The assembly of this active site is a multistep process involving three proteins, HydE, HydF and HydG. According to the current models, HydF has the key double role of scaffold, upon which the final H-cluster precursor is assembled, and carrier to transfer it to the target hydrogenase. The X-ray structure of HydF indicates that the protein is a homodimer with both monomers carrying two functional domains: a C-terminal FeS cluster-binding domain, where the precursor is assembled, and a N-terminal GTPase domain, whose exact contribution to cluster biogenesis and hydrogenase activation is still elusive. We previously obtained several hints suggesting that the binding of GTP to HydF could be involved in the interactions of this scaffold protein with the other maturases and with the hydrogenase itself. In this work, by means of site directed spin labeling coupled to EPR/PELDOR spectroscopy, we explored the conformational changes induced in a recombinant HydF protein by GTP binding, and provide the first clue that the HydF GTPase domain could be involved in the H-cluster assembly working as a molecular switch similarly to other known small GTPases. These proteins have different distribution, catalytic properties and molecular architectures, but they are both able to reversibly reduce protons to H2 by means of metal clusters with some key similarities, including the coordination to the polypeptide chain through four conserved cysteine residues and the presence of iron-bound CO and CN− ligands2. In the case of [FeFe]-hydrogenases, the active site (referred to as the H-cluster) is particularly complex since it is composed of a [4Fe4S] cubane linked via a cysteine bridge to a 2Fe subcluster comprising the CO and CN- molecules and an additional dithiomethylamine ligand5. This 2Fe subcluster needs a specific set of maturation proteins to be first assembled in the active form and then inserted into the target functional [FeFe]-hydrogenase. Three conserved proteins are involved in its biosynthesis and delivery, i.e. HydE, HydF and HydG, discovered in the unicellular green alga Chlamydomonas reinhardtii and then found in all microorganisms containing a [FeFe]-hydrogenase6. Both HydE and HydG are radical S-adenosylmethionine (SAM) proteins whereas HydF is a GTPase carrying a [4Fe4S] cluster binding motif6. The 3D crystal structures of all these maturases have been solved10, and several in vitro and cell-free experiments using purified recombinant proteins have been performed to understand their functions, allowing to propose a two-steps model that describes a H-cluster biosynthetic pathway in which a 2Fe precursor is assembled and chemically modified on a scaffold protein prior to the transfer to the apo-hydrogenase 2CN by HydG, with two synthons needed to synthesize a [2Fe]H subcluster16. From the other side, a synthetic [2Fe] precursor was loaded into HydF to finally yield the active hydrogenase (HydA)22, suggesting a key role of HydF in the last steps of the H-cluster maturation and delivery. As a scaffold protein, HydF must efficiently interact with the two maturation partners (i.e. HydE and HydG), and with the target apo-hydrogenase itself, keeping them in close proximity in order to get a functional unit able to follow the ordered biosynthetic pathway described in the proposed two-step model. Several functional insights have been gained from the 3D structure of the apo-HydF (i.e. devoid of both GTP and FeS cluster), as well as from several spectroscopic analyses of the FeS cluster carrying protein in solution. HydF is a dimer in which each monomer is composed of three distinct domains, two with the consensus sequences for the binding of GTP (domain I) and of a [4Fe4S] cluster (domain III)8; the third domain (domain II) allows two monomers to associate through a large surface, giving rise to a stable, left-handed helical shaped dimer with an open and accessible surface enabling it to interact with potential partners8. The [4Fe4S] cluster coordination sphere of HydF has been thoroughly investigated by spectroscopic analysis of the protein in solution29, which provided several clues on how the H-cluster precursor is kept in-site by the scaffold during biosynthesis and chemical modifications by HydE and HydG; however, the exact mechanism by which the mature precursor is transferred from the scaffold to the hydrogenase has not been clarified. A further unresolved issue is the specific role of the HydF GTPase moiety, which is essential for the [FeFe]-hydrogenase maturation and activation30. We found that the binding of GTP to HydF induces the dissociation of HydE and HydG from the scaffold31, suggesting that the GTPase domain could be involved in a dynamic network of interactions with the other maturases. Interestingly, the crystal structure of the apo-HydF protein showed the existence of flexible loops in this domain, which could undergo structural rearrangements upon GTP binding8. This could in turn have an impact on the capability of the holo-protein to interact with HydE and HydG in the maturation machinery, and to drive the proper delivery of the H-cluster precursor to the target hydrogenase.Biohydrogen production, one of the most promising frontiers in the field of renewable energies, is achieved in nature by several prokaryotic and eukaryotic microorganisms through a general class of evolutionarily unrelated metalloenzymes called hydrogenases. Among them, [FeFe]- and [NiFe]-hydrogenases are the most widespread32. Combining CW-EPR (electron paramagnetic resonance) and PELDOR (pulse electron-electron double resonance) spectroscopic analysis, either in the absence or in the presence of GTP, we monitored the local mobility of the spin label at the selected sites and the distance between couples of labels, respectively. We found that the binding of the nucleotide to the isolated HydF GTPase domain does not induce large conformational effects, at least at the level of the positions investigated. Instead, small changes in the distance between spin labels were observed, suggesting diffuse rearrangements upon GTP binding at the level of these structural elements32.In a previous work, we investigated the intrinsic conformational changes triggered in HydF upon GTP binding using a recombinant form comprising only the HydF domain I. Nitroxide spin labeled cysteine residues were introduced at diagnostic positions in different elements of the protein secondary structure, with the aim of monitoring large rearrangement of the structure34 containing the GTP-sensitive switch regions 1 (sw1) and 2 (sw2). Strikingly, we recognized sw1 and sw2 regions in the HydF GTPase domain and showed that, upon GTP binding, the protein undergoes conformational changes which are likely instrumental in promoting HydF activity in the maturation process of hydrogenases36.Since the presence of the other two domains may be important in producing structural constrains in HydF, by directing and/or amplifying the conformational changes induced at the GTP binding site, in the present work we used CW-EPR and PELDOR analysis by mapping the GTP-induced conformational changes along the entire HydF protein. We provide the first hint that the HydF GTPase domain functions as a molecular switch, similarly to other small GTPasesin vivo and in vitro30. Sequence analysis indicated that the HydF GTPase domain contains the consensus motifs shared by all NTPases and essential to bind and hydrolyze GTP, i.e. the P-loop: (GRRNVGKS) and G2 to G4 loops 30. However, the role of GTP binding and/or hydrolysis in the H-cluster assembly is still elusive. By structural analysis, we found a similarity of the HydF folding with that of small GTPases, which are well-established regulators of several cellular functions, such as FeoB, MnmE, RbgA and TrmE. These GTPases alternate between GDP-bound and GTP-bound forms, differing by the conformations of the so-called switch 1 (sw1) and switch 2 (sw2) regions, and of other, more protein dependent, structural elements37  contains two asparagine residues, namely Asn19 and Asn27, which are highly conserved in the K+ activated G-proteins37, as indicated by a detail of the multiple sequence alignment reported in panel C of Fig. + activated GTPases, the first conserved asparagine is a ligand to the potassium ion, which is also coordinated by three oxygen atoms from the GTP nucleotide and two backbone carbonyl groups from the sw1 region.An active HydF GTPase domain is essential to produce a functional [FeFe]-hydrogenase, both +, sw1 must adopt a particular structure in which its ‘K-loop’ lies directly over the nucleotide binding site. The GTP-bound sw1 conformation is a unique feature of the cation dependent GTPases. Moreover, in K+-activated GTPases the second conserved asparagine residue forms hydrogen-bonds with the backbone of sw1, and contributes in positioning it in the proper conformation. Interestingly, the putative sw1 of the HydF GTPase domain was not resolved in the X-ray structure of the apo-protein8, likely due to the high flexibility of this loop, which in the mentioned homologous proteins undergoes large conformational rearrangement upon GTP binding. Since the structure of HydF in the presence of either GTP or GDP is not yet available, the hypothesis of a structural analogy of its sw1 in the GTP-bound state with those of other K+-activated GTPase guided our experimental design aimed to detect possible rearrangements upon nucleotide binding. Panel A of Fig. +/Mg2+/GDPAlX4]-structures of the GTPase domain of FeoB, a membrane protein that imports Fe2+ 38, taken as reference structure of a K+-activated GTPase. Note that the putative sw1 region of HydF (residues 31–46) is mostly missing, since it was unresolved in the X-ray structure8, while the two conserved asparagines are highlighted.In order to coordinate K+-activated GTPases, the structure of sw2 and its rearrangement vary in a much more protein-dependent way with respect to sw137. Thus, it is difficult to foresee the conformational change of this protein region upon GTP binding/hydrolysis. The same consideration holds for other protein segments, which may be involved in the specific interactions with other proteins or domains in relation to the protein function.As reported above, sw2 is another common protein region of the GTPases, which usually undergoes structural modification upon nucleotide binding/hydrolysis. This part is well resolved in the HydF X-ray structure and corresponds to a long loop ending with an α-helix Fig. , panel AEscherichia coli (see below), as shown in Fig. We obtained a first hint of a structural change induced by GTP in HydF from the circular dichroism spectra of a recombinant HydF protein expressed in 39. All native cysteines must be eliminated in order to obtain a protein carrying a single cysteine introduced in the position of interest. This cysteine is then chemically modified with a sulfhydryl-specific EPR probe. To this end, a recombinant HydF protein was expressed in E. coli in frame with a 6His-tag at the N-terminus, as described in details in the Methods section, and purified by combining a NiNTA affinity and a gel filtration chromatography. Due to the presence of cysteine residues in wild type HydF at site 91 (GTPase domain I) and sites 302, 353 and 356 (FeS cluster binding domain III), we first substituted these native cysteines with serines by means of site-specific mutagenesis, in order to obtain a cysteine-less pseudo-wild type mutant protein. Only in one case C356 was maintained and spin labeled itself (see below). It should be noted that the removal of the cysteines in the domain III, where a [4Fe4S] cluster is bound to the holo-protein, precludes the cluster assembly; however, since the GTPase domain is not directly affected by the absence of the FeS cluster, the analysis of the cysteine-less mutants are meaningful. Moreover, the effect of the GTP binding in the CD spectrum of the cysteine-less mutant was the same as that of the recombinant wild type protein giving a comparable analysis in terms of secondary structure composition change upon nucleotide binding  the switch regions ; 2) the interface region between the GTPase domain and the catalytic domain ; 3) the catalytic domain (C356); 4) the helix connecting the GTPase domain with the long loop leading to the dimerization domain (T164C); 5) the long loop itself (I175C); 6) the dimerization domain (V261C). Figure The spin labeling positions were selected based on the All the spin labeled mutants showed changes of the CD spectrum upon GTP binding, indicating that the introduction of the spin label was not altering the capability of the protein to adopt the nucleotide-induced structural changes.40. Following protein site-directed cysteine mutagenesis, a nitroxide spin label binds to the mutated Cys residue and reports on local dynamics, conformational dynamics of protein domains, and possibly, global protein motion. The lineshape of the EPR spectrum of a spin label reflects its mobility and is therefore sensitive to conformational changes. Highly mobile spin labels, as those on the surface of a protein, have a characteristic narrow spectrum with three sharp peaks, whereas the reduction in mobility, due to intramolecular constrains, leads to the broadening of the spectrum and/or the appearance of an additional peak in the low field region41. We recorded the EPR spectra of purified HydF proteins individually spin-labeled with MTSSL at the 12 different positions mentioned above, and looked for mobility changes upon GTP addition  and electron paramagnetic resonance (EPR) is a well-established method to determine protein dynamics and conformationsion Fig. . While 5Minor, but still detectable, mobility changes were observed at positions 35 and 44 (sw1), 340 , 164 (α-helix of GTPase domain close to the loop connecting the dimerization domain), and 175 (belonging to the long loop connecting the dimerization domain) , 356 , and 261 (dimerization domain).4  and GTPγS  were also explored. The results are shown in Fig. 4, while GTPγS induced the same effect as GTP .To better characterize the role played by the nucleotides in determining the conformation of HydF, the effects of GDP, GDP-AlF31, detection of the EPR spectrum at different delay times after the GTP addition showed that only after several hours the spectrum returned to the lineshape preceding the nucleotide addition  experiments42. It is well known that this pulse EPR technique, based on the measure of dipole-dipole interaction between unpaired electron spins, has become the most widely used method for measuring distances between electron spins in (bio)macromolecules. The V261C HydF mutant was chosen to perform intra-dimer distance measurements because, based on the X-ray structure, the expected distance between spin labels belonging to the two moieties composing the dimer is 3.5 nm, which is in the suitable range of reliable distances measured by PELDOR. Moreover, according to the X-ray structure, residue 261 is located in a portion of the dimerization domain, that does not interfere with the folding of the β-sheet forming the dimeric structure of HydF, thus representing a good choice to detect conformational changes induced by GTP at the level of the dimer structure.Since HydF adopts a dimeric structure43, it seems clear that such differences are present not only in the region corresponding to the 261-261 distance, as for the single labeled mutant V261C, but also at the level of the other inter- and intra- monomer distances. Validation of the distance analysis performed with DeerAnalysis2015 is reported in Supplementary Fig. The spectra of samples frozen in the absence of GTP and immediately after its addition are reported in Fig. + activated GTPase family is a strong indication of its possible role as a molecular switch. The suggested double function of HydF as scaffold and carrier of the 2Fe unit of the [FeFe]-hydrogenase (HydA) H-cluster precursor may be facilitated by conformational changes of the protein during the cycle of interaction with HydG, HydE and/or HydA. Shepard and coworkers previously showed that the HydF-dependent GTP hydrolysis in vitro increases in the presence of HydE or HydG19, suggesting the existence of a HydF GTPase domain function/structure relationship driving the interactions of this scaffold with the other two maturases. More recently, Vallese et al. showed, based on Surface Plasmon Resonance experiments performed by injecting the nucleotide during the step of HydE and HydG dissociation from HydF, that the binding of GTP increases the dissociation rate31. This could be related to the maturation mechanism by which the displacement of an interaction partner from the scaffold occurs, allowing subsequent association of a different protein. Thus, it seems likely that a conformational switch due to the GTP binding to HydF is responsible for a fast release of the other two maturases.The sequence homology of the HydF GTPase domain with those of proteins belonging to the KWith the aim to prove the occurrence of conformational changes of HydF upon GTP binding, we first used CD spectroscopy. The addition of GTP induced a clear change of the secondary structure, reflected in the CD spectrum. However, the technique is not very sensitive to the rearrangement of unstructured regions, such as loops or random portions of the protein. Therefore, to get further insight into the protein regions undergoing structural rearrangement, we used SDSL EPR spectroscopy, which, with the introduction of a unique spin label at specific sites, reports on localized regions of a protein. If the nucleotide binding induces some changes in the protein structure, the lineshape of the EPR spectrum of a spin label is expected to change, when the probe is located at a site involved in/close to the rearrangement. A highly mobile spin label on the surface of a protein has a characteristic narrow spectrum with sharp peaks, whereas the reduction in its mobility due to intramolecular constrains or “trapping” in a protein-protein interaction interface is reflected in the broadening of the spectrum.To provide adequate coverage of the protein structure analysis by EPR, we expressed and purified 12 cysteine single mutants, modified them with spin labels and compared their EPR spectra taken before and after GTP addition. In this way, we were able to obtain information concerning different protein regions. Most of the changes detected in the EPR spectra of HydF are not as dramatic as one would expect for a large protein reassembly and reshaping. The 12 positions probed by our experiments, however, are indicative of structural changes taking place at different extent depending on the protein region.+ dependent GTPases at the level of sw1, it seems likely that in HydF, after GTP addition, residue 38 could become very close to the K+ binding region, starting from an extended loop far from this site. This would be consistent with the observed reduced mobility of the backbone, as revealed by the 5-MSL spin label. The MTSSL spin probe at the same positions has a more complicated behaviour, described by three components. The most immobilized component undergoes a reduction in intensity as well as the most mobile, while the intermediate regime component gains intensity. This suggests that MTSSL, having higher flexibility compared to the 5-MSL, may adopt different local conformations. Since both spin probes reveal a redistribution of the components of the EPR spectrum upon GTP binding, a significant structural change leading to many local effects is clearly taking place. Residues 35 and 44, which are very close to 38, undergo similar changes in terms of redistribution of components, although less pronounced. Changes of EPR lineshapes are observed for these two residues when 5-MSL is used as spin label and in the presence of glycerol, when their mobility is reduced. Looking at the spectra of the two proteins labelled with MTSSL it appears that residues 35 and 44 are both characterized by relatively high intrinsic mobility either in the presence or in the absence of GTP. For residue 44 this was expected on the basis of a possible structural analogy of HydF with FeoB  and a helix (a2). The unique location of a2 between the nucleotide-binding site and the GDI domain of NFeoBLp suggested for this helix a function of relay element transmitting the signal induced by nucleotide binding to the GDI and transmembrane domains46. In this way, nucleotide binding to the G domain in FeoB regulates ferrous iron uptake across the membrane. The 3D structure of HydF indicates that sw2 contains a long loop constituted by 14 residues followed also by a helix (a2). R88 is located at the base of a2, facing domain III. Thus, an effect similar to that observed in FeoB proteins could take place in HydF with the conformational change of sw2 upon nucleotide binding transmitted to the catalytic domain via a2, as suggested by the change in mobility experienced by the spin probe at site 340. In this respect, it is worth noting that the EPR spectrum of HydF [4Fe4S] cluster was found to be sensitive to GTP19. We did not observe changes in the spin label mobility at position 356 where a Cys ligand of the [4Fe4S] cluster is present in the wild type protein, however the lack of the cluster in the recombinant mutant protein may alter the response of the spin label at this site due to the absence of structural constrains imposed by the cluster itself in the holo-protein.The structure and length of the sw2 region are more heterogeneous in the GTPases compared to those of sw1. The conformational change in sw2 upon nucleotide binding differs among distinct GTPases, ranging from small rearrangements, such as in Ras+-dependent GTPases MnmE and TrmE48. To explore the response of the loop to the GTP binding in HydF, we considered the spin label at position 175. Interestingly, we found that the spin label bound at this site, although far away from the GTP binding site, undergoes a detectable change in the mobility upon nucleotide binding. We also monitored the possible effects in the dimerization domain by SDSL at site 261 and measuring the 261-261 inter-monomer distance by PELDOR. Clear effects were found on the order of few Å displacement upon GTP binding, showing that the changes occurring in the GTPase domain are felt by distant residues belonging to the dimerization domain as well. The effects induced on the dimer structure were further confirmed by the PELDOR analysis of the double mutant T164-V261.HydF is characterized by a dimerization domain, which is directly connected to the GTPase domain through a long loop see Fig. . Similar34. Thus, it was of primary importance to investigate the effects of the different nucleotides. The addition of GDP and GDP-AlF4 to the protein did not produce any observable effect in the EPR spectra, while GTPγS induced the same effect as GTP, strengthening the hypothesis that the trigger of the conformational change is given by the binding of the nucleotide rather than by its hydrolysis. Thus, as common for many other GTPases, the conformation of HydF in the presence of GDP is the same as that of the apo-protein. From the time evolution of the EPR spectra after the addition of GTP, we found that the return to the apo-conformation is very slow compared to the kinetic of hydrolysis of GTP. This inertia could be due to the absence of some cellular effector in our in vitro experiments compared to the in vivo conditions, as observed for a number of GTPases needing effectors to perform the GTP/GDP cycle34, but could even be functional to generate a rest time for the switch, allowing other steps of the maturation process to take place. This would fit with the previously proposed stepwise model of the H-cluster assembly on the HydF scaffold12.Proteins acting as GTPase switches show conformational changes, induced by a cycle of GTP hydrolysis, with different mechanisms. Changes in protein forms can be promoted either by GDP binding or by GTP binding/hydrolysis+ activated GTPase19. Our EPR experiments show that the absence of K+ does not preclude the conformational change induced by GTP binding, however its presence favours the switch of the structure. According to the known structures of the K+ activated GTPases, the cation contributes to the coordination of the sw1 upon nucleotide binding, thus the observed effect in HydF is in agreement with the rearrangement of sw1 and with the contribution to the stabilization of the switched conformation.Functional and sequence analysis clearly indicate that HydF is a Kin vivo, might restrict the conformational space of the protein generating a limited number of conformations compared to the in vitro conditions adopted in this work. In this respect, it is interesting to note that