At the NPC, the FG motifs of nucleoporins may exert this cohesive effect intermolecularly as well as intramolecularly to form a malleable yet cohesive quaternary structure composed of highly flexible polypeptide chains. Dynamic shifts in the equilibrium or competition between intra- and intermolecular FG motif interactions could facilitate the rapid and reversible structural transitions at the NPC conduit needed to accommodate passing karyopherin–cargo complexes of various shapes and sizes while simultaneously maintaining a size-selective gate against protein diffusion.The nuclear pore complex (NPC) provides the sole aqueous conduit for macromolecular exchange between the nucleus and the cytoplasm of cells. Its diffusion conduit contains a size-selective gate formed by a family of NPC proteins that feature large, natively unfolded domains with phenylalanine–glycine repeats (FG domains). These domains of nucleoporins play key roles in establishing the NPC permeability barrier, but little is known about their dynamic structure. Here we used molecular modeling and biophysical techniques to characterize the dynamic ensemble of structures of a representative FG domain from the yeast nucleoporin Nup116. The results showed that its FG motifs function as  The nuclear pore complex is a molecular filter that gates macromolecular exchange between the cytoplasm and the nucleoplasm of cells. It contains a size-selective diffusion barrier at its center composed of proteins named FG nucleoporins. These nucleoporins feature large, structurally disordered domains that are highly decorated with phenylalanine–glycine (FG) sequence motifs. The dynamic structure of these disordered FG domains excludes them from classical structural biology analyses such as X-ray crystallography; thus, new approaches are needed to characterize their shape. Here computational and biophysical approaches were used to elucidate the ensemble of structures adopted by the FG domain of a nucleoporin. The analyses showed that the FG motifs function as intramolecular cohesion elements that compact the shape of the FG domain, forcing it to adopt loosely knit globular configurations that are constantly reconfiguring. Within the nuclear pore complex, dozens of these nucleoporin FG domains may stack as loosely knit globules forming a porous sieve that gates molecular diffusion by size exclusion. The nuclear pore complex is a supramolecular protein structure in the nuclear envelope that controls nucleo-cytoplasmic traffic and communication  [1]. A kS. cerevisiae FG nups is unusual because their 150–700 amino acid (AA) FG domains are natively unfolded S. cerevisiae indicated that some FG domains (the GLFG-rich domains) bind to each other weakly via hydrophobic attractions between their FG motifs, whereas other FG domains (the FxFG-rich domains) do not form such cohesions The three dimensional structure of It is generally assumed that natively unfolded proteins have some preferred 3-D structures dictated by intra-molecular cohesion Since traditional experimental methods for elucidating protein structure cannot be used with natively-unfolded proteins, new approaches are needed to study and describe their dynamic ensemble of structures. In this emerging area of research, Jha et al intramolecular cohesion elements that impart structure. Apart from its cell biological significance, we chose this protein as a model system to investigate how a combination of molecular dynamics simulations and biophysical measurements can be used to characterize the ensemble of structures adopted by a natively unfolded protein, such as the FG domain of a nucleoporin.Here we conducted molecular dynamics simulations and biophysical measurements on a small FG domain from the yeast nucleoporin Nup116 (Q02630) to test the hypothesis that phenylalanines in its FG motifs function as intramolecular cohesion of coils  in the dynamic ensemble of nup structures. The simulated FG domains were then expressed in bacteria, purified to homogeneity, and analyzed by NMR spectroscopy and sizing columns to quantify their average shape through measurements of diffusion coefficient and Stokes radii. Finally, mathematical and biophysical analyses were combined to estimate the tertiary structure that best describes the natively unfolded domain of the representative FG nucleoporin.In the analysis that follows we first used MD simulations to generate a statistical ensemble of coil conformations for a 111 AA region of the Nup116 FG domain containing ten FG motifs (wild-type), and of a mutant version thereof lacking the phenylalanines in the ten FG motifs (F>A mutant) . The MD Rg values during the last 3 ns (data not shown).Twenty independent MD simulations were performed at 300 K (25°C) on the wild-type (6 ns) and F>A mutant (5 ns) versions of a Nup116 FG domain (AA 348–458) starting from a fully-extended conformation. The goal of these simulations was to sample the conformational distribution of the proteins as close as possible to their native distribution in solution. As soon as the simulations started, within the first 100 ps, the extended FG domains collapsed into a more cohesive or compact ensemble of structures with small patches of unstable (see below) secondary structure. Since the wild-type and mutant FG domains are highly flexible and disordered, the resulting end-structures from each of the twenty simulations did not resemble one another as expected for natively unfolded proteins see  and by sTo describe quantitatively the structural dynamics of the FG domains, we calculated the auto-correlation function of a vector of the 118 Φ and 118 Ψ angles along the peptide backbone of FG domain structures sampled every 1 ps from the MD trajectory. 10-helix). In general, no significant difference in overall helical content between wild-type and mutant FG domains was observed. The alpha- and 310- helical structures that did form ranged in size from 2–6 AA residues and did not persist for more than 35 ps on average (data not shown). The maximum duration of an α-helix and a 310-helix was 97 and 699 ps, respectively (data not shown).The ensemble of structures for each of the twenty MD trajectories generated for the wild-type and mutant FG domains were sampled at 1 ps intervals during the final 3 ns of the simulations, yielding a total of 60,000 structures for each protein. The secondary structure content was then analyzed in detail to determine the fraction of time during the simulations that each AA residue spent as part of a “helical” structure (either an α-helix or a 3Rg) and the end-to-end distance between terminal residues. The average (±1 standard deviation) end-to-end distance for the wild-type FG domain simulated at 300 K was 20.42 Å (±9.51), and for the mutant was 20.69 Å (±7.78) (data not shown). The predicted radius of gyration was 14.52 Å (±1.18) for the wild-type and 14.41 Å (±1.24) for the mutant FG domain .Using the same set of 60,000 structures, two measures of protein compactness were calculated: the radius of gyration . The average Rg was 17.40 Å (±3.11) for the wild-type FG domain and 23.68 Å (±6.05) for the mutant domain . Consistently, the average end-to-end distance for the wild-type FG domain was 29.95 Å (±16.04) compared to 52.56 Å (±25.31) for the mutant  for the ten sites that correspond to the phenylalanine  or to the substitute alanine  residues in the various FG motifs. The distances used were from the MD simulations at 350 K, which yielded structures  that betTo permit comparisons of the average inter-residue distances, the probability distributions obtained for the Cβ-to-Cβ distances were fit to a single Gaussian distribution even though in some cases there were multiple distinct peaks . This waProbability distributions of all inter-residue distances obtained from the MD simulations were subjected to clustering using the Pearson squared correlation. This was done to determine how any two of the distributions sampled in regular intervals of the MD simulations are correlated with each other. The correlation coefficient does not depend on the specific measurement units used because other correlation coefficients, such as Euclidian distance metric, yielded similar clustering effects (data not shown). To obtain a broader view of the dynamical correlation between inter-residue distances in the FG domains, the Pearson correlation coefficient between all 990 distinct interresidue distances in all 20 simulation replicates of each FG domain were calculated, yielding 19,800 correlation coefficients. In this case, a value of 1.0 would indicate a perfect linear correlation between two inter-residue distances ; a value of 0.0 would indicate no correlation between a distance pair; and a value of −1.0 would indicate perfect anticorrelation. intramolecular distances between FG motifs in the wild-type and mutant FG domains are quantifiably different from each other. There was a tendency for FG motifs in the wild-type FG domain to be proximal to each other , which was absent in the mutant. This conclusion is consistent with the hypothesis that the FG motifs in the wild-type Nup116 FG domain interact intra-molecularly in a manner similar to what has been observed for intermolecular interactions between this Nup116 FG domain and other FG domains of nups To better describe the relationship between FG motifs in the FG domain, the distances between F–F pairs (or substitute A–A pairs) were also categorized into groups representing distances of 10–15, 15–20, or >20 Å. These are shown in intra-molecular interactions within the Nup116 FG domain. In principle, a change in the dynamic ensemble of FG domain structures resulting from the substitution of all Phe's to Ala's could be detected by NMR. A less-ordered mutant FG domain would exhibit a slower diffusion coefficient. The wild-type and F>A mutant versions of the Nup116 FG domain were purified to `homogeneity and subjected to NMR analysis. Plots of the one-dimensional 1H NMR spectra are shown in The structural predictions made by the in silico modeling prompted us to seek physical evidence that the phenylalanine residues in FG motifs function as structural cohesion elements that form putative Dsexpt) values of 13.17 (±0.26) and 12.18 (±0.12)×10−11 m2 s−1 for the wild-type and mutant FG domains, respectively. This indicated slower diffusion for the less-ordered mutant FG domain. Despite the mass of the F>A mutant domain being smaller (11.9 kDa) than wild-type (12.6 kDa) (due to the replacement of 10 Phe for Ala) the diffusion constant of the mutant was smaller on average, suggesting that its effective hydrodynamic volume is larger. As expected for unfolded proteins Experimental self-diffusion measurements (intensity vs. product of the area of gradient pulse strength and the diffusion length) of the FG domains and the corresponding exponential fits are also shown in Rs = 23.5Å), ovalbumin , and BSA . The Stokes radius for the wild-type FG domain was measured at 25.2 (±0.6) Å  Å , which i(±0.6) Å . This ap(±0.6) Å  and with(±0.6) Å . The obsThe hydrodynamic volume or Stokes radius of a protein in different structural configurations  can be estimated from its mass using mathematical equations S. cerevisiae nucleoporin Nup116 (AA 348–458) and of a mutant version thereof (F>A) lacking the phenylalanines in its predominantly GLFG motifs , together with the scaling relations developed by Uversky's group across the NPC transport conduit from their tether sites within the NPC scaffold  to native pre-molten globular configurations .Our finding that the FG motifs can function as c states . Alternac states . Accordiintramolecular or intermolecular, is indeed an important question whose answer may rely largely on four parameters: the distance between FG domain anchor sites at the NPC, the volume of space occupied by each FG domain, the space available at the NPC for each FG domain, and the steric hindrance effect between neighboring FG domains z-axis; see inter-molecular FG motif interactions. This structural assembly could take the form of a meshwork of intertwined polypeptide chains intra- and intermolecular FG motif interactions could facilitate the fast structural changes in the NPC permeability