This paper explores the structural continuum in CATH and the extent to which superfamilies adopt distinct folds. Although most superfamilies are structurally conserved, in some of the most highly populated superfamilies  there is considerable structural divergence. While relatives share a similar fold in the evolutionary conserved core, diverse elaborations to this core can result in significant differences in the global structures. Applying similar protocols to examine the extent to which structural overlaps occur between different fold groups, it appears this effect is confined to just a few architectures and is largely due to small, recurring super-secondary motifs . Although 24% of superfamilies overlap with superfamilies having different folds, only 14% of nonredundant structures in CATH are involved in overlaps. Nevertheless, the existence of these overlaps suggests that, in some regions of structure space, the fold universe should be seen as more continuous. The initial expansion of the protein databank (PDB) in the mid-1990s inspired the creation of several hierarchical SCOP,  and nonhThe first detailed analysis of CATH, published in 1997, was based on ∼8,000 domains classified from the PDB at that time. CATH version 3.1 has expanded by over 10 fold to 93,885 domains, with twice the number of fold groups (1100 from 505) see  targetinOne key feature of the structural universe identified by our original analysis was the recurrence of common motifs  that cause overlaps in fold space as the result of a phenomenon called the “Russian doll effect” . This phMore recently, a detailed look at the domains within larger CATH superfamilies has revealed the extent to which the structures adopted by different homologs can be extended or embellished in different ways, beyond the conserved structural core. Indeed, in some superfamilies there is significant structural variation whereby some relatives contain three times more secondary structure elements than others . AlthougSeveral evolutionary mechanisms  underlie this structural variability and can result in the insertion and rearrangements of secondary structure elements . For hieDespite the controversies around structural classifications, structure-based domain classifications have been shown to be valuable for deriving phylogenetic trees  and reveAnother advantage of organizing domain structures into superfamilies and fold groups has been the detection of bias in the populations of these classification levels. An analysis of CATH revealed the existence of ten superfolds, which were overrepresented in the PDB  and appeWith the significant increase in the number and population of fold groups in CATH over the last fifteen years, we have decided to revisit the distribution of structures across the classification and to examine whether the structural divergence in some superfamilies and overlap between fold groups is posing a serious challenge to the concept of a hierarchical classification scheme.Our analysis has revealed that, although the number of domain structures in CATH has increased by over 10 fold, the numbers of architectures and core folds have grown more slowly. Furthermore, many of the fold groups shown to dominate the classification 15 years ago are still among the most highly populated. We show that for a small percentage of superfamilies (4%), significant structural divergence is observed among relatives. If a threshold of normalized RMSD <5Å , is usedAlthough most superfamilies do not structurally overlap with other fold groups in CATH, there are clearly overlaps between superfamilies in different folds in a small subset of architectures. These overlaps tend to occur between small domains, comprising fewer than six secondary structures, and are largely associated with super-secondary motifs , which recur or account for a large proportion of the fold. Although 24% of superfamilies are involved in overlaps, only 32% of nonredundant structures within them overlap with different folds . However, the presence of these overlaps suggests that for these architectures, fold space should be viewed as more continuous in nature.Although extreme structural divergence in superfamilies and structural overlaps between fold groups potentially challenge the notion of a hierarchical classification in CATH, we present strategies for coping with these phenomena. The T-level in CATH will group superfamilies sharing a common topology or folding arrangement in the evolutionary conserved cores of their domains. These similarities will be identified by manual inspection guided by automated structure comparison and analysis tools. In addition, CATH will also formally identify structural links between domains in different superfamilies to capture the more continuous nature of the relationships that exist in some regions of the structural universe.There are 93,885 domains in version 3.1 of CATH, a 10-fold increase since the last detailed analysis of CATH in 1997 . DespiteIn CATH, the fold level is manually assigned, guided by automatic structure comparison. If a newly solved domain structure does not superpose on any classified domain in CATH with a normalized RMSD <5Å see  and exhiAs first noted in 1994  and suppCATH classifies all structures that have diverged from a common ancestor into superfamilies see . RelativIt can be seen from If we define highly structurally diverse superfamilies as those comprising five or more SSGs, it can be seen that the two and three layered β and αβ-architectures contain a disproportionately higher number of highly diverse superfamilies than other architectures see . FurtherAmong the superfamilies exhibiting extreme structural drift, there are four Rossmann fold (3.40.50) superfamilies. For example, in the P loop nucleotide hydrolase superfamily (3.40.50.300), all nonredundant relatives are structurally diverse , and these domains occur in many different domain contexts see . A totalAlthough there are only 4% of superfamilies in CATH with five or more SSGs, these superfamilies are very highly populated in the genomes, accounting for nearly 40% of predicted domain structures in the sequences of completed genomes in Gene3D, and most of them are universal to all kingdoms of life . InteresAnother phenomenon challenging the CATH hierarchy is the existence of structural overlaps between different folds. Previously, we commented on a Russian doll