We present CATHEDRAL, an iterative protocol for determining the location of previously observed protein folds in novel multidomain protein structures. CATHEDRAL builds on the features of a fast secondary-structure–based method (using graph theory) to locate known folds within a multidomain context and a residue-based, double-dynamic programming algorithm, which is used to align members of the target fold groups against the query protein structure to identify the closest relative and assign domain boundaries. To increase the fidelity of the assignments, a support vector machine is used to provide an optimal scoring scheme. Once a domain is verified, it is excised, and the search protocol is repeated in an iterative fashion until all recognisable domains have been identified. We have performed an initial benchmark of CATHEDRAL against other publicly available structure comparison methods using a consensus dataset of domains derived from the CATH and SCOP domain classifications. CATHEDRAL shows superior performance in fold recognition and alignment accuracy when compared with many equivalent methods. If a novel multidomain structure contains a known fold, CATHEDRAL will locate it in 90% of cases, with <1% false positives. For nearly 80% of assigned domains in a manually validated test set, the boundaries were correctly delineated within a tolerance of ten residues. For the remaining cases, previously classified domains were very remotely related to the query chain so that embellishments to the core of the fold caused significant differences in domain sizes and manual refinement of the boundaries was necessary. To put this performance in context, a well-established sequence method based on hidden Markov models was only able to detect 65% of domains, with 33% of the subsequent boundaries assigned within ten residues. Since, on average, 50% of newly determined protein structures contain more than one domain unit, and typically 90% or more of these domains are already classified in CATH, CATHEDRAL will considerably facilitate the automation of protein structure classification. Proteins comprise individual folding units known as domains, with a significant proportion containing two or more (multidomain structures). Each domain is thought to represent a unit of evolution and adopts a specific fold. Detecting domains is often the first step in classifying proteins into evolutionary families for studying the relationship between sequence, structure, and function. Automatically identifying domains from structural data is problematic due to the fact that domains vary substantially in their compactness and geometric separation from one another in the whole protein. We present a novel method, CATHEDRAL, which iteratively identifies each domain by comparing a query structure against a library of manually verified domains in the CATH domain database through computational structure comparison. We find that CATHEDRAL is able to outperform the majority of popular structure comparison methods for finding structural relatives. Furthermore, it is able to accurately identify domain boundaries and outperform other methods of structure-based domain prediction for the majority of proteins. CATHEDRAL is available as a Webserver to provide domain annotations for the community and hence aid in structural and functional characterisation of newly solved protein structures. Proteins comprise individual folding units known as domains, with a significant proportion containing two or more units (multidomain structures) . Each dohttp://www.rcsb.org/pdb) [More than 7,000 new proteins structures were deposited in the Protein Data Bank   in 2005.The number of larger multidomain structures has been gradually increasing since the formation of the PDB, with improvements in techniques for structure determination. We can expect this trend to continue, as recent analyses of completed genomes have suggested that the proportion of multidomain structures in some organisms, particularly eukaryotes, may be as high as 80% . Figure A further complication is that approximately 20% of domains from multidomain proteins in the PDB are discontiguous  Figure ; that isVarious computational methods have been developed to automatically detect domain boundaries in multidomain structures (see ) throughAlthough each method reports 70%–80% accuracy in benchmarking tests, our experience of updating the CATH database suggest that these methods frequently (∼80%–90% of the time) produce results that are inconsistent with one another. As a consequence, manual validation becomes the only secure way to resolve these conflicting predictions. A recent analysis by Holland et al.  showed tAlthough many of the algorithms described above effectively delineate domains for a large percentage of protein chains in the PDB (even those which contain novel folds), they provide no indication as to how similar each predicted domain is to folds already classified within the CATH database. Therefore, it is still necessary to compare the excised domain against the CATH library to classify the fold. Since manual validation of domain boundaries and structure-based database scans are both slow, this has remained one of the major bottlenecks in the CATH classification process.As discussed above, there are a limited number of folds, and a novel multidomain structure could well comprise those that have already been classified. This concept of recurrence is not new, and has been successfully exploited by other structural classifications. For example, the DALI algorithm is used to detect recurrent folds for classification in the DALI Domain Database , while tMany methods exist to find recurring domains using pure sequence approaches  algorithm to discover putative fold matches for a given protein domain/chain structure, which could subsequently be more accurately aligned using a residue-based method exploiting DDP. To evaluate the performance of GT and DDP for fold recognition and accurate alignment, we first created a dataset of single CATH–SCOP domains and compared this approach with other publicly available methods before optimising the algorithm for discovering domain folds in multidomain chains. This benchmarking was also performed on a larger dataset of domains in the nonredundant CATH library (version 2.6) and produced almost identical results.. Given that the DDP algorithm is 100–1,000 times slower than GT, depending on the size of the structures being compared, taking the top ten folds identified by GT forward for more accurate scoring by DDP maintains coverage yet vastly reduces the number of DDP comparisons undertaken. We subsequently refer to the combined use of GT and DDP as CATHEDRAL.The coverage and precision of the GT and DDP algorithms was compared by scanning the benchmark CATH–SCOP dataset see  against To benchmark CATHEDRAL, we compared its performance against several other publicly available methods  using the same CATH–SCOP dataset see . KolodnyIt can be seen from As well as the ability of the SAS to correctly discriminate between true and false fold matches across a range of score cutoffs, it is important to identify the closest relative within a given fold group to obtain the best alignment. Therefore, the correct fold should rank highly in the list of matches.It can be seen from Alignment accuracy was initially assessed using a similar strategy to that devised by Kolodney et al. . The autIn some domain architectures , it is clear that large structural motifs  can overlap between domains in different folds, yet these do not always coincide with equivalent secondary structures. Furthermore, any similarity score based on RMSD will be dependent on the number of superposed residues, and hence aligning more residues in variable parts of two structures can give a disproportionately high score, even if the alignment is actually more biologically relevant. For the purposes of applications such as domain boundary recognition, it is clearly important to identify an alignment between two domains that maximises the number of equivalent residues. Consequently, for a given pair of fold matches, we looked at the average number of residues aligned by each method. We also examined the size of the domain fragments aligned with a given SAS. A better way to assess the biological significance of automatic structure alignments is to compare them to a manually validated dataset. To this end, we compared all methods against curated alignments from the BAliBASE see . Figure In summary, our benchmarking results suggest that GT and DDP are complementary approaches that may be combined to produce a fast yet reliable domain boundary recognition algorithm. The performance of CATHEDRAL for this purpose is described in the next section.domain level to identify fold relatives in a domain library. In this section, we develop CATHEDRAL to scan a novel multidomain structure against a library of representative single-domain folds in order to identify constituent domains, thereby locating domain boundaries. A GT approach is initially used to find putative folds in CATH within the protein structure. Representatives from each superfamily in each of the high-scoring fold groups are then aligned against the putative domain region in the chain using a more accurate, residue-based DDP algorithm. DDP was modified to allow the common secondary structures determined by GT to guide a residue alignment by DDP, which reduces the search space and hence results in a notable speed increase. The significance of the subsequent matches is assessed using a support vector machine (SVM), which accounts for the percentage of overlapping residues, geometric similarity, and number of aligned residues. Once a domain has been assigned, it is excised from the chain, and the remaining regions are iteratively recompared using the same protocol until all known domains have been found. An optimal balance between coverage and speed was achieved by only realigning domains in the top ten folds identified by GT using DDP.The preceding sections dealt with the use of the CATHEDRAL protocol at the As discussed above, it is essential to consider both the RMSD of the structural alignment of two domains, as well as the percentage of aligned residues in the largest protein. This problem becomes more complex when detecting individual domains in a multidomain chain, as the constituent domains may be unknown. When comparing a chain with a domain library, the best RMSD can sometimes be obtained by small domains with similar secondary structure motifs but different folds, which might inhibit the detection of truly equivalent domains.The highest-scoring match recognised by CATHEDRAL is used to determine the boundaries of the domain. Residues associated with that domain are then excised from the multidomain chain before rescanning the chain against the fold library. Therefore, it is important that this top match is frequently the best global match that can be obtained. As well as using information on the number of residues that superpose well (provided by the SAS), it is also important to consider the proportion of residues equivalent between the two domains, domain sizes, and protein classes. All these features have been found to be important in the manual curation process used to classify domains in CATH. We used an SVM to explore whether some optimal combination of these features resulted in a measure that was more discriminating than simply using a single structural similarity score.From Once CATHEDRAL has identified putative domain matches for a query multidomain structure, all domain hits to the chain are ranked by the SVM score, and domain boundaries are assigned using the protocol described in Methods. CATHEDRAL was able to assign 90% of domains in the query dataset to the correct fold group, with 80% of these within ten residues of the actual boundary . AlthougCATHEDRAL was developed as a method to be applied unilaterally to all protein chains to be classified into the CATH database. As it is not known a priori whether a given chain contains more than one domain, it is important that the algorithm does not split whole-chain domains unnecessarily. To analyse whether this would pose a problem, the iterative version of CATHEDRAL was also applied to the single-domain CATH–SCOP dataset. In less than 4% of cases, CATHEDRAL predicted that these structures contained more than one domain.The major speed increase in CATHEDRAL is due to the fact that GT preselects representatives for DDP to align to the query chain. By default, it takes all relatives (nonredundant at 35% sequence identity level) in each of the top ten top-scoring fold groups identified by GT, even if this results in thousands of comparisons, as occurs in large fold groups such as the Rossmann and TIM barrel folds. This can produce very long running times for some query chains. Nevertheless, it is important to find the closest structural relatives for each assignment to reduce the number of insertions and deletions and therefore increase the accuracy of the domain boundary.We explored whether only a limited number of relatives from each fold could be taken without compromising the fidelity of the domains boundaries. However, given that GT does not accurately discriminate between homologues and domains with the same fold, it was decided to take at least one relative from each superfamily in the target fold group and explore the effect of varying this number.fr) taken from each superfamily to be aligned by DDP was varied. fr parameter.CATHEDRAL was run as described above (by targeting the top ten fold groups at each iteration), but the number of nonredundant representatives , there is 2-fold or more variation in the sizes of the domains (as measured by the numbers of secondary structures in the domain) . TherefoTo place the performance of CATHEDRAL in context, we compared its ability to assign domains boundaries with two other methods: hidden Markov models (HMMs) and domain predictions from structure (PDP).Our dataset of protein chains was scanned against HMMs built from each structure in the CATH library using the HMMer suite of programs . Domain CATHEDRAL finds domain boundaries for a query chain by using structural alignment to known folds in CATH. To compare our approach with other methods that do not exploit the concept of fold recurrence, but instead are based on ab initio analysis of structural properties such as residue contacts, we applied the PDP method to our multidomain chain dataset. PDP was able to predict correct domain boundaries (within ten residues) for 67% of the chains in the dataset. Although this is lower than CATHEDRAL, it is substantially higher than the 33% achieved by HMM methods. Furthermore, the performance of PDP is still impressive given the problem of distinguishing domain units in a chain based purely on structural properties such as internal contacts and