barrier, which are presumably coupled to the passage of karyopherin-cargo complexes of different shapes and sizes during transit across the NPC  intramolecular interactions. This could cause the FG domains to fold back on themselves , effectively opening the permeability barrier by suddenly occupying less space.What type of FG motif interaction dominates at the NPC, either  the NPC . As karyintermolecular cohesions with each other via FG motifs intramolecular cohesions of their own FG motifs to adopt compact configurations. According to the “virtual gate” Xenopus Nup153 FG domain It remains to be determined whether other types of nup FG domains, which do not display Secondary structure analysis: For the final 3 ns of each simulation, the structure was analyzed every 1 ps using a standard program for identifying secondary structure from atomic coordinates  Radius of gyration and end-to-end distance analyses: For the final 3 ns of each simulation, radii of gyration and the end-to-end distances between terminal residues were calculated using the program CARNAL and ptraj, distributed with AMBER 7 High temperature molecular dynamics simulations: Molecular dynamics were performed at elevated temperatures for each of the 20 wild-type and mutant FG domain simulations. The GB/SA simulations were all restarted after 5 ns coupled to a heat bath at 325 or 350 K with all other parameters of the simulation kept the same. The simulations were run for 1 ns, and the final 500 ps were used for analysis.MD simulations of individual FG domains were started from a fully extended backbone structure . A different random number seed was chosen for each of the different simulations to randomize the initial atom velocities. Twenty separate simulations were run for either 6 ns (wild-type) or 5 ns (mutant) each using different initial atomic velocities and analyzed at 1 ps intervals. The wild-type fragment required an additional nanosecond of dynamics to have its radius of gyration converge. All MD simulations were performed with AMBER To determine the degree of dynamical change in the ensemble of FG domain structures, the autocorrelation function was derived for a vector  composed of the 118 Φ and 118 Ψ angles  to form a histogram of distance distributions. Probability distributions were calculated for each of the twenty simulations independently, and the values obtained were averaged at the end. A similar procedure was adopted for the mutant FG domain where the distance between the Cβ atoms of the Ala residue was used. Final probability distributions were used without any normalization.Clustering analysis: The correlation between different F–F probability distribution reflects the degree to which these variables (F–F distances) are related. The most common measure of correlation is the Pearson Product Moment Correlation (http://www.r-project.org/) and reflects the degree of linear relationship between the two variables. In order to determine whether probability profiles of the F–F interaction correlate, a similarity matrix with a Pearson square metric was calculated. The correlation was used to indicate the presence (or absence) of relationship between various F–F interactions.As a first approximation, the probability distributions were fit to a Gaussian distribution (probability versus distance). This is a conservative approach and is expected to be valid considering the number of structures generated  during the molecular dynamics simulations and in the absence of any constraints. The center of the Gaussian is considered as the mean distance between the F–F (or A–A), while the width at half-maximum is used as the allowed variation in the constraint. S. cerevisiae DNA using PCR and was cloned into the vector pGEX-2TK in frame with the coding sequence for glutathione S-transferase (GST) at the 5′ end, and in frame with the coding sequence for six contiguous histidines at the 3′ end. Site directed mutagenesis was then used to alter the coding sequence for the mutant F>A FG domain. The correct coding sequences were confirmed by DNA sequence analysis. The FG domains were expressed in a E. coli BL21+ strain as fusion proteins with GST (glutathione S transferase) at the N-terminus and a HIS tag (six contiguous histidine residues) at the C-terminus. Glutathione coated Sepharose beads were then used to isolate each GST-FG domain fusion from crude bacterial cell extracts. The isolated FG domains were eluted from the beads by specific thrombin proteolysis of the GST tag. Nickel-coated beads were then used to capture and isolate the FG domain through its C-terminal His-tag, and the captured proteins were eluted from the beads using imidazole. Finally, the eluates were concentrated in a Centricon 3 unit and were size fractionated in an FPLC Superdex 200 sizing column that was equilibrated in 50 mM NaH2PO4, pH of 6.5 for the NMR analysis, or in an FPLC Superdex 75 column equilibrated in 20 mM Hepes, pH 6.8, 150 mM KOAc, 2 mM Mg(OA)2 for determination of Stokes radii.The coding sequence for the representative 111 AA Nup116 FG domain was amplified from genomic 2, and 0.5 ml fractions were collected. The FG domain elution profiles were monitored by UV absorbance at 280 nm and by SDS-PAGE analysis of the eluates. The nup elution profiles were compared to those of carbonic anhydrase , ovalbumin , and BSA , which served as molecular size standards. The elution volume of the standards was plotted in relation to their Stokes radii, allowing for estimation of the FG domain Stokes radii from the resulting linear regression formula.Tandem-affinity purified wild-type and F>A mutant Nup116 FG domains were subjected to size-fractionation through an analytical-scale FPLC Superdex 75 column. FG domains (100 µl of 7.5 mg/ml) were injected at a flow rate of 0.5 ml/min at 4°C into a column that was preequilibrated in 20 mM Hepes pH 6.8, 150 mM KOAc, 2 mM Mg(OAc)2PO4, pH 6.5. Final protein concentrations were ∼0.5 mM for both wild-type and mutant FG domains. NMR experiments were performed in a Varian INOVA 600 MHz spectrometer equipped with a 5 mm probe with a single-axis  shielded magnetic field gradients. One dimensional 1H NMR experiments were obtained using the water suppression scheme 1-3-3-1 Water-gate dephasing) sequence NMR experiments were performed on tandem-affinity purified FG domains dissolved in 50 mM NaHTranslational diffusion tensor values were calculated based on the beads-model approximation of García de la Torre and Bloomfield Rh) for the wild-type and mutant Nup116 FG domains was calculated from the radius of gyration (Rg) values obtained from the simulations using the scaling relationship given in Rh = Rg/0.77, and for proteins in strong denaturing conditions, the scaling relationship is Rh = Rg/1.06. For the wild-type and mutant Nup116 FG domains simulated at 300 and 325 K, the hydrodynamics radius was obtained by Rh = Rg/0.77. In the 350 K simulations, some of the protein conformations were highly extended (as in denaturing conditions) and a single scaling value was not appropriate. In this case, if the Rg for a structure was less than 30.7 Å for wild-type and 29.6 Å for the mutant, it was scaled by 1/0.77; if the Rg was greater, the value was scaled by 1/1.06. The Rg cutoff values of 30.7 Å (wild-type) and 29.6 Å (mutant) were obtained by using Uversky's relationship: Rh (8 M urea) = (0.22)*M0.52, where M is the molecular mass Rh values were 22.5±4.0 for the wild-type FG domain and 28.6±5.2 for the mutant. To compare these Rh values to the Stokes radii values  for the purified FG domains in sieving columns, the contribution of a C-terminal His-tag (6 histidine residues/841 Da), which was added (post simulations) to the FG domains to aid in the purification of only full-length FG domains, had to be factored in. This was done using Uversky's scaling relationship by calculating Rs for the FG domains with the additional tag assuming a native pre-molten globular configuration for the wild-type and a native coil configuration for the mutant (see Rs estimated from the molecular dynamics simulations for the wild-type and mutant FG domains were multiplied by the ratio (Rs (His-tag)/ Rs (no-tag)) to yield the final values of 23.1 (±4.1) Å for the wild-type FG domain and 29.6 (±5.4) Å for the mutant FG domain reported in The hydrodynamic radius Click here for additional data file.Table S2List of distance constraints obtained from the Gaussian fit to interresidue distance distribution.(0.11 MB DOC)Click here for additional data file.Table S3The dimensions and locations of GLFG-rich domains of nups at the NPC.(0.07 MB DOC)Click here for additional data file.Figure S1Rg) in units of Angstroms (Å) over the last 3 ns of the MD simulations for twenty replicate simulations at 300 K for the wild-type and F>A mutant FG domains. The plots show no systematic increase or decrease in the Rg values during this time window.Plots of radii of gyration versus time of the simulated Nup116 FG domains. Radii of gyration ((6.97 MB TIF)Click here for additional data file.Figure S2Structural dynamics of the simulated FG domains. Autocorrelation vector of all phi and psi angles calculated with a 1 ps time step averaging over all 2800 autocorrelation windows in the last 3 ns simulations time. A separate line is plotted for each of the twenty wild-type replicates (blue lines), F>A mutant replicates (red lines), and the fibroblast growth factor 1, as reference (green line).(2.24 MB TIF)Click here for additional data file.Figure S3Comparison of internal structural correlation between the simulated FG domains. Back-to-back histograms of the Pearson correlation coefficients calculated between all F–F (or A–A) distance pairs measured every 1 ps during the last 3 ns of simulation time at 300 K in all 20 replicates. Note that the wild-type FG domain shows a systematic bias towards higher correlation coefficients, indicating more internal structural correlation in comparison to the F>A mutant.(0.74 MB TIF)Click here for additional data file.Figure S4http://bioserv.impmc.jussieu.fr/hca-file.html. The AA sequences used are listed in A hydrophobic cluster analysis (HCA) of the Nup116 FG domain. (A) HCA analysis of the wild-type and F>A mutant FG domains (AA 348–458) was performed using the web server (3.51 MB TIF)Click here for additional data file.Figure S5intra- and intermolecular interactions. (B) Fluctuations in the dimensions of FG domains (which are intrinsic to natively unfolded structures) and steric hindrance effects between FG domains (due to their close anchoring) could cause the FG domains to extend further out into the transport conduit. The cohesive properties between FG domains within the conduit  could transiently stabilize some of the extended conformations. In this panel, the FG motifs are shown in competition between intra- and intermolecular interactions. (C) During transit across the nuclear pore complex, karyopherin-cargo complexes likely separate and bridge FG domains by interacting with their FG motifs. In this panel, the FG motifs are shown in competition between intra- and intermolecular FG motif interactions as well as in competition with karyopherins. In all panels, the nuclear envelope is shown in gray, the nuclear pore complex in green, its cytoplasmic fibrils in light yellow, its nuclear basket in light red, the GLFG-rich domains of nucleoporins in blue, and their intra- and intermolecular FG motif interactions in bright red. For panel C, karyopherins are shown in black and their cargo in bright yellow.The dynamic behavior of GLFG-rich domains of nucleoporins within the transport conduit of the yeast nuclear pore complex. These FG domains are depicted as a dynamic ensemble of premolten globular structures that can fluctuate widely in dimensions. Other types of FG domains are excluded for simplicity. (A) In their “ground state” the GLFG-rich domains are too small to span across the NPC transport conduit from their tether sites within the NPC scaffold, but are large enough to contact each other locally within a single NPC spoke (see top panel) and between adjacent spokes (see bottom panel), based on their close anchoring at the NPC see . In this(1.58 MB TIF)Click here for additional data file.


