Orthologous protein detection software mostly uses pairwise comparisons of amino-acid sequences to assert whether two proteins are orthologous or not. Accordingly, when the number of sequences for comparison increases, the number of comparisons to compute grows in a quadratic order. A current challenge of bioinformatic research, especially when taking into account the increasing number of sequenced organisms available, is to make this ever-growing number of comparisons computationally feasible in a reasonable amount of time. We propose to speed up the detection of orthologous proteins by using strings of domains to characterize the proteins.We present two new protein similarity measures, a cosine and a maximal weight matching score based on domain content similarity, and new software, named porthoDom. The qualities of the cosine and the maximal weight matching similarity measures are compared against curated datasets. The measures show that domain content similarities are able to correctly group proteins into their families. Accordingly, the cosine similarity measure is used inside porthoDom, the wrapper developed for proteinortho. porthoDom makes use of domain content similarity measures to group proteins together before searching for orthologs. By using domains instead of amino acid sequences, the reduction of the search space decreases the computational complexity of an all-against-all sequence comparison.We demonstrate that representing and comparing proteins as strings of discrete domains, i.e. as a concatenation of their unique identifiers, allows a drastic simplification of search space. porthoDom has the advantage of speeding up orthology detection while maintaining a degree of accuracy similar to proteinortho. The implementation of porthoDom is released using python and C++ languages and is available under the GNU GPL licence 3 at http://www.bornberglab.org/pages/porthoda.The online version of this article (doi:10.1186/s12859-015-0570-8) contains supplementary material, which is available to authorized users. Bioinformatic programs to detect orthologous proteins have become indispensable in everyday biological research. These programs allow the classification of protein sequences sharing evolutionary origins and provide a better understanding of the evolutionary forces acting on organisms.Many algorithms have been developed to predict clusters of orthologous proteins. The algorithms can be grouped into two families: tools using phylogeny-based methods ,2 and toA solution to reduce the large computational time burden is to decrease the number of pairwise comparisons. Instead of comparing all proteins against all, proteins could be clustered together into smaller groups. These smaller groups correspond to a sub-search space in comparison with the huge orthology search space created by the all-against-all pairwise comparison. However, the creation of the clusters of proteins should be fast and therefore should not rely on amino acid sequence comparisons. A coarse-grained, biologically relevant description of the protein should instead be used, for example, one based on protein domains. Domains correspond to conserved portions of sequences that can be found in different proteins and in combination with identical or other domains. A domain can be seen as an extension of François Jacobs’ statement that nature tends to reuse already existing material to create novelties . By theiIndeed, DA similarity analyses have been successfully used to study the evolutionary relationship between proteins ,9,10 froMethods to compare and group proteins based on the similarity of their DAs have already been proposed -17. All Here, a new method is proposed using the evolutionary information carried by the domain content of proteins and using a domain-domain similarity score in a continuous space. The continuous similarity scores allow non-identical domains to be similar, thus removing the problem of binary similarity measures. Two scores are introduced to compute the similarity between DAs using this continuous similarity measure between domains.First, the scores are evaluated, independently of the orthology detection method, against manually created and curated benchmarks: a dataset used by Song et al.  and the Next, the best DA similarity measure is determined, then used in combination with an orthology detection method and evaluated on a test case of 32 arthropod proteomes.The methods comprise two parts. The first part describes the development of the domain-based similarity measures between proteins. Different measures are tested on reference classifications of protein families and machine learning techniques are used for the comparison and the evaluation of the measures. The second part describes the implementation of porthoDom, the wrapper around proteinortho used to produce clusters of ortholgous proteins. The porthoDom and proteinortho methods are then evaluated on a real dataset of proteomes and compared against an external database of orthologous proteins as a blind validation.All the measures developed rely on a pairwise domain similarity matrix. The similarity matrix between domains is created based on the direct comparison of domain models and provides a continuous similarity score.The different similarity measures are then tested on three manually created and curated benchmarks and the results compared between each measure and the NeighbourhoodCorrelation (NC) method ,19, whicThe methods are compared using ROC curves and AUC scores and subsequently, the best-performing method is selected to be used as a pre-processing step of the protein orthology detection method.2 pairs of aligned HMMs. Furthermore, the probability of a true positive match is used as a similarity score, as recommended in [To improve the comparisons between domains, a domain-domain similarity matrix was developed. The matrix was created using domains in Pfam-A  version ended in . The truThe matrix is available on the web site alongside the software.A cosine similarity measure is implemented to compute the distance between two DAs of any length. The cosine measure is a similarity measure often used for high dimensional spaces. Therefore, the measure is useful in the comparison of domain contents, as the number of domains contained in the Pfam database is higher than our classical three-dimensional space.x and y as follows:The cosine measure is computed between two vectors ABCC and DBBCE, then DX and DY correspond to two sets, each one made up of the unique domains extracted from their respective proteins. The domain universe set is defined as the union of the two sets, DX={A;B;C} and protein Y to DY={B;C;D;E}. The domain universe set then becomes UXY={A;B;C;D;E}.The cosine similarity between two proteins is calculated as follows: Let X and Y be two proteins of respective domain arrangements: i are defined as follows:For each protein, a similarity vector of the size of the domain universe set is created. In this example, two vectors of length 5 will be created for protein X and protein Y respectively, noted If a domain is present in a protein, the similarity is equal to 1, otherwise the similarity will be taken as the similarity value between this domain and the most similar domain in the corresponding protein.S=0.8), and domain E, a domain sharing no similarity with any of the domains of the protein X, =0, S=0, S=0). The universe vector of protein X will then be DX, D and A are similar and E shares no similarity with the other domains. The universe vector of protein Y DY These two vectors will lead to closely related positions in domain space. In consequence, the cosine similarity between two universe vectors of two proteins with no domain in common will be 0, and the cosine similarity between two universe vectors of two proteins with the same domains but different arrangements will be 1.In the example, let domains A and D be evolutionarily closely-related domains, . The COS and MWM measures described above correspond to such O1 measures (COSO1 and MWMO1). Similarity measures with an order of 2 (O2) are introduced by using pairs of consecutive domains instead of single domains.When only the domain content is compared between two proteins, the measure will here be referred to as an order 1 measure .The mean similarities replace the values in the cosine vector equation  and the O2 parameter tend to be higher than the scores computed with an O1 parameter. This effect is due to a smaller number of comparisons in the O2 domain universe set and to the usage of a mean similarity when pairs of domains are compared.Next, the effect of a weighting scheme is evaluated for the COS and MWM measures. The purpose of applying the weighting scheme is to enhance the weight of highly similar domains in the computation of the final similarity measure. The scores produced with an AB is compared to the pair AC, and C and B do not share any similarity, the similarity between the two pairs will be 0.5 according to equation O1 parameter corresponds to a direct medium similarity score between domains. Therefore, the scores computed with an O2 parameter can result in the grouping of proteins of two different clusters by creating false links between them. To give more importance to scores computed between similar pairs with an O2 parameter, the scores are weighted depending on the order parameter used. More details are given in the Additional file If a domain pair COSO1, COSO2, MWMO1 and MWMO2 with and without a weighting scheme applied.In total, eight different similarity measures are tested: After selecting the best-performing similarity measure, the measure is implemented in a piece of software built around proteinortho. In this section, an explanation of the software implementation and testing is provided.The newly-developed method, named porthoDom, is made up of a python wrapper and a C++ program for proteinortho  (versionThe python implementation uses the numpy (version 1.8.1), and networkx (version 1.8.1) libraries. The clustering is done using the kmedoids algorithm of Pycluster (version 1.52) .Starting with a list of proteomes, proteins are annotated by the pfam_scan.pl script and the DAs of the proteins are extracted. Alternatively, the user can provide precomputed annotation files of the proteomes.From the list of DAs of all the proteins in all proteomes, a list of the unique DAs is created. The pairwise similarity between all the unique DAs is then computed.Unique DAs are clustered using the kmedoids algorithm, set to look for 100 clusters by default. The clusters correspond to sub-spaces of the initial all-against-all protein search space.Protein and amino-acid sequences corresponding to each sub-space are retrieved and processed into new files and folders onto which proteinortho can be applied independently.Proteinortho is then used on each sub-space, at the sequence level.A classical proteinortho formatted output file is created by gathering the results of all runs of proteinortho.In detail, the method follows these steps:COS01 is the default domain content similarity measure implemented in porthoDom, but other measures can be set optionally as a parameter. Default parameters of proteinortho can be changed by providing a configuration file. The wrapper can also start the orthology prediction on a precomputed dataset with different parameters, a useful and conserved original feature of proteinortho.A;B;B;C;B;C} becomes {A;B;C;B;C} where tandem repeats are collapsed.In biological datasets, a possible bias in domain content similarity computation can result from tandem repetition of domains -30. To eIn the following section, the developed similarity measures, COS and MWM, are evaluated against the NeighbourhoodCorrelation (NC) method ,19. The Two manually curated datasets are used for evaluation: a dataset used by Song et al.  (later o+) and without (SD −). The measures are then evaluated on both datasets and any potential bias induced by the presence of the kinase family in the SD + can be detected by comparison with the results obtained from SD −.In accordance with previous studies ,19, the +, the SD − and the OB datasets using receiver operating characteristic (ROC) curves and area under the curve (AUC) scores. The ROC curves and AUC scores are used to compare the true and false positive rates (TPR and FPR) of the COS, MWM and NC similarity measures. These comparisons are made using the standard python machine learning library scikit-learn [The COS, MWM and NC measures are evaluated on the SD it-learn .