effect whereby folds were linked by overlapping motifs . Many siSpecifically, to detect “structural overlap” between domains in different CATH fold groups, we identified cases where structures overlapped with a normalized RMSD <5Å. This criterion is the same one used to examine structural divergence in superfamilies. In addition, at least 60% of residues in the larger domain should overlap with residues in the smaller domain. This overlap constraint was imposed to ensure significant “fold” similarities between the domains as opposed to small “motif” similarities. For 76% of superfamilies, there was no overlap with structures in different fold groups see . This fiFor the remaining 24% of superfamilies that overlap with different fold groups, only 32% of the nonredundant relatives within these superfamilies are involved in the overlaps, and many of the overlaps disappear if more stringent thresholds are imposed  see . FurtherAlthough 495 superfamilies are involved in overlaps, nearly half (48%) of all overlaps are associated with folds in the α-bundle and α-orthogonal architectures and involve superposition of an α-hairpin motif see . The higA large number of overlaps also feature small domains adopting β-roll architectures (2.30). In these very small domains, the overlap of a β-meander motif can constitute a very significant proportion of the domain structure see . The αβ-The extent of divergence within, or overlap between, superfamilies is clearly dependent on the thresholds used to recognize significant structural similarity. Using the threshold of a normalized RMSD of <5Å, the majority (32 of 40) of architectures exhibit no, or very few, overlaps. However, in eight architectures that are the most highly populated with sequences of completed genomes, there are structural overlaps between some fold groups.Overlaps can also be visualized as connected networks, with the thickness of the connection determined by the extent of overlap see . By depiTherefore, whether it is sensible or useful to represent the current structural universe as captured by CATH as discrete islands, a structural continuum, or something in between depends on how the classification will be exploited or applied. These issues are considered in more depth below.The concept of a fold is clearly meaningful as it allows us to characterize the topological arrangements of secondary structures in a domain structure. Furthermore, fold similarity can be assessed quantitatively following superposition of domains. However, even if we applied a liberal threshold for recognizing similar folds , some large CATH superfamilies would effectively contain multiple fold groups. Since CATH traditionally places the T-level or fold group above the superfamily (H-level), this phenomenon could potentially break the CATH hierarchy or result in fragmentation of some superfamilies into multiple fold groups. However, if the superfamily is considered to be the major interest for biologists—this will certainly be the case for those exploiting the classification to understand protein evolution or infer function—homologs should be classified together in the same superfamily, despite structural variability.It is possible to group homologs within the same H-level, and therefore T-level, if we consider structural similarity across the superfamily in the common domain core. As reported by The phenomenon of structural divergence has become more apparent over the last few years as a result of the development of highly sensitive sequence-based methods  architectures. Previous analyses of 31 of these superfamilies  demonstrEvolution is influenced by this tolerance to structural change. Paralogous relatives with structural variations that modify the active site or protein-protein interaction surfaces, thereby expanding the functional repertoire of the organism, are likely to be expressed and retained within the organism. The structural plasticity, therefore, provides some rationale for the wide expansion of these superfamilies in the genomes . Analysihttp://beta.cathdb.info/cathnode/3.40.50.620 for a Rossmann fold superfamily. Details of the methods used to identify conserved and variable regions are presented on the same web site and also in our Since the divergent superfamilies account for nearly 40% of sequences in the genomes with predicted structures, it is important that structural classifications derive strategies for characterizing them. In CATH, this phenomenon will be managed by identifying the topology of the evolutionary conserved core motif shared by all relatives and the various secondary structure embellishments to this common core. To capture information on structural diversity, the number of diverse SSGs within each superfamily will be recorded, and Rasmol images for each SSG will be displayed, highlighting conserved secondary structures across the superfamily  and secondary structure embellishments to this core. An example of the additional information that will be presented in CATH to capture information on structural diversity across fold groups and superfamilies is given in When we consider the overlap between different superfamilies and fold groups, the data presented here suggest that, for some thresholds , there is little structural overlap between superfamilies and fold groups. When the RMSD threshold is relaxed to 5 Å or more, overlap is observed between superfamilies in some architectures  often as a result of common super-secondary motifs. The frequent lack of any close functional relationship between the superfamilies that are overlapping suggests that these structural matches are more likely to be the result of physico-chemical constraints on folding or packing of the polypeptide chain—that is, convergence to a stable 3D arrangement. Although, as Nearly half the overlaps involve common α-hairpins in superfamilies adopting α-bundle and α−orthogonal architectures. In addition, other small single super-secondary motifs overlap between domains . In this sense, fold space is perhaps better represented as a galaxy with dense and sparse clusters. However, some overlaps comprise larger motifs of four or more secondary structures, such as split αβ-motifs or recurring αβ-motifs. For these cases, it is possible to link from one fold to the next and to the next, via these motifs, as in a Russian doll effect, and this is more suggestive of a fold