Staphylococcus aureus is an opportunistic pathogen, commensal of the human skin and nares, but also responsible for invasive nosocomial as well as community acquired infections. Staphylococcus aureus adheres to the host tissues by means of surface adhesins, such as SdrC, SdrD, and SdrE proteins. The Sdr family of proteins together with a functional A domain, contain respectively two, three or five repeated sequences called B motifs which comprise the CnaB domains. SdrD and SdrE proteins were reported to be protective in animal models against invasive diseases or lethal challenge with human clinical S. aureus isolates. In this study we identified a 126 amino acid sequence containing a CnaB domain, conserved among the three Sdr proteins. The three fragments defined here as CnaBC2, D5 and E3 domains even though belonging to phylogenetically distinct strains, displayed high sequence similarity. Based on the sequence conservation data, we selected the CnaBE3 domain for further analysis and characterization. Polyclonal antibodies raised against the recombinant CnaBE3 domain recognized SdrE, SdrC and SdrD proteins of different S. aureus lineages. Moreover, we demonstrated that the CnaBE3 domain was expressed in vivo during S. aureus infections, and that immunization of this domain alone significantly reduces the bacterial load in mice challenged with S. aureus. Furthermore, we show that the reduction of bacteria by CnaBE3 vaccination is due to functional antibodies. Finally, we demonstrated that the region of the SdrE protein containing the CnaBE3 domain was resistant to trypsin digestion, a characteristic often associated with the presence of an isopeptide bond. Staphylococcus aureus is a Gram positive opportunistic pathogen associated with asymptomatic colonization of the skin and mucosal surfaces. This microorganism is responsible for infections in humans and animals, ranging from mild localized impetigo and cellulitis, to life threatening systemic infections such as endocarditis, osteomyelitis, toxic shock syndrome and gastroenteritis  * 100, where Time 0 is the number of colonies before the incubation and Time 1 is the number of colonies after 1 hour of incubation.Human promyelocytic leukemia cells HL-60 (ATCC CCL240) were maintained in enriched medium and differentiated into phagocytes using 0.8% N,N-Dimethylformamide . Following heat inactivation , mouse SdrE and CnaBE3 antisera were prediluted 1:50 in Hank’s Balanced Salt Solution (HBSS) buffer (with Ca2 (dissolved in water) and EDTA, were used at up to 1mM. The SYPRO orange dye 5000X (Invitrogen) was used at the final concentration of 5X in each well. The reaction mixtures were 40 µl in 50 mM Tris-HCl buffer, pH 7.6. Fluorescence intensities were monitored using an Mx3005 RT-PCR instrument (Stratagene) using the FAM (492 nm) and ROX (610nm) filters for excitation and emission, respectively. Samples were heated from 25°C to 95°C at a 1 °C/min scan rate. The melting temperature (Tm) values were extrapolated by fitting the raw data to a Boltzmann model using GraphPad Prism 5.0 software. Each experiment was performed at least in triplicate.DSF experiments were performed using thin wall PCR plates (Axigen). SdrE was used at a final concentration of 10 µM; both CaCl2, pH 7.6 or in 50mM Tris HCl, 1mM EDTA, with one change of buffer. Dialyzed proteins were treated with 0.1% RapiGest™ (Waters), boiled at 100°C for 10 min. Samples were allowed to cool and were digested with sequencing grade modified trypsin (Promega), using an enzyme/substrate ratio of 1:25. The reaction was carried out at 37°C. After 24h of incubation, 15 µl were drawn out and mixed with boiling SDS sample buffer. SDS-PAGE analysis was performed using NuPage 4-12% Bis-Tris gradient gels (Invitrogen) according to the manufacturer’s instructions followed by Coomassie Blue staining.SdrE protein, CnaBE3 domain and CnaBE3 domain short form were dialyzed overnight either in 50mM Tris HCl, 1mM CaCl280, and apparent molecular masses were determined from a standard curve. The purified polypeptide identified from the collected fractions was successively used for N-terminal sequencing by Edman Degradation.The 37kDa resistant fragment was purified by Ultraperformance liquid chromatography SEC. The overnight trypsin digested SdrE sample was purified using Bridged Ethyl Hybrid 200 (BEH200) column in UPLC (Waters) at 0.5 ml/min with 20 mM phosphate buffer, pH 8.0. The overnight trypsin digested SdrE was loaded onto the column, peaks were assigned at A2 laser) were recorded at an acceleration of 25 kV in the reflector mode. About 200 single spectra were accumulated for improving the signal/noise ratio and analyzed by FlexAnalysis . Peptide identiﬁcation was performed using BioTools and Sequence Editor 3.0 . Initial searches against SdrE sequence using Mascot search engine version 2.0.05 (Matrix Science) identified the peptides from the resistant fragment in the CnaB region of the protein only.As previously reported , spots oTo confirm the identity of the purified overnight trypsin major resistant fragment of SdrE, the UPLC SEC purified resistant fragment of SdrE was subjected to amino-terminal sequence analyses by Edman degradation . Amino-tFigure S12+ stabilizes SdrE protein.CaSdrE protein was tested for its capacity to bind calcium in Differential Scanning Fluorimetry (DSF) experiments. SdrE protein was incubated in absence of Ca2+ ions or in presence of either 1mM CaCl2 or 1mM of EDTA. In presence of 1 mM CaCl2 the melting temperature (Tm) of SdrE protein increased up to 54.43°C, whereas after the incubation with EDTA the Tm decreased to 39.72°C, below the standard Tm of SdrE protein that was equal to 45.26°C. These data indicate that calcium binds to SdrE protein, favoring the structural stability of the protein.(TIF)Click here for additional data file.Figure S2Aminoacid sequence comparison between B3 repeat of the SdrE protein and subdomain D1 of the CnaB1 domain of the S. aureus protein Cna.A) Sequences alignment between B3 repeat of the SdrE protein (BE3) and subdomain D1 of the CnaB1 domain of the Cna protein. Identical residues are highlighted and the hypothesized new (green box) and current (light red box) putative CnaB domains are shown. B) Amino acid sequences of B repeats of the Sdr proteins are shown. The putative CnaB domains so far reported are encompassed in a light red box, whereas a green box highlights the suggested new CnaB domain sequences. Moreover a light blue box delimits a consensus Ca2+ binding EF-hand loop, present in all the B repeats of the Sdr proteins.(TIF)Click here for additional data file.