− .A comparison of the different measures is also performed based on AUC scores. The AUC scores are computed from the TPR and FPR of the different measures. Table + and OB in terms of AUC scores. The AUC scores from SD − are similar for all measures tested, but are slightly better for NC. The AUC score of the NC measure from SD + is lower than the AUC scores of the COS and MWM measure and confirm that the NC classification is more sensitive to the presence of the kinase family in SD +.The COS and MWM measures outperform the NC method for SD − dataset than the COS measures. However, the opposite can be seen for the SD + dataset where the COS measures have higher AUC scores than the MWM measures.AUC scores are similar between the different COS and MWM measures. When COS and MWM are compared with the same order and weighting scheme, the MWM measures perform better on the SD On the OB dataset, MWM and COS measures produce very similar AUC scores, slightly higher in general for the MWM measures.COSO1 and MWMO1 measures perform as well or better than the COSO2 and MWMO2 measures on all datasets. Moreover, the weighting scheme slightly increases the performance of the COS and MWM measures on the OB dataset.The O(V2E), with V being the number of vertices and E the number of edges, whilst the COS measure has a linear complexity. Therefore, the computational complexity of the COS measure is lower than that of MWM. Based on these results, the COS algorithm is chosen for the preprocessing stage of proteinortho.As the COS and MWM produce highly similar results, in order to select one domain content similarity measure, the complexity of both measures are compared. MWM has a complexity of Next, a direct application of the domain content similarity measure to protein orthology detection is presented with an evaluation of the predicted orthologous groups of proteins. Proteomes of 32 arthropods were downloaded from the Ensembl Metazoan website, version 20 . As the detection of orthologous proteins is not a trivial task, different methodologies will often lead to different results, so the comparisons of proteinortho and porthoDom to an external reference allow the methodologies to be evaluated strictly on their general behaviour.Predicted groups of orthologous proteins produced by proteinortho and porthoDom (abbreviated as PGOP for predicted group of orthologous proteins), are compared to the groups of orthologous proteins in the OrthoDB dataset (abbreviated as RGOP for reference group of orthologous proteins).a superset: a PGOP is a superset compared to an RGOP if all the proteins of an RGOP are present in the PGOP and some of proteins of the PGOP are not present in the RGOP.a subset: a PGOP is a subset compared to an RGOP if the proteins of the PGOP are all present in the RGOP but some other proteins of the RGOP are not in the PGOP.identical: a PGOP is identical to an RGOP.absent: a PGOP cluster is absent compared to the list of RGOP clusters if no proteins of the PGOP cluster are found in the RGOPs.new: a PGOP cluster is new compared to the list of RGOP clusters if the PGOP cluster is composed of parts of several RGOPs.A PGOP can be classified into five non-overlapping categories, depending on the relationship between the PGOP and the RGOP  and collapsing or not collapsing of domain repeats. Proteinortho is used with the default parameters in both porthoDom and the standalone version. The results of the proteinortho and porthoDom predictions are evaluated against the reference dataset and against each other.The influence of the following porthoDom parameters on these categories are evaluated: a domain content similarity cut-off , domain content similarity order . The decrease in the number of identical clusters is accompanied by an increase in the number of superset, subset, new and absent clusters.The comparison shows that setting the initial number of k-medoids from 10 to 1000 decreases the run-time (4 times faster). However, the number of identical clusters between porthoDom and proteinortho is reduced from 13255 to 11102 (Currently, no automatic method is implemented to set the initial number of k-medoids but a reasonable number seems to be one twentieth of the number of unique domain arrangements present in the dataset.Despite the differences coming from the direct comparison of the results, the comparisons against an external dataset give similar predictions. The orthology prediction power of porthoDom and proteinortho can therefore be considered as equivalent.The porthoDom wrapper is designed to be similar to proteinortho. Once the pairwise sequence comparison is generated, it can be reused with different parameters without again calling the full pipeline. A time comparison is performed on a full pipeline run between porthoDom and proteinortho. It is first important to note however, that porthoDom requires an extra step compared to proteinortho: the domain assignment using the Pfam annotation script.Table The reduction of the computational time is a clear consequence of the replacement of the all-against-all protein comparisons by sub-space searching for orthologs. Even with the speed progress made on the recent HMMER3 package , the hmmOrthologous protein detection is a crucial bioinformatic methodology for a wide range of analyses. Most methods for protein orthology detection use a pairwise Blast  all-agaiIn this paper, two new such measures of protein similarity are presented based on a cosine distance and a maximal weight matching algorithm. The measures use domain contents of proteins and a new continuous similarity score between domains to compare proteins against each other. The accuracy of the two measures has been benchmarked on curated datasets and both show an ability to efficiently group proteins from the same family together.The cosine measure, due to its better performance and its lower complexity, is chosen to be combined with the orthologous protein detection tool, proteinortho, in a method named porthoDom. It is also important to note that the developed methodology could in theory be combined with any other sequence-based orthologous proteins detection tool, such as OrthoMCL .The crucial parameter of porthoDom appears to be the number of initial domain-based clusters creating the search sub-spaces. Too high a number of initial clusters will result in the creation of too many search sub-spaces, leading to fast predictions but very different results compared to a normal proteinortho analysis. As proteinortho orthology predictions are based on the E-value similarity between protein sequences from a Blast comparison, too many initial clusters will lead to too few cluster members and can impact the resulting E-values. In contrast, too few initial clusters will give orthology predictions similar to the predictions of proteinortho alone but without a significant increase in speed.Direct comparison of the two methods shows that the majority of clusters created by porthoDom are identical to the clusters created by proteinortho. porthoDom also creates clusters that are subsets of proteinortho. This result seems to be the consequence of the proteins grouped by domain content; if a protein is not placed in the correct cluster during the domain-based step, the resulting clusters based on sequence similarity will be smaller. However, porthoDom and proteinortho results are similar when compared using an external reference database of orthologous proteins. These results emphasize that protein orthology detection is not a trivial task and that different software often produces different results. Therefore, the clustering results created by both methods should be considered equivalent.porthoDom should be seen as a new class of method for orthology detection, using both domain and amino-acid sequence similarities to create groups of orthologous proteins. Moreover, the pre-filtering step based on domain content similarity speeds up orthology detection by a factor ranging from 2.5 to 5.7.A drawback of the method is that not every protein may be assigned a domain and, consequently, many proteins may not be amenable to further processing. In the arthropod dataset, proteins with an annotated domain represent 65% of the total number of proteins. Nonetheless, the number of known domains is increasing and therefore reducing the number of proteins without annotation, as well as improving the annotation of existing proteins.Our ever-increasing knowledge of domains should positively affect the precision and efficiency of domain-based orthology detection methods.


Sulfolobus solfataricus N-terminal domain and Pyrococcus furiosus ATPase domain. We discuss three major findings: 1) a novel conformation for the A-subdomain that could play a role in MCM regulation; 2) interaction of a universally conserved glutamine in the N-terminal Allosteric Communication Loop with the AAA+ domain helix-2-insert (h2i); and 3) a recessed binding pocket for the MCM ssDNA-binding motif influenced by the h2i. We suggest that during helicase activation, the h2i clamps down on the leading strand to facilitate strand retention and regulate ATP hydrolysis.In a previous Research article , we suggDOI:http://dx.doi.org/10.7554/eLife.03433.001 Hexameric MCM rings act as the replicative DNA helicase , encirclPyrococcus furiosus MCM N-terminal domain (PfMCMN) bound to ssDNA revealed an MCM single-stranded binding motif (MSSB) that binds ssDNA  and the AAA+ domain of PfMCM, Sso-PfMCM , after it loads to encircle dsDNA, or after its activation (encircling ssDNA). The two-tiered structure is consistent with electron microscopy studies  MCMN based on electron microscopy  show severe DNA-binding and unwinding defects  (SsoMCM (Aeropyrum pernix MCM (In each subunit of our p’ (ACL)  due to ip’ (ACL) . Althoug . Specifically, the non-hexameric ring architecture or the alternative topology for the helix-2-insert region, both of which we consider artifacts of the N-terminal truncation, could fully explain the lack of unwinding by this domain. Thus, although SsoMCMN does enhance the unwinding activity of PfMCMAAA, this could simply be due to enforcing a hexameric architecture or by disallowing the unusual h2i topology in favor of the canonical topology.We tested the DNA unwinding activity of activity . The inaSso-PfMCM structure for interdomain interactions that could be conserved in native proteins. The structure reveals a fully conserved glutamine, Q198, of the ACL interacts with the main-chain amide atoms of the h2i  was predicted for MCM proteins in defining AAA+ Clade 7  fused to PfMCMAAA . It corresponds to a full-length MCM protein lacking the short (aa 967–1049) C-terminal helix-turn-helix domain  incorporating a silent NotI mutation at the junction. This plasmid served as the PCR template to generate constructs of Sso-PfMCM  and PfMCMAAA . The Sso-PfMCM chimera construct was generated by overlap extension of PCR fragments encoding the N-terminal domain of SsoMCM  and PfMCMAAA that had 93 bases of overlap at the junction. The full-length SsoMCM expression construct (pEE045.1) was prepared via PCR amplification of the full SsoMCM gene. The Q198A mutant (pJM005.5) was generated by site-directed mutagenesis of pJM001.3. DNA sequencing verified the integrity of the coding region of all constructs. Proteins were purified as described previously 2. Crystals of Sso-PfMCM with Mg/ADP grew by hanging drop by mixing 2 μl of protein:ADP solution  and 2 μl of well solution  PEG 3350). Crystals were cryoprotected by quickly passing through a 1:3 ethylene glycol:well solution and flash frozen in liquid nitrogen. Data were collected at SER-CAT beamline 22-ID. Data were collected at 1.0 Å wavelength in 0.25° oscillations for 112.5° at a temperature of 100 K. All data were scaled and integrated using the HKL-2000 software package  and two copies of a monomer of SsoMCMN, PDB 2VL6  were collected. All were strongly isomorphic with crystals grown with Mg/ADP, and no evidence of a γ-PO4 or its analog was ever detected in resulting electron density maps. We therefore conclude that all crystal datasets adopt a highly similar structure most consistent with an ADP-bound state and that the γ-PO4/analog hydrolyzes over the course of crystallization or is crystallographically disordered.Crystallographic datasets for crystals grown in the presence of several nucleotide cofactors , and we therefore include the details of its structure determination. However, the resolution of PfMCMAAA, (3.80 Å) limits the overall detail of the structure. Crystals of PfMCMAAA with Mg/ADP grew by hanging drop by mixing 2 μl protein/Mg/ADP  with 2 μl well solution . A Crystal was flash frozen in liquid nitrogen and data were collected at SER-CAT beamline 22-BM at 1.0 Å wavelength in 0.5° oscillations for 360° at a temperature of 100 K. The data were scaled and integrated using the HKL-2000 software package (Sso-PfMCM (above), the coordinates were updated, and the h2i region was rebuilt. The structure was subjected to coordinate and group B-factor refinement with strict 16-fold NCS in CNS , and reactions were incubated at 69°C for 60 min. For time-course experiments, 500 nM protein was incubated at 69°C for 1 to 90 min. Prior to the addition of ATP to initiate unwinding, time points were incubated at 69°C for 5 min to allow for thermal equilibration. For all samples, reactions were stopped by the addition of 5 μl of loading buffer containing 40% (vol/vol) glycerol, 5% (wt/vol) sodium dodecyl sulfate (SDS), and 50 mM ethylenediaminetetraacetic acid (EDTA), and a 20 μl aliquot was loaded on a 4–20% 1× TBE gradient PAGE gel  and run at 150 V for 90 min. Gel imaging was performed with a Fuji LAS-4000 using a 15-min exposure time and a SYBR-Green filter.All unwinding experiments were performed with a Y-shaped DNA substrate with a 55-mer double-stranded region, a 50-mer poly-dT 3ʹ-arm, and a 30-mer 5ʹ-arm with a fluorescein label at the 5ʹ- end. The substrate was prepared by annealing a 5ʹ-fluorescein-labeled oligonucleotide (5ʹ-TTGAACCACCCCCTTGTTAAATCACTTCTACTTGCATGCCTGCAGGTCGACTCTAGAGGATCCCCGGGTACCGAGCTCGAATTCG–3ʹ with an unlabeled oligonucleotide (5ʹ- CGAATTCGAGCTCGGTACCCGGGGATCCTCTAGAGTCGACCTGCAGGCATGCAAGTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT-3ʹ), Sigma–Aldrich, St. Louis, MO). The 85-mer oligonucleotide was identical to a previously published substrate that had been annealed to M13 plasmid ssDNA to study nwinding . HelicasSso-PfMCM hexamer is sufficiently large to accommodate dsDNA based on a channel radius minimum of 12.7 Å for its polyalanine model. For comparison, the polyalanine model of BPV E1:ssDNA . For Topoisomerase I . Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.eLife posts the editorial decision letter and author response on a selection of the published articles . An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent  and 3 reviewers, one of whom is a member of our Board of Reviewing Editors.Thank you for sending your work entitled “Analysis of the crystal structure of an active MCM hexamer” for consideration at The following individuals responsible for the peer review of your submission have agreed to reveal their identity: Michael Botchan (Reviewing editor), and James Berger and Alessandro Costa (peer reviewers).The Reviewing editor and the other reviewers discussed their comments before we reached this decision, and the Reviewing editor has assembled the following comments to help you prepare a revised submission.eLife to add something important to an already published piece. And this advance does show how the amino tier might be affected by the full deal. However, the following points need to be addressed in a revised manuscript before publication.This Research advance provides the first crystal structure of an Mcm hexamer and the use of a chimera fusing the N-terminal domain of one archeal Mcm to the AAA+ domain of another to achieve the goal is novel. The structure provides some surprises in particular that the A domain in the N-terminal tier undergoes a big shift in conformation from the monomer to the hexamer. For now this discovery is a heuristic one and the ideas that such a switch might be regulatory in going from one inactive state to another and active is intriguing. That the P104 residue which undergoes a big Psi/Phi change from one state to another is conserved (though there is no need for it) in the eukaryote Mcm 2 and 6 feeds the speculation especially since this could affect how Cdc45 interacts with Mcm2 in the CMG. We feel that this advance is what we are aiming for in 1) The helicase assay is somehow a bit disappointing and we would like to see the intact unwound substrate actually enter into the gel and not be hung up in the well. This would give one assurances that the circle is intact and that the displaced oligo is somehow not an artifact of a nuclease that might require ATP. But even more important would be a side by side comparison of this full hexamer with the AAA+ domain itself which should also be active. Would the full length hexamers be more active than the AAA+ tier alone? That might help the manuscript and support the interaction ideas put forward. Surely the authors have that hexamer in hand to compare. Mutations in a few key residues might also test easily a few points if these can be tested in the time required for a re-submission. Further the reviewers ask for this specifically.A time-course assay and an enzyme concentration titration need to be performed, and these data should be compared side-by-side with wild-type SsoMCM or PfuMcm. If the helicase activity of the chimera is severely compromised in relation to the native enzymes, then the significance attributed to certain structural states/interactions (such as the h2i-MSSB conformation) may have to be revised.eLife can be recommended.2) We agree that the idea that the different A-domain conformations might regulate interactions with other factors or control helicase function is intriguing, however, this concept is not tested. In general the impact of the glutamine-h2i interaction on MCM activities is not examined, nor is it clear how the current h2i conformation might (or might not) control ssDNA binding by the MSSB. At least some degree of follow-up that better relates the structural findings to MCM function is needed before publication in Specifically these points must be addressed: concerning the statement that “…during helicase activation, the h2i clamps down on the leading strand to facilitate strand retention and prevent premature ATP hydrolysis”: it is hard to see how this idea might work. The MMSB binds ssDNA in a manner that is perpendicular to the axis of any dsDNA duplex that might thread its way through the central helicase pore following loading. However, ssDNA isn't formed until after (or perhaps commensurate with) any activation event. Moreover, ATP hydrolysis by the MCMs has been reported to be important for activation (and hence ssDNA formation). This concept needs to be more clearly articulated or revised.3) Concerning the statement, “The relative positions of MCMN and MCMAAA differ considerably compared to crystal structures of monomeric MCM”: how so? These differences could be shown/discussed more clearly to better highlight what is new here compared to Brewster et al. or Bae et al. 1) The helicase assay is somehow a bit disappointing and we would like to see the intact unwound substrate actually enter into the gel and not be hung up in the well. This would give one assurances that the circle is intact and that the displaced oligo is somehow not an artifact of a nuclease that might require ATP. But even more important would be a side by side comparison of this full hexamer with the AAA+ domain itself which should also be active. Would the full length hexamers be more active than the AAA+ tier alone? That might help the manuscript and support the interaction ideas put forward. Surely the authors have that hexamer in hand to compare. Mutations in a few key residues might also test easily a few points if these can be tested in the time required for a re-submission. Further the reviewers ask for this specifically.A time-course assay and an enzyme concentration titration need to be performed, and these data should be compared side-by-side with wild-type SsoMCM or PfuMcm. If the helicase activity of the chimera is severely compromised in relation to the native enzymes, then the significance attributed to certain structural states/interactions (such as the h2i-MSSB conformation) may have to be revised.The Sso-PfMCM chimera shows an unwinding activity that appears faster than that of wild-type SsoMCM (see below). We therefore feel that this is a very good model system for studying the structure of the MCM hexamer.Manufacturer literature indicates that M13 plasmid DNA is too large to enter the lowest percentage TBE gel available, and we therefore adopted a smaller Y-shaped substrate for the unwinding experiments. The intact form of this Y-shaped substrate readily enters the gel and does not hang up in the well. We have performed protein concentration titrations and time-course assays for Sso-PfMCM, full-length SsoMCM, and Sso-PfMCM Q198A. Protein concentration assays that compare Sso-PfMCM with SsoMCM are now presented in 2) We agree that the idea that the different A-domain conformations might regulate interactions with other factors or control helicase function is intriguing, however, this concept is not tested. In general the impact of the glutamine-h2i interaction on MCM activities is not examined, nor is it clear how the current h2i conformation might (or might not) control ssDNA binding by the MSSB. At least some degree of follow-up that better relates the structural findings to MCM function is needed before publication in eLife can be recommended.Specifically these points must be addressed: concerning the statement that “…during helicase activation, the h2i clamps down on the leading strand to facilitate strand retention and prevent premature ATP hydrolysis”: it is hard to see how this idea might work. The MMSB binds ssDNA in a manner that is perpendicular to the axis of any dsDNA duplex that might thread its way through the central helicase pore following loading. However, ssDNA isn't formed until after (or perhaps commensurate with) any activation event. Moreover, ATP hydrolysis by the MCMs has been reported to be important for activation (and hence ssDNA formation). This concept needs to be more clearly articulated or revised.The A-domain conformation was a complete surprise because all previous crystal structures had shown a consistent, but different, conformation. We were further intrigued by a plausible mechanistic similarity to the well-known mcm5-bob1 mutation. We agree that at this stage, the possibility of alternative A-domain configurations playing a role in MCM regulation/activity is not tested, but we feel that the definitive demonstration of an alternative A-domain orientation in a MCM hexamer is sufficiently intriguing to be described in detail.We now show that mutation of the conserved glutamine (Q198A) shows no defect in the unwinding activity of Sso-PfMCM , consistOur description of a possible role of the h2i/Q198 interaction during helicase activation was confusing. This was intended to show how the AAA+ domain might participate in the activation mechanism suggested in Figure 7 of the earlier manuscript. We now show this in We feel that the figure helps clarify the relative motions that we describe for the two domains. We intend to remain neutral on several aspects of helicase mechanism. This led us to initially describe the roles of the protein modules vaguely. The design of 3) Concerning the statement, “The relative positions of MCMN and MCMAAA differ considerably compared to crystal structures of monomeric MCM”: how so? These differences could be shown/discussed more clearly to better highlight what is new here compared to Brewster et al. or Bae et al.We now show these differences explicitly in


PDZ domains are one of the most promiscuous protein recognition modules that bind with short linear peptides and play an important role in cellular signaling. Recently, few high-throughput techniques  have been applied to determine in-vitro binding specificity of PDZ domains. Currently, many computational methods are available to predict PDZ-peptide interactions but they often provide domain specific models and/or have a limited domain coverage.Here, we composed the largest set of PDZ domains derived from human, mouse, fly and worm proteomes and defined binding models for PDZ domain families to improve the domain coverage and prediction specificity. For that purpose, we first identified a novel set of 138 PDZ families, comprising of 548 PDZ domains from aforementioned organisms, based on efficient clustering according to their sequence identity. For 43 PDZ families, covering 226 PDZ domains with available interaction data, we built specialized models using a support vector machine approach. The advantage of family-wise models is that they can also be used to determine the binding specificity of a newly characterized PDZ domain with sufficient sequence identity to the known families. Since most current experimental approaches provide only positive data, we have to cope with the class imbalance problem. Thus, to enrich the negative class, we introduced a powerful semi-supervised technique to generate high confidence non-interaction data. We report competitive predictive performance with respect to state-of-the-art approaches.Our approach has several contributions. First, we show that domain coverage can be increased by applying accurate clustering technique. Second, we developed an approach based on a semi-supervised strategy to get high confidence negative data. Third, we allowed high order correlations between the amino acid positions in the binding peptides. Fourth, our method is general enough and will easily be applicable to other peptide recognition modules such as SH2 domains and finally, we performed a genome-wide prediction for 101 human and 102 mouse PDZ domains and uncovered novel interactions with biological relevance. We make all the predictive models and genome-wide predictions freely available to the scientific community.The online version of this article (doi:10.1186/1471-2164-15-S1-S5) contains