continuum. Superfamilies with no overlap at all tend to have very distinctive folds  comprising rather unusual motifs or unusual combinations of common motifs.http://beta.cathdb.info/cathnode/3.40.50.620 for an example). In addition a matrix showing all overlaps between nonredundant representatives in CATH is downloadable from the CATH web site .Although only 24% of superfamilies and less than 32% of the nonredundant structures within them  are involved in structural overlaps with different folds, these superfamilies account for a significant percentage of sequences in completed genomes. It is possible, therefore, that as more structures from these superfamilies are solved, more overlaps will be revealed. It is important that CATH should also reflect these lateral links, which traverse the traditional hierarchy. Consequently, in addition to the traditional hierarchical classification, CATH will also present horizontal links involving structural matches between different superfamilies and fold groups. Any significant structural overlaps between a domain and domains in different fold groups will be presented on the individual web page for that domain  has been used to explore structural diversity within CATH superfamilies and structural overlaps between fold groups. Using this measure, we observe that, in most superfamilies, domains tend to be structurally similar to other relatives. However, a small set of 78 superfamilies are highly divergent, comprising five or more distinct SSGs, where SSGs contain relatives superposing with a normalized RMSD of <5Å. Moreover, these superfamilies account for 25% of all SSGs identified in CATH superfamilies and are highly populated, accounting for nearly 40% of predicted domain structures in genome sequences.A large proportion of superfamilies are structurally “distinct” from superfamilies in other fold groups. However, 24% show structural overlaps with other fold groups. While fewer than 32% of the nonredundant relatives within them are involved in overlaps, the superfamilies they belong to are highly populated with domain sequences in the genomes. Furthermore, since analysis of the genome sequences suggests that many more diverse relatives remain to be structurally characterized , new strClasses by secondary structure content . They are then classified according to their Architecture , then Topology/fold (where the connectivity between secondary structures are taken into account), followed by Homologous superfamily . Domains are also clustered into subfamilies with increasing sequence similarity . The term Sreps is used to describe domain representatives clustered at 35% sequence identity into S35 subfamilies.CATH is a hierarchical classification of protein domain structures according to sequence, structural, and functional similarity. Domains are initially sorted into four There have been substantial developments in the CATH update protocol  see Sup enablingProbably the best known method for measuring structural similarity is the root mean square deviation (RMSD) . StructuWe examined the degree of structural divergence between close relatives in CATH S35 sequence families. It can be seen from A structural overlap score was calculated to assess whether protein domains from one superfamily were significantly structurally similar to domains in other fold groups or architectures. A CATHEDRAL structural comparison  was perfA superfamily was considered to overlap another one if there was at least one overlap between Sreps observed. Structural overlaps were analyzed between different superfamilies within the same fold and between superfamilies in different folds and architectures.Architectures 2.10, 2.20, 3.100, and 4.10 were omitted from both structural drift and overlap analyses as these are not well-defined architectures but collections of structures with irregular secondary structures arrangements. Superfamilies within these collections have small populations (<3 Sreps). Superfamilies from regular architectures that were not sufficiently well populated  were also omitted from the structural drift analysis. This gave 559 highly populated superfamilies (3 or more Sreps) used in this analysis.A new method (foldspin) for representing structural diversity across a superfamily was used to visualize diverse relatives from structurally divergent superfamilies. Foldspin selects the most representative Srep relative from the superfamilies  and then calculates the normalized RMSD between this relative and other relatives in the superfamily. A two-dimensional plot is then generated that presents diversity across the superfamily by radially drawing lines from the central representative so that the length is proportional to the structural distance from the superfamily representative. Selected relatives are visualized on the plot using the MOLSCRIPT program .In 2002, a sister resource, Gene3D , was est


Domains are the basic functional units of proteins. It is believed that protein-protein interactions are realized through domain interactions. Revealing multi-domain cooperation can provide deep insights into the essential mechanism of protein-protein interactions at the domain level and be further exploited to improve the accuracy of protein interaction prediction.In this paper, we aim to identify cooperative domains for protein interactions by extending two-domain interactions to multi-domain interactions. Based on the high-throughput experimental data from multiple organisms with different reliabilities, the interactions of domains were inferred by a Linear Programming algorithm with Multi-domain pairs (LPM) and an Association Probabilistic Method with Multi-domain pairs (APMM). Experimental results demonstrate that our approach not only can find cooperative domains effectively but also has a higher accuracy for predicting protein interaction than the existing methods. Cooperative domains, including strongly cooperative domains and superdomains, were detected from major interaction databases MIPS and DIP, and many of them were verified by physical interactions from the crystal structures of protein complexes in PDB which provide intuitive evidences for such cooperation. Comparison experiments in terms of protein/domain interaction prediction justified the benefit of considering multi-domain cooperation. and .From the computational viewpoint, this paper gives a general framework to predict