hydrophobicity.http://cathwww.biochem.ucl.ac.uk/cgi-bin/cath/CathedralServer.pl).More than 50 structural comparison algorithms have been published in the literature in the last 30 years, the vast majority of which are not in regular use by the bioinformatics or structural biology communities. Those which have gained popularity tend to have a Web-based interface for users to submit their own structures or structures from the PDB. CATHEDRAL has been implemented as a crucial part of the CATH classification protocol, and a new Webserver was created to provide users to investigate domain assignments and homologue recognition with their protein structure of choice (We have developed a protocol for domain boundary assignment in multidomain proteins (CATHEDRAL) that exploits the recurrence of folds in different multidomain contexts. This was devised because a high proportion (currently >90% ) of domaCATHEDRAL first scans a query structure against a library of folds from the CATH databases. The algorithm first uses GT to perform a secondary structure–based comparison and identify putative domain fold matches in the query structure. A representative sample of nonredundant superfamily relatives from the top ten folds are then recompared to try to obtain a better alignment and refine the domain boundaries. This latter step exploits a DDP algorithm that has been guided by information on equivalent secondary structures identified by the GT match.CATHEDRAL combines the power of two established structural comparison algorithms in order to develop a fast and accurate protocol for homologue recognition and domain assignment. CATHEDRAL misses ∼10% of the domains in the target dataset. Of these, ∼30% are too small (fewer than three secondary structures) and so are ignored by the CATHEDRAL protocol. Manual inspection revealed that a further ∼20% are distorted or irregular structures giving poorly defined graphs. The remaining ∼50% are missed because they do not pass the score similarity cutoff, as the relatives are too distant and related structural motifs in neighbouring fold groups are better matched.The CATH classification of protein folds gives a discrete description of fold space. However, there are difficulties in identifying distinct folds in some populated regions of fold space where the structural universe could be more reasonably represented as a continuum . In manyp-value to the boundary and fold assignments predicted by CATHEDRAL. Furthermore, at present, CATHEDRAL assigns domains to a query chain in an iterative fashion. It could be conceived that a better prediction of boundaries and fold assignments could be attained by considering a number of different classifications. The best of these could be identified as the prediction with the highest confidence value.For nearly 80% of the test set, all domain boundaries within the multidomain were correctly assigned within ten residues. This is a considerable improvement over a previous consensus protocol  describSince CATH aims to maintain high quality domain boundary assignments , resultsCATHEDRAL and DDP  were beThere are several publicly available methods that have been endorsed by widespread community use and/or validation by comparative benchmarking against established methods. We selected a range of methods, many of which had been previously benchmarked by Kolodny et al.  for theiProtocol used to compare the performance of CATHEDRAL and full DDP in fold recognition and alignment accuracy with other established methods—fold recognition. Structure alignment methods were compared using ROC curves. These plot true positive rate (sensitivity) against the false positive rate (1 − specificity) for different similarity scores returned by the individual methods. A binary classifier was defined by the CATH hierarchy whereby a positive match is one in which both domains share the same fold classification. The matches for each method were ordered by the structural similarity score of their alignment, and the number of true positives and false negatives were calculated at varying thresholds.Protocol used to compare the performance of CATHEDRAL and full DDP in fold recognition and alignment accuracy with other established methods—alignment accuracy.matN represents the number of aligned residues and 1L/2L represents the length of the respective domains. The different measures attempt to balance the different properties that describe a “good” alignment, weighting the RMSD against the length of the alignment as a fraction of the length of the proteins aligned.Kolodny and coworkers tested several measures for assessing the accuracy of structural alignments . They idAs CATHEDRAL is exploiting fold recognition to obtain reliable domain boundary assignment, we developed a further measure that scores the global alignment accuracy. As opposed to SiMin, which gives a good score for a small fold appearing as a conserved motif within a much larger fold, SiMax (Equation 3 below) takes account of the proportion of residues aligned in the larger domain structure to determine whether a significant fold match has been achieved.All the measurements are in angstroms, and the percentage of alignments within a particular distance in angstroms were calculated for each measure .In addition to these geometric measures, alignment accuracy was also assessed by comparison against a set of manually curated alignments. BAliBASE is a database of manually refined multiple structure alignments specifically designed for the evaluation and comparison of multiple sequence alignment programs. The alignments in BAliBASE are selected from the FSSP  or HOMSTmf, which quantifies the number of amino acids correctly aligned in the structural alignment divided by the total number of aligned residues in the BAliBASE alignment. CE was not included in this analysis, as it only identifies the largest continuous motif.A total of 14 BAliBASE multiple alignments were selected, comprising 108 pairwise structural comparisons. All the alignments represented single-protein domain chains that shared less than 25% sequence identity, making alignment nontrivial. All three major protein classes were represented, and the quality of the alignments generated by the different structure comparison methods are measured by the score, CATHEDRAL was benchmarked to calculate its ability to delineate domains within multidomain proteins, as well as correctly recognising the fold of the constituent domains. A set of representatives from 1,071 multidomain S35 sequence families (clustered by single linkage at 35% sequence identity) was selected. From this set, those chains containing domains from folds with less than two S35 sequence families were removed. The remaining set contained 964 chains comprising 1,593 domains. These originated from 245 distinct fold groups and 462 superfamilies.To identify domain boundaries in a novel multidomain structure, CATHEDRAL scans the query structure against a library of folds classified in the CATH database (see Text S1 for description of CATH hierarchy) that are derived from contiguous domain representatives from each sequence family (in which relatives have at least 35% sequence identity) in version 2.6 of the CATH database. This comprised 4,707 domains, covering 907 folds. A secondary structure graph of each domain was generated as described in Harrison et al. .Iterative protocol used by CATHEDRAL. CATHEDRAL uses an iterative protocol illustrated in fr representatives from each superfamily in the selected folds are compared against the matched region using the DDP algorithm.As matches to small domains (fewer than five secondary structures) can produce insignificant E-values (see ) when coA variety of different scoring schemes were assessed for their ability to recognise true matches, together with a combination of several measures using an SVM (see below). If the score suggests that the match is valid, the region is accepted as a putative domain and the alignment used to indicate the residues that can be excluded from the multidomain structure (and score matrix) in future searches. A new graph is constructed from the remaining secondary structures, and the GT and subsequent DDP search is repeated to identify another putative domain. CATHEDRAL continues for up to ten iterations or until there are fewer than three secondary structures left to be assigned.Identification of corresponding secondary structures in the multidomain protein and a single domain structure using GT. GT was first introduced for protein structure comparison by Artymiuk and coworkers [oworkers . CATHEDRoworkers   / 2 edges. Matches identified using GT are therefore more secure when the clique is large, independent of the residue similarity score. Furthermore, because the scoring scheme for graph-matching breaks down for the very small folds (fewer than three secondary structures [ructures ), to maiAs CATHEDRAL iterates toward a solution, the CATH database is repeatedly scanned. However, some large folds contain structural motifs that match well to small folds. These motif matches sometimes rank higher in the match list because the geometry is very well conserved, and the selection of these matches over equivalent folds can therefore confuse the identification of domain boundaries. This effect can be avoided by attempting to match only large domains first; that is, two passes of CATHEDRAL are performed. The first pass only allows matches to folds in CATH that have graphs of five or more nodes. Once CATHEDRAL has reached its termination, it is applied again to the folds in CATH that have graphs with three or four nodes.This strategy results in the smallest folds only being compared against regions of the multidomain protein that are not part of a large fold, as well as typically increasing CATHEDRAL's speed by 50% or more since fewer searches are required. Hence, CATHEDRAL essentially assigns all large domains first before attempting to align smaller domains to any remaining unassigned regions. To aid the assignment of discontiguous domains, in the first iteration, the top hit is also required to be contiguous .Scoring the structural similarity of the domain region aligned by DDP. To assess whether a given structural hit represents a true fold match within the multidomain protein, several measures of similarity are calculated. The structural similarity score returned by the DDP algorithm is normalised to lie in the range of 0–100  irrespective of the protein sizes [β atoms of equivalent residues in the aligned proteins and is normalised to take account of the size of the largest domain being compared.in sizes . This scα atoms is calculated, and a cutoff can be imposed on the local structural similarity (see above) to select only the most similar residue pairs when generating the superposition of the structures. A cutoff of 30  is used to ensure the most equivalent residues pairs are used to calculate the SAS.A rigid body superposition of the structures is also generated from the equivalent residues identified by the alignment. RMSD of the aligned CUsing an SVM to validate structural matches. Determining domain boundaries in protein chains through iterative fold assignment presents several challenges. For example, there is the problem of mis-assigning folds that simply match a large structural motif that does not correspond to a significant “global” match to the domain region. Discontiguous domains can also present problems for structural alignment algorithms. Several similarity measures can be considered when gauging whether a match is valid. Manual experimentation can be used to explore and optimise the combination of these measures, or machine learning methodologies can be used. In CATHEDRAL, we exploited an SVM to perform the optimisation automatically and to determine when a significant domain structure match to a classified fold in CATH was occurring.In addition to the similarity measures provided by the GT and DDP algorithms, we also considered other features  to help improve recognition of global similarity between domain structures. We used the SVMLight package  to combiTo train the SVM, 5-fold cross-validation was used to assess the performance of the SVM models. That is, the dataset was split into five sets, and each one was successively used as the test set, while the model was trained on the other four sets. This reduces any potential bias caused by random fluctuations in the composition of the training and test sets. The error cost for positive examples was weighted according to their ratio to negative examples.Features included the raw score, E-value, and clique size (number of matched secondary structures) returned by the GT comparison. In addition, the raw score derived from the DDP algorithm together with the residue overlap , CATH domain size, sequence identity, and SAS. To improve the ability of the classifier to avoid bias toward one feature, each was normalised between 0 and 1.Identifying domain boundaries and handling discontinuous domains. The individual DDP similarity score of equivalent residue pairs, normalised to lie between 0–100, indicates where residue similarity is good (high), where it is poor (low), and where it is nonexistent (residue score is zero). Since only individual domains from CATH are scanned against the multidomain structure, the alignment can be used to find domain boundaries, because the residue pair score falls to zero at the boundary.When CATHEDRAL determines which fold to assign to a region of the protein chain, it is also making a judgment of where the domain boundaries lie. The fidelity of this latter process is arguably dependent on the structural similarity between the domain region in the chain and the domain it has matched in the library. Although domain boundaries can be assigned to the chain in the same step as taking the highest scoring hits to each region of the chain, the accuracy can be improved by modifying the boundaries once all assignments have been made.Subsequently, domain assignments that contain regions of the chain that overlap with one another are processed as a last step in the protocol. Conflicts are resolved by assuming that the highest-scoring domain is most likely to have the correct boundaries. The boundaries of the overlapping domain are cropped to exclude the shared region. Second, some chains may contain small regions at the start and end that are unassigned. This is often fewer than 20 residues and is unlikely to contain another domain, or comprise an additional segment of a discontiguous domain. In these instances, CATHEDRAL assigns the extra residues at the beginning and end of the chain to the first and last domains, respectively. Similarly, some chains contain small regions between assigned segments. In these cases, CATHEDRAL splits the unassigned residues equally between the two neighbouring segments.Text S1(48 KB PDF)Click here for additional data file.