in cell-free experiments an addition of cellular extract is always necessary to allow the [FeFe]-hydrogenase maturation process to take place49. Although it is difficult at this stage to discriminate between the a) and b) possibilities, the main result remains, showing that the GTPase domain of HydF may undergo conformational changes upon GTP binding. The presence of a correct cluster assembly and/or the interaction with other unknown effectors/maturases may well enhance the differences between energies of the conformations that we have detected shifting the equilibrium between different forms, which are however intrinsically determined by the nucleotide binding. EPR experiments will be performed in the near future by using unnatural aminoacids carrying a spin label to avoid the substitution of native cysteine residues and allow refolding of the protein in the presence of the [4F-4S] cluster in the catalytic domain, with the aim of studying the influence of the cluster presence on the conformational equilibrium shift induced by GTP.Most of the sites investigated by SDSL EPR reveal the presence of more than one conformation, both in the apo- and in the GTP-bound state. We never observed a complete conversion of the EPR spectrum from one form to another, even in large excess of either GTP or GTPγS. For instance, in the case of R88C the change in the lineshape of the EPR spectrum induced by GTP can be assigned to a 30% population shift between the two components needed for the simulation of the spectra  are all involved in ribosome biogenesis, exceptions are reported. For instance, FeoB is a membrane protein that imports FeWe have provided evidence that EPR is a suitable technique to follow the changes induced by GTP binding and hydrolysis in HydF and monitor the states of nucleotide cycle. The effects monitored at different protein sites, by using SDSL-EPR techniques, showed that the structural changes upon GTP binding in HydF are diffuse, and indicate that not only the GTPase domain but the whole protein undergoes conformational rearrangements. This is in agreement with previous data suggesting that GTP alters the EPR signal of the reduced [4Fe4S] cluster of HydF, and facilitates the dissociation of HydE and HydG from HydF. The interaction areas between the two maturation proteins and HydF are not known, however they likely involve extended protein regions not only sw1 and 2. Thus, the diffuse conformational changes detected in HydF upon GTP binding may well be functional to a variation of interaction with the other maturases. Experiments to measure these effects in the HydF-HydG and HydF-HydE spin labeled complexes will help to confirm this hypothesis.51, which is essential for hydrogenase maturation/activation. Size exclusion chromatography and cross-linking studies demonstrated that the binding of GTP triggers the dimerization of HypB. The HypB GTP-dependent dimerization facilitates nickel delivery to hydrogenase by loading nickel to the metal-binding site at the dimeric interface. Thus, a GTPase-dependent molecular switch may be a common strategy adopted by the different hydrogenases in the assembly of their complex metal active sites.As a final remark, it is worth noting that a GTP-dependent step in the maturation process is found also in [NiFe]-hydrogenases. The maturase HypB is a metal-binding GTPase involved in this stepThermotoga neapolitana hydF gene was isolated from purified genomic DNA by PCR amplification and subcloned in frame with a 6His-tag sequence at the N-terminus in a pET-15b vector (from Novagen®), as described in ref. hydF at selected sites was performed with the QuickChange® II Site-Directed Mutagenesis Kit (from Agilent Technologies), using as template pET-15b/hydF recombinant plasmid and the couples of primers listed in Supplementary . E. coli Rosetta (DE3) cells were transformed with the obtained plasmids and positive clones selected by antibiotic resistance. The expression of the wild type and mutant 6His-tagged HydF proteins was induced by adding 1 mM isopropyl-β-thiogalactopyranoside (IPTG) in LB medium and incubating the cells at 30 °C overnight. Proteins were purified starting from 0, 5 to 1 L cultures and combining affinity chromatography and gel filtration. Briefly, cells were harvested by centrifugation, resuspended in lysis buffer  and lysed by French press. The supernatant fractions were isolated from cell debris by centrifugation and the proteins purified to homogeneity by a nickel affinity chromatography  and a gel filtration chromatography using a Superdex 200 GL 10 300 column , equilibrated in a buffer containing 25 mM Tris-HCl pH 8.0, 200 mM KCl, and 1 mM MgCl2 . To estimate the molecular weight of the analyzed samples, the column was equilibrated in the same buffer and calibrated with the standards thyroglobulin (669 KDa), ferritin (440 KDa), β-amylase (200 KDa), bovine serum albumin (67 KDa), carbonic anhydrase (29 KDa) and cytochrome c (12 KDa). The eluted fractions containing the HydF dimer were finally pooled together and concentrated by centrifugal filters  to a volume suitable for spectroscopic analysis (see below), giving rise to a final concentration up to 600 µM, as determined spectroscopically using ε280nm = 26360 M−1cm−1. Purified proteins were analyzed by 12% SDS-PAGE.The 2), and both solutions were thoroughly degassed before each titration. The solution (75 μM wild type HydF protein) in the cell was stirred at 200 rpm to ensure rapid mixing. Typically, 7 μL of titrant (500 mM either GTP or GTPγS) were delivered every 10 s with an adequate interval (4 min) between injections to allow complete equilibration. Titrations continued until no further complex formation following addition of excess titrant was detected. A background titration, consisting of identical titrant solution and buffer solution in the sample cell, was subtracted to account for heat of dilution. The data were collected automatically and then analyzed by the Windows-based Origin software package supplied by MicroCal. A one-site binding model was used.ITC measurements were carried out at 25 °C on a MicroCal OMEGA ultrasensitive titration calorimeter. The titrant and sample solutions were made from the same stock buffer solution , in a 0.5 mM Tris-HCl buffer, pH 8.0, containing 4 mM KCl and 20 μM MgCl2), and concentration by centrifugal filters. Twenty microliters of each sample, with a protein concentration of about 600 µM  were loaded into quartz capillaries with 0.6 mm ID and 0.84 mm OD. In GTP binding experiments, GTP (up to 10 mM) was added to the samples and EPR measurements were performed immediately after the addition. EPR spectra were collected at room temperature (298 K) on an Elexsys E580-X-band spectrometer (Bruker) using a Super High Sensitivity Probehead cavity. The field modulation frequency was set at 100 kHz, with a field-modulation amplitude of 0.5 G and the microwave power was 6.4 mW. Time constant was set at 40.96 ns and conversion time at 81.92 ms; data collection was carried out acquiring 1024 points. The center of the field was set to 351 mT and the sweep width to 10 mT. Simulations of the CW-EPR spectra were performed using a program based on the stochastic Liouville equation and adopting the MOMD model as standard for spin-labeled proteins52. The overall rotational correlation time of the HydF dimer was estimated using the program by Zerbetto et al.53. Details are reported in Supplementary information.Samples for EPR labeled with either MTSSL or 5-MSL were obtained by adding to the purified protein (at a concentration of about 150 μM) a fivefold molar excess of spin label, either MTSSL or 5-MSL, dissolved in DMSO and ethanol respectively, and incubating the protein at 4 °C overnight in the dark. Excess of non-ligated spin label was removed from the protein by several cycles of dilution with final buffer  was also added to the samples before freezing. The final protein concentration was about 150 µM for all the samples. In the nucleotide binding experiments, GTP was added to a 10 mM final concentration. All the samples, loaded into quartz capillaries with 1.1 mm ID and 1.6 mm OD, were quickly frozen. Q-band pulse EPR experiments were performed with the same EPR spectrometer used for CW-EPR (Elexsys E580) equipped with a Bruker EN 5107D2 resonator (microwave frequency = 33.86 GHz) and an Oxford CF935 cryostat. The measurements were performed at a temperature of 50 K. A standard four pulse sequence was applied; the microwave power was adjusted to obtain an observer sequence of 28/56/56 ns and a pump pulse of 56 ns. The difference between the pump and observer frequency was set to 80 MHz. A two-step phase cycle was applied for base-line correction, while deuterium nuclear modulations were suppressed using an 8 step τ cycle from a 180 ns starting value with 56 ns increment steps. Data on each sample were collected for about 15 hours. Distance distributions were extracted from PELDOR traces by using DeerAnalysis2015Supplementary information


Jumonji domain-containing protein 6 (JMJD6) is a member of the Jumonji C family of Fe(II) and 2-oxoglutarate (2OG) dependent oxygenases. It possesses unique bi-functional oxygenase activities, acting as both an arginine demethylase and a lysyl-hydroxylase. JMJD6 has been reported to be over-expressed in oral, breast, lung, and colon cancers and plays important roles in regulation of transcription through interactions with transcription regulator BRD4, histones, U2AF65, Luc7L3, and SRSF11. Here, we report a structural mechanism revealed by NMR of JMJD6 recognition by the extraterminal (ET) domain of BRD4 in that a JMJD6 peptide (Lys84-Asn96) adapts an α-helix when bound to the ET domain. This intermolecular recognition is established through JMJD6 interactions with the conserved hydrophobic core of the ET domain, and reinforced by electrostatic interactions of JMJD6 with residues in the inter-helical α1-α2 loop of the ET domain. Notably, this mode of ligand recognition is different from that of ET domain recognition of NSD3, LANA of herpesvirus, and integrase of MLV, which involves formation of an intermolecular amphipathic two- or three- strand antiparallel β sheet. Furthermore, we demonstrate that the association between the BRD4 ET domain and JMJD6 likely requires a protein conformational change induced by single-stranded RNA binding. The majority of proteins in this family have been assigned as histone lysine demethylases and are involved in chromatin-mediated transcription. The remaining members catalyze protein oxidation and generate a stable hydroxylated modification3. JMJD6 has been originally identified as a phosphatidylserine receptor on the cell membrane responsible for phagocytosis of apoptotic cells4. However, after JMJD6 was shown through structural bioinformatics to have catalytic activity similar to dioxygenase in the nucleus5, JMJD6 was soon described as a bi-functional oxygenase. It is the first discovered arginine demethylase that is able to remove the methyl moieties on methylated arginines of histones (such as H3R2me2 or H4R3me2) and on non-histone proteins, including methylated ERα, RHA, HSP70 and TRAF608. JMJD6 also acts as a lysyl hydroxylase by catalyzing C-5-hydroxylation of the splicing regulatory factor U2AF65, of multiple lysine residues of histones H3 and H4, and p53 (on K382), and auto-hydroxylation of internal lysine residues9. The initial report on the biochemical role of JMJD6 in histone arginine demethylation had been challenged by other results, which could not verify N-methyl arginine demethylation activity for JMJD6, but rather confirmed JMJD6’s lysine hydroxylation of histone peptide10. JMJD6 was also shown to interact with different proteins such as U2AF65, Luc7L3, SRSF11, histones and BRD414. Its overexpression is observed in many human malignancies including oral, breast, lung, and colon cancers, suggesting a role in tumorigenesis20. A biochemical study indicated that JMJD6 can interact with single-stranded RNA (ssRNA)21, but not with ssDNA, dsRNA and dsDNA.Jumonji domain-containing protein 6 (JMJD6) is a member of the Jumonji C family of Fe (II) and 2-oxoglutarate (2OG) dependent oxygenases21. Like the common structural fold of all 2OG oxygenases, JMJD6 contains a distorted double-stranded β-helix (DSBH or cupin) fold that is surrounded by characteristic secondary structure elements. This barrel-type