supplementary material, which is available to authorized users. Protein-protein interactions are the most essential cellular process in eukaryotes that involve many important biological activities such as signal transduction, maintaining cell polarity etc –3. Many β strands and 2 α helices. The second β strand, second α helix and a GLGF loop of the PDZ domains collectively form the binding pocket, which recognizes the hydrophobic C-terminal peptide of a target protein [X[T/S]Xφ-COOH (Class I motif), XφXφ-COOH (Class II motif) and a minor X[D/E]Xφ-COOH (Class III motif), where X represents any natural amino acid and φ represents hydrophobic amino acid [PDZ domains are one of the most widespread peptide recognition modules (PRMs) that predominantly found in signaling proteins in multi-cellular organisms and play an important role in the establishment of cell polarity, neuronal signaling, protein trafficking etc –3. It ha protein , 10. Alb protein . In earlino acid , 12. Nevino acid , 14.To determine binding specificity of PDZ-peptide interactions, several high-throughput techniques  have been employed , 16. WhiChen et al. used structural information from a reference PDZ-peptide complex structure to build a Bayesian model for predicting PDZ-peptide interactions [We can distinguish two types of approaches to tackle this problem. The first type uses structural information to improve specificity. It was recently observed that structural information can be used to improve the prediction of binding sites for DNA-binding or RNA-binding proteins  in 2007, which uses in effect a mixture of position specific scoring matrices (PSSMs) [Li et al. applied a nearest neighbor approach (based on domain sequence identity and ligand binding specificity) to extend the training set for each domain, building models for 174 PDZ domains in total [The other type consists of approaches that cope with the problem of few data points by combining the experimental evidence for domains with similar binding preference. As was recently shown by two high-throughput experiments, PDZ domains can be classified into 16 different specificity classes , 16 with (PSSMs) . However (PSSMs) , 27. Li H. sapiens), mouse (M. masculas), fly (D. melanogaster) and worm (C. elegans), using a machine learning approach. The importance of our method is five fold: (i) clustering of a very large set of PDZ domains based on their sequence identity. This comprehensive study allowed us to construct specialized models for 43 PDZ families, covering 226 PDZ domains, which are more accurate compared to the state-of-the-art and offers models for the largest set of PDZ domains to date. (ii) The data obtained from high-throughput experiments are often found to lack of non-interacting data (i.e. negative data) and thus lead to a great class imbalance problem. Previous research showed that the performance of many machine learning methods are significantly poorer on highly imbalanced data [In this work, we present a cluster based prediction of PDZ-peptide interactions for human (ced data –30. To dH. sapiens), 16597 mouse (M. masculas), 3182 fly (D. melanogaster) and 3382 worm (C. elegans) proteins. A large set of 548 PDZ domains, comprising 271 human, 234 mouse, 27 fly and 16 worm PDZ domains, were derived.For retrieving all the annotated PDZ domains from human, mouse, fly and worm proteomes, we used UniProtKB/Swiss-Prot database, which is a well known manually curated and reviewed database . At the Li et al. have proposed that PDZ domain pairs with greater than 50% sequence identity share similar binding specificity [We clustered all the available PDZ domains using Markov clustering algorithm (MCL) based on their global sequence identity . MCL is cificity . Thus, wcificity . In the D value) was applied to determine the positive and negative classes [D cutoff of 100 µM. We used same KD cutoff value as mentioned in [We used a protein microarray screening data to analyze the specificity of PDZ domains, comprising 157 mouse PDZ domains and 217 fluorescently labeled genome-encoded peptides [For the human phage display experiment, we considered a total of 1389 interactions that involved 54 human PDZ domains and 1211 peptides . Note thFrom PDZBase , which iFor retrieving all the available PDZ-peptide complex structures, we used Protein Data Bank (PDB), which contains experimentally solved protein structures . At the We have combined all the positive and negative interaction data from dataset I, dataset II and dataset III. Five C-terminal residues of the peptides were considered since they are the most important for determining PDZ domain specificity , 16. FinDatasets derived from high-throughput experiments usually suffer from a lack of reliable negative interaction data. In our study, we were only able to obtain the negative interaction data from a microarray experiment although the dataset had an imbalanced problem. Other data sources (i.e. phage display and PDZBase) provide only positive interaction data. Previous study showed that machine learning methods work poorly when the dataset is highly imbalanced –30. In oFinally, the predicted unlabeled peptides having the probability of 0.5 to 0.8 towards the negative class were considered. We ignored very high scoring (probability more than 0.8) predicted negative peptides since they might be very far from positive class and therefore could produce low quality models. We randomly chose negative data from the pool of predictive negatives, added them to the training data, and re-trained the classifier. In general, there was five times more negative data than positive data .Note that we need both positively and negatively labeled data to apply the described SSL approach since we need to train the base classifier with both positive and negative data. Hence, we could only employ the SSL approach to domains that occur in dataset I as it contains both classes. For those PDZ domains where only the positive data was available, we chose the negative data randomly from C-terminal peptides of the respective organism from UniProtKB/Swiss-Prot . Note thPrevious studies show that the C-terminal residues of a peptide are the most important for PDZ-peptide binding specificity . We follx, living in a 20 × 5 = 100 dimensional space. I.e., for each position, we reserved 20 dimensions (one for each amino acid type) and encoded the amino acid type with a 1 in the corresponding dimension and 0 elsewhere.In the sequence-based feature encoding, a peptide sequence was mapped into a binary vector Chen et al. [MAFFT [x, living in a 20 × 20 = 400 dimensional space. All the position pairs were then encoded in a binary vector of size 400 × n, where n is the number of binding pairs. Finally the sequence-based encoding was concatenated with the contact-based encoding, which produced a binary vector of size 100 + 400 × n.For the contact-based feature encoding, we used an approach similar to the one described by n et al. . Here, t. [MAFFT . We thenD, we have compiled a data set encoded as a set of pairs ,.., where, xi is the binary feature vector for peptide Pi with the class label ci ∈ {−1, 1}. The class label is +1 if the domain D interacts with peptide Pi and -1 otherwise.For each domain We formulated a learning problem for each PDZ domain family. The predictive performance for each problem was assessed by computing 5 measures: sensitivity, specificity, precision, area under the receiver operating characteristics curve and area under the precision recall curve. These are defined as: In recent years, enormous amounts of interaction data have been generated by various high-throughput experiments thus computational methods are invaluable to analyze these data. One of the major problems while analyzing these data is sufficient amounts of data may be available for certain domains but completely missing or much less available for another domain. For example, there are only two positive interactions for PDZ 1 and PDZ 2 domains of human DLG2 and DLG4 available in the literature. To overcome this limitation our first goal was to combine the PDZ domains that are similar in substrate specificity and therefore build a single classifier for these similar domains. Hence, this approach enables us to make separate models for each domain family.MAFFT and built a phylogenetic tree [Material and methods for details). All the available PDZ domains (548) were classified into 138 families. Out of all 548 domains, we were unable to classify 33 PDZ domains since the sequences are too diverse. The biggest family consists of 20 PDZ domains from human, mouse and fly. Finally, we have mapped the 138 families on the phylogenetic tree of all PDZ domains for better visualization  annotated in UniProtKB/Swiss-Prot by using tic tree . We thentic tree . MCL is tic tree  . Families with at least 10 positive interaction data were considered for modeling. In summary, we built models for 43 families covering 226 PDZ domains.For the sequence-based modeling approach, we followed the literature and considered five C-terminal residues of peptide sequences as an input, where the position of C-terminal residue is given P0 and going upstream P-1, P-2 and so on. To define the positional features, we extracted amino acids from peptides and mapped them into a binary vector Chen et al. in 2008 [n, where n is the number of binding pairs . We concatenated sequence-based features with contact-based features and finally, we built models for 10 families covering 70 PDZ domains.The contact-based modeling approach combines the peptide sequence information with PDZ-peptide complex structure information. We followed the similar approach taken by  in 2008 . For thelight software was used to build the SVMs [We employed a Gaussian kernel support vector machine to build predictive models . SVMlighthe SVMs . We usedγ and the cost parameter C) for each fold were optimized using 5-fold grid search method over the training sets. See Additional file For the sequence-based approach comprising models for 43 families covering 226 PDZ domains, only 22 families covering 136 PDZ domains met this criteria and therefore used in cross-validation. The hyper parameters  of sequence-based and contact-based approaches   and DomPφ) over a scoring threshold (τ), specific for each domain [i, if φi/τi>1. Table 1 lists the scores for all 20 validated interactions as calculated by MDSM and by our method.In another experiment, we tested our method with MDSM on a validated dataset. We could not test DomPep since many of the test instances were present in the DomPep training set and hence a fair comparison was not possible. The test data was retrieved from an experimentally validated database, called PDZBase . We comph domain . A peptiThe advantages of our approach compared to aforementioned tools are threefold: i) using an accurate clustering approach allows our method to achieve a higher domain coverage; ii) we have employed a powerful semi-supervised learning technique to identify high confidence negatives, which increase the model quality; and iii) our approach is based on a non-linear model to address the issue of the correlation between amino acid positions.We performed a genome-wide prediction of PDZ domain mediated interactions in human and mouse proteomes. The idea was to identify novel interaction partners for human and mouse PDZ domains. In order to do so, we extracted the set of peptides from UniProtKB/Swiss-Prot database , releaseIUPred algorithm over the full-length protein sequence to get a disorder score between 0 and 1 for each residue of a protein [Akiva et al. [IUPred score less than 0.4.Previous study showed that PDZ domains have a tendency to bind with intrinsically unstructured proteins (IUPs) , thus we protein . Finallya et al. . To thisAs a second filter, co-cellular localization was applied to avoid unlikely interactions. More clearly, we have considered only those interactions where the peptide containing proteins and the PDZ containing proteins share at least one cellular localization term annotated in Gene Ontology Database .Finally, the eligible peptides were scored by the trained models and sorted according to SVM scores. We have observed C-terminal peptide (IETHV) from Connector enhancer of kinase suppressor of ras 2 protein  was targeted by 40 PDZ domains, represented 16 families, in human and mouse. See Additional file In our comprehensive study, we propose a cluster based computational method to accurately predict the binding partners of PDZ domains using support vector machine (SVM). First, we used an efficient MCL algorithm to cluster all PDZ domains from different organisms and thereafter built prediction models for each PDZ family found by our clustering. Our method offers the largest number of prediction models for PDZ domains to date. We showed that our clustering method maximizes the training datasets, which is important to build powerful prediction models. In the clustering method, we combined all the PDZ domains that share high sequence identity and therefore have similar binding specificity. There are, however, additional cases where the binding preference is very similar despite a low sequence identity. For example, MAGI1-5 and MAGI3-4 domains share similar specificity despite of low sequence identity (24% in mouse) . Since tModels are available under the URL:http://www.bioinf.uni-freiburg.de/Software/PDZPepInt/PDZPepInt.tar.gzGenome-wide predictions are available under the URL:http://www.bioinf.uni-freiburg.de/Software/PDZPepInt/Genome-wide-predictions.tar.gzDatasets are available under the URL:http://www.bioinf.uni-freiburg.de/Software/PDZPepInt/PDZ-datasets.tar.gzOperating systems: LinuxAdditional file 1: Figure S1: Peptide logos for each PDZ domain cluster. Figure S2: Performance comparison of sequence-based and contact-based approach. Table S1: Performance of sequence-based approach. Table S2: Performance of contact-based approach. Table S3: List of proteins that targeted by highest number of PDZ domains in human. Table S4: List of proteins that targeted by highest number of PDZ domains in mouse. (PDF 336 KB)