N). Furthermore, no long range interactions were observed between the domains, as has been previously predicted. Flexibility appears to be a common feature of Gag “late” domains required for virus release during budding. Residues near the N-terminus of CAN that form a β-hairpin in the mature CA protein are unfolded in p12-CAN, consistent with proposals that hairpin formation helps trigger capsid assembly.The Gag polyproteins of gammaretroviruses contain a conserved p12 domain between MA and CA that plays critical roles in virus assembly, reverse transcription and nuclear integration. Here we show using nuclear magnetic resonance, that p12 is unstructured in a Moloney murine leukemia virus (MMLV) Gag fragment that includes the N-terminal domain of CA (p12-CA All retroviruses encode a polyprotein called Gag that serves as the major structural protein of the virus and is capable of assembling into virus-like particles in the absence of any other viral constituent. Gag proteins contain three major domains: an N-terminal matrix (MA) domain that regulates intracellular trafficking and membrane targeting, a capsid (CA) domain  that promotes virus assembly and forms the capsid shell of the viral core during proteolytic maturation, and a nucleocapsid (NC) domain that is responsible for genome selection and encapsidation. Atomic level structures have been determined for several MA, CA and NC domains, and in general, the domain structures do not vary significantly among different genera of retroviruses Gag proteins contain additional polypeptide elements and spacers that are often critical for proper virus assembly. In particular, short, proline-rich segments have been identified that facilitate viral budding at late stages of the viral lifecycle, and mutations within these segments typically give rise to elongated or partially formed virus-like buds that are not released from the plasma membrane N).Proteolytic cleavage of the p12-CA junction is critical for viral infectivity and precedes cleavage of both the MA-p12 and CA-NC sites N, with a C-terminal His-tag, into pET11a (Novagen) using NdeI and BamHI restriction sites, and subsequently transformed into BL21 codon plus RP (DE3) cells (Stratagene). Cells were grown in LB or M9 minimal media supplemented with 99.9 % enriched 15N-ammonium chloride and/or 99.8 % enriched 13C-glucose as the sole nitrogen and/or carbon sources. Protein expression was induced in shake flasks with 1 mM IPTG. The cells were harvested and lysed with a microfluidizer (Micorfluidics), clarified by centrifugation, and the target protein was purified to homogeneity using cobalt affinity , cation exchange and size exclusion chromatographies (Amersham). Fractions containing pure protein were concentrated using Centripreps  and Centricons . The mass of p12CAN was confirmed by mass spectroscopy.The pNCA MMLV proviral plasmid N in buffer containing 50 mM sodium phosphate pH 5.5, 100 mM NaCl, 5 mM DTT and 10% D2O. Backbone assignments were obtained using 2D 1H,15N-HSQC, 3D 15N-edited TOCSY, and 3D HNCA and HN(CO)CA experiments 15N-edited NOESY-HSQC, 4D 13C,15N-edited NOESY and 4D 13C,13C-edited NOESY experiments were used for side chain assignments and to identify long range intra- and interdomain  NOEs NMR data were collected at 35°C with a Bruker AVANCE 600 MHz spectrometer equipped with a cryoprobe using samples of 0.8–1.0 mM p12CA1H}–15N steady-state heteronuclear NOE (XNOE) data were obtained for the backbone 15N nuclei 0) of its 15N/1H correlation peak in the presence of 3 sec proton saturation (I) and in the absence of proton saturation (I0). Peak intensities and the XNOE value were calculated using the HetNOE Analysis module in NMRVIEW. Errors were estimated from the baseline noise in the two spectra. All NMR data were processed using NMRPipe To gain insight into the dynamical properties of MMLV p12, {1H,15N-HSQC spectra for p12CAN revealed a large subset of signals with poor chemical shift dispersion in the proton dimension, suggestive of a largely unstructured domain within the protein  provide information on local secondary structure viations , in agreN domains of several retroviruses, and in all cases, the first ∼15 residues form a β-hairpin stabilized by a salt bridge between Pro 1 and the carboxyl group of an Asp residue subsequent to proteolytic induced maturation N domain of p12CAN indicate that the β-hairpin observed in the X-ray structure of the mature MMLV CAN domain is similarly unfolded in the immature p12CAN construct.Atomic level structures have been determined for the mature CA1H}–15N steady-state heteronuclear NOE (XNOE) data were obtained for the backbone 15N atom N that were well resolved in the 2D 1H,15N-HSQC spectra N domain, confirming that the β-hairpin is unfolded in the immature p12CAN construct. In contrast, residues within the α-helices of the CAN domain exhibit relatively large XNOE values, consistent with a regularly folded tertiary structure.To gain insight into the dynamical properties of N-MLV p12, {1H-1H NOE data  were also obtained to probe for intra- and potential inter-domain contacts. Except for the N-terminal residues of the CAN domain, which did not exhibit long-range interactions, the intra-CAN NOEs observed in all the NOE data were fully consistent with the X-ray structure of the mature CAN hexamer. No long-range intra- or inter-domain interactions were observed for residues of p12.Multi-dimensional 1H-1H NOE NMR data indicate that the p12 domain of MMLV p12CAN is conformationally labile. The folded regions of the HIV-1 MA and CA domains are also separated by a stretch of flexible residues (∼20 in the MA-CAN NMR structure) The combined CSI, XNOE and N exhibits structural/dynamical properties similar to those observed for HIV-1 p6, despite that fact that p12 is twice as large as p6, is located in a very different region of Gag, and has additional functions during the early phase of viral replication.In summary, the MMLV p12 domain of p12CA


The first two structures contain a single PHb domain that forms a dome-shaped, oligomeric ring with C5 symmetry. The third structure has an additional helical hairpin attached at the C-terminus and forms a similar but much larger ring with C12 symmetry. Thus, both molecular assemblies exhibit rare, higher-order, cyclic symmetry but preserve a similar arrangement of their PHb domains, which gives rise to a conserved hydrophilic surface at the intersection of the β-strands of adjacent protomers that likely mediates protein–protein interactions. As a result of these structures, additional families of PHb domains were identified, suggesting that PH domains are much more widespread than originally anticipated. Thus, rather than being a eukaryotic innovation, the PH domain superfamily appears to have existed before prokaryotes and eukaryotes diverged.Pleckstrin homology (PH) domains have been identified only in eukaryotic proteins to date. We have determined crystal structures for three members of an uncharacterized protein family (Pfam PF08000), which provide compelling evidence for the existence of PH-like domains in bacteria (PH Proteins containing PH-like domains are essential in eukaryotes and PH-like domains are among the most abundant modules (fourth) in the human genome.The Pleckstrin homology (PH) domain is a common protein module in eukaryotes found in proteins with a wide range of functions, involved in intracellular signaling and cytoskeletal organization.b) domains are likely evolutionarily related to eukaryotic PH-like domains. The PHb proteins form oligomeric ring assemblies of different sizes and symmetries, providing an excellent example of how molecular architecture can evolve, where the general form of the molecular assembly is preserved, but its size is expandable. Furthermore, we show that several other uncharacterized protein families are likely to contain PHb domains. With more than 1000 prokaryotic homologs identified so far, it seems that this structural module is also widespread among bacteria and archaea.Despite the abundance of PH-like domains in eukaryotes, they had not been previously identified in bacteria. As part of our structural genomics effort in targeting novel protein families, we determined crystal structures of three bacterial proteins from a previously structurally uncharacterized protein family Pfam PF08000 [also known as domain of unknown function family 1696 (DUF1696)],Methanobrevibacter smithii, an archaeal member of the human gut microbiota. Most members of this family have a sequence length of ∼ 125 residues (group 1), but a second group is longer (∼ 204 residues) with a C-terminal extension, and such sequences are detected only in Bacillaceae . Similarities in the core residues suggest that both groups share a common evolutionary origin, but group 2 likely has evolved more recently due to its narrower distribution in bacteria and its more restricted sequence diversity . A third group of more distant homologs, containing the core sequence of the first group at their C-termini, is present in phages, such as Lactococcus phage bIL285  and Staphylococcus phage X2 .PF08000 defines a small protein family whose members are distributed in bacteria, bacteriophages, and archaea, with the majority in firmicutes and proteobacteria. As this family is of unknown function, as well as being structurally uncharacterized, it is also classified as DUF1696 in the Pfam database. One homolog has been identified in Shewanella loihica (sl), Shewanella amazonensis (sa), and Exiguobacterium sibiricum (es) were expressed in Escherichia coli as selenomethionine derivatives. As full-length (sl) and (es) failed to crystallize, different constructs with serial truncations of up to 16 residues at the N- and/or C-terminus, in steps of 4 residues, were screened in parallel. Only one N-terminal truncation construct of each protein, residues 9–124 for (sl) and 13–204 for (es), resulted in successful structure determination. Structure determination of full-length (sa) was enabled using reductive methylation of the protein prior to crystallization. All three structures were solved independently using the multiwavelength anomalous diffraction (MAD) phasing method.To gain insights into this family of proteins, we determined crystal structures of three bacterial members (that represent groups 1 and 2) of the PF08000 family at the Joint Center for Structural Genomics JCSG using itb1) {S. loihica  and S. amazonensis } and from group 2 (PHb2) [E. sibiricum 255-15 ]. The two PHb1s are closely related (80% sequence identity); PHb1(sa) can be aligned to PHb1(sl) without gaps except for an extra glycine at its C-terminus  . PHb2 (PDB ID: 3b77) was solved in tetragonal space group P4 at 2.42 Å resolution with six molecules per asu (two 1/4 dodecamers) (Rcryst = 21.6/Rfree = 25.4). The mean residual errors of the coordinates for PHb1(sl), PHb1(sa), and PHb2 were estimated at 0.16, 