The origin of metabolism has been linked to abiotic chemistries that existed in our planet at the beginning of life. While plausible chemical pathways have been proposed, including the synthesis of nucleobases, ribose and ribonucleotides, the cooption of these reactions by modern enzymes remains shrouded in mystery. Here we study the emergence of purine metabolism. The ages of protein domains derived from a census of fold family structure in hundreds of genomes were mapped onto enzymes in metabolic diagrams. We find that the origin of the nucleotide interconversion pathway benefited most parsimoniously from the prebiotic formation of adenine nucleosides. In turn, pathways of nucleotide biosynthesis, catabolism and salvage originated ∼300 million years later by concerted enzymatic recruitments and gradual replacement of abiotic chemistries. Remarkably, this process led to the emergence of the fully enzymatic biosynthetic pathway ∼3 billion years ago, concurrently with the appearance of a functional ribosome. The simultaneous appearance of purine biosynthesis and the ribosome probably fulfilled the expanding matter-energy and processing needs of genomic information. Thus, the making of amino acids and nucleotides must precede polypeptides and nucleic acids. In other words, some chemistries of metabolism must be of prebiotic origin and should have been established prior to peptide bond biosynthesis and nucleic acid replication. Cellular metabolism is driven by a complex collection of enzymes, most made solely of proteins and few also involving nucleic acids  The principle of continuity also dictates that the thousands of protein domain structures of the cell must appear progressively in time – the time of origin of individual domains establishes their relative age. The MOLECULAR ANCESTRY NETWORK (MANET) database traces evolution of protein domains in biological networks and is therefore useful to study the history of modern metabolism de novo from simple precursors by biosynthetic pathways or by catabolic and salvage pathways that recycle nucleotide derivatives from turnover and degradation of nucleic acid compounds Purine and pyrimidine nucleotides and their cognate bases can be produced FFnd  = 0; deep red) to the most recent . An old domain is here defined as one that had an earlier evolutionary origin when compared to a recent domain. The patchy distribution of colors in this subnetwork reflects the pervasive trend found in almost all 136 MANET subnetworks that confirms previous inferences that recruitment is the driving evolutionary force of metabolic expansion The origin and evolution of the purine metabolic network was first explored using the MANET database MANET 1.0 and 2.0 use domains structures defined at fold level. This is problematic. Folds are ambiguously associated with molecular functions and cannot dissect recruitment patterns without additional information FFnd  = 0) and were only later followed by enzymes in BIO (FFnd  = 0.057) and then CAT (FFnd  = 0.069) pathways (FFnd  = 0.057–0.367)  . This tiFFnd) is provided in a relative zero-to-one scale, with FFnd  = 0 representing the origin of the protein world. However, we found that the ages of domain structures at fold or FSF levels show strong linear correlation with geological time scales measured in billions of years (Gy) The age of FFs  interconversion of the INT pathways appeared at the start of the protein world embodied in the ATPase domain-like FF (c.37.1.12) of ABC transporters, ∼3.8 Gy ago . In turn = 0.187 . This tiFFnd  = 0.057), the BC N-terminal domain-like FF  and the BC ATP-binding domain-like FF , which are shared by the GMP synthetases (EC 6.3.4.1 and EC 6.3.5.2) of the INT pathway and the AIR carboxylase (EC 4.1.1.21), FGAM synthetase (EC 6.3.5.3) and AICAR formyltransferase C-N ligases (EC 6.3.4.–) of BIO (FFnd  = 0.069) is responsible for most salvage reactions of the CAT pathway and is shared with the BIO functions of AMP pyrophosphorylase (EC 2.4.2.7) and amidophosphoribosyltransferase (EC 2.4.2.14). In turn, the INT and BIO pathways share the L-aspartate/fumarase FF , while the RecA-like protein like ATPase-domain FF  enabled crucial GDP-GMP interconversions in the INT pathway.A group of six FFs crucially associate with the early expansion of the purine metabolic pathways, which is portrayed in a time series . The thr) of BIO . The morFFnd  = 0.188) of the enzyme EC 6.3.2.6 of the BIO pathway was not recruited from within the expanding purine metabolic network and appears to represent a unique and late development of purine metabolism. This is compatible with previous proposals that gene duplications mediated patchwork recruitments in the pathways of CAT The internal recruitment of structures as these appear in the timeline is responsible for the functional make up of the BIO and CAT pathways. Six out of the 11 reaction steps involved inter-pathway structural recruitment and another four involved intra-pathway recruitment. Only the SAICAR synthase FF  and the AAA-ATPase domain  FFs The enzymatic founders of nucleotide metabolism that appeared ∼3.8 Gy ago are embodied in primordial ATP phophohydrolases (EC 3.6.1.3 and EC 3.6.4.1) with the P-loop hydrolase fold structure. These enzymes enable ATP/ADP-mediated energy interconversion reactions. They include the first and second domain structures of the protein world, the ABC transporter ATPase domain-like  enabled a direct shortcut to fumarate in the citrate cycle (that was probably running in reverse FFnd  = 0.094). Similarly, the phosphate binding protein-like FF  enabled the biosynthesis of 5-phosphoribosylpyrophosphate (PRPP) precursors in the pentose phosphate pathway (CAR 00030) that could have been used for primordial semienzymatic biosynthetic steps. One of these, originally proposed by Becerra and Lazcano Most central reactions of the INT and CAT pathways were already established 3.5 Gy ago . In factA fully functional BIO pathway replaces the need for purine abiotic synthesis and is fully functional ∼3 Gy ago , with a An ancient RNA world with ribozymes mediating the many enzymatic activities of metabolism is less parsimonious and incompatible with timelines of domain discovery and many other lines of evidence In the absence of early ribozymes, two possible scenarios can be used to explain the patchy age distribution of enzymes in metabolism: (1) Early proteins that are highly multifunctional could have provided enzymatic functions for missing steps in pathways, albeit with low efficiency Phylogenomic reconstructions show that the first proteins were P-loop hydrolase mechanoenzymes of nucleotide metabolism that appeared ∼3.8 Gy ago and were responsible for primordial energy interconversion reactions . Since tNucleoside constituents. Nucleobases were likely produced by abiotic synthesis from HCN via its tetramer diaminomaleonitrile (DAMN) Nucleoside formation. The attachment of nucleobases to ribose via the glycosidic bond is troublesome. Purine nucleosides can be obtained by dry phase heating of purine bases with ribose in the presence of sea salts and magnesium Nucleotides. Nucleotides can be readily synthesized from nucleosides by dry heating with for example ammonium dihydrogen phosphate During the 3.5–3.8 Gy time interval following the inception of primordial membrane-linked enzymes, the central α-β-α interleaved fold design typical of P-loop hydrolases was revisited in six new fold families necessary for enzymatic steps of the INT and CAT pathways . EnzymatDuring the 3.0–3.5 Gy time interval, more efficient enzymatic steps of the INT and CAT pathways replaced the enzyme-coupled abiotic reactions and completed a fully functional biosynthetic pathway . RNA wassensuOur phylogenomic study makes explicit a ‘metabolism-first’ model for origins of life exploration that takes into account the coexistence of abiotic and biotic metabolic reactions. The study attempts to merge prebiotic chemistry ‘nomothetic’  and evolutionary genomic ‘ideographic’  frameworks. In combination, the discovery operations sensu of thesehttp://MANET.illinois.edu). MANET 1.0 (July 2006) provides 1,255  structural assignments  to enzyme activities in a total of 133 (out of 137) subnetworks of KEGG. Conversely, MANET 2.0 (July 2007) provides 1,405  structural assignments  to enzyme activities in 136 (out of 221) subnetworks. Both versions trace the ages of protein folds in the enzymes of metabolism, with ages derived from a structural phylogenomic census of 784 folds defined by SCOP 1.67 (May 2004) in the proteomes of 174 organisms with genomes that have been completely sequenced  E of 10−4) against a library of advanced linear hidden Markov models (HMMs) of structural recognition in SUPERFAMILY FFnd) can be calculated directly from rooted phylogenomic trees using a PERL script that counts the number of nodes from the ancestral structure at the base of the tree to each leaf and provides it in a relative zero-to-one scale. We stress that the evolutionary model applies to the world of proteins, does not constrain the spread of domain structures in proteomes , and is agnostic about how domain growth occurs in proteomic lineages  defined SCOP domains at fold, FSF and FF levels in SCOP. For example, c.23.16.1 represents the structure of the amidotransferase domain of the formylglycinamide ribonucleotide amidotransferase from Salmonella typhimurium  ccs were used to identify taxa in trees and label structures in diagrams.Concise classification strings  or by using the largest FFnd value in the set of FF domains belonging to an FSF . We also found that some domains without fold assignments in MANET had FF assignments (e.g. d.284.1.2 of EC 3.5.4.2) and could be incorporated in the analysis at this level of abstraction.HMM analysis of sequences of enzymes with no PDB models was conducted again on the set of purine metabolism enzymes. Since NUC 00230 had 83% structural coverage in enzymatic entries (83%), only few new structures were found associated with enzyme activities  and none were relevant to the pathways analyzed in this study. Only SCOP entries for EC 3.5.4.2 sequences had to be revised . When assigning FF age, some FFs did not have nd +3.814) and FSF (t = –3.831 nd +3.628) levels The molecular clocks for protein domains at fold . Domain structures defined at the FF level of structural abstraction that are associated with individual enzymatic activities (described in EC nomenclature) are painted according to their age, in a scale of node distance (FFnd) that ranges from 0 (the oldest enzymes) to 1 (the most recent).(EPS)Click here for additional data file.Table S1Enzyme activities and associated FF assignments in purine metabolic pathways.(PDF)Click here for additional data file.


Curation of such a database and creation of the frozen parameter estimates is time-consuming; therefore, fRMA has only been implemented on the most widely used Affymetrix platforms.A novel method of microarray preprocessing - Frozen Robust Multi-array Analysis (fRMA) - has recently been developed. This algorithm allows the user to preprocess arrays individually while retaining the advantages of multi-array preprocessing methods. The We present an R package, frmaTools, that allows the user to quickly create his or her own frozen parameter vectors. We describe how this package fits into a preprocessing workflow and explore the size of the training dataset needed to generate reliable frozen parameter estimates. This is followed by a discussion of specific situations in which one might wish to create one's own fRMA implementation. For a few specific scenarios, we demonstrate that fRMA performs well even when a large database of arrays in unavailable.By allowing the user to easily create his or her own fRMA implementation, the frmaTools package greatly increases the applicability of the fRMA algorithm. The frmaTools package is freely available as part of the Bioconductor project. The vast majority of preprocessing algorithms require multiple arrays to be analyzed simultaneously, and in general such multi-array preprocessing algorithms outperform single-array algorithms [In microarray data analysis, the process of converting probe-level flourescent intensities from a scanner to gene-level expression estimates is commonly referred to as gorithms . Therefogorithms , gcRMA [gorithms , MBEI [4gorithms  - are muHowever, multi-array preprocessing algorithms restrict scientific inquiry because it is necessary to analyze all arrays simultaneously. Because data preprocessed separately cannot be combined without introducing artifacts -10, the frozen. These frozen parameter estimates can then be used to preprocess new array(s). In addition to allowing arrays to be preprocessed individually or in small batches and combined for subsequent analysis, fRMA resulted in improved performance when analyzing microarray data in the typical multi-array setting, especially when the new data consisted of arrays from different batches [Recent work by McCall et al. 2010) provided a method of single-array preprocessing, Frozen Robust Multiarray Analysis (fRMA), that retains the advantages of multi-array preprocessing. The fundamental idea behind fRMA is a simple but powerful one - parameter estimates are precomputed based on a large biologically diverse database of microarrays and then  providedIt should be noted that fRMA allows the user to easily combine Affymetrix microarrays from different batches; however, these arrays must all be from the same platform. For example, fRMA cannot be used to preprocess Affymetrix Human Genome U133A and U133 Plus 2.0 arrays together. To combine different platforms, one would need to first preprocess the data from each platform separately using fRMA and then combine the data using an additional batch correction algorithm such as ComBat  or SurroThe primary limitation of fRMA is that it requires a curated database of microarrays to generate the frozen parameter vectors. As such, fRMA has currently been implemented for only the three most widely used Affymetrix microarray platforms - Human Genome U133 Plus 2.0, Human Genome U133A, and Mouse Genome 430 2.0. Efforts are underway to implement fRMA on two more platforms - Arabidopsis ATH1 Genome and Rat Genome 230 2.0.To address this limitation, we have created an R package, frmaTools, designed to allow the user to easily create the frozen parameter vectors necessary to run fRMA. By decentralizing fRMA implementation, each group can contribute a database and parameter estimates specific to their area of research. We begin by describing the frmaTools package itself and how it interfaces with the standard frma package. Next we examine the size and scope of data necessary for a platform-wide implementation of fRMA. We then turn our attention to situations in which one might want to generate custom parameter vectors for a specific experiment.