H. pylori Csd3 (HP0506), together with other peptidoglycan hydrolases, plays an important role in determining cell shape. Its crystal structure in the latent state is reported. Helicobacter pylori is associated with various gastrointestinal diseases such as gastritis, ulcers and gastric cancer. Its colonization of the human gastric mucosa requires high motility, which depends on its helical cell shape. Seven cell shape-determining genes  have been identified in H. pylori. Their proteins play key roles in determining the cell shape through modifications of the cell-wall peptidoglycan by the alteration of cross-linking or by the trimming of peptidoglycan muropeptides. Among them, Csd3  is a bifunctional enzyme. Its d,d-endopeptidase activity cleaves the d-Ala4-mDAP3 peptide bond between cross-linked muramyl tetrapeptides and pentapeptides. It is also a d,d-carboxypeptidase that cleaves off the terminal d-Ala5 from the muramyl pentapeptide. Here, the crystal structure of this protein has been determined, revealing the organization of its three domains in a latent and inactive state. The N-terminal domain 1 and the core of domain 2 share the same fold despite a very low level of sequence identity, and their surface-charge distributions are different. The C-terminal LytM domain contains the catalytic site with a Zn2+ ion, like the similar domains of other M23 metallopeptidases. Domain 1 occludes the active site of the LytM domain. The core of domain 2 is held against the LytM domain by the C-terminal tail region that protrudes from the LytM domain. It colonizes approximately half of the world’s population and its infection of the gastric mucosa has been associated with various diseases of the upper gastrointestinal tract, such as chronic gastritis, peptic ulcer, mucosa-associated lymphoid tissue (MALT) lymphoma and gastric adenocarcinoma . However, increasing antibiotic resistance requires new therapies and the discovery of new antibiotics  and N-acetylmuramic acid (NAM) disaccharides, with a pentapeptide linked to NAM  activity, which cleaves the d-Ala4-mDAP3 peptide bond in cross-linked dimers of muramyl tetrapeptides and pentapeptides  indicated that Csd3 has an additional d,d-carboxypeptidase  activity that cleaves the d-Ala4-d-Ala5 bond of the muramyl pentapeptide to produce the muramyl tetrapeptide and d-Ala  of the peptidoglycan to produce the muramyl dipeptide (muramyl-l-Ala1-γ-d-Glu2) and mDAP. Although both Csd3 and Csd4 play essential roles in determining the helical cell shape, their amino-acid sequences are unrelated. Genetic interaction studies between the csd3 and csd4 genes revealed that Csd4 d,l-CPase activity does not depend on Csd3 CPase/EPase activity and vice versa . It consists of three domains: domain 1 (residues Glu42–Ile124), domain 2 (residues Ile125–Gly228 and Ala360–Phe403) and the C-terminal LytM domain (residues Phe229–Thr359). Csd3 domain 1 and the core of domain 2 (residues Ile125–Gly228) share a common fold despite a very low level of sequence identity. The LytM domain of Csd3 is structurally similar to the corresponding domains of other MEROPS M23 family metallopeptidases. Substrate binding to the active site of the LytM domain is blocked by domain 1 in our structure, suggesting that domain 1 is the inhibitory domain and that our Csd3 structure is in the latent state. The core of domain 2 is held stably against the LytM domain by the C-terminal extended tail region that protrudes from the LytM domain. This work could serve as a foundation for the discovery of novel inhibitors that could prove to be helpful in fighting infections by the major human pathogen H. pylori.To provide a structural framework for a better understanding of the molecular function of 2.2.1.Δ41) of H. pylori Csd3 (HP0506 from strain 26695) was PCR-amplified and cloned into the expression vector pET-21a(+) (Novagen) using the NdeI and XhoI restriction-enzyme sites. The recombinant protein, which was fused to a hexahistidine-containing tag (LEHHHHHH) at its C-terminus, was overexpressed in Escherichia coli Rosetta 2(DE3)pLysS cells. The cells were grown at 37°C in Terrific Broth culture medium containing 50 µg ml−1 ampicillin. Protein expression was induced by 0.5 mM isopropyl β-d-1-thiogalactopyranoside and the cells were incubated for an additional 15 h at 30°C. The cells were harvested by centrifugation at 5600g for 15 min at 4°C and subsequently lysed by sonication in ice-cold buffer A  which was supplemented with 1 mM phenylmethylsulfonyl fluoride, 60 mM ammonium chloride and 15 mM magnesium acetate. The lysate was centrifuged at 36 000g for 1 h at 4°C to discard the cell debris. The supernatant was applied onto a HiTrap Chelating HP affinity-chromatography column  which was previously equilibrated with buffer A. The column was eluted with a linear gradient of imidazole from 50 mM in buffer A to 500 mM in buffer B, which consists of 20 mM Tris–HCl pH 7.9, 500 mM NaCl, 500 mM imidazole, 10%(v/v) glycerol. The Csd3 protein eluted at 125−150 mM imidazole concentration was further purified by gel filtration on a HiLoad 16/60 Superdex 200 prep-grade column  at two different salt conditions, either with buffer C  or buffer D . Two different batches of Csd3 yielded different crystal forms, as described below. The purified protein was homogeneous as analyzed by SDS–PAGE. Fractions containing recombinant Csd3 were pooled and concentrated to 10 mg ml−1 (0.24 mM) for crystallization using an Amicon Ultra-15 Centrifugal Filter Unit (Millipore).The gene encoding the N-terminally truncated form  PEG 4000, 20%(v/v) glycerol] and 0.4 µl purified protein in buffer C. Elongated rectangular crystals grew to approximate dimensions of 0.3 × 0.3 × 0.3 mm within a few days. For form 2 crystals, the recombinant Csd3Δ41 protein purified in buffer D was pre-incubated with buffer D supplemented with 1 mM zinc chloride under ice for 30 min prior to crystallization setup. Sitting drops were prepared by mixing 0.3 µl reservoir solution  and 0.3 µl protein solution. Hexagonal bipyramidal crystals grew to approximate dimensions of 0.2 × 0.2 × 0.3 mm within a few days.Crystals were grown at 23°C by the sitting-drop vapour-diffusion method using a Mosquito robotic system (TTP Labtech). We obtained two types of native crystals (form 1 and form 2) under different crystallization conditions. For form 1 crystals, the sitting drops were prepared by mixing 0.4 µl reservoir solution [160 m2.3.P212121, with unit-cell parameters a = 62.6, b = 112.1, c = 112.9 Å. Assuming the presence of two Csd3 monomers in the asymmetric unit, the Matthews coefficient and solvent content are 2.33 Å3 Da−1 and 47.2%, respectively. Form 2 crystals were soaked for several seconds in a cryoprotectant solution consisting of the reservoir solution supplemented with 25%(v/v) glycerol and were flash-cooled in a nitrogen-gas stream at 100 K. Form 2 crystals diffracted to 1.98 Å resolution and belonged to space group P6522, with unit-cell parameters a = b = 91.5, c = 187.0 Å. Assuming the presence of one Csd3 monomer in the asymmetric unit, the Matthews coefficient and solvent content are 2.65 Å3 Da−1 and 53.6%, respectively.Native X-ray diffraction data were collected on beamline BL-5A at Photon Factory, Japan using an ADSC Q315 CCD detector and on beamline BL-44XU of SPring-8, Japan using a DIP6040 imaging-plate/CCD hybrid detector Table 1. Form 1 Δ41 protein was not expressed in E. coli. Therefore, we prepared a Pt derivative of form 2 crystals by soaking them for 3 min in 2 µl of a heavy-atom-containing cryoprotectant solution, which consisted of the reservoir solution supplemented with 25%(v/v) glycerol and 30 mM K2PtCl4. Single-wavelength anomalous diffraction (SAD) data were collected at 100 K from the Pt-derivative crystal at a wavelength of 1.0720 Å using an ADSC Q270 CCD detector at the BL-7A experimental station of Pohang Light Source, Republic of Korea. The raw data were processed and scaled using the HKL-2000 program suite . Anomalous difference maps were calculated using FFT  and three different protein concentrations . The protein concentration of the recombinant Csd3Δ41 was estimated using ∊280 nm = 39 770 M−1 cm−1 and a molecular mass of 42 562 Da, which includes the C-terminal hexahistidine-containing tag. The partial specific volume of the protein and the buffer density were calculated using SEDNTERP  despite extensive screening of crystallization conditions. This prompted us to try overexpressing a number of shorter constructs encompassing residues 40–403, 40–397, 40–393, 40–390, 42–403, 42–397, 42–393, 42–390, 45–403, 45–397, 45–393 and 45–390. Among these, only the N-terminally 41-residue truncated Csd3 (Csd3Δ41) comprised of residues 42–403 was expressed in soluble form, and it yielded two different forms  of well diffracting crystals  in a disordered loop near the metal-binding site as well as four residues Gly333–Thr336 and two histidines at the end of the C-terminal affinity tag. The model of the form 1 crystal was refined at 2.00 Å resolution to an Rwork and Rfree of 20.3 and 25.6%, respectively (Table 1A and B) of Csd3Δ41 in the asymmetric unit. Chains A and B are related by noncrystallographic twofold symmetry. In both chains A and B, four residues Gly333–Thr336 and the C-terminal affinity tag (LEHHHHHH) are disordered. Chains A and B are highly similar to each other, with an r.m.s. deviation of 0.67 Å for 359 Cα atoms. However, they show larger structural deviations from the chain of the form 2 crystal, with r.m.s. deviations of 1.61 and 1.85 Å for 353 Cα atoms in chains A and B, respectively. The largest deviations occur in the η1 and α6 helices, with deviations of 6.65 and 5.58 Å at the Cα atoms of Pro167 and Gly366, respectively . The observed structural variation is likely to be owing to the inherent flexibility of these regions and also owing to different crystal contacts.We have determined the structure of Csd341 Fig. 1 using SAy Table 1. The fory Table 1. The for3.2.2 per monomer (6.0% of the monomer surface area), whereas the largest buried surface area in the form 2 crystal is 710 Å2 per monomer (3.8% of the monomer surface area). The bulk of crystal interfaces have areas below 1000 Å2, with very few representatives above this value , domain 2 (residues Ile125–Gly228 and Ala360–Phe403) and the LytM domain (residues Phe229–Thr359)  of Csd3Δ41 resembles an outer-membrane protein from Neisseria meningitidis  and a putative lysostaphin peptidase from Vibrio cholerae   Fig. 1. A struca). However, their charge distributions on the surface are distinct from each other . The C-terminal helix (α6) and β-strand (β22) protruding from the LytM domain are tightly associated with the core of domain 2, covering another face of the β-sheet and extending the β-sheet, respectively.Domain 1 of Csd3 has an α/β fold consisting of a five-stranded antiparallel β-sheet (β1↓–β2↑–β3↓–β4↑–β5↓) and three short α-helices Fig. 1. A clustDALI search was performed separately against domain 1 , the highest structural similarity is observed to the corresponding domains from VC0503 and NMB0315, with Z-scores of 7.6 and 5.0, respectively. Besides these, domain 1 displays a very remote structural similarity to single-chain monellin , with a Z-score of 3.5. When the DALI search was performed separately against the core of domain 2 , the highest structural similarity is observed to the corresponding domains from VC0503 and NMB0315, with Z-scores of 14.6 and 14.5, respectively. Besides these, domain 2 exhibits a very remote structural similarity to the core of a single-stranded DNA-binding protein from Aeropyrum pernix K1 , with Z-scores of 4.5 and 4.4, respectively.When a 10-helix (η3)  of Csd3Δ41 exhibits significant structural similarity to the corresponding domains of the M23B