protein interactions in a more accurate manner by considering the information of both multi-domains and multiple organisms, which can also be applied to identify cooperative domains, to reconstruct large complexes and further to annotate functions of domains. Supplementary information and software are provided in  Many proteins involved in signal transduction, gene regulation and other biological activities require interaction with other proteins or cofactors to achieve specific processes ,2. EluciIn recent years, a number of computational algorithms have been developed to infer protein-protein interactions, such as those methods based on gene fusion (Rosetta Stone) ,8, phyloDomain-based protein interaction prediction assumes that proteins are composed by a set of recognition elements which are referred to as domains, and protein-protein interactions are achieved through domain interactions . A typicRecently, Han et al. studied domain combinations in protein interactions ,23. In tSimilar to proteins in a complex which cooperatively bind to each other so as to achieve specific functions , there iIn this paper, we firstly aim to identify cooperative domains from protein interaction data by extending two-domain interactions to multi-domain interactions. Cooperative domains mean that the strength of their cooperative interaction with some domain is stronger than the corresponding domain-domain interactions. Then, by employing the information of both multi-domains and multiple organisms, we propose a general framework based on a Linear Programming with Multi-domain pairs (LPM) and an Association Probabilistic Method with Multi-domain pairs (APMM), to predict protein interactions in a more accurate manner. Experimental results demonstrate that our approach not only can identify cooperative domains effectively but also has a higher accuracy for predicting protein interactions than the existing methods. Cooperative domains, including strongly cooperative domains and superdomains, were detected from major interaction databases, e.g. MIPS and DIP, and many of them were verified by checking physical interactions from the crystal structures of protein complexes in PDB (Protein Data Bank). These crystal structures of complexes provide intuitive evidences for such cooperation. In addition, comparison experiments in terms of protein/domain interaction prediction also justified the benefit of considering multi-domain cooperation.M domains D1, …, DM involved in the experimental interaction data. We use  to represent a two-domain pair, one domain in a protein and the other in another protein, and use  to denote a generalized pair i.e. a three-domain pair, where Dm and Dr appear in one protein (denoted by Dm-Dr) and Dn in another protein. A multi-domain pair means a two-domain pair or a three-domain pair. In Figure P1, P2). A cooperative-domain pair (cooperative-domain interaction) implies a generalized pair  in which two domains Dm-Dr referred as cooperative domains coexist in a protein P1 and cooperatively interact with Dn in another protein P2. The cooperative-domain pair should have a stronger interaction effect than the corresponding two-domain pairs  and . A strongly cooperative-domain pair (strongly cooperative-domain interaction) is a cooperative-domain pair  which satisfies that there is an interaction effect of Dm or Dr on Dn only if the domains Dm and Dr appear together. In Figure D1 and D2 are strongly cooperative domains interacting with D3 because all other domain pairs involving D1 or D2 have no interactions. A superdomain implies two 'combined' domains Dm-Dr which are special cooperative domains and always appear together in individual proteins. Note that we extend two-domain interactions only to three-domain interactions because the cooperation of more than three domains is believed to be rare compared with the cases of two and three domains according to statistics [In this paper, we investigate domain cooperation in protein interactions by extending two-domain interactions to multi-domain interactions. We first define the types of domains and domain pairs, and then describe the main results. Assume that there are atistics .Dm-Dr, Dn) is larger than those of the corresponding two-domain pairs  and , this three-domain pair can possibly be a cooperative-domain pair and considered in the model. Secondly, there is no redundant correlation between different domain combinations in our work. For example, if a three-domain pair  is considered in our method according to the rules of selecting variables , the two-domain pairs  and  will not be included into the consideration as interacting domains in the same protein pair to eliminate the redundancy, in contrast to the high correlation among Han et al.'s domain combinations [The concept of cooperative domains in our work seems to be similar to Han et al.'s "domain combination" ,23. Howeinations ,23.dm, n = 1) represents the probability that domain Dm interacts with Dn. Pr represents the probability that domains Dm and Dr cooperatively interact with Dn. Similarly, Pr represents the probability that domains Dn and Dr cooperatively interact with Dm. Our approach for detecting cooperative domains in protein-protein interactions can be summarized as three steps. First, we extend the conventional probabilistic model for inferring domain interactions to accommodate multi-domain pairs. Then, the interaction probabilities of multi-domain pairs are estimated by the proposed approach. Finally, according to the interaction probabilities of multi-domain pairs, cooperative domains and superdomains are detected. The detailed information on the methodology is given in Methods.In the following text, Pr <Pr and Pr <Pr from the results of LPM or APMM. From the definitions, domains in a superdomain or in a strongly cooperative-domain pair are expected to have similar biological functions. We applied our approach to protein physical interaction data in MIPS1 (see Methods) to get reliable cooperative-domain interactions.Our method is able to identify biologically meaningful superdomains and putative cooperative domains. We illustrate this feature by using MIPS data set. A cooperative domain pair has a stronger interaction effect than their corresponding two-domain pairs. Therefore, domains Totally we found 5187 two-domain interactions (with no-zero interaction probability), 83 superdomains