DSBH fold conserves binding motifs for Fe(II) and 2OG oxygenases21. However, in comparison to representative structures from other lysine hydroxylase proteins such as JMJD2A and FIH, JMJD6 only contains the similarity of the cupin fold, and is otherwise dramatically different in overall structural conformation from the others, suggesting distinct functions of JMJD621. The JMJD6 structure contains a total of 15 short α-helices with α2, α3, α5, α6, α9, α10, and α11 displaying only one-turn and α4 and α8 two-turns. These one- and two-turn helices are distributed all over the surface of the protein molecule, are loosely connected by a variety of coil loops, and are likely flexible in a solution. These structurally unique small helices of JMJD6 have no clear function, but may be needed to engage interactions with different protein substrates.Human JMJD6 consists of a JmjC (Jumonji C) domain, three apparent nuclear localization signals (NLS), a DNA binding domain (AT-hook domain), a putative sumoylation site, and a polyserine (polyS) domain12, which is a member of the bromodomains and extra-terminal domain (BET) protein family22, and characterized by tandem N-terminal bromodomains (BrDs) followed by an extraterminal (ET) domain25. BRD4 has important cellular functions in transcription, DNA replication and DNA repair27. It has also been implicated in development of cancers including acute myeloid leukemia, multiple myeloma, Burkitt’s lymphoma, NUT midline carcinoma, and colon and breast cancers, and is thus recognized as a promising cancer drug target29. BRD4 and JMJD6 interact with the positive transcription elongation factor b (P-TEFb) complex in its active form to regulate Pol II promoter-proximal pause release for transcriptional activation of a large cohort of genes7. BRD4 and JMJD6 interact with each other on distal enhancers, so-called anti-pause enhancers (A-PEs), where JMJD6 is able to demethylate both histone H4R3me2 and the methyl cap of 7SK snRNA. The location of BRD4-JMJD6 on A-PEs binding leads to the dismissal of the 7SK snRNA/Hexim1 inhibitory complex. After removal of repressive histone marks, JMJD6 and BRD4 remain co-bound to anti-pause enhancers and active P-TEFb. Meanwhile, both JMJD6 and BRD4 are capable of attracting and retaining the P-TEFb complex on chromatin, leading to its activation, promoter-proximal Pol II pause release, and transcriptional activation7.Recently, JMJD6 was reported to interact with BRD412, but the underlying molecular basis has remained elusive. The ET domain function as a protein interaction domain was first suggested in human BRD2 and BRD4 interactions with the C-terminal segment of Kaposi’s sarcoma-associated herpesvirus (KSHV) latency-associated nuclear antigen (LANA), which facilitates KSHV episome integration into host chromatin for KSHV latency30. The ET domain mediates transcriptional activation through recruitment of several cellular transcription regulators including NSD3, ATAD5, CHD4, GLTSCR1, and JMJD631. The structures of the BRD4 ET domain bound to MLV Integrase, LANA and NSD3 were recently reported33, and the general ET domain binding motif and specificity were also explained. However, it has remained unclear how the ET domain selectively interacts with JMJD6 considering its conserved β-sheet cupin core and multiple small unique helices spreading around the protein surface. In this study, we report the NMR 3D structure of the BRD4 ET domain in complex with a peptide derived from one of helices on the JMJD6 surface, and describe detailed structural basis for a unique mode of protein-protein interactions for JMJD6 recognition by the BRD4 ET domain. We further show that BRD4/JMJD6 recognition is likely promoted by ssRNA binding to JMJD6 that induces protein conformational change.Proteomics and knockdown experiments identified that the ET domain of BRD4 is responsible for BRD4 and JMJD6 interaction33. As shown in Fig. 15N-HSQC spectra of the ET domain displayed backbone amide perturbations induced upon the addition of the JMJD6 peptide. The pattern is nearly identical to that of a longer ET domain construct with the same JMJD6 peptide (data not shown), confirming the conserved core ET domain binding to JMJD6. The affinity of the protein/peptide complex (Kd) was determined to be 158 ± 14 μM using isothermal titration calorimetric (ITC)  was used in NMR titration experiments to assess its binding to a JMJD6 peptide  that resembles the consensus amphipathic ET domain binding motif found in other effector proteins including NDS3 and KSHV LANATC) Fig. , which i21. Notably, as from our thermal shift binding analysis in Fig. We next conducted a thermal-shift study to characterize the full-length JMJD6 binding to the ET domain. When 100 μM of the ET domain was mixed with 1 μM of the full length JMJD6, the melting temperature of JMJD6 in the ET/JMJD6 complex was increased by 2.5 °C (from 48.0 °C to 50.5 °C) as compared to the free JMJD6 protein, indicating that binding of the ET domain improves the thermal stability of JMJD6 Fig. . AdditioTo validate the binding activity between JMJD6 and the BRD4 ET domain, we generated a domain-specific deletion that removes the ET domain (BRD4ΔET) in the context of full-length BRD4 and we utilized stable HEK293T cells expressing Flag-tagged wild type BRD4 (BRD4WT), BRD4ΔET and BRD4 ET mutant (E651A/E653A) as well as His-tagged JMJD6 (1–403) to perform immunoprecipitations (IP) followed by Western blotting with antibodies for Flag-tag, JMJD6, NSD3 and H4ac. As shown in Fig. 21. HEK293T cells were double-transfected with Flag-BRD4 and His-JMJD6, and different amounts of ssRNA and RNase inhibitors were added to cell lysates, followed by the same IP protocol and Western blotting analysis. As shown in Fig. To determine whether ssRNA can enhance JMJD6 and ET domain interaction, we choose the same 27-nt ssRNA probe used in a previous JMJD6 study34. The solution structure of the protein/peptide complex was determined using a total of 2199 NMR-derived distance and dihedral angle restraints  structure of the BRD4 ET domain (residues 601–683) in complex with a JMJD6 peptide  by using triple-resonance NMR spectroscopy methodsts Table . The str2 of solvent-accessible surface area of the protein, which consists of negatively charged exterior residues Asp650, Glu651, Glu653, Asp655, and Glu657 in the α1-α2 loop, and the interior hydrophobic core residues from α1 and α2 in their inter-connecting α1-α2 loop  on the α6 helix in the JMJD6 crystal structure are not completely exposed to exterior solvent, they instead partially interact with the α8 helix , or through helix to helical bundle interactions (for JMJD6) as shown in this study. As compared to the structure of the BRD4 ET domain/JMJD6 complex, the structure of the ET domain/NSD3 complex  α2 Fig. . Only tw α2 Fig. ; two pos α2 Fig. . In addi α2 Fig. . Three hKd of 158 ± 14 μM; and NSD3, Kd of 140.5 ± 2.2 μM), the underlying thermodynamic mechanisms are apparently different. As revealed by the thermodynamic parameters determined by ITC, the free energy change  of the ET domain binding to the JMJD6 peptide is contributed collectively by a favorable entropy change  and a small favorable change in enthalpy , whereas the ET domain binding to the NSD3 peptide  is driven by a dominated favorable entropy change  that overcomes a moderately unfavorable enthalpy change . The binding of BRD4 ET with the LANA peptide is almost identical, structurally and thermodynamically, to the NSD3 peptide32, which is consistent with their mode of ligand recognition.Notably, although ITC results show similar affinity of BRD4 ET binding to JMJD6 and NSD3 peptide  is stronger than the latter (1000x). The MLV IN peptide recognition by the ET domain features more extensive contact by a three-stranded intermolecular anti-parallel β-sheet. As compared to the ET/JMJD6 complex, the β1 strand of the ET domain when bound to the MLV IN peptide is pushed outward to provide greater interface for intermolecular interactions  peptide  is thermodynamically similar to the ET domain association with JMJD6ons Fig. . As showons Fig. ; residueons Fig. . In addions Fig. . These r11. However, the molecular mechanisms by which it engages in these processes have remained elusive. BRD4 plays important roles in multiple cellular functions, such as transcriptional elongation, epigenetic regulation, and DNA repair7. Its functional importance in the development of multiple diseases has supported its recognition as an attractive drug target. Previous work has shown that the interaction of BRD4 and JMJD6 with the positive transcription elongation factor b (P-TEFb) complex regulates Pol II promoter-proximal pause release, leading to activation of a large cohort of genes7. The interaction of BRD4 with JMJD6 was found to occur through the BRD4 ET domain12, but the structural details of this interaction were not clearly understood.JMJD6, a jumonji C domain-containing protein demethylase and hydroxylase, has been associated with multiple biological processes, including regulation of transcription and splicing via post-translational modification33. This demonstrates the functional versatility of the conserved three-helix bundle fold of the ET domain for modulating protein-protein interactions, which likely facilitate its ability to engage in multivalent interactions involving not only protein-protein but also protein-RNA interactions. Our results suggest that the ET domain recognition of the α6 helix of JMJD6 requires significant structural re-arrangement of the latter, which is likely made possible through allosteric effects of JMJD6 binding to single-stranded RNA. Surprisingly, this effect was not observed with DNA. We speculate that active transcription of these enhancers, leading to generation of nascent ssRNA, may need to occur prior to JMJD6 and BRD4 interaction and/or recruitment. Understanding the dynamics of this interaction, as well as the role that this plays in regulation of transcription by enhancers, will require a combination of both structural analysis and functional epigenetics. Our study presented here provides a clear guidance for further mechanistic characterization of the multifaceted functions of JMJD6 in coordination with BRD4 and other proteins in regulation of transcription during various normal and pathologic processes in biology.In our study, we provide the structural mechanism of JMJD6 recognition by the ET domain of BRD4, which is distinctly different from that of the ET domain recognition of other effector proteins including NSD3, LANA of herpesvirus, and integrase of murine leukemia virus, which feature the formation of an intermolecular antiparallel β sheet33. Briefly, His-Tagged BRD4 ET domain and His-Tagged JMJD6 were overexpressed in Escherichia coli pRIL plasmid BL21-CodonPlus cells and induced with 0.3 mM isopropyl-b-D-thiogalactopyranoside at 18 °C. His-Tagged BRD4 ET and His-Tagged JMJD6 were purified by HiTrap IMAC . After removing His-Tag with thrombin or TEV treatment, protein samples were further purified with Superdex 75 column or Superdex 200 column . Uniformly 15N-, 15N/13C-labeled proteins were prepared by growing the bacteria in M9 minimal medium containing 15NH4Cl with or without 13C-glucose and prepared as unlabeled protein.The human BRD4 ET domain  and full-length JMJD6 subcloned in pNIC28 vector were expressed and purified using a procedure as described previously2H2O (9/1) or 2H2O. All nuclear magnetic resonance (NMR) spectra were acquired at 25 °C on Bruker 500, 600, and 800 MHz spectrometers equipped with z-gradient triple-resonance cryoprobes. The backbone 1H, 13C, and 15N resonances were assigned using standard three-dimensional triple-resonance HNCA, HN(CO)CA, HN(CA)CB, and HN(COCA)CB experiments34. The side-chain atoms were assigned from three-dimensional HCCH-TOCSY, HCCH-COSY, and (H)C(CO)NH-TOCSY data35. The NOE derived distance restraints were obtained from 15N- or 13C-edited three-dimensional NOESY spectra. The JMJD6 peptide was assigned from two-dimensional TOCSY, NOESY, ROESY, and 13C/15N-filtered TOCSY and NOESY. The intermolecular NOEs used in defining the structure of the complex were detected in 13C-edited (F1), 13C/15N-filtered (F3) three-dimensional NOESY spectra (unlabeled JMJD6 peptide bound to 13C/15N-labeled ET protein)36. Spectra were processed with NMRPipe and analyzed using NMRVIEW38.The BRD4 