metallopeptidases, such as NMB0315  and VC0503 . It is also structurally similar to the LytM domains of Staphylococcus aureus glycylglycine endopeptidase, another M23B metallopeptidase , and the virulence factor LasA from Pseudomonas aeruginosa, an M23A metallopeptidase . M23A metallopeptidases are distinguished from the more numerous M23B enzymes by structural features that include disulfide bridges and the possession of an additional C-terminal subdomain as well as alterations to the active-site region that are manifested in differences in the sequence spacing between the His and Asp residues of the HxxxD motif of the M23B family  and a smaller, three-stranded antiparallel β-sheet (β14↓–β19↑–β17↓). These β-sheets share a long strand (β17), which is bent like the letter J. Another long strand (β20) associates with a short strand (β21) to form a mini, antiparallel β-sheet (β20↑–β21↓), which is followed by a 33) Fig. 1. As expeAfter leaving the LytM domain, the polypeptide chain is connected to a C-terminal α-helix (α6) and a short β-strand (β22) that fold back onto the core of domain 2, making a tight interaction with the core of domain 2 Fig. 3. The α6 3.4.2+ ion by calculating anomalous difference maps using anomalous data collected at the zinc absorption edge of 1.2820 Å . In all Csd3Δ41 structures we observed a tetracoordinated Zn2+ ion in the active site, with the expected three amino-acid ligands  and one amino-acid ligand (Glu74∊2) from the α3 helix of domain 1  are 2.9 and 2.0 Å, respectively. This coordination differs from that in the uncomplexed structure of LasA from P. aeruginosa  with three conserved metal ligands and two conserved water molecules. The distances between Zn2+ and the water O atoms (Wat-1 and Wat-2) are 2.1 and 2.7 Å, respectively. In the tartrate-complexed structure of LasA, tartrate O atoms occupy nearly identical positions as both Zn2+-coordinated water molecules, with the distances between Zn2+ and tartrate O atoms being 2.5 and 2.1 Å, respectively. However, the Zn2+ coordination in the tartrate-complexed structure has been described as a tetrahedral geometry . Loop I of Csd3 shows the most significant deviation among the homologues. In form 1 crystals, it is involved in crystal-packing interactions by forming sulfate-mediated salt bridges . In form 2 crystals, it is not involved in crystal packing and is disordered. Therefore, we conclude that the observed structural difference of loop I is largely owing to differences in crystal packing and also to its inherent flexibility. In all structures of Csd3Δ41, three residues (Gly333-Leu334-Ser335) of loop III are disordered. Loop III has significant sequence conservation and immediately precedes the HxH motif , a phosphate ion, as a substrate candidate, is fixed in space in the active site by multiple hydrogen bonds to Asn286 , loop II (the β15–β16 loop), loop III (the β19–β20 loop) and loop IV (the β20–β21 loop) Figs. 2 and 4 ▶.3.5.2+ coordination sphere. Several negatively charged residues from domain 1  and positively charged residues  as well as the Zn2+ ion from the LytM domain form strong salt bridges at the interface between these domains. As described above, the Zn2+ ion in the active site of the LytM domain is tetrahedrally coordinated by the three conserved residues (His259 and Asp263 of the HxxxD motif and the second histidine His341 of the HxH motif) of the LytM domain as well as the nonconserved Glu74 in helix α3 of domain 1. There is a hydrogen bond between Glu74 and the conserved His306 of the LytM domain. Asp72 interacts with Arg257 in loop I of the LytM domain by forming a salt bridge. Glu78 forms a salt bridge with Arg301 in the β17 strand of the LytM domain; it also makes a hydrogen bond to Tyr289 in loop II of the LytM domain. In addition, Asp105 in the β3–β4 loop of domain 1 forms a salt bridge with Arg349 in the β21 strand of the LytM domain. Tyr65 in the α2 helix of domain 1 forms a hydrogen bond to the main-chain N atom of Gly288 in loop II of the LytM domain. Ser103 in the β3–β4 loop of domain 1 is hydrogen-bonded to Tyr260 in the β13 strand of the LytM domain. On the basis of these extensive interactions between domain 1 and the LytM domain, we suggest that Csd3Δ41 is in an auto-inhibited state in the crystal. This is similar to the occlusion of the active site by an N-terminal segment in structurally related three-domain proteins in the inhibited conformational state . In NMB0315, the N-terminal short β3–β4 loop stretches into the active site and tightly associates with the catalytic domain  without any ordered water molecules. Glu74 is not conserved, whereas His259, Asp263 and His341 are conserved  and it was suggested that auto-inhibition is a critical feature of the regulation of peptidoglycan amidases required for cell division in Gram-negative bacteria  using mass analysis. The muramyl pentapeptide (5 mM) was incubated at 37°C for 150 min with recombinant Csd3Δ41 (5 µM) in the presence of 2.5 mM metal ion (either Zn2+ or Mg2+) or 2.5 mM EDTA. We used two different buffers for dissolving the reaction mixture: (i) 20 mM HEPES pH 7.9, 100 mM NaCl or (ii) 20 mM sodium phosphate pH 6.0. No reaction product was observed in all of the reaction conditions that we tested. As a positive control, we could detect the muramyl tetrapeptide product when the muramyl pentapeptide was incubated with Enterococcus faecium VanY. This result is consistent with a latent, inactive state of Csd3Δ41 in the crystal.We have determined the first crystal structure of Csd3 from H. pylori Csd3 may occur by autoproteolysis or may require proteolytic cleavage by other endopeptidases to free the catalytic LytM domain from the inhibitory domain 1. When we tried limited proteolysis with trypsin, chymotrypsin and pepsin, no discrete cleavage at specific sites took place. To physically remove the inhibitory domain 1 from Csd3, we tried to overexpress various constructs  that lacked helix α3. However, none of them was expressed in E. coli in a soluble form. Alternatively, a large conformational changed induced by an allosteric regulator may lead to opening of the active site. For instance, a large conformational change culminates in opening of the active site to permit substrate entry when an allosteric site ∼60 Å distant from the d,d-transpeptidase active site is occupied in penicillin-binding protein 2a (PBP2a) from S. aureus  was not possible because this substrate is neither commercially available nor easy to synthesize.To provide details of the interaction of the substrate with Csd3, we soaked the form 1 and form 2 crystals in a cryoprotectant solution that was supplemented with the muramyl pentapeptide. No electron density for the substrate peptide was observed. We also tried to co-crystallize Csd3H. pylori Csd1 (HP1543), Csd2 (HP1544) and Csd3 (HP0506) contain a LytM domain of the M23B metallopeptidase family; they all catalyze the same reaction as EPases. Csd2 could be a nonpeptidase homologue of the M23B family, as the first histidines of the characteristic HxxxD and HxH motifs in its LytM domain are mutated to Glu at residue 165 and Lys at residue 246, respectively. Csd3 exhibits CPase activity, while Csd1 may or may not have such an activity. H. pylori Csd3 (with 403 residues) is considerably longer in sequence compared with Csd1 (312 residues) and Csd2 (308 residues). Sequence alignment of Csd1 and Csd2 with either domain 1 or the core of domain 2 of Csd3 reveals that the proregions of both Csd1 and Csd2 have higher levels of sequence similarity to domain 1. The significantly shorter lengths of Csd1 and Csd2 indicates the presence of only one domain in their proregions. This may affect their CPase activity. However, no information on the three-dimensional structures of Csd1 and Csd2 is available at present and thus structural studies on them are required to compare the structures of Csd1 and Csd2 with Csd3 and to understand the functional differences in structural terms.H. pylori is able to enter a viable but nonculturable state, in which the microorganism modifies its morphology from a spiral to a coccoid form (Cellini, 2014H. pylori strain N6 led to a transformation from rod-shaped to viable cocci-shaped bacteria (Bonis et al., 2010H. pylori but also for inhibiting the morphological transformation into the persistent coccoid form.Bacteria may change their morphology to fit the circumstances for survival (Young, 2007Csd3, 4rnyPDB reference: 4rnzPDB reference: 10.1107/S1399004715000152/tz5068sup1.pdfSupporting Information.. DOI:


There are strong theoretical arguments for using narratives in teaching and learning within medicine, but little is known about how they are used in medical lectures. This study explores the types of narratives lecturers use, the attitudes of lecturers and students to the use of narratives in teaching, and the aspects of learning that narratives may facilitate.Observation of three medical lectures was followed by one-to-one interviews with the respective lecturers, and separate focus group interviews with medical students who attended each of the three lectures.Lecturers used a variety of narratives on a range of themes, from clinical cases to patient experience narratives or narratives about their professional careers. Students and lecturers highlighted key aspects of narrative learning: for example providing a relevant context, as a “hook” to engage the audience, and as a memory aid.The findings support existing literature which suggests that narratives may be a useful tool for learning in medicine. This study suggests that narratives tap into several key learning processes including providing a relevant context for understanding, engaging learners, and promoting memory. For medical students in lectures, narratives may be particularly relevant in promoting humanistic aspects of medicine, including professional identity and empathy. There are strong pedagogical and theoretical arguments for the benefits of narrative learning  in medical education—particularly in the areas of meaning-making (“making sense”) , the devThe frames of meaning within which learning occurs are constructions which grow out of our impulse to emplot or thematise our lives” (.R3: It’s probably one of the clearest stories that I remember him saying, cause that’s useful, and that’s something we would need to remember in an emergency situation.Students also saw narratives as memorable because they clearly outlined the relevance for students in their future work. A story about opiod overdose in A&E for example:The focus on memory appears to back up the theoretical links between the use of narrative and memory, and it is particularly interesting that it was the “story” genres of narrative, with their character driven plots, struggles and goals, that provoked the most discussion around memory. There were however several instances where students would volunteer what the author had classified as “stories”, unprompted, as the most memorable part of the lecture.real world experience and clinical case categories—drew comments about empathy and compassion. Lecturer 1 particularly, told several narratives which seemed to be promoting the patient’s point of view, encouraging students to step inside a patient’s shoes. A story in Lecture 1 about a patient with locked-in syndrome triggered some strong reactions in some students:R1: I remember her saying this, and me thinking, oh my God that would be a horrible situation to be in.I: You were thinking that?R1: Yeah I remember her telling us about locked-in syndrome and me thinking that it would be such a horrible thing to experience.Several narratives—especially in the R4: Maybe just realising what it’s like for a patient in these conditions. It’s relating it to something you’ve probably never actually experienced it, but you could almost imagine more realistically what it’s like. It’s like with the mobile, it is annoying being once or twice, whereas if it’s every minute you can start to imagine going, hang on, whereas these poor people, nothing, it’s all about relating it to how you … it’s enabling you to empathise with the patient isn’t it.Another story, about sleep apnoea, encouraged students to imagine what it would be like to be woken every few minutes by their mobile phone going off…L1: And what you’re demonstrating to me, which I couldn’t have told you if you hadn’t read them all out, is that these narratives frequently come from a patient perspective.This is an example of a finding which was originally derived from observation and analysis of the lecture transcript, but which was later validated by asking the students and the lecturer herself if she agreed with my analysis. Before it was mentioned to her, the lecturer was unaware of the patient perspective nature of the narratives she was using:The promotion of empathy and compassion is a strong theoretical benefit of narrative pedagogy—these findings suggest that even in basic science lectures, this aspect