and 650 cooperative-domain pairs, among which 525 pairs are strongly cooperative domain interactions according to the above definition. Some detected (strongly) cooperative domains and superdomains in MIPS1 are respectively listed in Tables t-test value is significant by comparing with randomly generated domain pairs).In this section, we verify the detected cooperative domains by checking their physical interactions from the crystal structures of protein complexes in PDB and examine the essential mechanism of protein interactions at the domain level. The complex crystal structures in PDB can be regarded as a gold standard to verify protein interactions and domain interactions. The seq2struct web resource  was usedα atoms are within the distance threshold 10Å, which is consistent with the more accurate computation given in PROTCOM. This example provides an intuitive evidence for the cooperation among domains in the interaction of P02994 and P32471.Figure Furthermore we also revealed some complexes in PDB which are not reported by PROTCOM . For exaIn addition to identifying superdomains and cooperative domains, our approach has a higher prediction accuracy for protein interactions by exploiting the information of both multi-domains and multiple organisms. In this section, we compared LPM and APMM with the existing methods, such as association based methods , and EMThe performance of each method in terms of RMSE and elapsed training time for fivefold cross-validation is summarized in Table The results for Ito's dataset in five rounds are not so consistent because there may exist bias in five divided subsets due to the small size of this dataset. Another larger dataset used for cross-validation is Krogan's confidence data   computed by comparing with randomly predicted domain pairs (see Methods). We can see that domain interactions predicted by our approach have a significant overlap with iPfam and domain interactions predicted from multiple-organism data have larger overlap with iPfam owing to exploiting more information. Note that the overlap proportion is not so big. This is mainly because the amount of data in iPfam is highly incomplete and many domain interactions do not appear in iPfam.The comparison results of the predicted domain interactions by LPM and APMM with those in iPfam are summarized in Table p-values, which shows that the overlap with InterDom is significant and the interaction probabilities of predicted domain pairs are positively correlated with those in InterDom, i.e. a higher threshold corresponds to a higher mean confidence score. In addition, when selecting the same number of top high-scoring predicted domain interactions, the prediction result exploiting multiple-organism data has a much higher mean confidence score than that based on single-organism data, which is shown in Figure The comparison results of the predicted domain interactions by LPM and APMM with those in InterDom are summarized in Table The domain interactions in iPfam have been used as a gold standard set to evaluate the predicted domain-domain interactions ,19. We c<44/300), but when the threshold is lower, we found more cooperative domains. This is mainly due to the difference between the definitions of cooperative domains and domain combinations. In our approach, if a two-domain pair has a stronger interaction, the three-domain pair in the same protein pair will not be included as potential cooperative domains. If two-domain pairs have weaker interactions than the corresponding cooperative-domain interaction, this three-domain pair will be considered and the two-domain pairs will be excluded in the same protein pair. In other words, we eliminate the redundancy between domain combinations, whereas in Han et al.'s method, each domain combination is included.As for identifying cooperative domains, we made a rough comparison with Han et al.'s domain combination approach ,23. Han In this work, cooperative domains, strongly cooperative domains and superdomains in MIPS and DIP were detected, and many of them were verified by the crystal structures in PDB. Functional relations in superdomains and cooperative domains were examined by the terms of GO. Among the detected superdomains, we found that two domains in most of them belong to a same family and have similar or identical functions. Such fact is biologically reasonable because the two domains in a superdomain always appear together in individual proteins and participate in interaction processes simultaneously. It is interesting that many domains that act as superdomains are binding domains, such as snoRNA binding, ATP binding, DNA binding, FAD-binding, NAD-binding, GTP binding, protein binding, and Lum-binding. For cooperative domains, some of them have dissimilar functions partly because domain cooperation needs complementary functions . CooperaMulti-domain cooperation information can be explored to reconstruct the structure of a large protein complex. Protein complexes are key molecular entities that integrate multiple gene products to perform cellular functions. Recently, tandem-affinity-purification coupled to mass spectrometry (TAP-MS) which combines affinity tags-based protein purification technique and mass spectrometry for identifying a tagged protein and its interaction partners  has beenAs an example, Figure Domains are viewed as the basic functional units of proteins, and it is believed that protein interactions are achieved through domain interactions. Most existing methods for inferring protein interactions from experimental data assume that two-domain pairs are dominating factors for protein interactions. However, like the cooperation of several proteins in a complex, many domains may be cooperative in achieving the interaction of a protein pair. In this paper, we focus on revealing such domain cooperation by considering multi-domain pairs as the basic units of protein interactions. In addition, in contrast to the existing methods which mainly use the data from single organism, data sets for multiple species with different reliabilities were exploited in this paper to make full use of the available information. From the computational viewpoint, this paper provides a general framework based on APMM and LPM to predict protein interactions in a more accurate manner by considering the information of both multi-domain pairs and multiple organisms, which