0.17, and 0.24 Å, respectively, by an Rfree-based diffraction-component precision index method.b1(sl) [NB: similar scores for PHb1(sa)] and an all-atom clash score of 6.82, 1.9% rotamer outliers, and 97.7% of residues in the favored region of the Ramachandran plot (five outliers in regions of poor density) for PHb2. The final model of PHb1(sl) contains 561 amino acid residues, 1 chloride ion, 7 (4S)-2-methyl-2,4-pentanediols, and 322 waters in the asu. The PHb1(sa) model consists of 585 protein residues, 2 polyethylene glycol (PEG) fragments, 6 glycerols, and 242 waters, and PHb2 has 1106 residues and 173 waters. Data collection, model, and refinement statistics are summarized in Both PHb1 and PHb2 share a common domain that is structurally similar to eukaryotic PH domains (b domain (PH domain in bacteria). PHb1(sl) (residues 9–124) has only a single PHb domain that consists of the canonical seven β-strands in two β-sheets (β1–β4 and β5–β7) and a C-terminal helix (α1) (b2 contains the PHb domain at its N-terminus (residue 14–136) and a helical-hairpin structure (∼ 68 residues) at the C-terminus (b domain. A short helix at the C-terminus (α3) docks between α1 and the β2–β3 (L23) loop and, as a result, links the other end of the hairpin to the PHb domain. This hairpin attachment is crucial for formation of the PHb2 homododecamer.PH domains a–c, whiclix (α1) a. It hasterminus b that exb domain resembles a butterfly of two β-sheets with the β3–β4 loop (L34) and the β5–β6 loop (L56) at the tip of each wing. The PHb domains in two PHb1 orthologs contain a structurally conserved core, except for the N-termini (before α0), the L34 loop, and the L56 loop regions that are highly variable in conformation (b1 monomers [from the two pentamers of PHb1(sl) and PHb1(sa)] can be superimposed with an average rmsd of 1.31 Å for 94 aligned Cα atoms; the rmsd values between different monomers are significantly higher if all variable regions were included (b1(sl) construct, are ordered in two monomers (A and B) of PHb1(sa) due to crystal contacts and form a short helix . The conformation of the N-terminal region differs significantly between PHb1(sa) and PHb1(sl) (b1(sa) does not contain the first strand (β0) found in PHb1(sl). These structural variations, which likely reflect differences in the contact environments in the crystals, demonstrate the inherent flexibility of the PHb domains . The L34 loop of PHb2 is exposed to solvent and disordered. The PHb domains of PHb1 and PHb2 can be superimposed with an average rmsd of ∼ 3.0 Å for ∼ 110 aligned Cα atoms (b domains in PHb1(sl) and PHb2 are found in α0, the second β-sheet (β5–β7), α1, and the loop regions  c. PHb1 is buried inside the protein where its carboxyl side chain forms multiple hydrogen bonds with the main-chain amide (NH) of a conserved threonine (Thr47 in PHb1 and Thr55 in PHb2) and the side chain of an arginine (Arg50 in PHb1 and Arg76 in PHb2). These interactions, combined with the conserved hydrophobic core, are expected to be important for the structural integrity of PHb domains. Furthermore, Arg50 and Arg76 also participate in a water-mediated hydrogen-bond network at the oligomeric interfaces. The conserved residues that are clustered near the L34 and L56 loops are solvent exposed and are, thus, more likely to be of functional significance.Mapping of conserved residues within the PHL56 loop . A highlb domain from either PHb1 or PHb2, identified a large number of proteins from the eukaryotic PH superfamily. The top hits included proteins of diverse function, such as the GRAM-PH domain of myotubularin family phosphoinositide phosphatase MTMR21zvr, Z = 9.9, rmsd = 2.3 Å for 94 aligned Cα atoms, 14% sequence identity), GLUE domains of VPS36 of human and Saccharomyces cerevisiae2hth, Z = 9.6, rmsd = 2.1 Å for 90 aligned Cα atoms, 16% id; PDB ID: 2cay, Z = 9.3, rmsd = 2.4 Å for 90 aligned Cα atoms, 13% id), and Dcp12qkl, Z = 8.8, rmsd = 2.3 Å for 93 aligned Cα atoms). Three previously solved structures of uncharacterized bacterial proteins, two by the JCSG (PDB IDs: 2ra9 and 2re3) and one by the Northeast Structural Genomics Consortium , also appear to contain a domain with PH fold but have one less β-strand (β1) and are structurally more distant. The closest eukaryotic protein in terms of structural similarity to PA2021 is the Ras-related protein RalA1zc3, Z = 3.4, rmsd = 2.5 Å for 59 aligned Cα atoms, 10% id). Structural comparisons of the PHb1 domain with PA2021 and representative eukaryotic PH-like domains are shown in bs packs perpendicularly with the β5–β7 sheet through hydrophobic contacts. An additional connection between the two β-sheets is mediated through the conserved C-terminal α-helix (α1). The most significant structural difference between PHb domains and canonical eukaryotic PH-like domains is the length and conformation of the β1–β2 hairpin. The longer hairpin of the eukaryotic PH-like domain curves towards the open edge of the β5–β7 sheet, such that β1 interacts with β7 to form an extended antiparallel β-sheet, thus producing a closed barrel. In contrast, the β1–β2 hairpin of PHbs, including the two-residue L12 loop, is shorter and does not interact with the β7 strand directly. The first ∼ 30 N-terminal residues of PHbs are not conserved across the PH-like superfamily of proteins. However, some proteins in the PH-like family do contain extra structural elements that are functionally equivalent for stabilization of the β1–β4 surface. For example, Dcp1 contains a helix that is equivalent to α0 of PHb1.DALI structural similarity searches,b1 and PHb2 form oligomeric rings with rarer, higher-order, cyclic symmetry.b1(sl) pentamer and the PHb2 dodecamer are illustrated in b1(sl) and PHb1(sa) form a disk with non-crystallographic C5 symmetry that is normally only encountered in viruses with icosahedral capsids. A hole in the center of the pentamer narrows from the top towards the base. The PHb1(sl) pentamer buries a total surface area of 9080 Å2 . The PHb1(sa) pentamer is similar to that of PHb(sl); thus, we refer only to PHb1(sl) in the discussion hereafter. PHb2 is also a dome-shaped assembly, but with C12 cyclic symmetry that is generated by crystallographic C4 axis and a non-crystallographic C3 axis. The PHb2 assembly is significantly larger with an outer diameter of 150 Å, that is, twice that of PHb1(sl). The PHb2 dodecamer buries a total surface area of 46,700 Å2 or ∼ 50% of total surface area (3890 Å2 per monomer). Its central channel is also significant larger with a diameter of 90 Å at the base and 15 Å at the top . The openings of both central channels are guarded by positively charged residues [Lys72 and Lys99 in PHb1(sl) and Arg161 in PHb2]. Both oligomeric states are consistent with size-exclusion chromatography experiments. Despite significant differences in the sizes of the two assemblies, the orientations of the PHb domains within the rings are highly similar. The base of each ring consists of repeating β-blades, while the top of each ring is primarily helical. If we superpose one subunit from the PHb1(sl) pentamer with one from the PHb2 dodecamer, then the average rotation angle that relates adjacent monomers from the two assemblies is 43° , which is very close to the expected value of 42° (360°/5 − 360°/12), if the deviation in rotation angle between monomers is uniform over the pentamer.PHb domain resembles those of other intermolecular β-sheet forming assemblies of structurally different folds, for example, the Sm-like ribonucleoproteins (SCOP ID: 50182) and the TRAP-like superfamily of proteins (SCOP ID: 51219) in SCOP.bs.The oligomerization mode of the PHb1(sl) is mediated primarily through hydrogen-bonding interactions (b2) helps to stabilize the β4 N-terminus with the β5 C-terminus of the next protomer and is likely important for PHb function since the proposed binding site is in close proximity (see below).The formation of the pentamer in PHractions a and b. 10 helix region (residues 25–29) of the adjacent protomer. Lys117 adopts different conformations in different promoters and makes polar contact with different partners, such as the carbonyl groups of Met27 and Gly24 and the side chain of Asp29. Asn121Oδ1 forms another hydrogen bond with the main-chain amide of Asp29. Additionally, a water-mediated hydrogen-bonding network is observed for polar/charged residues buried in the interface . Overall, the PHb1(sl) pentamer interfaces lack specific, conserved, side-chain interactions, except for Thr81.The second contact point in the pentamer involves α1 (residues 113–121) from one monomer and the 3b2 α1 is significantly longer and forms a hairpin appendage with an additional helix α2 at the C-terminus (distance = 6.8 Å and Ω = − 159° to α1). The hairpin constitutes the primary mediator of the central core of the dodecameric PHb2 ring, mainly through hydrophobic interactions (2) of the overall buried surface within the dodecamer. The packing of hairpins involves the docking of surface hydrophobic residues of α1 (around Ile156) and α2  into a hydrophobic pocket formed by the adjacent hairpin of the neighboring protomer (The PHractions c and e. protomer e. Helix b domains in the PHb2 dodecamer is remarkably similar to that of the PHb1 pentamer (b1(sl)] are formed between the two strands due to the separation at the N-terminus of β4 and C-terminus of β5 to accommodate the packing of the α1–α2 hairpins. The second interface between PHb domains in PHb2 also resembles that of the PHb1 and involves contacts between the N-terminal 310 helix of PHb (residues 31–37) and a section of helix α1 (residues 120–130) of two adjacent promoters. The interface involves van der Waals contacts (33% of nonpolar residues) but no hydrogen bonds. A region of helix α3 (residues 200–201), which is unique to PHb2, also contributes to these interactions (Tyr201/Asp37).Despite significant difference in the size of the oligomers, the arrangement of the PHpentamer c and d. b domain of PHb2 was observed, presumably to facilitate formation of the dodecamer ring  and an N-terminal region containing a 310 helix. Interestingly, this mode of protein–protein interaction, involving the open edge of the β5 strand, is commonly observed in eukaryotic PH-like domains (70PYxxI74) exists at the C-terminus of β4 in PHb1 and adopts a similar main-chain conformation to PTB peptides, such as in Shc.bs is different; the tyrosine in the PTB peptides plays a significant role in PTB binding, while the tyrosine is buried inside PHbs and not involved in protein–protein interaction.The oligomerization of PH domains . In PTB b oligomerization interface overlaps with a common site of protein–protein interaction in eukaryotic PH domains. Furthermore, the mode of PHb oligomerization through β-sheet extension is similar to the mode of protein recognition by eukaryotic PH domains.Another example of the β5–β7 β-sheet extension at β5 is observed in the recent