of narrative learning is indeed at work. Students and lecturers both acknowledged that the value in many of the narratives used was in promoting a patient perspective.L2: When you are telling a story, they are not going to take notes of a story—it’s obvious it’s a story and therefore they have to stop, and as soon as they stop writing then they are thinking. Then maybe you’re hoping you’re engaging a different pathway so that maybe now they think okay now I understand better because I have actually stopped to listen.Although not a formal element of the lectures, reflection did come up in discussions. For example, lecturers talked about using narratives as punctuation marks in lectures, to give the students a rest, and time to reflect on what’s gone on before.R6: Just putting a break to the number of lists and facts.I: So, almost like punctuation, and a breathing gap.R6: Yeah, exactly.R1: It’s a really good moment for consolidation, in that sense, so, we’ve had the dry list at the start, and then if you get an anecdotal story or whatever afterwards it’s not only a breathing space but you can sit back, you’re not taking notes for a moment, but it all puts into context what you’ve just written down.Students seemed to agree that this was another positive feature of narratives in lectures:R3: Stories are good from our perspective, the ones that relate to general day to day life on the ward, cause they teach you what to look out for and put in context what you need to know. But you’re aware that if you were gonna publish a study or something you’d leave out the anecdotal evidence because it’s not viewed as scientific. So, I think those kinds of stories are good in the right context, so to speak.Students, on the whole, were very receptive to the idea of lecturers using narratives or stories in their science lectures, particularly given their view of medicine as a distinctly human endeavour. Students also seemed to distinguish between the (legitimate) use of narratives to aid student learning for a clinical context, and the (discouraged) use of stories in formal scientific discourse:R2 I find the anecdotes interesting, but I don’t think they necessarily help me to learn the material any more. They’re interesting, but I think they’re an aside. So, personally, I don’t think having them there improves my understanding.R3 …sometimes there can literally be just too many referring to, not the actual lecture itself but all the kind of anecdote stories and things, and then you're not going to remember them, and you just lose interest as well.But a minority of students had negative comments to make about the use of narratives in lectures. The three main themes here were that narratives/anecdotes may be more to do with entertainment than learning, that they are too often irrelevant asides (and particularly not focussed on passing exams), and that too many of them can become boring:L2: No, I like fluffy so I don’t have a problem with the slightly less scientific. I don’t even mind telling a story that can be scientifically irrelevant or even inaccurate sometimes if it’s making a point.The lecturers were also open to using narratives in their lectures, and felt quite passionately that their “non-scientific” nature did not detract from their value. One didn’t accept my suggestion that narratives might be perceived as rather “fluffy” in a scientific institution:L2: There are the students who actively rebel against it and get a bit annoyed and think it’s a waste of time. Whether there are particular types of learners who need that straight narrative to maintain concentration, and then by telling a story you are actually distracting them and therefore they disengage with the lecture; I guess that’s the only downside.But lecturers were aware that not all students were well-disposed towards the use of narratives in lectures. Lecturer 2 wondered whether this was because some students simply don’t learn that way:L2: Yes, definitely; so historical stuff I guess just for consistency. Personal stuff and historical stuff you can apply in a lot of instances and then outside of that you are trying to find anything real world that you can apply to your teaching just to try and give it more interest and get them to think about it.I was interested too in whether the lecturers used narratives deliberately, or were even aware of the fact that they were using narratives and stories at all. Lecturer 2 was aware that he was using certain types of narratives, and did use them deliberately:L2: …it’s even the things like the opioid overdose without necessarily thinking of it as a story so that’s interesting. I guess that kind of thing it might make you more inclined to try and make your stories more clinically relevant more often…But he was less aware that he was telling some other categories of narrative:L1: I think it’s the word story that’s quite tricky, because it’s kids and it’s bedtime reading. And so it doesn’t have the same level of impact than if you can call it something else. We need to find a more sophisticated name for it.Finally, Lecturer 1 felt that while she had no problem with the concept of using narratives in teaching and learning, she did perceive a problem with the actual words “story” and “narrative”:From the findings presented, it appears that lecturers are using many narratives in their basic science lectures – even when they don’t think they are. Some of them are using narratives purposefully, but usually only on certain topics such as the history of medicine. The typology describes a variety of narrative themes, from clinical cases  to narratives about patient experience or their own professional careers. Certain categories of narrative seem to carry particular shared meanings between students and lecturers, and these echo the theoretical benefits of narratives described in the literature , 5, 6—foWhilst students felt that narratives or stories would not be appropriate to use in a formal scientific research paper, they were mostly receptive to the idea of using narratives for learning in lectures. A minority of students though, felt that narratives could be an irrelevant distraction, particularly if not related to learning objectives to be tested in exams. This may reflect different learning style preferences in different students. It is interesting that lecturers often underestimated the number and range of narratives or stories they were telling; this in itself might reflect a reluctance to consider them as useful learning tools. However on direct questioning, lecturers were even more positive than students in their attitudes to using narratives and stories, although there was some feeling that the word “story” was problematic as it had connotations of childishness. There was little sense, as some have suggested, that the use of stories and narrative in medical education is undervalued, even maligned .engage learners by offering a connection with existing knowledge and experience, and by making the unfamiliar seem familiar. For example Holt and others [Most students  felt that narratives can be a valuable learning tool. Several key parts of the learning process came to light in this study which resonate with the existing literature. Both students and lecturers highlighted narrative’s value in providing a relevant context for them—by drawing them in to the story and engaging them, stories seemed to help them understand the material better. A key step for any learner—including medical learners—is to create meaning from what they experience; to make it make sense. Meaning-making is very closely linked to comprehension or understanding—a learner must comprehend before he or she can create meaning. There have been many studies of patient’s illness narratives which add empirical weight to the theoretical centrality of narrative in meaning-making. Hanninen, for example, found that patients with addictions were unable to promote self-recovery until they were able to construct a successful narrative of their addiction . But anod others , 35 haved others .Students and lecturers focused a great deal on the value of narratives in promoting memory, something which is likely to help in the development of useful illness scripts to be retrieved later on in their careers. Clinical case narratives seemed the most relevant category of narrative in this regard. Hunter, from her observational studies in educational settings within hospitals, has postulated that narratives may be a useful way for medical students to retain information . FernaldIn medicine, parables often start with “I had this great case” or “When I was an intern.” What ensues is a story about a fascinating medical case with a moral about what it means to be a doctor.The literature suggests that narratives are central in professional identity development, passing down cultural values and norms of professional behaviour through generations of doctors :In medicBased on her scholarly studies of narratives in medicine, Hunter identifies an important role of narrative in aiding socialisation and professionalization of medical learners . NarratiFinally, of all the narratives identified, it was the ones categorised as “stories” with a character-driven plot, and some emotional struggle towards achieving a goal, which provoked the most discussion and positive remarks about nearly all the aspects of learning I have highlighted. This resonates with Haven, Bruner, Ricoeur and others –41, thathow stories are used in learning, or the themes they cover.There are several limitations to this project. For practical reasons it was only possible to observe three lectures (and therefore three interviews and focus groups), which raises the possibility of various biases; this may not have captured the full range of narrative categories that a larger sample of lectures would have demonstrated, and the sampling of lecturers may well have favoured those who use narratives more often than average. Equally, by disclosing the focus on narratives in the invitation to students, there may be a selection bias within the focus groups. However, neither of these issues would necessarily bias findings with regard to There may also have been an important observation bias, where participants behave differently purely as a result of being observed. As a result of being transparent about the narrative nature of the study it is possible that lecturers may have included more narratives than they normally would. However, they were asked directly if they had included more narratives than usual; all denied this was the case, and the narratives were ones they used every year in the same lecture. In addition, this would not affect the analysis of meaning or elements of learning process – only the number and types of narratives used.Students may also have been reluctant to criticise their lecturers. Students may have felt under pressure from the author as a senior member of Faculty to be enthusiastic about narratives . However, it was reassuring that several students felt quite happy to make critical observations about both lecturers’ styles and their use of narratives or stories. It is also possible that in choosing audio over video, the study may have missed some important opportunities to observe key audience reactions or contextual features of narrative learning, though field notes included visual observations.The findings support existing literature in this area which suggests that narratives may be a useful tool for learning in medicine. This study suggests that narratives tap into several key learning processes including providing a relevant context for understanding, engaging learners, and promoting memory. For medical students in basic science lectures, narratives or stories may be particularly relevant in promoting the more humanistic aspects of medicine, including professional identity, and empathy. More research is needed to provide evidence of the impact of narratives or stories on learning outcomes for students. However, for lecturers or faculty interested in using narratives—especially stories—as a teaching tool, this study may offer greater awareness of their potential and some guidance in how to use them in a more focused way.A sequence of events connected together in a way that gives them meaning.My working definition of narrative, derived from a wide literature, including the fields of narratology, literary criticism and linguistics is:a telling or recounting of a string of events with at least three basic elements: 1) a situation involving some predicament, conflict or struggle, 2) an animate protagonist who engages with this situation for a purpose, 3) a plot during which the predicament is somehow resolved…For simplicity I adopted Haven's definition for this study: "A detailed, character-based narration of a character“s struggles to overcome obstacles and reach an important goal" .Within this over-arching definition of narrative sit many different genres of narrative .