ET domain/JMJD6 peptide (84–96) complex was used for structure determination. NMR samples of the ET domain (0.5 mM) in complex with a JMJD6 peptide  of 1.0 mM were prepared in 100 mM phosphate buffer (pH 6.5) containing 5 mM perdeuterated DTT and 0.5 mM EDTA in H2O/39. Initial protein structure calculations were performed with manually assigned NOE-derived distance constraints. Hydrogen bond distance, ϕ and ψ dihedral-angle restraints from the TALOS-N prediction were added at later stage of structure calculations for residues with characteristic NOE patterns41. The converged structures were used for the iterative automated NOE assignment by ARIA refinement. Structure quality was assessed with CNS, ARIA, and PROCHECK analysis43. Total 143 intermolecular NOE-derived distance restraints were added in the structure determination of the ET/JMJD6 peptide complex. A family of 200 structures was generated and 20 structures with the lowest energies were selected for the final analysis.Structures of the BRD4 ET domain/JMJD6 peptide were calculated with a distance-geometry simulated annealing protocol with CNS44. Peptide concentration was determined by weight and confirmed by NMR, and protein concentration by absorbance measured at 280 nm (A280). The protein sample (0.5 mM) was placed in the cell, whereas the micro-syringe was loaded with a peptide (7.5 mM) in the ITC buffer. The titrations were conducted using 19 successive injections of 2.0 μl (the first at 0.4 μl and the remaining 18 at 2.4 μl) with a duration of 4 s per injection and 150 s between injections. The collected data were processed using the Origin 7.0 software program (OriginLab) supplied with the instrument according to the “one set of sites” fitting model.Experiments were carried out on a MicroCal auto-ITC200 instrument at 20 °C while stirring at 750 rpm in ITC buffer (pH 7.4), consisting of PBS buffer and 0.5 mM dithiothreitol as described previously45. SYPRO Orange dye (Life Technologies) was incubated at ratio of 1:1000 (w/w) with ~0.05 mg/mL of full-length JMJD6 (1 μM) in PBS (pH 7.4). If samples also include the ET domain, the concentrations were 1 μM to 100 μM. If samples also include ssRNA, dsRNA, ssDNA, or dsDNA, those concentrations were 1 μM, respectively. All oligonucleotides were purchased from eurofins. The sequence of ssRNA was 5′-AUACGAUGCUUUACGGUGCUAUUUUGU-3′; 27 nt. The sequence of ssDNA was 5′-TTTCTAGATCAGGAGCTGTCGGAAGCTATCC-3′; 31 nt. 40 μL of the dye/protein solution was aliquoted into a 96-well PCR plate, and an emission of 610 nm was measured in a real-time PCR instrument  as the temperature was increased from 25 °C to 95 °C at 1 °C/min. The fluorescence was fit by regression analysis using IGOR Pro  using a simple equation such as the Boltzmann equation,a denotes the slope of the curve within the inflection point of the transition curve (Tm). Using Tm and a obtained by the fitting, the thermal shift data were normalized to remove temperature dependency of fluorescent intensities and effect of protein aggregation that distort an intrinsic transition curve.DSF measurements were performed using a method described byThe mammalian expression plasmid, pcDNA3-F:hBrd4 (FL) and pcDNA3-F:hBrd4 (E651A E653A) were gifts from MMZ lab. To generate ET domain deletion mutant of hBrd4, we used pcDNA3-F:hBrd4 (FL) as template. There are two unique restriction enzyme cleavage sites near ET domain, Bsu36I and AleI. Overlap extension PCR was employed to generate domain-specific deletion of ET by using a pair of primers flanking the region where the deletion will be made (primers 1 and 4), and two complementary primers comprising a region of −19 bp to +26 bp related to the junction point (primers 2 and 3). The amplified product was then digested with Bsu36I and AleI at 37 °C for 3 h for subsequent cloning. The resulting clone, after bacterial transformation and DNA sequencing, was named pcDNA3-F:hBrd4 (ET-deletion).The day before performance of transfections, cells were seeded such that they would reach 70% confluence at the time of transfection. HEK-293T cells were transfected with the indicated plasmids using a 3:1 PEI/DNA ratio according to the instructions of the manufacturer . After 48 hours transfection, cells were lysed in lysis buffer  supplemented with protease inhibitor cocktail (Roche). Cell lysates were sonicated at 30% intensity for 6s (3s on and 3s off and repeat twice) and clarified by spinning at 14000rpm for 15 min. Add same volume of IP binding buffer  to lysate, then add anti-Flag  or anti-Brd4 (Bethyl A301-985A) and pre-cleared protein G dynabeads (Thermo Fisher). Incubate at 4 °C overnight. After 5 washes with the wash buffer , the bound protein was eluted in SDS Laemmli buffer and size fractionated on Bis-Tris gels prior to blotting with the indicated antibodies. For immunoprecipitation adding Single-stranded RNA(ssRNA), we also need to add ssRNA and RNase inhibitor  to the cell lysate.PDB: coordinates for the solution structures of the BRD4 ET domain in complex with JMJD6 peptide is deposited at PDB under accession numbers PDB: 6BNH, and the NMR spectral data are deposited at BioMagResBank (BMRB) under accession numbers BMRB: 30373, respectively.Supplementary Information


While the local-mode HMMER3 is notable for its massive speed improvement, the slower glocal-mode HMMER2 is more exact for domain annotation by enforcing full domain-to-sequence alignments. Since a unit of domain necessarily implies a unit of function, local-mode HMMER3 alone remains insufficient for precise function annotation tasks. In addition, the incomparable E-values for the same domain model by different HMMER builds create difficulty when checking for domain annotation consistency on a large-scale basis.In this work, both the speed of HMMER3 and glocal-mode alignment of HMMER2 are combined within the xHMMER3x2 framework for tackling the large-scale domain annotation task. Briefly, HMMER3 is utilized for initial domain detection so that HMMER2 can subsequently perform the glocal-mode, sequence-to-full-domain alignments for the detected HMMER3 hits. An E-value calibration procedure is required to ensure that the search space by HMMER2 is sufficiently replicated by HMMER3. We find that the latter is straightforwardly possible for ~80% of the models in the Pfam domain library (release 29). However in the case of the remaining ~20% of HMMER3 domain models, the respective HMMER2 counterparts are more sensitive. Thus, HMMER3 searches alone are insufficient to ensure sensitivity and a HMMER2-based search needs to be initiated. When tested on the set of UniProt human sequences, xHMMER3x2 can be configured to be between 7× and 201× faster than HMMER2, but with descending domain detection sensitivity from 99.8 to 95.7% with respect to HMMER2 alone; HMMER3’s sensitivity was 95.7%. At extremes, xHMMER3x2 is either the slow glocal-mode HMMER2 or the fast HMMER3 with glocal-mode. Finally, the E-values to false-positive rates (FPR) mapping by xHMMER3x2 allows E-values of different model builds to be compared, so that any annotation discrepancies in a large-scale annotation exercise can be flagged for further examination by dissectHMMER.http://xhmmer3x2.bii.a-star.edu.sg/.The xHMMER3x2 workflow allows large-scale domain annotation speed to be drastically improved over HMMER2 without compromising for domain-detection with regard to sensitivity and sequence-to-domain alignment incompleteness. The xHMMER3x2 code and its webserver  are freely available at Reviewed by Thomas Dandekar, L. Aravind, Oliviero Carugo and Shamil Sunyaev. For the full reviews, please go to the Reviewers’ comments section.The online version of this article (doi:10.1186/s13062-016-0163-0) contains supplementary material, which is available to authorized users. Over the past decade, the OMICs frenzy from arrays to sequencing has swarmed genomic research with voluminous amount of data and elucidated lists of candidate genes/proteins. Yet, many of these genes/proteins remained not well-understood in relation to the observed phenotype. The major gap to our full understanding stems from our lack in complete gene/protein functions which, in turn, impedes researchers from assembling the sets of biomolecular mechanisms that can sufficiently explain the observed phenotype in these OMICs experiments; the knowledge of gene/protein functions is a premise necessary for delivering the big promises in personalized medicine.Despite so, experimental characterization of gene/protein function still receives insufficient attention nowadays. This can be attested by the dwindling number of characterized genes/proteins reported over the past decade. Sadly, one can expect this number to continue to grow at a slow rate.On this basis, the only viable approach is to computationally transfer function annotation of the well-studied gene/protein sequences to the less-studied or novel ones for functional hints. Implicitly, this entails that these in-silico function annotations must always be kept consistent regardless of the revisions made to function annotation databases or associated software tools used during the computations.The value of biomedical and biotechnological applications from biomolecular sequence information is generally limited by the degree of functional annotation of non-coding genomic regions, protein-coding genes and the proteins themselves , 2. WherWhile lexical analysis and certain simple sequence motif-function correlations are helpful for understanding functions for non-globular protein segments, the homology concept based on significant levels of sequence similarity  is the foundation for understanding functions of globular protein regions –5. A paiSince an element of chance implies a chance for error, unjustified annotations can arise and percolate into databases –9. Thus,Traditionally, large-scale domain function annotation is achieved through the HMMER –18 algorTaken together, the preceding conditions give rise to the utility and design rationale behind xHMMER3x2. First, the basic idea is to utilize HMMER3’s improved speed to first scan for statistically significant sequence-to-domain hits. Then, the sequence-to-full-domain alignments of these hits are reproduced through the glocal-mode of HMMER2. Overall, this approach will greatly reduce the search space for HMMER2 so that xHMMER3x2’s overall speed will be most similar to HMMER3 while retaining the more desirable glocal-mode alignments. The major aspect is then to ensure that the search space of glocal-mode HMMER2 is sufficiently replicated by HMMER3 local-mode for any given E-value cutoff as faithfully as possible. We achieve this goal through the receiver operating characteristic (ROC) calibration of the domain library to be used by xHMMER3x2 so that the E-values for every domain model from different builds are made comparable through the false-positive rate measure.In hindsight, the application of the calibration procedure on the Pfam domain library reveals three post-calibration findings that underline some of the fundamental differences between HMMER3’s search style and HMMER2’s glocal mode that justify for the necessity of xHMMER3x2 to unify both HMMER variants into a common framework. Another important outcome is that the false-positive quantification by xHMMER3x2 results in a key step for flagging discrepancies in large-scale domain annotation for manual scrutiny.The function annotation task via the HMMER algorithm is innately coupled to the domain libraries. In this work, the Pfam library (release 29) with a total of 16295 domain models was examined. Furthermore, to capture the same search space of glocal-mode HMMER2 via the local-mode HMMER3, a calibration  procedure is needed to relate the different E-values generated by either tool for the same sequences and domain models  and the consensus sequences of the remaining 16294 domain models .Briefly, this entails the objective comparison of the receiver operating characteristic (ROC) curves generated by the two HMMER builds  for each domain model. The query sequence set is specific to every domain model and it is composed of a positive set of i negative E-values are sorted in ascending order and labelled with its order from 1 to i. The order is in fact the FP value and the FPR range for this domain is, hence, between 1/16294 and i/16294.Figure −3 and 10−8.Over the full body of the Pfam library, we