structure of the TFIIH p62 subunit (PH domain) in complex with the C-terminal acidic domain of the general transcription factor TFIIE.b assemblies by analyzing the sequence conservation patterns in the context of the protein structure, particularly for clustering of conserved residues on the protein surface. Aside from the few buried, charged residues discussed above, all conserved surface residues cluster to a single location on each β-blade at the base of the dome (b1(sl), these residues include Asp42 from β2, Asp55 and Gln57 from β3, and Lys63 from β4, as well as residues from the adjacent protomer: Glu80 from β5 and Asp86, Asp88, and Glu90 from β6 . The surface is stabilized by residues  that form hydrogen bonds with adjacent structural elements. These PHb1 residues are also highly conserved in PHb2. Thus, PHb2 also contains a similar site due to the similarity in the arrangement of its PHb domain in the oligomer  is likely relevant for the function of PHbs.The bipolar electrostatic potential distribution  displayed by many PH domains characterized to date is believed to facilitate their interaction with the membrane.near L34 . Thus, Pthe dome e and f. et al. noted that, if bacterial homologs were to exist, they would likely be undetectable by the sequence methods of the time.b1 and PHb2 structures reported here, we used sequences from the PF08000 family to iteratively identify other potential homologs. Profile-based sequence similarity search methods implemented in PSI-BLASTbs and eukaryotic PH domains. Another profile-based fold recognition method, FFAS, suggested that PHb domains were likely related to the GLUE domain (97% confidence).b domains and eukaryotic PH domains. In the absence of strong sequence similarity, remote relatives of a highly divergent superfamily can be detected by analyzing unique structural motifs that are important for either fold or function.PH-like domains are ubiquitous in eukaryotes. The current SMART database (v6)b secondary-structure elements are highly related to those of eukaryotic PH-like domains, with similar highly conserved buried hydrophobic residues  in the 310 helix preceding Ile74 in PHb1. This region is located at a strategic location where the first β-sheet (β1–β4), second β-sheet (β5–β7), and the C-terminal helix α1 pack together and is likely to be important for correct folding of the domain. This structural feature is widely conserved in the PH-like superfamily , supporting its importance for the PH-like fold. Another conserved structural feature of PHb1 domains is a highly conserved, mostly buried salt bridge  and DUF1696 was the third highest . These analyses suggest that DUF2244 also possibly belongs to the PHb domain family. Based on the known PHb structures, we noted that the current Pfam definition of DUF2244 includes two N-terminal transmembrane helices that anchor this family to the membrane, much like DUF1200. Interestingly, alignments of each of these families suggest that they lack the β1 strand, similar to PA2021, and raise the interesting possibility that PHb domains lacking a β1 strand could represent a more simplified, ancestral-like structure  are expressed, indicating that PHbs are functional in bacteria.B. subtilis is induced under antibiotics, heat shock, and alkaline shock, suggesting a role in cell stress responses.W, a member of extracytoplasmic function (ECF) σ factors that often control functions associated with the cell surface or transport. σW likely plays a role in defending the cell against antimicrobial agents.Bacillus licheniformis.b function may be provided by the presence of PHb1 in the lysogenic modules of bacteriophages, such as Lactococcus bacteriophage ul36 (ORF124)Microbacterium nematophilum (ORF77).bs may also play a role in the phage life cycle.As discussed above, a conserved hydrophilic site near the rim of the dome is likely to be of functional importance for both PHB. subtilis is found in the sub-membrane fraction and was predicted as a peripheral protein.b1 homologs were also identified in the insoluble sub-proteome of other bacteria, such as Streptomyces coelicolorQ9F325) and Oceanobacillus iheyensisQ8ELK9), an alkaliphilic and halotolerant deep-sea bacterium. These results raise the possibility that the activity of PHb domains may be associated with the membrane. However, the structures of PHbs do not seem to support a direct interaction, since the electronegative conserved binding sites of the PHb oligomers are not suitable for interacting with a negatively charged bacterial membrane. Furthermore, the PHb domain also lacks the actual site and electropositive surface corresponding to the eukaryotic lipid binding site. Instead, we predict that the PHbs likely interact with other proteins through the conserved binding site at their base. A eukaryotic example, which utilizes a similar location for protein interaction, is the TFIIE/TFIIH complex where the β5–β6 hairpin region of TFIIH is involved in the interaction with an extended peptide from TFIIE . It may also be significant that these oligomers are assembled from higher-order cyclic symmetries that are rare in proteins analyzed to date,b domains and assemblies, but the identification here of PHb domains provides fascinating new insights into their involvement in all kingdoms of life.YvbH of om TFIIE . From thb1(sl) (Swiss-Prot: A3QB43), PHb1(sa) (Swiss-Prot: A1S3D0), and PHb2 (Swiss-Prot: Q41E03). Clones were generated using the Polymerase Incomplete Primer Extension (PIPE) cloning method.Shewanella sp. PV-4, S. amazonensis SB2B, and Exiguobacterium sp. 255-15) using PfuTurbo DNA polymerase (Stratagene) and I-PIPE (Insert) primers that included sequences for the predicted 5′ and 3′ ends. The expression vector pSpeedET, which encodes an amino-terminal tobacco etch virus (TEV) protease-cleavable expression and purification tag (MGSDKIHHHHHHENLYFQG), was PCR amplified with V-PIPE (Vector) primers. V-PIPE and I-PIPE PCR products were mixed to anneal the amplified DNA fragments together. E. coli GeneHogs (Invitrogen) competent cells were transformed with the V-PIPE/I-PIPE mixture and dispensed on selective LB-agar plates. The cloning junctions were confirmed by DNA sequencing. Using the PIPE method, the section of the gene encoding residues Met1-Met8 was deleted for PHb1(sl) (Met1-Phe12 was deleted for PHb2). Expression was performed in a selenomethionine-containing medium. At the end of fermentation, lysozyme was added to the culture to a final concentration of 250 μg/ml, and the cells were harvested and frozen. After one freeze/thaw cycle, the cells were homogenized in lysis buffer  and the lysate was clarified by centrifugation at 32,500g for 30 min. The soluble fraction was passed over nickel-chelating resin  pre-equilibrated with lysis buffer, the resin was washed with wash buffer , and the protein was eluted with elution buffer . The eluate was buffer exchanged with TEV buffer  using a PD-10 column  and incubated with 1 mg of TEV protease per 15 mg of eluted protein. The protease-treated eluate was passed over nickel-chelating resin  pre-equilibrated with Hepes crystallization buffer , and the resin was washed with the same buffer. The flow-through and wash fractions were combined and concentrated for crystallization trials by centrifugal ultrafiltration (Millipore). For PHb1(sa), lysines were reductively methylated by adding 40 μl 0.98 M dimethylaminoborane and 80 μl 3.26% by weight formaldehyde, per milliliter of protein, for 2 h in the presence of crystallization buffer at 4 °C.The same protocol was used for the cloning, expression, and production of PHb1(sl), concentrated to 13.3 mg/ml, was crystallized by mixing 100 nl protein with 100 nl crystallization solution in a sitting drop above a 50-μl reservoir volume using the nanodroplet vapor diffusion methodPHb1(sa) crystals were obtained in multiple conditions. The crystal used for refinement was obtained using a precipitating reagent consisting of 10% glycerol, 5% PEG 3000, 20% PEG 400, and 0.1 M Hepes, pH 7.3. (4S)-2-Methyl-2,4-pentanediol was added to the crystal as a cryoprotectant to a final concentration of 10% (v/v). The crystallization reagent yielding the crystal used for the second MAD data set consisted of 5% PEG 3000, 22% PEG 400, 10% glycerol, and 0.1 M Hepes, pH 7.5. Glycerol was added to the crystal as a cryoprotectant to a final concentration of 15% (v/v). Both cubic-shaped crystals (dimensions: ∼ 30 μm × 20 μm × 20 μm) were harvested after 50 days at 293 K. The protein concentration was 19 mg/ml.PHN,N-bis(2-hydroxyethyl)glycine, pH 9.0. The diamond-shaped crystal (dimensions: ∼ 40 μm × 40 μm × 40 μm) was grown at 277 K. Ethylene glycol was added to the crystal as a cryoprotectant to a final concentration of 10% (v/v). The protein concentration was 13.6 mg/ml.The crystallization reagent consisted of 10% (w/v) PEG 6000 and 0.1 M b1(sl) and PHb2 were determined using a 1 × 30 cm Superdex 200 column  in combination with static light scattering (Wyatt Technology). The mobile phase consisted of 20 mM Tris, pH 8.0, 150 mM NaCl, and 0.02% (w/v) sodium azide.In order to identify the crystals with the best possible diffraction, we screened all harvestable crystal hits for diffraction using the Stanford Automated Mounting systemS. loihica PHb1) and SSRL beamlines 9-2 (S. amazonensis PHb1) and 11-1 (PHb2). Data were collected at wavelengths corresponding to the inflection, high-energy remote, and peak of a selenium MAD experiment at 100 K using ADSC Q315 [PHb1(sl)] and MarCCD325 [PHb2 and PHb1(sa)] detectors. Data processing, structure solution, and refinement were carried out independently for each of the three proteins using the following protocol. The MAD data were integrated and reduced using XDS and then scaled with the program XSCALE.b1(sa), an additional MAD data set was collected using another crystal after initial higher-resolution SAD data did not provide enough phasing information. Structural solution was possible with combination of both data sets, which improved the accuracy of phases, especially at lower resolution. Model completion and refinement were performed with Cootb2 except for flexible loop regions, while loose or no non-crystallographic symmetry restraints were used for PHb1s. Each monomer was defined as a TLS group. Experimental phases were used as restraints during refinement. Analysis of the stereochemical quality of the model was accomplished using MolProbity.b domains and the average rmsd value were calculated using MATT.α atoms in both structures. Alignments of PHb domains in MAD data were collected at the Advanced Light Source  beamline 8.2.2 (b1s at 2.0 Å resolution and PHb2 at 2.42 Å resolution have been deposited in the PDB under accession codes 3dcx, 3hsa, and 3b77, respectively.Atomic coordinates and experimental structure factors for both PH