can also be applied to identify cooperative domains. Experiment results demonstrated that our method not only can find superdomains and putative cooperative domains effectively but also has a higher prediction accuracy of protein interactions than the existing methods. Cooperative domains, strongly cooperative domains and superdomains in MIPS and DIP were detected, and many of them were verified by the crystal structures in PDB. Comparison experiments on protein/domain interaction prediction confirm the benefit of considering multi-domain cooperation. More detailed results and software can be found at our website.In this work, we validated our approach using several types of experiments which employed various experiment data sets as follows.When we test our method for detecting cooperative domains and PPI prediction based on multiple-organism data, we used binary interaction data in which the information is whether two proteins interact or not. In other words, there is not a confidence score for each protein-protein interaction. We collected 4103 physical interactions in yeast from MIPS [S.cerevisiae, worm C.elegans and fly D.melanogaster with respectively 5295, 4714 and 20349 protein interactions. The protein-domain relationships for each protein are extracted from PFAM [For PPI prediction based on multiple-organism data, in order to make comparison convenient, we used the same training data and testing data collected by Liu et al. . These drom PFAM  and SMARrom PFAM . Among tFor comparison experiments at the domain interaction level, we used protein-protein interactions and protein domain composition dataset in Riley et al.  and GuimNumerical interaction data are defined as opposite to binary interaction data. It means that each protein-protein interaction has a score to denote the interaction strength. It includes experiment ratio data based on IST  and confThe domain information for proteins was extracted from Pfam 14.0 . For MIPCooperative domains were confirmed using structural data of protein complexes from PDB and PROTOCOM. Protein sequences were mapped from Swiss-Prot/TrEMBL database to their corresponding structure files using the seq2struct web resource . PROTOCOIn this section, we describe an improved probabilistic model for protein interactions by considering the multi-domain pairs, which is the essential basis of our method.K species (or data sets), there are Nk proteins in dataset k respectively denoted by k = 1, …, K, with M domains in all of these proteins represented by D1, …, DM . Let i of dataset k. Define Dm, n to represent a domain pair . We also introduce a symbol Dm, rn for a cooperative-domain pair  to represent the case that domains Dm and Dr in protein Dn in protein Dm, rn has a similar implication. In our probabilistic model, Assume that in the protein interactions of Dm and Dn) be represented by a random variable dm, n). Accordingly we introduce random variables dmr, n to denote whether domains Dm and Dr cooperatively interact with domain Dn or not. The probabilistic model [Let the interaction between ic model ,13 for ik, and Pr represents the probability that domain Dm interacts with Dn. Pr represents the probability that domains Dm and Dr cooperatively interact with Dn. Pr has a similar meaning. For each protein pair in (1), if there is a cooperative interaction of domains Dm - Dr with domain Dn in the second multiplying term, then  and  must be excluded from the first multiplying term in order to maintain the independence assumption; otherwise,  should be deleted. The third multiplying term for  should be checked in the same way. Clearly, the first multiplying term represents the effect of two-domain pair interactions while the second and third multiplying terms stand for the effects of cooperative-domain interactions. In next section, we will show how to determine those independent variables.where Pr can be further extended to four-domain pair and above but with the sacrifice of the computational efficiency. Figure dm, n, dmr, n and dr, n according to the following strategy. DefineIn order to make the variables of model (1) independent with each other, we will delete dependent variables among Imn is the number of interacting protein pairs in the training set that contain domain pair Dm, n, and Nm, n is the total number of protein pairs in the training set that contain Dm, n. Rmr, n and Rr, n are similarly defined. For variables dm, n, dmr, n and dr, n, the variable deletion strategy is described by the following procedure.where Rmr, n< Rm, n or Rmr, n< Rr, n, it indicates that the appearance frequency of domain pair Dmr, n in interacting protein pairs is not higher than those of Dm, n and Dr, n. We consider that there is no cooperation between Dm and Dr in their interacting with Dn, so we keep the variables dm, n, dr, n and delete the variable dmr, n in (1).1. If Rmr, n ≥ Rm, n and Rmr, n ≥ Rr, n, for Dm, n2. If Rmr, n> Rm, n and Imr, n = Im, n, the appearance frequency of domain pair Dmr, n in interacting protein pairs is higher than those of Dm, n and Dr, n, and furthermore, Dm, n does not appear in any other interacting protein pair without Dr. Hence, we consider that Dm and Dr are cooperative when interacting with Dn, and thereby the variable dm, n is deleted, but the variable dmr, n is kept in (1);• when Rmr, n = Rm, n and Imr, n = Im, n, it means that Dm and Dr always appear together in individual proteins. Hence, Dm and Dr are considered as a superdomain and can be merged to one. For such a case, we delete variable dm, n but keep the cooperative-domain pair dmr, n.