found in our test calculations that the false-positive (FP) values span between 1 and 626 for HMMER2 and between 1 and 2143 for HMMER3. In turn, this translates to the sampled FPR ranges of between 6.14e-5 (1 out of 16294) and 3.84-2 (626 out of 16294) for HMMER2 and between 6.14e-5 and 1.32e-1 (2143 out of 16294) for HMMER3. Then, at each sample FPR or FP points, the median E-values of the negative domain hits of the Pfam library were taken to give the median E-value ranges of between 2.13e-9 to 2.40e + 1 for HMMER2 and between 1.35e-20 and 7.1e-2 for HMMER3. A reproducible feature to note for HMMER2 software suite is the occurrence of a breakpoint due to the switch in statistical model from the extreme value distribution (EVD) to the logistic model when the similarity score grows larger. In our previous work, the median E-value of Pfam domain (release 23) for this switch occurs at 1.0e-7  which faSeparately, the upper FPR limit of HMMER2 is lower than that of HMMER3 since it detects much less false-positive hits than HMMER3. This is unsurprising since HMMER2 enforces complete sequence-to-domain alignment when operating in glocal-mode, while HMMER3 does not (and the occurrence of a sequence similar to a fraction of the domain suffices). And for the intended purpose of capturing the search space by HMMER2, the common upper limit of FPR at 5.60e-3  across the sampled range. This is consistent with the HMMER manual’s recommended E-value cutoff of < <1 for HMMER3 and <0.1  for HMMER2 to gather trusted hits. As an example, when one imposes the recommended trusted domain E-value cutoff of 0.1 for HMMER2, the corresponding FPR will be at 5.60e-3 . The latter translates into 91 false-positive hits. Meanwhile, the equivalent HMMER3 E-value will be 4.15e-7 at this FPR setting. If extreme stringency is applied to achieve zero FP , then the required HMMER2 and HMMER3 E-values will be at 2.13e-9 and at 1.35e-20 respectively  where the IQR of the two plots start to overlap. Essentially, this implies that there can be cases where the HMMER2 E-values can exhibit higher stringency than its HMMER3 equivalent, therefore bucking the general trend of HMMER3’s stringency over HMMER2. As such, a direct comparison of E-values from different HMMER builds even for comparable sequence-to-domain alignments is not always a straightforward exercise.x. These evaluated FP values, which correspond to respective FPRs (in parentheses), are x1 = 1 (6.14e-5), x2 = 5 (3.07e-4), x3 = 10 (6.14e-4), x4 = 20 (1.20e-3), x5 = 30 (1.80e-3), x6 = 40 (2.50e-3), x7 = 50 (3.10e-3), x8 = 60 (3.70e-3), x9 = 70 (4.30e-3), x10 = 80 (4.90e-3), x11 = 90 (5.50e-3) and x12 = 100 (6.14e-3). Furthermore, to enhance the stability of the model selection, a predefined set of FP values A  can generally be expected from local-style search mode (as in HMMER3) compared with the glocal mode regime (as in HMMER2) since alignments over the full domain model are not enforced in the first case. Fortunately, HMMER3 provides larger number of hits and, as a trend, better sensitivity than HMMER2. However, on the flip side, HMMER3’s tendency towards partial domain coverage is insufficient to justify for function annotation transfer since a full domain necessarily implies a unit of function. The extent of HMMER3’s partial coverage over various FPR values is quantified in the next section.In a strict sense, partial sequence-to-domain alignments generally do not justify for a sequence segment having the function of the domain hit since the completeness of a 3D structural unit (as implied by the domain unit) is a precondition for functional annotation transfer. The latter is especially relevant to HMMER3 since it can only operate in local-mode.To study the completeness of domain coverage by HMMER3, we first computed the average “coverage per domain model” by taking the total coverage of detected positive hits divided by the total detected positive hits at a FPR for a given domain model. Finally, an overall average is taken over all 16295 HMMER3-build Pfam models. Figure Although the average “coverage per domain model” remains relatively stable across all FPR values, incomplete domain coverage is expected to have different impact especially in cases of relatively long and of the shorter domain models. Figure Figure N (3066 ~ 3502) HMMER2 and M (13229 ~ 12793) HMMER3 models can be utilized for the search (where N < <M) based on the sensitivity evaluation between HMMER2 and HMMER3 models in Fig. In the first stage of xHMMER3x2, Generally speaking, since the total number of utilized HMMER3 models far exceeds that of HMMER2 (80% versus 20%), the speed of xHMMER3x2 is expected to be closer to the faster HMMER3 than the slower glocal-mode HMMER2. As such, reasonable computational speed can be achieved without sacrificing for detection sensitivity but with the caveat that the HMMER3 hits might be fragmented to various extents. Upon the selection of the appropriate model builds, the respective hmmpfam (HMMER2) and hmmscan (HMMER3) runs are executed with the respective sub-libraries of HMMER2 and HMMER3 domain models.K local-mode and L glocal-mode sequence-to-domain alignments respectively for a given query sequence. The resulting K local-mode alignments will be searched against the same K domain models but in glocal-mode HMMER2. The latter is considered as the full domain re-alignment step as intended by glocal-mode HMMER2 originally. Since the number of models used in the realignment step is expected to be much less than the total number of HMMER3 models used , this is an area of major speed gain by xHMMER3x2 over pure HMMER2. At the end of stage two, xHMMER3x2 would have produced K pairs of HMMER2/HMMER3 and L HMMER2 sequence-to-domain hits over K + L domain models for every query sequence.In the second stage, the two runs from the dominant HMMER3 and the minority HMMER2 will result in The third stage of xHMMER3x2 estimates the FPR (and FPs out of 16294) values of the domain hits based on their E-values. For each domain model, the previously calibrated HMMER2/HMMER3 ROCs are purposed to assign the particular domain hits to the appropriate FPR interval based on their E-values. The intervals are bounded by the evaluated FPR (FP) points: 6.14e-5 (1), 3.07e-4 (5), 6.14e-4 (10), 9.21e-4 (15), 1.20e-3 (20), 1.50e-3 (25), 1.80e-3 (30), 2.50e-3 (40), 3.10e-3 (50), 3.70e-3 (60), 4.30e-3 (70), 4.90e-3 (80), 5.50e-3 (90) and 6.14e-3 (100) as depicted in Fig. http://xhmmer3x2.bii.a-star.edu.sg for local installation. Since xHMMER3x2 requires the carefully-selected domain models for its proper performance, the list of calibrated models for the recent Pfam domain libraries  are also provided at the same site. Alternatively, an online webserver (for up to 1000 FASTA-formatted sequences) exists at the download site as well. When multiple sequences are inputted, the result is returned via email in tabulated form as electronically readable file containing query sequences, Pfam model hits together with their alignments E-values and standardized FPRs. Upon input of a single sequence, the HMMER2 and HMMER3 results are presented graphically-enhanced for easy comparison.The overall xHMMER3x2 workflow is implemented as a software suite in PERL language. It is freely available for download at ftp://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/complete/uniprot_sprot.dat.gz.To evaluate the real-world performance of xHMMER3x2, the sequences and Pfam domain annotations of the human UniProt/SwissProt sequences were evaluated. The source file was extracted from the public resource at To create a reference set for evaluation purpose, the sequences were re-annotated via the glocal-mode HMMER2 against the Pfam domain library (release 29). Only the re-annotated domains that overlap with the existing UniProt/SwissProt Pfam annotations were retained. The latter aims to exclude UniProt annotations that cannot be reproduced by this computation as a result of direct inheritance from previous releases, percolation errors, etc. Overall, our re-annotation resulted in 27878 (out of 27988 reported annotations) Pfam domain hits which span across 17517 sequences over 5719 unique domain models. Since the complete human proteome is a moving scientific target , the re-N = 3502, N = top 1500/1000/500 and N = 0) against the Pfam library (release 29). The top most sensitive HMMER2 domain models were ranked according to the AUC evaluation via Eq. To examine the trade-offs between computational speed and domain-detection sensitivity, these 16175 sequences were subjected to hmmscan/hmmpfam searches via HMMER3 (as reference), glocal-mode HMMER2 and 5 configurations of xHMMER3x2  which is only 1.1 times  slower than HMMER3 .For the various setting of xHMMER3x2, the fastest is achieved by running all HMMER3 models  decreases from 3502,1500,1000 to 500 (see second to fifth boxplots from left), xHMMER3x2’s sensitivity decreases from 99.8, 99.3, 99.1 to 98.6% respectively. In contrast, its speed increases accordingly from 32× (Mh2/(Mh3 + Kh3) = 101.50; see right-hand side of Eq. Mh2/(Mh3 + Kh3) = 101.23), 12× (Mh2/(Mh3 + Kh3) = 101.07) to 6× (Mh2/(Mh3 + Kh3) = 100.80) slower than HMMER3. Alternatively, this translates to 7 × (222÷32), 13 × (221÷17), 18.5 × (221÷12) and 37 × (221÷6.2) faster than HMMER2 respectively. Overall, the best compromise is achieved with the top 1000 most sensitive HMMER2 domains while the remaining as HMMER3 models. For most occasions, this configuration is the recommended and default mode in the xHMMER3x2 webserver since it achieves the best balance between domain-sensitivity (at 99.1%) and speed (12× slower than HMMER3 but 18.5× faster than HMMER2). However, when fast annotation is the main consideration, xHMMER3x2 offers the “glocal-mode HMMER3” as its fastest mode that is almost as fast as the native local-mode HMMER3.Meanwhile, the trade-offs between speed and sensitivity can be observed when the various sets of selected HMMER2 models were invoked by xHMMER3x2. Based on Fig. In summary, the xHMMER3x2 framework will always produce the glocal-mode alignments and it can be configured to achieve a balance between speed and domain-detection sensitivity. On one extreme, it can be configured for full speed when all HMMER3 models are selected, thus mimicking a ‘glocal-mode’ HMMER3. On the other end, when all HMMER2 models are deployed, xHMMER3x2 reverts back to HMMER2 with the highest level of domain-detection sensitivity.At the end stage of xHMMER3x2, the FPR (FP) values for all the sequence-to-domain hits would have been estimated accordingly. To evaluate the consistency of domain annotation, the 23622 common domain hits to both HMMER2 and HMMER3 from the preceding analyzed set of 16175 human UniProt/SwissProt sequences were further examined.We define annotation consistency as the concordance in statistical assessment of same domain hits between HMMER2 and HMMER3 for a particular sequence segment. Figure y = 0.72x, R2 = 0.91 (matched cases) and y = 0.77x, R2 = 0.22 (unmatched cases). In other words, the paired HMMER2/HMMER3 E-values are not equivalent, and non-linear to each other particularly for the unmatched cases given the bad R2 (coefficient of determination) values. Additionally, about 86.5% of the pairs show higher stringency of HMMER2 E-values over HMMER3, and vice versa for the remaining pairs. To reiterate, direct comparison using E-values to determine concordance proves to be difficult. It is also important to remember that the evaluated Pfam annotations in UniProt are generally constrained to one domain hit per sequence segment. In more realistic settings, the same sequence segment can have multiple significant domain hits. In the absence of FPR estimation, the actual error rates associated to the overlapping domain hits will simply be masked behind a set of inseparable E-values.Based on the linear regression analysis, the mathematical relationship of HMMER2 against HMMER3 E-values was found to be H2-FPH3) values were taken. The histogram of the 23622 pairs of FP difference values were shown in Fig. H2 = FPH3 or 0 (see blue dot). When the small difference of FPH2-FPH3 = ±4  are also considered since FP values are approximated to their nearest values, the overall statistical concordance of the HMMER pairs makes up about 98% (addition of another 766) of the data points. As such, FP quantification by xHMMER3x2 offers an easy