Domains, evolutionarily conserved units of proteins, are widely used to classify protein sequences and infer protein function. Often, two or more overlapping domain models match a region of a protein sequence. Therefore, procedures are required to choose appropriate domain annotations for the protein. Here, we propose a method for assigning NCBI-curated domains from the Curated Domain Database (CDD) that takes into account the organization of the domains into hierarchies of homologous domain models.Our analysis of alignment scores from NCBI-curated domain assignments suggests that identifying the correct model among closely related models is more difficult than choosing between non-overlapping domain models. We find that simple heuristics based on sorting scores and domain-specific thresholds are effective at reducing classification error. In fact, in our test set, the heuristics result in almost 90% of current misclassifications due to missing domain subfamilies being replaced by more generic domain assignments, thereby eliminating a significant amount of error within the database.Our proposed domain subfamily assignment rule has been incorporated into the CD-Search software for assigning CDD domains to query protein sequences and has significantly improved pre-calculated domain annotations on protein sequences in NCBI's Entrez resource. A major goal in the post-genomic world is to infer protein function from sequence information. One popular approach is to classify protein families or domains by grouping homologous sequences and annotating the groups with properties such as general function, intracellular location, three-dimensional structure, conserved sequence patterns or motifs, evolutionary origin, and binding and active sites. Novel proteins can be characterized quickly by assigning a group via profile search methods. However, more than one family or subfamily may exhibit similarity to overlapping sequence intervals and to a degree that seems convincing Figure . AssigniDomains are evolutionarily conserved units in proteins and frequently correspond to recurrent structural and functional units. The particular function of a protein depends on its combination of domains; two-thirds of prokaryotic proteins and 80% of eukaryotic proteins have more than one domain. To create new protein functions, novel domain architectures arise through domain rearrangement and recombination, frequently through gene duplication and fission or fusion events ,6. A domTo refine protein annotation, domains models may be subdivided to represent more specific functions or conserved features. CDD curators apply phylogenetic and structural analysis to construct hierarchies of homologous domain models, related by common descent, to reflect aspects of their evolutionary histories ,4. CuratIt may seem natural that once the profiles have been defined, the most significant match to a query sequence is the correct one. Indeed, domains from Pfam ,10 and SThe systematic arrangement of CDD domains requires identifying the most suitable level of resolution among domain models that offer more or less fine-grained descriptions of a protein. We take the viewpoint that if a protein cannot be associated unambiguously with a specific subgroup or may be a member of a subgroup that has not been defined, the protein can be assigned a more generic domain model or the superfamily in general. Consequently the ideal domain assignment to a query sequence will be the most specific domain, within a candidate hierarchy, with a strong match to the sequence. Here, we analyze a set of correct domain assignments from CDD to establish an improved method for assigning domains to query sequences. The effectiveness of a traditional alignment score and domain-specific threshold is of particular interest, as this method is efficient and makes use of alignment information that is already computed for CDD.To benchmark domain assignment heuristics, a reference set of domain assignments is constructed from the NCBI-curated portion of CDD v. 2.12. This set contains every sequence fragment present among the MSAs and its domain assignment. The NCBI-curated domains have undergone rigorous testing to optimize the MSAs and distributions of representative sequence fragments.The correct or most specific domain for each sequence in a hierarchy is defined as the domain having no descendant that contains an overlapping sequence interval. Two sequence intervals from one protein are said to overlap if one sequence interval contains at least 30% of the positions of the other. While each sequence has been placed in the most specific domain model that characterizes it, this step is required as parent and child domains share overlapping sequences Figure . Sequenchit, for brevity. We call a match between a sequence region and its correct domain a self hit to distinguish it from other hits to overlapping sequence regions. Other hits to the sequences in the reference dataset serve as examples of incorrect domain assignments.A significant PSSM-sequence alignment is called a CDD v 2.12 contains 3078 NCBI-curated domains in 495 hierarchies, including 298 single-domain "hierarchies" and 197 trees with 2357 leaf and 423 internal domains. Many sequence fragments used to construct the NCBI-curated domain profiles come from proteins that have been replaced with newer versions or declared obsolete. Among the 109186 representative sequences in NCBI-curated domain hierarchies, over 21% have no hits and more than 90% of those sequences are no longer present in Entrez. This analysis excludes the 149 curated domains without corresponding live data in Entrez, leaving 2929 domains.We begin by assessing the performance of the previous method for assigning NCBI-curated domains from CDD. The NCBI CD-Search tool  has histWe define a score threshold for each domain to be the lowest self-hit score to that domain among all of its sequences in the benchmark set. This additional heuristic, in particular, reduces incorrect assignments to subclasses as only 9.1% of hits to subclasses score above the thresholds for those subclasses Table . The thrWe propose to label a single domain as correct or specific for a protein sequence region if its alignment score is highest among all domains that align to overlapping regions of the protein sequence and the score exceeds a pre-calculated threshold for the domain, defined as the minimum alignment score among confirmed members of the domain. Sequence intervals that are difficult to group with a specific subclass with high confidence following this rule may receive only generic domain assignments. Assuming that the set of overlapping domains represents an ancient domain superfamily, such a generic assignment would be characterized as membership with the respective superfamily.A more concrete picture of the effect of the proposed rule may be gleaned by quantifying misclassifications, defined to be either descendants of the correct domain or domains that lie in other branches of the correct hierarchy. Averaged over domains in multi-domain hierarchies and counting only sequences with self hits, the misclassification rate using high scores only is 2.6%. Incorporating score thresholds to eliminate low-scoring best hits reduces the misclassification rate to 0.85%. Misclassifications may also be used to estimate error due to missing subfamilies. Not all subclasses in a domain hierarchy may have been identified as the available sequence databases only provide a terse snapshot of protein domain diversity. We simulate a cross-validation experiment to ask, if an existing domain model were missing from a hierarchy, what fraction of its sequence intervals have best hits to other models in the hierarchy that are not ancestors of the correct model? Averaged over leaf domains, 50.9% of domain assignments made from high alignment score alone are misclassifications, compared to 6.0% of domain assignments after thresholds are used to screen hits.To illustrate the effect of our proposed method, we examine domain assignments from the glycyl radical enzymes (RNR_PFL hierarchy). Its subgroups have distinct and important functions, including ribonucleotide reductases (RNRs), which synthesize deoxyribonucleotides, and pyruvate-formate lysases (PFLs), a family of catabolic enzymes. The proposed method places the sequence [Entrez:CAA42118] into RNR class 1 and places [Entrez: AAZ61477] into RNR class-1-like domain. The functions of these proteins are inferred by their subclass. Other proteins receive generic assignments to this family. For example, PFL2 (cd01677) is the best match to [Entrez:ABX41552] and [Entrez:EDQ26237] with alignment scores that fall short of the PFL2 threshold. The first alignment includes a long insertion (gap), and the latter exhibits weak sequence similarity; in both of these scenarios the transfer of functional annotation may not be straightforward.Chlorobium limicola DSM 24 has RNR_3 proteins (e.g. [Entrez:ZP_00512827]) as well as an enzyme ([Entrez:ZP_00512727]) that matches RNR_1_like, suggesting that RNR_1_like, a subfamily lacking experimental characterization, may contain non-oxygen dependent versions of RNR_1.Domain assignments also help to make biological insights. RNRs fall into classes that use different mechanisms and/or cofactors. Class 1 is oxygen dependent and class 3 is used by strictly or facultative anaerobic organisms. RNR_1_like has a similar active site to class 1 and at the time of curation, no specific literature was available about this subclass. We observed that the strictly anaerobic organism While many sequences can be classified by sequence similarity, profiles of protein domain families make it possible to quickly classify more distant homologs  and can Although the sequence and domain databases evolve rapidly, we expect our findings to provide an accurate snapshot for some time. A version of our proposed method has been incorporated into the current version of the CD-Search program and the pre-calculated annotation of proteins with domains in NCBI's Entrez system. Domain assignments to specific orthologous subfamilies or ancient subfamilies are distinguished from non-specific assignments to a domain superfamily. High-confidence annotation of functional sites is also provided following these results. We hope the improved ability to quickly and accurately classify proteins will be a valuable step toward simplifying protein sequence analysis and the computational annotation of genomes.The authors declare that they have no competing interests.JF carried out the experiments and drafted the manuscript. AMB conceived the study, participated in its design, and helped to draft the manuscript.Extended Results section. Extended presentation of all analyses, including additional data and discussion.Click here for file