<platform>frmavecs and are automatically used by frma. To extend the fRMA algorithm to a new platform, one need only create a data package or supply ones own frozen parameter vectors directly to frma.The computational tools described here are written in the open-source statistical language R and are available as part of the Bioconductor project , a collaTo preprocess single arrays using fRMA, one needs 5 frozen parameter vectors: (1) the reference normalization distribution, (2) the probe effect estimates, (3) the within batch residual variance, (4) the between batch residual variance, and (5) the within probeset average standard deviation. Optionally, a sixth vector of median standard errors is necessary to compute a measure of array quality. By default, the frma function attempts to load the appropriate data package for the AffyBatch object given as input. However, it is possible for the user to supply some or all of the frozen parameter vectors allowing the user to preprocess data using their own fRMA implementation.The frmaTools package was developed to allow users to easily create their own frozen parameter vectors for use with frma. It contains two primary functions - makeVectorsAffyBatch and makeVectorPackage. The former is used to create the frozen parameter vectors used by frma; the latter both creates the vectors and builds a R data package for them. By creating a data package, one can share frozen parameter vectors with other researchers and track changes over time. The interface between the affy, frma, and frmaTools packages can best be understood by examining a typical workflow Figure .The frozen parameter vectors used in the fRMA algorithm are generated using a training dataset comprised of a balanced sample from multiple batches. For example, in the three current implementations of fRMA, 5 arrays from each of 200 different batches are used. In this case, batch is defined as a unique combination of tissue type and experiment id . Here the between batch variability in probe effects captures all the potential differences between experiments - different labs, different technicians, different environmental conditions, etc. - as well as differences due to the hybridized tissue type.One of the fundamental assumptions underlying fRMA is that the training data is both large enough and diverse enough that the vast majority of probes that differ in performance across batches can be detected as such in the training data. Some probes may only perform poorly in a small handful of tissues or laboratory conditions, so it is advantageous to include as many different tissues and experiments as possible to maximize our chance of detecting unreliable probes. When implementing fRMA on widely used platforms for which there is a plethora of publicly available data, it is advisable to use as many arrays as possible. This allows one to obtain the most information about the behavoir of probes across a wide variety of tissues and experiments. In these situations, the size of the training dataset should only be constrained by computer memory.For a fixed amount of memory corresponding to a fixed number of arrays in the training dataset, the primary question is whether it is better to include a greater number of batches (and thereby fewer arrays per batch) or a greater number of arrays per batch (and thereby fewer batches). To address this question, we examined a variety of training datasets of varying number of batches and batch size. For each combination we repeated the random sampling used to select the training arrays 100 times. For each set of parameter vectors, we preprocessed a single average-quality array (GSM282373) and computed the median absolute deviation (MAD) across the 100 replicates for each probeset. To assess the consistency of fRMA for a variety of training datasets, we report the median and inter-quartile range (IQR) of the within-gene MADs . In these cases, one might consider creating one's own custom fRMA implementation that is specific to a certain dataset, lab, or experiment.If it is infeasible (due to lack of data) or undesireable (due to properties of the data) to use a standard fRMA implementation to analyze one's data, it could be beneficial to implement fRMA in a manner that mimics RMA. While the primary advantage of fRMA over RMA is the ability to preprocess a single microarray, the fRMA algorithm also improved the RMA model by recognizing that some probes within a probe set were more variable than others and down-weighting such probes . For thiSuch an implementation can easily be accomplished using the frmaTools package. To illustrate the ability of fRMA to mimic RMA when analyzing a single dataset, we compared the expression values generated by RMA to those generated by 3 different fRMA implementations:1. the default fRMA implementation trained on a large diverse sample of arrays,2. fRMA trained on a balanced random sample from the experiment being analyzed,3. same as (2) but using all the arrays to form the reference normalization distribution.As one would expect, the differences in expression values are much greater between the default fRMA implementation and RMA than between the two custom fRMA implementations and RMA Figure . It is isignal detection slope, the slope from regressing observed expression on nominal concentration in the log2 scale. The signal detect slope is the expected difference in observed expression when the true difference is a fold change of 2, with the optimal result being a slope of one. We assessed precision by computing the standard deviation and 99.5th percentile of the null log-ratios, log-ratios from transcripts which were not spiked in and therefore should not be differentially expressed. The standard deviation is an estimate of the variability in log-ratios for non-differentially expressed genes; the 99.5th percentile assesses outliers - 0.5% of non-differentially expressed genes are expected to exceed this value. Finally, we report the signal-to-noise ratio (SNR) and the probability of a gene with a true log2 fold change of 2 being in a list of the 100 genes with the highest fold change (POT).We used this extended spike-in dataset to assess the performance of RMA and fRMA trained on only these data. Specifically, we assessed the accuracy, precision, and overall performance of both methods in three expression strata using the methodology proposed in . We asseIn all three strata, fRMA outperformed RMA with regard to precision and overall performance Table . In the Another reason one might wish to create a custom fRMA implementation is to analyze a dataset in which samples are added in small batches over a period of time and intermediate analysis would be beneficial. For example, suppose researchers are interested in investigating a gene signature for breast cancer prognosis when treated with a novel chemotheraputic drug. At the start of treatment, a biopsy of each patient's breast tumor is hybridized to a single microarray. As the results begin to trickle in, the researchers are able to train the fRMA model based on the currently available data and use that model to predict the response of patients currently undergoing treatment.In contrast, most multi-array preprocessing methods would require the entire dataset to be preprocessed each time a new array was added. This poses several potential problems. First, although preprocessing hundreds of arrays with RMA requires relatively little computational time, other multi-array preprocessing methods take significantly longer. Second, while RMA requires little computational time, it does require large amounts of memory - preprocessing 200 arrays requires over 11GB of RAM; for 500 arrays, this increases to over 30GB. Third, once an array has been preprocessed with fRMA, it's gene expression estimates will remain the same regardless of any additional arrays that are added to the data. The same cannot be said of multi-array preprocessing methods, which would provide different expression estimates for a given array each time an additional array is added and the data are preprocessed again.To assess the applicability of the fRMA algorithm in such a situation, we consider a dataset of 200 breast tumor samples (GSE11121) collected from patients over 477 days from Jan 29th, 2004 to March 20th, 2005. The arrays were grouped in 13 batches by the date on which the array was scanned. We defined a batch as consecutive dates over which at least 5 arrays were hybridized. Only 16 out of the 200 arrays did not fall into a batch.First, we trained the fRMA algorithm using 5 arrays randomly selected from each of the 13 batches and stored the resulting parameter vectors. We then trained the fRMA algorithm using only the first N batches where we let N vary from 3 to 9. We then used each of these sets of parameter vectors to preprocess all 200 arrays. Finally, we subtracted the expression estimates based on all 13 batches from the expression estimates based on each of the other subsets. As one would expect, the distributions of these differences are centered close to zero and their spread decreases as the number of training batches increases Figure . On the In the current implementations of fRMA, batch is defined as the combination of tissue and experiment. However, there are certainly batch effects present within many experiments ; therefoIn fact, even if the user eventually plans to use a preprocessing method other than fRMA, examining the frozen parameter vectors generated by frmaTools may provide insights in to batch effects present in the data that need to be addressed in some manner. Specifically, we can compute an F-statistic for each probe, defined as the ratio of between-batch and within-batch variance, to assess whether that probe displays a batch effect in a given data set. To illustrate this, we computed these ratios for the data presented in Section 3.4.1 and the data presented in Section 3.4.2. In the former case, 20.3% of probes show a statistically significant batch effect, while in the latter case, only 1.8% of probes show a batch effect. Thus by simply comparing the within- and between-batch probe variances produced by frmaTools, we can assess the extent of batch effects within a given dataset.We have described a novel R package, frmaTools, that allows the user to easily create his or her own fRMA implementation. We have described how this new package fits into a standard analysis workflow and the size and scope of data necessary to make use of it. This package will allow researchers to use fRMA on a much wider range of datasets and annotations. Furthermore, we have described several situations in which a dataset-specific custom fRMA implementation might be advantageous. The frmaTools package makes such custom implemenations straightforward. The frmaTools package is freely available as part of the Bioconductor project and as Additional File The authors declare that they have no competing interests.Project name: frmaTools• Project home page: http://www.bioconductor.org/packages/release/bioc/html/frmaTools.html• Operating system(s): Platform independent• Programming language: R• Other requirements: R >= 2.10• License: GNU GPL• Any restrictions to use by non-academics: None• MNM conceived the study, created the R package, and wrote the manuscript. RAI conceived the study and edited the manuscript. All authors read and approved the final manuscript.frmaTools package. The R/Bioconductor package frmaTools (version 1.4.0), providing tools for advanced use of the frma package.Click here for file