and quick way to check for general concordance of the paired HMMER2/HMMER3 hits.Utilizing the estimated FP values of the paired HMMER2/HMMER3 hits, the difference  data pairs exhibited higher HMMER3 FPR than that of HMMER2 despite similar sequence-to-domain alignments. These 389 outliers, which span across 140 unique Pfam domain models . Therefore, when the scores were added up as part of the total HMMER3 E-values, the latter becomes much less significant than that of HMMER2 and hence their larger FPRs. It is likely that HMMER3 cannot justify any independent non-globular alignment segments of the domain model without significant score compensation from other alignment pieces. Unfortunately, it is beyond the scope of this work to examine why the remnant segments of these particular HMMER3 models appeared to be so heavily penalized though this sheds some light on why certain HMMER3 models were less sensitive than their HMMER2 counterparts.Protein domain function annotation for the experimentally-unverified ones is at best a computational prediction that is based on the statistical evaluation of the sequence homology concept. With every new revision of any domain model library or the associated software tool, the corresponding annotation update becomes a frequent and routine task; the task of ensuring annotation consistency with every software or library migration is rarely cared for. Simultaneously, as more new genomes are being churned out, the need for a reliable and fast protein annotation workflow, that takes into account of annotation consistency, cannot be ignored.In this work, the proposed xHMMER3x2 annotation framework is squarely aimed at alleviating both annotation discrepancies and speed issues that can arise from practical large-scale protein annotations. Notably, its scalability will allow for the future inclusion of newer HMMER variant. It simply requires the calibration of this new variant over a common domain library to allow its E-values to be compared against the others for selecting the most sensitive variant-dependent domain model amongst all. The remaining task is then to carefully balanced for the annotation speed.The function annotation task via the HMMER algorithm is innately coupled to the domain libraries. The calibration procedure used here is generally applicable to any domain libraries to normalize between the two different model builds. It aims to capture the search space of glocal-mode HMMER2 via the local-mode HMMER3, therefore relating the different E-values generated by the two HMMER variants. In a nutshell, we determined the true-positive rate  and false-positive rate  for the two HMMER builds  of any domain model.α + 16294 sequences for every domain model. The query sequence set is specific to every domain model and it is composed of a positive set of α protein sequences  and the consensus sequences of the remaining 16294 domain models . The query sequences are extracted from the Pfam domain library alignments and are downloaded from ftp://ftp.ebi.ac.uk/pub/databases/Pfam/releases/Pfam29.0/Pfam-A.full.gz.The calibration workflow is schematically shown in Fig. α + 16294, so that the E-values will corroborate with hmmpfam(HMMER2)/hmmscan(HMMER3) search results in the subsequent xHMMER3x2 workflow. At the end of stage 1, two separate computations would have been executed and produced β HMMER2 and γ HMMER3 sequence-to-domain alignments over the space of α + 16294 query sequences.Aside applying the E-values cutoff of 1000 to capture as many hits as possible, the E-values were also calculated for the database size of 16295 (via the -z option) instead of β glocal HMMER2 and γ local HMMER3 sequence-to-domain hits can be stratified into paired HMMER2/HMMER3 hits, HMMER2-only hits and HMMER3-only hits. More specifically, the paired hits are HMMER2 and HMMER3 hits where the domain model sequences overlap . Th details , 13, 14.In stage 3 of Fig. It is important to note that marginally large FPRs or certain higher E-values can nevertheless suggest correct yet distant hits rather than totally wrong ones. Thus, our computed FPRs reflect the worst case thresholds. For a domain library like Pfam with clan classification of families of related domains, this is especially true.AUCdiff is the difference between the area under curves (AUC) of HMMER2 and HMMER3, x is a fixed false positive rate. Here, the AUC value is normalized to total area under evaluation where 16294 is the total number of negative domains for any Pfam domain (release 29) model. Furthermore, to ensure that the sensitivity of an evaluated domain model does not fluctuate at different absolute numbers of false-positives (FPs), a counting mechanism is enforced to ensure that Eq. , x2 = 5 (3.07e-4), x3 = 10 (6.14e-4), x4 = 20 (1.20e-3), x5 = 30 (1.80e-3), x6 = 40 (2.50e-3), x7 = 50 (3.10e-3), x8 = 60 (3.70e-3), x9 = 70 (4.30e-3), x10 = 80 (4.90e-3), x11 = 90 (5.50e-3) and x12 = 100 (6.14e-3).In our study, the set of selected cases of false-positive hits (FP) corresponding to respective FPRs (in parentheses) are: A of selected FP value, are as follows :Finally, the total number of selected HMMER2/HMMER3 and undetermined domain models for the set Theoretically, the speed improvement of xHMMER3x2 over the glocal-mode HMMER2 algorithm can be quantified in the following manner. First, let the total run time of xHMMER3x2 for one sequence be defined as:M, N is the number of HMMER3 and HMMER2 models being used for a given FPR setting, K is the number of HMMER2 models used to re-align the HMMER3-derived sequence-to-domain alignments , th3,i and th2,i refer to the HMMER3 and HMMER2 computational time required for domain model i.Also, let the run time of HMMER2 be defined as:f) is attributed to the set of M models. Therefore, let the relevant terms in xHMMER3x2  and hmmpfam (HMMER2) searches respectively, against a database of M domain models , fastest is HMMER3 glocal mode, but only 95.7% detection sensitivity. And again, all is well explained and as far as I can judge technically sound and well prepared I think what would now help to add, is a bit more practical experience  of HMMER2 optimal in your hands and in your experience? Finally, also comment how maybe future advances or other methods would influence this annotation procedure.We thank the reviewer for his comments. We have added in the discussion to advise users to run xHMMER3x2 configured with the top 1000 HMMER2 domains as the recommended mode since this has the best balance in terms of sensitivity and speed. Also, the webserver now flags this configuration as the recommended mode under the mode selection list. The discussion on how xHMMER3x2 will be influenced by future advances or other methods is included in the conclusion.Authors’ response: HMMER3 has revolutionized HMM based search approaches by speeding up the searches greatly. However, this has come at the cost of preciseness of domain annotation that HMMER2’s glocal mode allowed. The authors attempt to remedy this compromise by combining the initial search with HMMER3 for detecting the domain followed by a HMMER2-like alignment procedure for the detected hits. Overall the glocal search procedure of HMMER2 does seem to provide more complete alignments which are often useful to ascertain domain boundaries. I have not compiled the program to run locally but testing on the server shows that it runs at comparable speed to a Pfam scan of single sequence with HMMER3.I have no particular issues with the overall methodology. But I have some practical comments as a user who pays close attention to the quality of alignments: 1) Even though the technically the alignments produced by the web-server should be similar to a local HMMER2 Pfam scan, the examples I used produced “aesthetically” poorer alignments in that they were more gap-ridden. Is this arising from the attempt to cover the entire length of the Pfam model? 2) On trying a sequence where the match genuinely should not cover the entire Pfam model the alignments appear to be artificially stretched out to cover the entire length. While these issues might not greatly negatively affect an annotation pipeline for domains on a large-scale they might still affect the use of this search for in depth domain analysis in particular cases.We thank the reviewer for his comments. For this work, all positions in the domain models’ seed multiple sequence alignments were considered during HMMER model building. On a large-scale basis, it helps to simplify the HMMER2 versus HMMER3 sequence-to-domain hit comparisons, especially for cases where missing domain model positions can vary between HMMER2 and HMMER3 due to their parameterization differences.Authors’ response: At the same time, the quality of the sequence-to-domain alignment is also highly dependent on the curated seed alignments of the domain libraries. The visually poorer alignments as observed by the reviewer for the sequence example XP_002989768.1 was generated with Pfam release 29 for a pair of SBDS (PF01172) HMMER2/HMMER3 hits and a single SBDS_C (PF09377) HMMER2 hit. When the same example was tested on Pfam release 27, a stark improvement in alignment quality was made. Also, an additional SBDS_C (PF09377.5) HMMER3 domain hit, which covers about 2/3 of the model’s length, was also recovered. On closer examination of these alignments, we noted that the full model lengths of the SBDS and SBDS_C domains in Pfam releases 27 and 29 vary by 38 (132 versus 170) and 365 (234 versus 599) positions respectively. Mainly, the artificially stretched sequence-to-domain HMMER2 SBDS_C hit as observed with Pfam release 29, was due to a much longer domain model which includes more non-globular regions.On a separate note, we wish to highlight that the seed alignments from Pfam release 28 and upwards were constructed from the reference proteome instead of the UniProt sequences. Given the same Pfam model, any differences in the seed sequences between releases will have a major impact on the final sequence-to-domain alignment quality. The implication of this sequence database change, in terms of search sensitivity/specificity, alignment quality, etc. will require a separate scrutiny.The search server could simplify the delivery of results by having the results directly presented rather than needing an additional click on the xHMMER3x2 query status page that is presented first.The search server has now simplified the delivery of results by having the results directly presented rather than needing an additional click on the xHMMER3x2 query status page.Authors’ response: The manuscript submitted by Eisenhaber and Wong describes a novel computational approach for protein function annotation on the basis of sequence similarity. A web-server is also presented, to allow biologists to use this computational methodology. The basic idea is to couple two methods. One, HMMER2, is slow and the other, HMMER3, is fast. However, HMMER2’s glocal more alignments cannot be performed by HMMER3. Moreover, HMMER2 is probably more reliable than HMMER3, especially in the E-value-based domain assignments. The scientists lead by Eisenhaber and Wong designed an empirical interface, xHMMER3x2, which seems to be reliable and fast. Empirical parameters have been adapted based on PFAM-29.http://xhmmer3x2.bii.a-star.edu.sg. This would greatly help biologists to try to understand the basics features of this method. About the computational procedure. Large use is made of the ROC curves. I have nothing again it. However, it is a better idea, in my opinion, to couple the ROC with the precision-recall curves . EventuWe thank the reviewer for his comments. As suggested by the reviewer, a simplified description of our method is now available online and accessible via the “introduction” link right after the main title of the xHMMER3x2 webserver page. Alternatively, the link is given ashttp://xhmmer3x2.bii.a-star.edu.sg/introduction.html.Authors’ response: With regard to the use of ROC in our work, it is squarely aimed at calibrating between two different builds of the same domain mode to select for the more sensitivity one to be executed in xHMMER3x2. In fact, ROC was not used for performance measure. Rather, xHMMER3x2 was evaluated simultaneously for both speed and sensitivity. Nevertheless, the precision-recall (PR) curve helps to reveal any large class imbalance problem after the domain model