Protein domains are protein regions that are shared among different proteins and are frequently functionally and structurally independent from the rest of the protein. Novel domain combinations have a major role in evolutionary innovation. However, the relative contributions of the different molecular mechanisms that underlie domain gains in animals are still unknown. By using animal gene phylogenies we were able to identify a set of high confidence domain gain events and by looking at their coding DNA investigate the causative mechanisms.Here we show that the major mechanism for gains of new domains in metazoan proteins is likely to be gene fusion through joining of exons from adjacent genes, possibly mediated by non-allelic homologous recombination. Retroposition and insertion of exons into ancestral introns through intronic recombination are, in contrast to previous expectations, only minor contributors to domain gains and have accounted for less than 1% and 10% of high confidence domain gain events, respectively. Additionally, exonization of previously non-coding regions appears to be an important mechanism for addition of disordered segments to proteins. We observe that gene duplication has preceded domain gain in at least 80% of the gain events.The interplay of gene duplication and domain gain demonstrates an important mechanism for fast neofunctionalization of genes. Protein domains are fundamental and largely independent units of protein structure and function that occur in a number of different combinations or domain architectures . Most prKua-UEV [The question of what mechanisms underlie domain gains is related to the question of what mechanisms underlie novel gene creation ,8,9. TheKua-UEV . RecombiKua-UEV . The twoKua-UEV , which rKua-UEV , also knKua-UEV ; 2, exonKua-UEV ; and 3, Kua-UEV . InteresProtein evolution has frequently been addressed by studying the evolution of domain architectures ,23. SpecIn this work, we use the phylogenetic relationships between genes from completely sequenced metazoan genomes in order to address the question of what mechanisms underlie the gains of novel domains. To do this, we first identify a set of high-confidence domain gain events and then look at the characteristics of the sequences that encode these domains. Our results show that gene fusion through joining of exons from adjacent genes has been a dominant process leading to gains of new domains. Two other mechanisms that have been proposed as important contributors to gains of new domains in animals, retroposition and insertion of exons into ancestral introns through intronic recombination, appear to be minor contributors. Furthermore, we observe that most domain gain events have involved gene duplication and that domain gains often relied on DNA recombination. Based on the results presented here, we propose that these gain events were frequently assisted by NAHR, which played a role in creating gene duplicates and in the juxtaposition of the ancestral genes concerned.To find a set of high-confidence domain gain events, we used gene phylogenies of completely sequenced animal genomes from the TreeFam database . TreeFamIt is important to distinguish real domain gain events from domain gain calls caused by errors in gene and domain annotations. To obtain a set of high-confidence domain gains, we implemented an algorithm that ensured that a gain is not falsely called when other genes in that family had actually experienced multiple losses of the domain in question. We also took into account only those gains that had at least one representative sequence in a genome of better quality and we discarded gains where there was only one sequence with the gained domain, that is, gain was on the leaf of the phylogenetic tree. We did this to overcome the issue of erroneous gene annotations. We then refined the initial domain assignments to find domains that were missed in the initial Pfam-based annotation and then discarded all dubious domain gain cases where there was evidence that a domain gain was called due to incorrectly missing Pfam annotations. After filtering for confounding factors that could cause false domain gain calls and taking into account only examples where the same transcript contains both the ancestral portion of the gene and a sequence coding for a new domain, we were left with 330 events where we could be confident that one or more domains had been gained by an ancestral protein during animal evolution - we took into account only gains of new domains, and not duplications of existing domains. The final set will not be comprehensive, but these filtering steps were necessary to ensure that we have a set of high-confidence domain gain events. Moreover, none of these steps introduces a bias towards any one mechanism over another. The only mechanism of domain gain that we cannot detect after this filtering is the case where amino acid mutations in the sequence created signatures of a novel domain that was not previously present in any protein; for example, when point mutations in the mammalian lineage created signatures of a mammalian-specific domain.To investigate which molecular mechanisms have caused domain gains in our set of high-confidence domain gain events, we examined the characteristics of the sequences that code for the gained domains. As a requirement, each gain event in our set has as descendants two or more genes with the gained domain. To simplify the investigation, we only considered one representative protein for each gain event, and most (232 or 70%) of these were drawn from the human genome as its gene annotation is of the highest quality. Sometimes the same protein was an example for more than one domain gain that occurred during evolution. We projected intron-exon boundaries and intron phases onto the representative protein sequences to help identify the possible causative mechanism. We also compared each representative protein sequence with the orthologs and paralogs in the same TreeFam family that lacked the gained domain. This helped us to assign the characteristics of the gained domains.-13, Chi-square test; Additional file We recorded domain gain position  as well as the number of gained exons and whether the domain was an extension of an existing exon Figure . We obseSchistosoma mansoni and Tetraodon nigroviridis , and among the organisms with least reported gains were human and mouse ). We compared the high and medium confidence sets of gain events  set by excluding two out of the three filtering criteria . This suggests that the causative mechanism can be investigated by looking at the characteristics of the domain in one representative protein for each gain. Additionally, we tested whether deficiencies in the current transcript assignments introduce false domain gain calls and found that not more than 4% of domain gain calls could be due to discrepancies in gene annotations  to the divergence of primates (around 25 million years ago). We grouped descendants of each gain event into the evolutionary group  they span. Additional file SETMAR gene were most likely gained by retroposition and have an origin in the gene SUV39H1. Interestingly, this gene lies within the intron of another gene on the opposite strand, which implies a possible means for overriding the need for the evolution of novel regulatory signals. A similar observation has been reported for the examples of evolution of novel human genes [Drosophila melanogaster, have numerous examples (22 cases) of a gain of domain coded by one exon, leaving open the possibility that retroposition might be a more important mechanism for domain gain in insects than it is in other lineages. However, overall this seems to be a rare mechanism for domain gain in animals and there are also indications of the importance of adjacent gene joining [Drosophila lineage.Domains in the human lineage for which we can identify a potential donor protein and that are gained within a single exon are possible candidates for retroposition 26 cases). We checked these cases manually and found that only one of them was plausibly mediated by this mechanism Figure ; the pre cases. Wan genes -42. The  joining  and NAHR joining  in the fTerminal gains of domains coded by multiple novel exons are particularly interesting here because for these events there is only one plausible causative mechanism: joining of exons from adjacent genes Figure . Even thUEVLD gene ; in fruit fly the dominant category was extension of an exon at the carboxyl terminus (29% of domain gains); and in zebrafish it was a mixture of the two . For rat and chicken we had too few domain gains to draw conclusions.UEVLD gene: joining of exons from adjacent genes after gene duplication. For these two examples, however, there is also evidence of a likely connection between recent genomic duplication and domain gain. However, it is necessary to be cautious when assessing the possible role of the protein products of these genes. For both examples, there is only transcript evidence and some of the transcript products of these genes appear to have a structure that would lead to them being targeted by nonsense-mediated decay (NMD) [Recent segmental duplications in the human genome are a possible source of new genetic material  and theiay (NMD) . SometimOne advantage of using TreeFam phylogenies is the ability to distinguish between gene evolution that follows gene duplication and gene evolution that follows speciation. When comparing the observed versus expected frequency of duplication and speciation events after which domain gain occurred, we found that domains were gained 2.7 times more frequently after gene duplication compared to after speciation  and 4.5 times more frequently when numbers of nodes were compared . Additionally, when some sequences lacked a domain that other family members had, we used Wu-blastp to search that sequence against the domain sequences found in other members of the family. When a significant match was found  we added domain assignments to those sequences.We assigned Pfam domains (release 23.0) to all protein products of genes in the TreeFam database (release 6.0) using the Pfam_scan.pl software. Since domains in the same Pfam clan are evolutionarily related, we replaced domain identifiers with clan identifiers where applicable. Domain prediction methods can both fail to predict Domain refinements added Pfam domains to proteins that shared significant similarity with domain sequence but were not recognized by searching with the Pfam hidden Markov model library. However, apart from these clear cases of a lack of domain annotation, there are also cases where proteins share only moderate similarity with domain sequence and it is difficult to say whether a domain should be annotated to these proteins as well. To avoid false calls of domain gains, we excluded domain gain events where sequences in the same gene family shared a similarity with the gained domain but were not annotated with that domain. We chose a strict threshold and excluded all gain events where a domain sequence had 16% or more identical amino