that PRRS had been seen on the units, so both vaccinated and positive herds were included in the model as seropositive herds. Analysis was carried out in Stata SE 9.0 .Model 2 A three level mixed normal model was built in MLwiN version 2.1  to invesβX0 is the intercept and βX is a series of fixed effect vectors that varied at the herd (k) and pen (j). νk is the variance at herd level, υjk is the variance at the pen level and eijk is the variance of the log IRPC between pigs.Where Model 3 Is as Model 2, but includes those farms where young stock were seropositive.χ2 tests , with Fisher's exact test where appropriate. To reduce the number of predictor variables for consideration in the multivariable models, significance for the univariable screening of variables was set at P < 0.1 [For all models continuous predictor variables were investigated for linearity with the outcome variable using five quintiles. The variable was transformed into a categorical variable if the relationship was not linear by eye. Pairwise correlated variables were identified using Pearson's pairwise correlation coefficient (for continuous variables) and  P < 0.1 . ForwardA total of 4852 pigs from 103 herds were used in the analysis. Thirty five (34.0%) herds did not have any seropositive pigs, 41 (39.8%) had at least one seropositive pig and 27 (26.2%) were using vaccination . In both Models 2 and 3, the log IRPC of pigs changed with age Tables  and 5. IThe 103 herds sampled in this study were representative of the national herd in size, location and ratio of indoor to outdoor pig herds in 2004 . Age-relClassifying herds as virus negative on the basis of 20 seronegative 8 or 14 week old pigs (irrespective of whether seropositivity was non-homogeneously distributed within the two age groups) is supported by three arguments. First, there was no passive immunity, which declines within 4–10 weeks ,25 indicThe factors that may provide a pool of susceptible pigs and reduce the probability of herd immunity and so aid persistence of PRRSV on pig farms include production of susceptible piglets (approximately 22 per annum per sow) and the movement of pigs between farms, especially breeding stock, currently replaced at 45% per annum in the UK. These risks are correlated and decrease together as herd size decreases. There may be a threshold level when the probability of successful introduction reduces to below 1. In the current study, this appears to be at ~250 sows. So a smaller herd size might reflect an increased probability of reduced risk of introduction of virus and/or virus fadeout from lack of susceptible pigs. The association between fadeout of PRRSV and herd size has been reported previously .Herds with < 250 sows were correlated with multiplier and nucleus herds. Such herds were also more likely to be situated > 2 miles from the next nearest pig unit. It is not possible to state which of these factors, or the combination of factors, assists seronegativity because these farms are more likely to be more biosecure than commercial farms and might be deliberately situated further away from the main pig breeding areas. Consequently, cause and effect relationships cannot be separated. However, biologically, a small population is more likely to lead to virus fade out.PRRSV antibody negative herds were more prevalent Model 1) when they were > 2 miles away from the nearest pig unit. Seropositivity was also lower in herds that were more remote from other pig herds so local distant spread appears possible. The mechanisms by which virus may be transmitted between herds is currently not known, although aerosol transmission of PRRSV has been demonstrated over short distances ,27 and s when theAs well as geographical isolation, purchase of known negative stock and quarantine of stock before introduction onto a farm may limit introduction of PRRSV. Presence of quarantine facilities were not associated with antibody negative herds but were associated with herds that had all seronegative young stock (Model 2), which are likely to have been virus negative (see below). In addition, time that purchased stock spent in isolation was associated with lower IRPC values in pigs in virus positive herds (Model 3). This latter association could occur if pigs in isolation were more likely to be virus negative by the time they entered the unit. Isolation of new stock has been associated with a lower risk of introduction of PRRSV ,31,8.A lower mean IRPC in positive herds with purchased gilts rather than homebred replacements would occur if there was a higher probability that purchased gilts were seronegative compared with homebred gilts: this probability would be high if the mean herd seropositivity was higher than the mean of all herds. Purchase of PRRSV negative gilts into a PRRS virus positive herd might lead to disease in the herd if these gilts were infected when pregnant and this may explain some of the irregular disease patterns reported in positive herds .The presence of antibodies in breeding female pigs but not young stock Figures  and 3 haIt is likely that both truly virus positive and truly virus negative herds that use homebred replacements are the most clinically and immunologically stable, since the former encourages active immunity in pigs before their first gestation and the latter have no virus. Introduction of viraemic stock into positive herds might assist in persistence of PRRSV through re-introduction and would be of concern if a different strain of PRRSV was introduced. Conversely, introduction of negative pigs into a positive herd might cause disease because these gilts would be infected in their first gestation. Totally negative herds are at risk of PRRSV introduction if geographically close to another pig farm, of larger herd size or if purchasing and/or not isolating incoming stock. We are pursuing the development of mathematical models to better understand the transmission dynamics of PRRSV, the patterns of clinical disease it exhibits and appropriate control strategies.Approximately 51.5% and 64.8% of the total between-herd variance (amongst seropositive herds) were explained in the two multilevel models (Tables The decision to use ELISA log IRPC values as the continuous outcome variable in the multilevel models was based on < 100% sensitivity of PRRSV ELISAs ,33. A biWe conclude that PRRSV infection was far from consistent across this sample of farms, with herds ranging from seropositive pigs in all age groups, to seronegative in young stock and seronegative in all ages. The results suggest that PRRSV transmission dynamics exhibit viral fadeout and reintroduction rather than indefinite persistence on infected farms. Whilst fadeout may occur in smaller more geographically isolated herds with minimal introduction of infectious stock, persistence may be associated with large herds in pig-dense regions with continuous introduction of infectious stock. These results may explain why disease is variable between infected herds and indicate that different management strategies are required which depend on the current herd status.CME was involved in the analysis and interpretation of the results and statistical analyses. GFM was involved in the development of the research concept and ideas. LEG designed the study, participated in its coordination and was involved in the interpretation of results and the development of the research concept and ideas. All authors contributed to the drafting of the manuscript and have read and approved the final version.