• when Dr, n. For the case of Figure D1 - D2, D3) are deleted based on this procedure.The operations are performed in the same way for Rmr, n ≥ Rm, n and Imr, n< Imn. For this case, we cannot determine if or not there is a cooperative effect of domains Dm and Dr on their interacting with domain Dn since Dm, n also appears in the interacting pairs without Dr, thereby we keep all of them. This may affect the assumption of independence, but there are few such cases. For example, for the date set MIPS1, among 22325 multi-domain pairs, there are only 85 such cases. Hence, by the above variable deletion operations, the assumption can be primarily satisfied. In the following formulation, all the variables appearing in the formula are those kept after the deleting strategy, whereas the probabilities of all deleted variables are set to be zero. Note that, in contrast to the appearance frequency or interaction strength for selecting cooperative domains, the two domains in a superdomain are determined based on their co-occurrence, and the cooperativity are also indirectly confirmed by their identical or similar functions from GO annotations.Obviously, the above operations do not cover the case fpk and a false negative rate fnk·fpk = Pr and the formula (3), we havexm, n = ln), xmr, n = ln), xm, nr = ln) and Pr satisfying (5) for all observed protein interaction data, the domain interaction probabilities Pr, Pr and Pr fully consistent with the training data can be obtained. However, it is usually impossible to satisfy all constraints owing to the noise and incompleteness of experimental data. In such a case it is natural and reasonable to minimize the total error with respect to L1 norm. Therefore we can obtain the following Linear Programming with Multi-domain pairs (LPM):This is a set of linear equalities. If we can find where Solving (6), we can obtain a set of interaction probabilities for domain pairs. Then, new protein interactions can be predicted by these inferred domain interactions through the probabilistic model (1).Numerical experiments show that LPM performs well, but is computationally expensive for large scale problems. Therefore, we introduce a faster probabilistic method based on statistics. This method is based on a generalization of Association Probabilistic Method  with mulwhere |Px and Py is defined by the maximum number of index levels from the top shared by Px and Py. The smaller the value of function similarity, the broader is the functional category shared by the two proteins. The details can be found in Chen et al. [We validated our method using several types of experiments with different criteria. For computing the similarity of GO annotations , we adopn et al. .For protein interaction prediction on numerical PPI data, we use root-mean-square error (RMSE) to measure the difference between the observed probability values and the predicted probability values:P denotes a set of protein pairs (training set or testing set) including interactions and non-interactions. Non-interacting protein pairs may be those not appearing in the observed interaction data or those whose interaction probabilities are below a threshold.where For protein interaction prediction on binary PPI data, we use sensitivity and specificity to evaluate the performance of a method. Specifically, given a set of interacting protein pairs as positive set and a non-interacting protein pair set as negative set, sensitivity and specificity (denoted by SN and SP) are respectively defined aswhere the number of true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN) are estimated with respect to the given test set.p-value) by comparing with randomly predicted domain pairs:The evaluation of the predicted domain interactions in this work is based on the overlap with the gold standard set iPfam. We adopted binomial cumulative distribution function to compute the significance of the overlap , cooperative-domain interactions verified by PDB crystal structure , RMSE comparison results on all protein pairs , and the additional information on the LP model.Click here for file


SCOP and CATH are widely used as gold standards to benchmark novel protein structure comparison methods as well as to train machine learning approaches for protein structure classification and prediction. The two hierarchies result from different protocols which may result in differing classifications of the same protein. Ignoring such differences leads to problems when being used to train or benchmark automatic structure classification methods. Here, we propose a method to compare SCOP and CATH in detail and discuss possible applications of this analysis.We create a new mapping between SCOP and CATH and define a consistent benchmark set which is shown to largely reduce errors made by structure comparison methods such as TM-Align and has useful further applications, e.g. for machine learning methods being trained for protein structure classification. Additionally, we extract additional connections in the topology of the protein fold space from the orthogonal features contained in SCOP and CATH.Via an all-to-all comparison, we find that there are large and unexpected differences between SCOP and CATH w.r.t. their domain definitions as well as their hierarchic partitioning of the fold space on every level of the two classifications. A consistent mapping of SCOP and CATH can be exploited for automated structure comparison and classification..Benchmark sets and an interactive SCOP-CATH browser are available at  According to the SCOP authors, domains in a common fold have the same major secondary structures in the same arrangement with the same topological connections. In the same superfamily, domains share low sequence identities but their structures and, in many cases, functional features suggest that a common evolutionary origin is probable while domains clustered in the same family are likely to have a common evolutionary origin based on sequence similarity or functional evidence.The classification and comparison of the more than 50'000 protein structures deposited in the PDB  . The second layer, called architecture, clusters domains with common general features with respect to the overall protein-fold shape but does not take connectivity into account. The topology level is analogous to the SCOP fold level and groups structures that have a similar number and arrangement of secondary structure elements with the same connectivity. The last (major) level, homologous superfamily, clusters domains with a high structural similarity and similar functions, which suggest that they may have evolved from a common ancestor.The building process of CATH contains more automatic steps and less human intervention compared to SCOP. Analogous to SCOP, CATH starts at the class level defining three major classes of secondary structure content  has become an important topic with the faster growing number of PDB structures in order to analyze structural and functional features of proteins. Methods which are specifically suited for an accurate and automatic assignment of structures to their respective class often use SCOP or CATH as reference and template datasets or to evaluate their