acids aligned to any sequence in the same TreeFam family that lacked the gained domain. This further reduces the chances of erroneously calling domain gains due to a lack of sensitivity of some Pfam hidden Markov models.D. melanogaster (fruit fly), Xenopus tropicalis (frog), Danio rerio (zebrafish), Gallus gallus (chicken), Mus musculus (mouse), Rattus norvegicus (rat) or Homo sapiens (human) - that is, to a species whose genome is of a good quality. This left us with 653 gained domains that had representative transcripts that fulfilled all conditions. Since each representative sequence was chosen from the descendant with the genome of best quality for all gains in the human lineage, we chose representative human transcripts (proteins). Exclusion of leaf gains and selection of representative transcripts from better quality genomes were necessary to ensure that our gain events were not due to gene annotation errors. We then excluded all instances where the sequences from the same family that lacked the gained domain were found to have diagnostic motifs for that domain, as recognized by profile comparer [To identify the branch points in the phylogenetic trees at which new domains were gained, we used the TreeFam API . In Treecomparer , or to hWe used the TreeFam table map with gene structures to project the intron-exon boundaries and intron phases on the representative protein sequences for each domain gain event. In order to establish whether the gained protein domains were part of completely new exons or extensions of already pre-existing exons, we downloaded protein sequence alignments for each TreeFam family with a gained domain from the TreeFam website. Since it is unlikely that the gained sequence would exactly correlate with domain boundaries, we examined the similarity in regions close to exon boundaries. We considered that a domain was inserted into an existing exon if the region in the same exon close to the exon border shared partial similarity with an exon from those sequences in the same family that lacked the domain. We considered that the domain was gained within an existing exon when the boundary region of the exon - first or last third of the sequence outside of the domain - had 30% or more identical residues to one of the sequences without the inserted domain. We required that this 'boundary' region was at least seven amino acids long. However, because of this criterion that only a short stretch of sequence similarity is enough to claim that a gained domain is coded by an extended ancestral exon, the number of extended exons is likely to be an overestimate.When a new domain was encoded by a first or last coding exon, the gain was called as an amino- or carboxy-terminal gain, respectively. In addition, when an inserted domain was not coded by the terminal exons, we checked whether additional exons towards the termini were gained together with the ones coding for the gained domain. If there was no significant similarity between these exons and the ones in the sequences without the gained domain, the exons were called novel and the gain still called terminal. Conditions for calling an exon as novel were: 85% or more novel amino acids in an exon ; or less then 10% identity with any of the sequences without the domain. For short exons coding for 20 amino acids or less, we changed this requirement to less than 40% identity. All other domain gains were classified as middle.It is important to note that examining the sequences that surround the gained domains, when classifying the gains according to their relative position and as exonic or intronic, also helps to overcome the issue of imperfect domain boundary assignments, which could bias classification of gained domains.-4 with 60% or more of the domain sequence aligned. We visually examined the structures of the genes with gained domains and of their best hits using Ensembl (release 50) and the Belvu viewer [For all domain gain events that have a human descendant, the gained domain sequence from a representative protein was searched against the rest of the human proteome. For this we used Wu-blastp. The best significant hit that was not in one of the gene's paralogues was considered to be a potential donor of the gained domain. A set of paralogues for each gene was composed of other human genes from the same TreeFam family and Ensembl paralogues for that gene. The condition for a significant hit was an E-value < 10u viewer .We used the segmental duplication coordinates from the Segmental Duplication Database . We inveIR: illegitimate recombination; NAHR: non-allelic homologous recombination; NMD: nonsense mediated decay.MB participated in design of the study, carried out analyses and drafted the manuscript. AF analyzed gene structures of selected examples. AB conceived of the study and participated in design of the study and writing of the manuscript. All authors read and approved the final manuscript.Further discussion of different types of domain gain events as classified in Figure Click here for fileFlowchart of (a) methods and (b) analysis for the set of high-confidence domain gain events and for the set of medium-confidence domain gains. The numbers of gained domains we were left with after each filtering step are noted in (a). In some cases more domains were gained at the same time; hence, the number of gain events that we looked at for the high-confidence domain gains differs from the number of gained domains.Click here for fileDistribution of domain gain events according to the position of the domain insertion and the number of exons gained in the set of high-confidence domain gains and the set of medium-confidence domain gains. (a) The distribution of characteristics of domains from the high-confidence set of domain gains is identical to that in Figure (b) The distribution of characteristics of domains from the set of medium-confidence domain gains. There are in total 330 high-confidence domain gain events and 849 medium-confidence domain gains (of which 19 gains have ambiguous position and are not shown in the graph). The flowchart in Additional file Click here for fileAnalysis of supporting evidence for the representative transcripts for domain gain events.Click here for fileA table listing high-confidence domain gain events.Click here for fileAnalysis of evidence for retroposition and middle insertions by intronic recombination as mechanisms for domain gain.Click here for fileFusion of adjacent genes and NAHR as a mechanism that preceded gene fusions. Discussion of evidence for NAHR as a mechanism that frequently assisted domain gains.Click here for fileExamples of domain gains by joining of exons from adjacent genes. (a) TreeFam family TF323983 contains Cadherin EGF LAG seven-pass G-type receptor (CESLR) precursor genes. One branch of the family, containing vertebrate genes, has gained the Sulfate transport and STAS domains in addition to the ancestral cadherin, EGF and other extracellular domains. The gain occurred after the other vertebrates diverged from fish and homologues without the gained domains are present in all animals. A representative for the gain is the transcript CELSR3-207 (ENST00000383733) and its 3' end is shown on the left-hand side (the whole transcript is too long to be clearly presented). On the right-hand side is shown a gene that is the plausible donor of these domains. Namely, the gene SLC26A4 (ENSG00000091137) contains both domains, and its STAS domain is 31% identical to that in the CELSR3 gene. In addition, the alignment with the zebrafish genome is shown below the CELSR3-207 transcript. The yellow arrows represent the alignment with chromosome 8 in zebrafish, and pink arrows that with chromosome 6 (information taken from the USCS browser). The alignment with the fish genome shows that the synteny is broken exactly in the region where the new domain is gained. Therefore, the plausible scenario for domain gain involves gene duplication, recombination and joining of newly adjacent exons. (b) Another example of a domain gain after gene duplication and exon joining. Family TF334740 in the TreeFam database contains genes that code for the Rho-guanine nucleotide exchange factor (RhoGEF). However, the RhoGEF domain was not present in the ancestral protein but was inserted later on together with the C1_1 domain when mammals diverged from other vertebrates . The representative transcript for the gain event is AC093283.3-201 (ENST00000296794). The gene ARHGEF18 (ENSG00000104880) has both of these domains, and the two RhoGEF domains between the genes are 52% identical. Hence, ARHGEF18 is a plausible donor for this gain event. Again, the mechanism for the gain of these domains most likely involves gene duplication and exon joining. (c) An example of a domain gain after segmental duplication and exon joining. TreeFam family TF351422 contains only primate genes, and after a gene duplication event one branch of the family has gained the PTEN_C2 domain. A representative transcript for this gain is AL354798.13-202 (ENST00000381866). A few segmental duplications span across the gene AL354798.13 and one of them covers only the ancestral portion of the gene - without the gained domain. The pair of that segmental duplication is on the gene's paralogue that has not gained the domain, the gene AP000365.1 (ENSG00000206249). Hence, a possible scenario is that a recent duplication of a paralog gene has changed its genetic environment and brought it into proximity of the PTEN_C2 domain, which subsequently became part of the gene. (d) Another example of a gain of a domain-coding region by segmental duplication followed by exon joining. A branch with primate genes in the TF340491 family of vertebrate proteins that contain the KRAB domain has gained the additional HATPase_c domain. The representative transcript is the human PMS2L3-202 (ENST00000275580). The HATPase_c domain exists in the gene PMS2 (ENSG00000122512) and on the protein level the gained domain is 98% identical to the sequence in the protein product of PMS2's transcript, PMS2-001. A segmental duplication spans across the gained sequence in the transcript PMS2L3-202 and is a pair of the segmental duplication that covers the same domain in the gene PMS2. The pair of segmental duplication regions are presented as grey boxes and are connected with arrows. Therefore, the mechanism underlying this gain appears to be a segmental duplication of the sequence belonging to PMS2 after which the copy next to PMS2L3-202's ancestor was joined with it. An important caveat is that PMS2L3-202 has a structure that can be targeted by NMD.Click here for fileA table that lists domains that are classified as being gained by insertion of new exons(s) into the introns of ancestral genes.Click here for fileA table listing significant Gene Ontology terms for human genes that have been extended with a new protein domain during evolution.Click here for file