The non-receptor tyrosine kinase Ack1 belongs to a unique multi-domain protein kinase family, Ack. Ack is the only family of SH3 domain containing kinases to have an SH3 domain following the kinase domain; others have their SH3 domains preceding the kinase domain. Previous reports have suggested that Ack1 does not require phosphorylation for activation and the enzyme activity of the isolated kinase domain is low relative to other kinases. It has been shown to dimerize in the cellular environment, which augments its enzyme activity. The molecular mechanism of activation, however, remains unknown. Here we present structural and biochemical data on Ack1 kinase domain, and kinase domain+SH3 domain that suggest that Ack1 in its monomeric state is autoinhibited, like EGFR and CDK. The activation of the kinase domain may require N-lobe mediated symmetric dimerization, which may be facilitated by the N-terminal SAM domain. Results presented here show that SH3 domain, unlike in Src family tyrosine kinases, does not directly control the activation state of the enzyme. Instead we speculate that the SH3 domain may play a regulatory role by facilitating binding of the MIG6 homologous region to the kinase domain. We postulate that features of Ack1 activation and regulation parallel those of receptor tyrosine kinase EGFR with some interesting differences. Activated Cdc42-associated kinase, Ack1, belongs to one of the 10 families of mammalian nonreceptor tyrosine kinases (NRTK) The common core of the Ack family consists of the SAM domain at the N-terminus followed by the kinase domain and the SH3 domain . Thus thcasAck1 is expressed ubiquitously, though the highest expression levels seem to be in spleen, thymus and brain, and is phosphorylated in response to a number of stimuli including EGF, PDGF, insulin and cell adhesion Biochemically measured activity of Ack1 kinase is much lower than that of other NRTKs, and autophosphorylation increases the activity only marginally a la EGFR, may play a major role in the regulation of Ack1 kinase activity.We report a 3.23 Å crystal structure of the Ack1 protein encompassing the kinase domain and SH3 domain. The structure provides the first glimpse of the inactive state of Ack1 which resembles the Src/Cdk-like inactive state, also adopted by autoinhibited EGFR. It shows that the disposition of the SH3 domain with respect to the kinase domain precludes it from playing a direct role in the cis-regulation of Ack1 activity. But the modeling suggests an indirect role of SH3 domain in MHR mediated autoinhibition of the kinase activity by recognition of a unique stretch of proline-rich sequence of MHR. The structure reveals the unusual constitution of the ligand binding site of the SH3 domain. We also present a high resolution structure of the isolated kinase domain. Like the other known crystal structures of the Ack1 kinase domain, the protein is in the active conformation and shows the same dimeric interactions in the crystal lattice. The arrangement of the two molecules in the asymmetric unit provides a hitherto unnoticed hint of the role of dimerization in the kinase activation. It has been proposed that the N-terminal SAM domain mediates self association and subsequently increases kinase activity in cells There have been five crystal structures of the Ack1 kinase domain reported previously, all of which show the protein in its active conformation irrespective of its phosphorylation state 2 at the interface. The hydrophobic patch close to the αC helix of one molecule is packed symmetrically against the same region of the other. The N-terminal region, the catalytically important αC-helix, and the loop between strands β4 and β5 from one monomer are symmetrically packed against the same elements from its dimeric partner  . There aResidues 115–446 are modeled, with the exception of residues 279–291, for each of the four molecules. There is no appreciable electron density to model the activation loop region 279–291 for any of the molecules in the asymmetric unit. However, the N-terminal end of the activation loop shows strong electron density to model just under two turns of the helix, which is one of numerous structural features that seem to stabilize this inactive conformation of the enzyme. Helical beginning of the activation loop following the DFG-motif prevents the αC helix from moving into its catalytically competent state. Leu273 (immediately following the DFG-motif), Ala276 and Leu277 contribute to the hydrophobic cluster in this region, parts of which are contributed by the G-loop (Phe137), the rest of the β-sheet  and the αC-helix . In the process, they stabilize the outwardly rotated conformation of the helix, maintaining it in the catalytically incompetent state. The interaction between the ion-pair Lys158 and Glu177, highly conserved among active kinases, is broken. Glu177 instead makes an alternate ion-pair interaction with Arg275 of the α-helical part of the A-loop, contributing to the stability of the outwardly rotated conformation of the αC-helix.Other than the differing conformations of the activation loop, the activated and the inactive states of the enzyme differ from each other by virtue of two independent rigid body rotations in opposite directions relative to the C-terminal lobe . One of Besides the broad differences between the activated and the inactive states of the Ack1 kinase domain, there is a subtle difference between the main chain trace of the N-terminal region of the kinase domain in the two conformations . In the 10 loop that has 310 helical character. Two antiparallel β-sheets, one made by βb, βc and βd packing orthogonally against the other formed by the N-terminal βa, βb and C-terminal βe make up the five stranded β-barrel architecture. Overall, the Ack1 SH3 domain shares this fold, except that it has a four stranded β-barrel. The last part of the polypeptide chain corresponding to βe strand turns away from βa  with 1.15 Å RMSD between 52 equivalent Cα atoms . A typic from βa . SH3-SH32 (>20%) of the exposed surface area, which is the largest single region buried in this crystal form . MIG6 is a feedback regulator of ErbB family kinases. The MIG6 recognition region of EGFR has close sequence and structure resemblance to the corresponding region of the Ack1 kinase domain, and MHR has been proposed to bind and inhibit the Ack1 kinase domain in an analogous fashion Just upstream of the kinase recognition segment of the MHR, there is an insertion of ten residues (relative to the MIG6 sequence) . This inphox, the structurally equivalent Glu188 is seen making ion-pair interactions with the Arg of the substrate The substrate binding site of the Ack1 SH3 domain is quite unique, and could be expected to undergo some changes upon substrate recognition. Based on the known structures of other SH3 domains in complex with their cognate ligands, we identify Gln397, Glu401, Lys404, Ile416, Tyr423, Trp424, Arg426, Pro437, Arg440 and Asn441 as the residues constituting the surface of the SH3 domain where the substrate is expected to bind . This suTrp424 is the most highly structurally conserved residue among SH3 domains of known structures. It is surface exposed and is involved in van der Waal’s contacts with the poly-Pro ligand in substrate bound SH3 domain structures. Likewise, in the Ack1 kinase+SH3 structure it is highly exposed and expected to interact with the substrate peptide. Its mutation to Lys has been observed to increase autophosphorylation in cells A single somatic mutation, M409I, located in the SH3 domain of Ack1 has been identified in gastric adenocarcinoma. In the structure presented here, the site of mutation is surface exposed, and though the structurally equivalent residue is not seen interacting with SH3 domain substrates in other structures, it is proximal to the substrate binding site. This suggests that Met409 could be involved in the recognition of the poly-Pro region of Ack1 which may bring MHR in contact with the kinase domain to play its negative regulatory role.Based on the foregoing discussion, we speculate that the 10 residue insertion in the MHR (relative to MIG6) may interact with the SH3 domain and orient MHR for inhibitory interactions with the kinase domain, stabilizing an auto-inhibited state. This hypothesis would explain why a protein construct lacking the MHR region could not be activated by SH3 substrate peptides. The kinase-SH3-CRIB construct that was used for the study So the question is why does the protein construct encompassing the kinase domain and SH3 domain crystallize in the inactive state whereas the isolated kinase domain crystals always show the protein in the active state? We believe that the characteristic of the crystal packing may have allowed the protein to retain its inactive solution state even at high concentration in the lattice. As mentioned above, the extent of interfacial area buried at the SH3-SH3 interface in the crystal lattice is larger relative to the burial of the N-lobes in the activated dimeric structures of the kinase domain constructs. Based on this observation we believe that the SH3 domain mediated dimerization may be stronger than the N-lobe mediated interaction in the kinase domain+SH3 domain protein construct. Hence during the crystallization as the local protein concentration increases, SH3-domain mediated dimers may form and nucleate the crystal lattice before the N-lobe mediated activated dimer can form. Consistent with this hypothesis is the observation that the protein concentration in crystal lattice of the inactive form is about half that in the crystal lattice with the active kinase domain.We propose that the mechanism of regulation of the kinase activity in Ack1 largely parallels the model developed for EGFR One significant difference between EGFR and Ack1 kinase domains is the mode of dimerization; while activation of Ack1 is through symmetric dimer, that of EGFR is mediated by asymmetric dimerization.The Ack1 kinase domain containing residues 115–389 with N-terminal His6 tag and TEV protease cleavage site was subcloned into pFastbac vector and expressed in SF21 cells . The pellet was resuspended in 2X volume to weight lysis buffer  w/o EDTA) and microfluidized twice to lyse the cells. Triton X-100 was added to a final concentration of 0.1%. The lysate was centrifuged at 40,000 rpm for 30 min (Beckman ultra) at 4°C to obtain the soluble fractions. The supernatant was incubated with Ni-NTA agarose at 4°C for 2 hr. The Ni-NTA agarose was packed onto a column, washed, and eluted with 400 mM Imidazole. His6-TEV enzyme was added at 1:100-1:25 molar ratio. The solution was dialysed overnight at 4°C against 25 mM Hepes, pH 7.5, 10% Glycerol, 0.3M NaCl, 20 mM Imidazole, 0.5 mM TCEP. The His tag and His tagged-TEV protease were removed by passing through Ni-NTA agarose and collect the flow through. The flow through fractions were collected, concentrated and purified further on a Superdex 100 column equilibrated with 25 mM Hepes, pH 7.7, 10% Glycerol, 0.3 M NaCl, 0.5 mM TCEP, 20 mM MgCl2. The final protein was concentrated to 3–6 mg/ml and stored at −80°C.The Ack1 kinase+SH3 domain containing residues 115–453 was cloned and purified similarly as the kinase domain. The final buffer is 25 mM Hepes, pH 7.5, 20 mM MgCl2, 0.5 mM TCEP, 0.3 M NaCl, and 10% Glycerol.Unphosphorylated GST tagged Ack1 (residues 110–476) was purchased from Life Technologies; catalog number PV4807.2, 1 mM TCEP, 0.001% Tween-20, pH 7.5) containing various concentrations of ATP and peptide (KVIYDFIEKKKKG) in a total volume of 160 µL  in approximately 5 days. kinase+SH3 was set up similarly, with 2 µl drops in a 1:1 ratio of protein to well reservoir solution containing: 0.2 M AmSO4,.1M Bis-Tris pH 6.6, 22–24% PEG 3350, 10–20 mM TCEP pre-titrated to pH 7.0. Crystals grew as long, thin rods, and reached a size of 0.3×0.05×0.05 mm in approximately 7 days. Crystals from both constructs were soaked in a solution of their corresponding mother liquor containing 22% glycerol prior to being frozen in liquid nitrogen.X-ray data for the Ack1 kinase and kinase+SH3 domain crystals were collected on IMCA-CAT 17ID beamline at the APS. Data were processed using autoPROC.Ack1 kinase domain crystals were isomorphous with the published structure (PDBID: 3EQR), which was used as the starting model and refined using the program CNX.The kinase+SH3 domain structure was solved by molecular replacement using the program phaser in CCP4. There are four molecules in the asymmetric unit of these crystals. The C-lobes of all four kinase domain were first located by molecular replacement. The N-lobes were next built-in manually in the four-fold averaged Fo-Fc difference maps. Using improvement in the phases with four kinase domains modeled, three of the SH3 domains were placed in the final stage of the molecular replacement using phaser; the SH3 domain of Hck (PDBID: 1qcf) was used as the search model. Non-crystallographic symmetry was employed to place the SH3 domain of the fourth molecule. The model was refined using CNX.Structural similarities of the kinase domain and the SH3 domain were analyzed using the program CE Software tools were developed that used the structure based sequence alignments generated by CEEK to analyze the specific sequence and structural motifs of the SH3 domain.Figure S1Asymmetric unit of Ack1 kinase domain+SH3 domain structure. The color coding is the same as in (TIF)Click here for additional data file.Figure S2Representative ATP titration curves for each Ack1 construct in the non-phosphorylated and phosphorylated forms. Maximum rate was converted to kcat by dividing the calculated Rmax by 0.0055 and then by the enzyme concentration in uM. 0.0055 is the calculated extinction coefficient for NAD in the spectromax reader used for all assays using a fill volume of 160 ul per well. Enzyme concentrations used were 12.5 nM for GST-CD and pGST-CD, 100 nM for CD and pCD and 200 nM for KD, pKD, KD+SH3 and pKD+SH3.(DOCX)Click here for additional data file.Figure S3Representative raw data for an ATP titration using the CD construct.(DOCX)Click here for additional data file.