calibration step. As such, the xHMMER3x2 webserver now presents a link to both the ROC and precision-recall (PR) curves of every domain model in the returned results.In hindsight, we are mindful of the limitations of any ratio measures  that may mask the true characteristics of the underlying data. Therefore, xHMMER3x2 reports the absolute number of false-positives  that is associated to every sequence-to-domain alignment hit, much like how the E-values were intended for. Generally speaking, the FP range of 1 to 5 is relatively reliable for annotation purpose while any value beyond is considered for discovery purpose. As an example, Fig. depicts the ROC and PR curves  for the domain model PF03915 AIP3. From the figure, trusted AIP3 domain hits have the precision value of about 1.0 (see PR curve) with the true-positive/recall rate of about 0.4 (see ROC) as marked by FP = 1 and 5. Beyond that, the precision rate deteriorates with a synchronized increase in false-positive rate.The authors try to find a balance between two versions of HMMER (the more recent version offering a much greater speed at the price of slightly reduced sensitivity). They offer an umbrella approach combining both versions and adding estimation of false-positive rate. Although addressing a technical point, this work provides a potentially useful protein domain annotation software.I find that the analysis of FPR should be clarified. The analysis suggests that E-values for both versions of HMMER are not perfectly tuned. Is this due to specific assumptions in the statistical model of HMMER, or this is a result of the choice of the negative dataset?We thank the reviewer for his comments. Indeed, the reviewer’s first intuition is correct. The fundamental difference stems from the statistical models which gives rise to the different sets of HMMER2 and HMMER3 scoring parameters . As such, even comparable sequence-to-domain alignments can give quite different E-values. In addition, when comparing alignments between partial HMMER3 and full HMMER2 hits, only the overlapping segments were considered for re-deriving the HMMER2 scores. Despite so, these E-values are still not comparable.Authors’ response:


The lowest vitamin E activity (1.9 mg kg−1) was found in the cultivar C1PAN3118 from Ladybrand. High values of tocotrienols were obtained in whole flour of the cultivars Caledon (30.5 mg kg−1 in Clarens), Elands (35.5 mg kg−1 in Ladybrand), and Limpopo (33.7 mg kg−1 in Bultfontein). The highest tocotrienol to tocopherol ratio was found in white flour (2.83) due to higher reduction of tocotrienols than of tocopherols at fractionation. The quantity and composition of tocols can be governed in wheat flour, primarily by the selection of fractionation method at flour production, but also complemented by selection of genetic material and the growing environment. Wheat is a major component within human consumption, and due to the large intake of wheat, it has an impact on human nutritional health. This study aimed at an increased understanding of how the content and composition of tocols may be governed for increased nutritional benefit of wheat consumption. Therefore, ten South African wheat cultivars from three locations were fractionated into white and whole flour, the content and concentration of tocols were evaluated by high performance liquid chromatography (HPLC), and vitamin E activity was determined. The content and composition of tocols and vitamin E activity differed with fractionation, genotype, environment, and their interaction. The highest tocol content (59.8 mg kg Wheat is, together with rice, the major food crop in the world, supporting 20% of the daily energy for the human population . In certFractionation of the crushed grain during milling is known to have a critical implication for the distribution of many nutrients . The conThus, the aim of this study was to evaluate the effects of genotype, environment, and fractionation through milling on the quantity and composition of tocol components in South African wheat flour. Interactions as well as importance of the various evaluated factors will be investigated and conclusions drawn as related to opportunities to govern these health components in wheat flour. Ten South African bread wheat cultivars, Betta-DN, Caledon, Elands, Gariep, Komati, Limpopo, Matlabas, PAN3118, PAN3349, and PAN3377, grown in four replicates in each location, were used in the present study. The trials were conducted at three different locations in one season: Bultfontein , Ladybrand , and Clarens . The trials were planted under dryland conditions in a randomized complete block design. Trial plots consisted of five rows of 5 m length and inter-row spacing of 5 cm. Fertilization was done after soil analysis and according to normal production practices for each location. Milled grain samples were freeze dried for three days before tocol extraction. The extraction  was perfn-hexane/ethyl acetate/acetic acid (97.3:1.8:0.9 v/v/v) at a flow rate of 1.6 mL min−1. All peaks were detected by fluorescence and the wavelength of detection was set to 290 nm and emission wavelength of 330 nm. HPLC injection volume was 10 μL per injection. A standard solution was used to carry out the linearity test over the different concentration ranges (ng μL−1) close to the amount of tocols found in the samples: α-tocopherol 0.47–9.57 ng μL−1; β-tocopherol 0.23–4.7 ng μL−1; γ-tocopherol 0.65–13.1 ng μL−1; δ-tocopherol 0.62–12.4 ng μL−1; β-tocotrienol 0.54–10.82 ng μL−1. Total tocols were the sum of α-tocopherol, β-tocopherol, α-tocotrienol, β-tocotrienol, and δ-tocotrienol. A normal phase-HPLC method  with modp < 0.05, Spearman rank correlation analyses, and Principal component analyses (PCA) was carried out using the statistical package SAS . In order to explain the proportion of the contribution of variation by the environments, genotypes, and flour fractionation on the tocol composition, regression analysis was applied [Statistical evaluation applying ANOVA followed by mean comparison with Duncan post-hoc test at  applied . Vitamin applied . The Rec applied . p < 0.01). Similarly, the PCA analysis showed samples clearly clustering in two separate groups as related to flour type, although a number of whole flour samples from Ladybrand were also diverging into a separate group of whole flour samples based on lower values on the second principal component value  higher tocol concentrations than white flour, the latter having on average 40% of the concentration of whole flour . By doing so, 27% of the human daily requirement of vitamin E can be received by an average consumption of 200 g of wheat flour per day unless the tocols in the wheat flour are destroyed by harsh processing conditions.Tocotrienols are possibly even more important than vitamin E in their contribution from wheat towards a healthy diet. Content and composition of these compounds can also be governed in wheat, as large variations are present due to fractionation, cultivar, and cultivation location. However, to do so, an increased knowledge on the requirements as to total amount, composition, and ratios of the different compounds is needed.


Ube3a gene encodes an E3 ubiquitin ligase and exhibits brain-specific genomic imprinting. Genetic abnormalities that affect the maternal copy of this gene cause the neurodevelopmental disorder Angelman syndrome (AS), which is characterized by severe mental retardation, speech impairment, seizure, ataxia and some unique behavioral phenotypes. In this review article, I highlight the evolution of the Ube3a gene and its imprinting to provide evolutionary insights into AS. Recent comparative genomic studies have revealed that Ube3a is most phylogenetically similar to HECTD2 among the human HECT (homologous to the E6AP carboxyl terminus) family of E3 ubiquitin ligases, and its distant evolutionary origin can be traced to common ancestors of fungi and animals. Moreover, a gene more similar to Ube3a than HECTD2 is found in a range of eukaryotes from amoebozoans to basal metazoans, but is lost in later lineages. Unlike in mice and humans, Ube3a expression is biallelic in birds, monotremes, marsupials and insects. The imprinting domain that governs maternal expression of Ube3a was formed from non-imprinted elements following multiple chromosomal rearrangements after diversification of marsupials and placental mammals. Hence, the evolutionary origins of Ube3a date from long before the emergence of the nervous system, although its imprinted expression was acquired relatively recently. These observations suggest that exogenous expression and functional analyses of ancient Ube3a orthologs in mammalian neurons will facilitate the evolutionary understanding of AS.The human  The evolution of the brain in mammals is characterized by dramatic increases in size and complexity, especially in the neocortex  provides an excellent model for studies of gene evolution because of its brain-specific imprinting and implication in the neurodevelopmental disorder Angelman syndrome (AS). Ube3a is a homologous to the E6AP carboxyl terminus (HECT) domain-containing E3 ubiquitin ligase that was initially discovered as the protein involved in human papillomavirus E6-mediated p53 degradation -AS imprinting center (IC), which is located upstream of the adjacent SNURF (SNRPN upstream reading frame)–SNRPN  gene on the human 15q11-q13 chromosome region  that possess a single N-terminal RCC1-like domain (RLD) and a C-terminal HECT domain are also similar to Ube3a Marín, . HECTD2 Ube3a are commonly found in bilaterians including vertebrates and Drosophila  proteins demonstrate that the genomes of nerve-less basal metazoans and unicellular choanoflagellates contain core sets of scaffold protein orthologs, and these are co-expressed in a distinct cell type of Ube3a expression is imprinted in the brain but not in peripheral tissues in humans and mice  growth repressor  and a larger PWS subdomain that contains six paternally expressed genes  and two major clusters of the paternally expressed small nucleolar RNAs (snoRNAs) SNORD115 (HBII-52) and SNORD116 (HBII-85). Numbers of snoRNA genes in this region are highly variable across eutherian lineages  translocation of SNRPN next to Ube3a; (3) generation of SNURF and IC; (4) insertion and expansion of snoRNA repeats; (5) insertion of the three retroposed genes MKRN3, MAGEL2, and NDN, followed by the integration of NPAP1 in the primate lineage. A few key questions remain unsolved regarding the assembly of the PWS-AS imprinted domain. In particular, it is unclear why Ube3a was fused to SNRPN and became a part of the PWS-AS imprinting domain, and no marsupial progenitors of SNURF and IC have yet been identified  form the catalytic center of γ-secretase that processes amyloid precursor protein (APP) to produce amyloid-β (Aβ) peptide, and mutations in PSEN1, PSEN2 and APP are found in early-onset familial Alzheimer’s disease , amyloid precursor-like protein 2 (APLP2), and APP) have been identified only in multicellular metazoans, including Nematostella vectensis, and the amyloidogenic Aβ motif and γ-secretase cleavage sites are conserved only across APP orthologs from jawed vertebrates  expression of the Ube3a protein in nerve cells and its localization at functionally important subcellular compartments such as synapses; (2) colocalization and interaction with substrates that play essential roles in neuronal development and function; and (3) acquisition of genomic imprinting, leading to increased vulnerability of Ube3a to genetic damage. Thus, further studies of the expression and localization of Ube3a orthologs in primitive extant organisms, and exogenous expression and functional analyses of these orthologs in mammalian neurons, will broaden the evolutionary perspective of AS, as described for a few other synaptic proteins (Burkhardt et al., Recent studies suggest that diverse symptoms of AS are mediated by distinct circuits, cell types, substrates and downstream pathways that act at different developmental stages (Mandel-Brehm et al., MS wrote the manuscript.This work was supported in part by a grant from the Angelman Syndrome Foundation.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer DT and handling Editor declared their shared affiliation, and the handling Editor states that the process nevertheless met the standards of a fair and objective review.