This data supplements the article   Specifications TableValue of the data•The NOE network defines clearly the side chain orientations of particular ligand-binding residues;•* domain that are not observed in the NMR-derived structure of the domain;MD simulations provide atomistic descriptions of conformational fluctuations within the WW3•This data set serves as a reference for future studies involving WW domains.115N-edited and 13C-edited NOESY spectra, which were processed using NMRPipe χ1 angle restraints determined from combined 3Jαβ and 3JNβ couplings were used for structure calculation. The NOEs were picked manually and assigned in a semi-automated manner using the Aria 2.3.1 * domain is provided as supplementary material. The experiments performed to acquire chemical shift assignments, 3J couplings and NOE distance restraints are summarized in 3J couplings and the subsequently determined rotameric state for the WW3* domain are given in We have collected 1592 NOE distance restraints from three-dimensional 1 rotameric states of six key peptide binding residues  over 100 ns in the apo and hENaC peptide bound state of the WW3* domain is provided  from neuronal precursor cell expressed developmentally down-regulated gene 4-1 (Nedd4-1) was overexpressed in E. coli BL21 (DE3)pLysS cells, as described previously The WW32.2* domain  from human Nedd4-1 in 20 mM sodium phosphate buffer (pH 6.5), 50 mM NaCl, 0.1% (w/v) NaN3 and 1 mM DSS in a 93%/7% (v/v) H2O/D2O mixture. NMR spectra were recorded at 25 °C on NMR spectrometers equipped with cryogenically cooled z-gradient probes operating at 1H frequencies of 600 and 900 MHz. 1H, 15N and 13C chemical shift assignments of the WW3* domain were obtained using experiments in 15N-edited and 13C-edited NOESY spectra were recorded using mixing times between 150 and 180 ms. Backbone dihedral angles were obtained from TALOS+ 1HN, 1Hα, 13Cα, 13C’ and 15N) and 13Cβ chemical shifts. Sidechain χ1 dihedral angles were obtained from a combination of 3Jαβ and 3JNβ couplings derived from 3D HNHB Standard heteronuclear multidimensional NMR experiments 2.3MD simulations were performed using parameters described in


L is strongly semicontinuous if and only if L is semicontinuous and meet semicontinuous. It is proved that semi-FS domains are strongly semicontinuous. Some interpolation properties of semiway-below relations in (strongly) semicontinuous bc-domains are given. In terms of these properties, it is proved that strongly semicontinuous bc-domains, in particular strongly semicontinuous lattices, are all semi-FS domains.We are mainly concerned with some special kinds of semicontinuous domains and relationships between them. New concepts of strongly semicontinuous domains, meet semicontinuous domains and semi-FS domains are introduced. It is shown that a dcpo  A logic-oriented approach to domain theory to formalize the properties of computation is provided in  to denote all order-preserving functions from L to M, use [L↪M] to denote all the functions preserving suprema of prime ideals from L to M and use [L → M] to denote all the Scott-continuous functions from L to M. All of them are under the pointwise order. It is easy to see that [L → M]⊆[L↪M]⊆.Let L, M be dcpos. Then [L↪M] is a dcpo.Let G be a directed subset of [L↪M] and f(x) = ∨g∈Gg(x) for all x ∈ L. Then it is easy to see that f is order-preserving. For any prime ideal P ∈ PI(L), we havef ∈ [L↪M], showing that [L↪M] is a dcpo.Let L be a dcpo. If D⊆ is directed and supδ∈Dδ = IdL, then we say that D is an approximate identity for L.Let L be a dcpo. If L has an approximate identity D⊆[L↪L] such that δ(x) ⇐ x for all δ ∈ D and for all x ∈ L, then L is a strongly semicontinuous domain.Let x ∈ L, let A = {δ(x) : δ ∈ D}. Then A⊆⇓x ∩↓x. Note that D is directed and x ∈ ↓(∨A) = A↑↓. By the L is a strongly semicontinuous domain.For any L be a dcpo. A function δ : L → L on L is finitely separating if there is a finite set Fδ such that, for each x ∈ L, there exists y ∈ Fδ such that δ(x) ≤ y ≤ x.Let L is called a semi-FS domain if there is an approximate identity D⊆[L↪L] consisting of finitely separating functions.A dcpo L, there is an approximate identity D⊆[L → L]⊆[L↪L] for L consisting of finitely separating functions. So, an FS-domain is a semi-FS domain.For an FS-domain L be a dcpo. If δ ∈ [L↪L] is finitely separating, then, for all x ∈ L, δ(x) ⇐ x. Thus a semi-FS domain is a strongly semicontinuous domain.Let x ∈ L and P ∈ PI(L) with x ≤ ∨P. Since δ is finitely separating, there exists a finite set Fδ such that for each d ∈ P there exists yd ∈ Fδ with δ(d) ≤ yd ≤ d. Let Fδ′ = {yd ∈ Fδ : d ∈ P}, a nonempty finite subset of Fδ. Then for each y ∈ Fδ′, we can get dy ∈ P such that δ(dy) ≤ y ≤ dy. As P is a prime ideal, there exists d0 ∈ P such that y ≤ dy ≤ d0 for all y ∈ Fδ′. Hence for all d ∈ P, δ(d) ≤ yd ≤ d0 and δ(x) ≤ δ(∨P) = ∨d∈Pδ(d) ≤ d0. Therefore, δ(x) ∈ P. It follows from δ(x) ⇐ x.Suppose that L is a strongly semicontinuous domain.By The next example gives a strongly semicontinuous domain which is not a semi-FS domain, showing that the reverse of L = {0, b1, b2}∪{aj : j = 1,2,…} be the domain showing in bi < aj for i = 1,2 and j = 1,2,…. It is clear that L is an L-domain but not compact in the Lawson topology. By ⊆[L↪L]⊆ = [L → L]. So, [L → L] = [L↪L]. Since L is not an FS-domain, L is not a semi-FS domain either.Let Every strongly semicontinuous bc-domain is a semi-FS domain.L be a strongly semicontinuous bc-domain. For each x ∈ L, S ∈ Pfin(L), define δS : L → L by δS(x) = ∨{y ∈ S∩↓x : y ⇐ x}. If {y ∈ S∩↓x : y ⇐ x} = ∅, then δS(x) = ⊥, the least element of L. So, δS(x) is well-defined. It is easy to see that δS is order-preserving with δS(x) ≤ x for all x ∈ L. Next we show that δS preserves suprema of prime ideals. For each P ∈ PI(L), it suffices to show that δS(∨P) = δS(k) ≤ ∨p∈PδS(p), where k = ∨P. If {y ∈ S∩↓k : y ⇐ k} = ∅, then, by the definition of δS, we see that δS(k) = ⊥ ≤∨p∈PδS(p). Let {y1,…, yl} = {y ∈ S∩↓k : y ⇐ k} ≠ ∅, then m = ∨i=1lyi exists in L. By the definition of δS, δS(k) = m ≤ k. Since yi ⇐ k for i = 1,2,…, l, we have that m ⇐ k. By (SI≤) in m* ≥ m such that m ⇐ m* ⇐ k. It follows from m* ⇐ k = ∨P that m, m* ∈ P. Noticing that m ≤ m* and yi ≤ m ⇐ m* ∈ P, we have {y1,…, yl}⊆{y ∈ S∩↓m* : y ⇐ m*} which yields that ∨p∈PδS(p) ≥ δS(m*) ≥ δS(k). So, δS preserves suprema of prime ideals, and δ ∈ [L↪L].Let S, T ∈ Pfin(L) with S⊆T. It is easy to see that δS ≤ δT and D = {δS}S∈Pfin(L) is directed. Let FδS = {δS(x) : x ∈ L}. Then FδS is finite by the finiteness of S. So, δS is a finitely separating function. Since L is strongly semicontinuous, for each x ∈ L and δS ∈ D, δS(x) = ∨{y ∈ S∩↓x : y ⇐ x} ≤ x andSuppose that D = {δS}S∈Pfin(L)⊆[L↪L] is an approximate identity for L consisting of finitely separating functions, and L is a semi-FS domain.Therefore, Every strongly semicontinuous lattice is a semi-FS domain.