Podospora anserina is involved in a cell death process associated with non-self recognition. The prion forming domain (PFD) of HET-s adopts a β-solenoid amyloid structure characterized by the two fold repetition of an elementary triangular motif. [Het-s] induces cell death when interacting with HET-S, an allelic variant of HET-s. When templated by [Het-s], HET-S undergoes a trans-conformation, relocates to the cell membrane and induces toxicity.Prions are infectious proteins propagating as self-perpetuating amyloid polymers. The [Het-s] prion of het-S. STAND proteins are signal transducing ATPases which undergo ligand-induced oligomerisation. Homology modelling predicts that the NWD2 N-terminal region adopts a HET-s-like fold. We propose that upon NWD2 oligomerisation, these N-terminal extensions adopt the β-solenoid fold and template HET-S to adopt the amyloid fold and trigger toxicity. We extend this model to a putative prion, the σ infectious element in Nectria haematococca, because the s locus controlling propagation of σ also encodes a STAND protein and displays analogous features. Comparative genomic analyses indicate evolutionary conservation of these STAND/prion-like gene pairs, identify a number of novel prion candidates and define, in addition to the HET-s PFD motif, two distinct, novel putative PFD-like motifs.Here, comparing HET-s homologs from different species, we devise a consensus for the HET-s elementary triangular motif. We use this motif to screen genomic databases and find a match to the N-terminus of NWD2, a STAND protein, encoded by the gene immediately adjacent to We suggest the existence, in the fungal kingdom, of a widespread and evolutionarily conserved mode of signal transduction based on the transmission of an amyloid-fold from a NOD-like STAND receptor protein to an effector protein. Saccharomyces cerevisiaeP. anserinaPrions are infectious proteins that propagate information embedded in their conformational state. Prions are responsible for fatal neurodegenerative diseases such as CJD and kuru in humans, BSE in cattle, Scrapie in sheep and CWD in cervids P. anserina is involved in a non-self recognition process. het genes. Classically, it is proposed that incompatibility serves to limit the transmission of mycoviruses and other deleterious replicons between strains. The het-s/het-S genes constitute one of many gene pairs controlling heterokaryon incompatibility. A fusion between a het-s and a het-S strain is lethal. The protein encoded by the het-s gene is a prion and exists under a soluble form and an aggregated self-propagating prion form. Het-s strains acquire the prion state either spontaneously at a low frequency or systematically after a cell fusion with a prion-infected strain.The HET-s prion protein of the filamentous fungus het-s/het-S system The activity in heterokaryon incompatibility is associated with the prion form of HET-s Fusarium graminearum, was structurally and functionally characterized F. graminearum protein forms amyloids and behaves as a prion in vivo in heterologous hosts like yeast or P. anserina. FgHET-s(218–289) cross-seeds HET-s(218–289) and vice versaA number of HET-s orthologs have been identified in other pezizomycota and one of them, FgHet-s, from the plant pathogen Nectria haematococca is strongly suspected. The “secteur” phenotype of N. haematococca corresponds to a growth alteration and dark red pigmentation of the mycelium at the growth margin of the colony s. Certain mutations at the s locus can abolish σ formation while others make σ constitutive. Molecular cloning of the s locus was achieved and it was found to comprise two genes termed sesA and sesB. sesA encodes a protein of unknown function and sesB encodes a protein with a α/β hydrolase fold. Daboussi and co-workers proposed that σ corresponds to a modified form of the sesA and sesB proteins and that modified sesA is able to promote modification of sesB So far, no other prions than [Het-s] have been formally identified in filamentous fungi. But, the existence of a prion system in the species het incompatibility genes of P. anserina encode STAND proteins c to the WD-repeats, APAF-1 undergoes a transition from a monomer to an oligomeric superstructure termed apoptosome, a ring shaped heptamer in which the NOD domains form a central ring. The oligomerisation step groups the CARD domains into a hub where binding and activation of caspase-9 occurs. Other STAND proteins with immune functions, like the mammalian NOD-like receptors, also oligomerise upon ligand binding to the repeat domain Signal transducing NTPases of the STAND type play essential roles in the regulation of programmed cell death and immune response in plants, animals and fungi P. anserina involved in incompatibility  belong to a gene family designated NWD. This gene family comprises 10 members. All 10 members share a C-terminal WD-repeat domain and a central NACHT domain  P. anserina is HET-E (HNWD5). Incompatibility is triggered when HET-E interacts with specific allelic variants of a protein termed HET-C Het-c encodes a GLTP (glycolipid transfer protein). GLTPs are cytosolic proteins conserved in most eukaryotes which bind specific glycolipids but their exact cellular function remains debated N. haematococcaNWD1 associated domain). Finally, NWD2 stands out as an exception in this gene family since this protein lacks a defined N-terminal effector domain upstream of the NACHT nucleotide binding oligomerisation domain.The STAND proteins of N. haematococca and identify several other potential prion-like proteins that could be controlled by STAND proteins.In the present paper, based on sequence analyses, we propose that the NWD2 STAND protein is a functional partner of the HET-S protein. We propose a model in which ligand induced oligomerisation of NWD2 leads to amyloid transconformation of HET-S. These observations impose a re-evaluation of biological significance of the [Het-s] prion system and introduce the notion that propagation of an amyloid fold can be an integral part of a signal transduction mechanism. We expand this model to another putative prion model of the fungus F. graminearum, F. oxysporum, F. verticillioides, N. haematococca and Grosmannia clavigera. The sequences of the PFD region of 9 HET-s homologs were aligned with ClustalW and the sequence stretch matching the elementary r1 and r2 repeat motifs of HET-s were extracted. The elementary repeat motifs where then aligned anew in order to generate a consensus sequence of the elementary repeat unit of the β-solenoid fold . The nwd2 gene is located immediately adjacent to the het-s/het-S gene in the P. anserina genome . These two A. otae and the A. gypseum genes are also located adjacent to genes encoding STAND proteins. These STAND proteins display a N-terminal extension homologous to the extension found at the C-terminus of the HeLo domains  was performed and led to a consensus motif .We also identify 4 proteins in which the HeLo domain is part of a larger protein and associated to a second distinct domain (we designated Goodbye). In these 4 homologous proteins, this Goodbye domain is N-terminal to the HeLo domain. These proteins also display a short extension C-terminal to the HeLo domain. Genome sequence analyses revealed that the corresponding genes are all adjacent to genes encoding STAND proteins . These Sed above . In addius motif . This cohet-S/nwd2 clustering and the fact that sesB homologs are found associated with HET-S homologs  motifs and clustered with genes encoding STAND proteins lacking effector domains. When we analyzed fungal genomes for the presence of these 4 domains in other protein architectures, we found that these same 4 domains can also been found as N-terminal effector domains of STAND proteins with a NACHT or NB-ARC NOD domain and various superstructure forming repeats . For insectively . It thusAspergillus oryzae, Aspergillus terreus and Magnaporthea grisea and NAD/STAND pairs were found in F. graminearum and F. oxysporum , the RelSpo domain (interpro: IPR004811) and the NAD domain because these domains are found in genes of the NWD family. We also chose the UDP-phosphorylase domain (interpro: IPR000845) because it is among the most frequently found effector domains in fungal STAND proteins listed in the pfam or interpro directories of fungal NACHT and NB-ARC domain proteins (IPR007111 and IPR002182 respectively). We screened in Blast searches for proteins containing HET, RelSpo, NAD or UDP-phosphorylase domains as unique domains. We then looked for the presence of a gene encoding a STAND protein (lacking an effector domain) adjacent to the gene encoding the candidate domain. No RelSpo/STAND or HET/STAND pairs satisfying these search criteria were identified. In contrast, UDP-phosphorylase/STAND pairs were identified in xysporum  and 10A.atococca . When, tN. haematococca protein (EEU38904.1) displaying a C-terminal extension matching the σ-motif, but the gene encoding this protein was not adjacent to a STAND encoding gene.As an alternate approach, we also performed PSI-BLAST searches with the HET-s, PP- and σ-motifs in order to identify additional prion candidates. No other effector domain than the already identified HeLo, Goodbye-HeLo, sesA, sesB, NAD and UDP-phosphorylase domain proteins were identified in that way. We however identified a Together, these analyses reveal that the putative STAND/prion-like couples show a diversity of effector, NOD, repeat domains and PFDs. These systems involve at least five different effector domains , two types of NOD domains (NB-ARC and NACHT), four types of superstructure forming repeats . Finally, we distinguish three types of putative PFDs. Apparently, these different building blocks can be assorted in various combinations .in silico analyses we find that the NWD2 STAND protein encoded by the gene immediately adjacent to HET-S displays an N-terminal extension that is homologous to the HET-s PFD elementary structural motif and that these features are evolutionarily conserved in a range of fungal species. Based on the APAF-1 paradigm, we propose that the NWD2 protein can undergo ligand-induced oligomerization and that this oligomerisation brings the N-terminal PFD-like extensions into close proximity and allow their cooperative folding into the HET-s-like β-solenoid structure. This amyloid-fold would would then template HET-S into the β-solenoid fold and induce its toxicity. We thus envision the existence of two modes of activation of the HET-S HeLo domain, either by interaction with the prion form of HET-s (as occurs during incompatibility) or by interaction with the oligomeric form of NWD2.Herein based on a series of N. haematococca could also be regulated in the same way by a STAND protein. Our model, derived from fungal genome analyses, now awaits direct validation. Preliminary experimental data indicate that a NWD2 N-terminal peptide can form infectious amyloids and that NWD2 can induce [Het-s] prion formation in vivo thus providing some support for key aspects of the model .Further sequence analyses suggest that a putative prion model of nwd gene family (including nwd2) is subject to positive Darwinian selection and capable of very rapid diversification in trans as a distinct proteins (encoded by a tighly linked gene). Communication between the NOD/recognition and effector modules appears to be based on transmission of the amyloid prion fold. In that model, NWD2 senses a pathogen-derived non-self ligand and oligomerizes, this oligomerization brings about the β-solenoid template which then converts the PFD-like region of HET-S into the same β-solenoid fold which ultimately leads to refolding and activation of the HeLo toxicity domain. By extension, we propose that the other STAND/effector domain pair identified in this study might have related functions in host defense. Remarkably, the involvement of prion-like aggregates in an antiviral innate immune response in mammals has recently been reported It is an established (and yet under recognized) fact that fungi are like any other organism under the threat of pathogens het-c gene.The nature of cognate NWD2 ligand is presently unknown but based on the extreme sequence homology between NWD2 and other HNWD proteins such as HET-E and HET-D het-s strains in turn would be deficient in this activity because of the het-s HeLo domain fails to exert toxicity upon transconformation of the PFD region. In this model, the het-s allele thus represents a mutant allele having lost the function of the wild-type HET-S allele, namely the ability to induce cell death. In wild isolates of the het-s genotype, the nwd2 gene is inactivated by insertion of a transposon nwd2 (by the insertion of the transposon) or het-S (by a point mutation in position 33 of the HeLo domain) renders the other interaction partner orphan so that no selective pressure remains to maintain gene function. At present, it cannot be resolved whether nwd2 inactivation by the transposon insertion predated mutation of het-S or if these events occurred in the reverse order. Consistent with this hypothesis viewing the HET-s allele as a mutant form of HET-S, is the fact that based on sequence analysis of amino acid position 33 , HET-s homologs in other fungi are predicted to be of the HET-S and not HET-s type.The description of NWD2 as an additional player in the [Het-s]/HET-S system, forces us to reconsider our understanding of the [Het-s]/HET-S system. In the model proposed here, the toxicity induced by activation of the HeLo toxicity domain of HET-S represents a host defense reaction. het-s is simply an inactive form of het-S and het-s strain would present a deficit in host defense abilities. It should be recalled here that the het-s allele behaves as a meiotic drive element which skews mendelian segregation ratio in its favour in het-s x het-S sexual crosses het-s can explain the ongoing presence of het-s alleles in modern day populations even if the het-s allele is detrimental. The full implication of this first hypothesis is that the [Het-s] prion represents a -genetic and infectious- fungal disease. Alternatively, it can still be envisioned that a selective advantage is associated to [Het-s]/HET-S incompatibility as is classically proposed het-S would represent a gene involved in pathogen defense that doubles as a heterokaryon incompatibility gene, while het-s (or better said the [Het-s] prion) solely functions in heterokaryon incompatibility. In that second hypothesis, [Het-s] is an adaptive prion.The [Het-s]/HET-S incompatibility system should now be viewed as a derivation, an evolutionary by-product of the NWD2/HET-S complex. The inactivation of the HET-s HeLo domain unleashed the [Het-s] prion since HET-s, in contrast to HET-S, can propagate as a harmless prion. Two hypotheses then emerge on the biological significance of [Het-s]/HET-S incompatibility. In the first hypothesis, E. coli bacteria are amyloids. Our observations introduce a novel view on the biological significance of the amyloid fold transmission process as an integral part of cell fate determining signal transduction pathways. Several advantages of this mode of protein activation can be envisioned. In this model of propagation of the amyloid fold, the effector is turned into an inducer thus allowing for amplification of the signal, its propagation to adjacent cells and even hysteresis because the propagation of the response can become independent of the initial signal. In addition, when compared to the normal STAND signal transduction mechanisms (in which the effector module and the sensor module are carried on the same protein), the amyloid fold transmission mechanism allows a single STAND protein to activate simultaneously different effector domains and also to uncouple the expression level of the sensor/inducer (STAND) and effector moieties. The systems that we consider here are proposed to have their biological function in the inflammatory response to pathogenic non-self It is clear today that amyloids are not strictly associated to pathologies and that some amyloids have functional roles PIN+] prion is necessary the formation of [PSI+] Prion formation in yeast has been envisioned as an epigenetic switch of protein activity, a mechanism allowing for the establishment of a protein-based molecular memory N. haematococca het-eN N-terminus corresponding to this σ-motif forms amyloid fibrils in vitro .We identify three distinct PFD-like motifs in the different STAND/effector domain pairs: the HET-s motif, PP-motif and the σ-motif. The total length of these conserved regions is relatively similar, 21 amino acids for the elementary HET-s motif, 17/18 amino acids for the PP-motif and 25–27 amino acids for the σ-motif. It is naturally premature to make inferences about the structural organization of these newly identified motifs. Yet, we note that all three motifs show sequence features reported in other amyloid forming domains. In particular, the motifs show conservation of G, N/Q and aromatic residues and are relatively poor in charged residues. This biased amino acid composition as been noted for yeast prion forming domains, for instance the Sup35 PFDs from various species are also rich in N, Q, Y and G and rarely contain charged residues in cis mechanism) or communication between the STAND and effector involves transmission of the amyloid fold (in trans mechanism). Two evolutionary scenari can be envisioned to explain the existence of the two component organisation. First, the in cis organisation might be ancestral and the two component organisation appeared secondarily as a result of gene splitting event. Alternatively, the split organisation might be ancestral and a gene fusion event later generated the in cis organisation. At any rate, the co-existence of these two gene architectures in fungi suggest that both organisations posses intrinsic advantages.We identify a total of 6 domains  that could be regulated by the STAND/effector protein interaction we propose here. In all cases, STAND proteins in which these domains are found directly associated to the NOD/repeat moiety are also found in fungal genomes. It thus appears that two mechanistic solutions have been retained in the STAND signal transduction process. The effector domain can alternatively be located on the STAND protein  components is actually selected, it is as if the genes form a functional unit which is inherited as such. We find that PFD-like extensions apparently co-evolve within a gene cluster. This apparent concerted evolution of the effector and STAND protein extensions can be taken as a strong indication of the existence of a functional interaction between the effector domain and the STAND protein.What is the reason behind the genomic association of the P. anserina NWD2/HET-S pair. At least in the fungal kingdom it is of wide occurrence. The diversity of effector domain and NOD domain types indicates that the generation of these systems (by gene fusion or fission) is the result of multiple reoccurring evolutionary events. It is as if the PFDs (putative or confirmed) behave as portable units that can be assorted with a variety of effector and NOD domains. Early yeast prion studies have shown that PFDs can behave experimentally as portable units The mode of signal transduction that we propose here appears widespread both in terms of species distribution and the number of domains involved. We found STAND/effector domain pairs in at least 14 different pezizomycota species largely distributed in the fungal phylogenetic tree. Then, as mentioned above, we find that in addition to the HeLo toxicity domain, 5 other domains could be regulated in a similar manner. We distinguish three types of PFD-like domains. The σ-motif can be found associated to a variety of effector domains, in contrast the HET-s PFD is only found associated to the HeLo domain. In turn, certain effector domains like the HeLo and sesB domains can be found associated to different PFD-like motifs. Finally, the PFD-like extension are found associated to NOD domains belonging to two different clades of NOD domains (the NACHT and NB-ARC clade) and a variety of super-structure forming repeats. If our model is correct, it is clear that the proposed mechanism is not an isolated oddity concerning only the particular case of It will be of interest to determine whether similar systems exist in the genomes of organisms from other branches or if the presented mechanism is restricted to the fungal kingdom. If this is so, this might be related to the synthicial organisation of filamentous fungi in which the phenomenon of amyloid propagation could be exquisitely exploited for adaptive ends.P. anserina. More generally, we propose the existence of a common and evolutionary conserved functional interplay of STAND protein with effector domains in fungi ensured by transmission of amyloid folds.Our observations identify NWD2 as a functional partner of the HET-S of http://www.ncbi.nlm.nih.gov/) and the Broad Institute Fungal Genome Initiative web interface  in BLASTP and PSI-BLAST searches with default settings. Sequences alignment were performed with ClustalW2  and formatted using the BOXSHADE server (http://www.ch.embnet.org/software/BOX_form.html). Consensus sequence and motif searches were performed using the MEME suite (http://meme.sdsc.edu/meme/intro.html). All sequence described and analyzed in the present study have been grouped in Fungal protein sequences were retrieve from ncbi (http://geno3d-pbil.ibcp.fr/cgi-bin/geno3d_automat.pl?page=/GENO3D/geno3d_home.html). The template for modeling was HET-s(218–289) structure . The results of the modelling are presented as a superposition of the 10 models calculated by Geno3D. For homology modeling, the chimeric sequences have been generated by replacing the r1 and r2 regions of the HET-s(218–289) with the 21 amino acid sequence of the STAND proteins.Homology modeling was performed using the Geno3D server Click here for additional data file.