performance. Among those methods which heavily use SCOP or CATH for structure prediction from the sequence are AutoSCOP  and PFREAlso, various methods which aim at the prediction of a proteins structure from the sequence  have beeAlthough the two hierarchies have become the gold standard in the field, their goals and the methods used to classify structures are not the same which leads to different classifications of the same protein. Differences are found with respect to the domain partitioning of the protein chain, as well as in the classification of a domain into its corresponding structure class. Differences and similarities between SCOP and CATH have already been evaluated ,18 and tSCOP and CATH are often used as 'standard of truth' datasets and inconsistencies and differences in the hierarchies unavoidably lead to problems in the training phase (since wrong or misleading concepts are learned) as well as in the testing/benchmarking phase. Proteins classified to be different by one hierarchy may indeed be similar according to the other classification leading to an overestimation of the errors made. To overcome those problems, we extract sets of pairs of protein domains from our SCOP-CATH mapping which are consistently classified in both hierarchies. Those pairs represent a novel and comprehensive benchmark (training) set which allows for a more consistent evaluation (and training) of protein structure comparison and protein structure prediction methods.Finally, we utilize our mapping as orthogonal evidence in order to identify potential connections between different folds in one hierarchy which may be revealed via a connection of the two folds suggested by the respective other hierarchy. Such connections between different folds (which are supposed not to be evolutionary related due to the SCOP or CATH definition) provide interesting starting points to further analyze the protein sequence-structure space.. The website and the benchmark datasets will be updated regularly when new versions of SCOP and CATH are released. SCOP 1.73 contains 34'495 proteins deposited in the PDB (97'178 domains) which are classified into 11 classes, 1'283 folds, 2'034 superfamilies and 3'751 families. CATH comprises 30'028 PDB proteins which are partitioned into 93885 domains and sorted into 4 classes, 40 architectures, 1'084 topologies and 2'091 homologous superfamilies. The union set of the proteins in the two classification schemes contains 36'970 proteins. 27'553 PDB proteins are classified in both hierarchies. Please note that throughout this article we regard the following levels of SCOP and CATH to correspond to each other: SCOP family/superfamily ↔ CATH homologous superfamily, SCOP fold ↔ CATH topology, SCOP class ↔ CATH class.For our analysis we use the most current version of SCOP  as well as CATH version 3.1.0 (January 2007) which contains a similar number of proteins. The mapping containing the more recent CATH version 3.2.0 can be found on the supplementary website at In the following we present the results of our analysis of similarities and differences between SCOP and CATH. We will first discuss the results of mapping the different domain definitions of SCOP and CATH onto each other, showing that there are surprisingly large differences between SCOP and CATH with respect to their domain definitions. We will then use the set of mappable domains (for which domain definitions largely agree), restrict the respective hierarchies to those domains and compute the mapping of inner nodes of the two restricted hierarchies. We then analyze this mapping of inner nodes in detail which turns out to be very complex indicating many inconsistencies between SCOP and CATH. For interesting examples see Additional File .Our analysis depends on whether we map SCOP to CATH or vice versa. We present the results of the (non symmetric) mapping of SCOP → CATH in the following. The results for the mapping of CATH → SCOP are available in the supplementary material on iff the overlap o, as defined in the Methods section, is greater than 0, i.e. we map all domains which have at least one residue in common with the query domain. The results are shown in Table In order to analyze the different domain definitions in SCOP and CATH, we keep a domain defined in one hierarchy fixed and count how often one or more domains from the respective other classification are mapped onto it. A domain is mapped To = 0.8. This implies a unique and bijective mapping of domains onto each other but leaves many domains unmapped. Including protein domains which overlap to only a small extent would lead to additional problems when comparing the two hierarchies, especially since domains are also classified according to their secondary structure elements and content . In SCOP, those domains are classified into 11 classes, 754 folds, 1'258 superfamilies and 2'228 families which means that on the other hand, for 538 folds, 776 superfamilies and 1'523 families already the domain definitions of SCOP and CATH differ to such a large extent that they can not be meaningfully mapped onto each other. According to CATH, the proteins belong to 4 classes, 38 architectures, 736 topologies and 1'462 homologous superfamilies. Two architectures, 348 topologies and 629 superfamilies of CATH remain unmapped. Those values show a surprisingly large number of domains in either of the two hierarchies which are defined in a very different manner in the respective other classification scheme according to their domain boundaries and result in the fact that for only 70% of the proteins the classifications can be compared. Moreover also only 70% of all SCOP families and CATH superfamilies are retained in the mapping due to differing domain assignments.As shown in the following, differing domain assignments have a large impact on the resulting classification. Out of the 27'553 proteins which are classified in both hierarchies, for only 19'266 (about 70%) the domain definitions are similar enough leading to 56'104 domains in the final dataset , the mapping of inner nodes and, therefore, the partitioning of the fold space according to SCOP and CATH appears to be more complicated than expected and many inconsistencies can actually be observed. When we require a certain quality for a mapping, i.e. setting the F-measure threshold to 0.8, a large number of inner nodes do not find a partner in the other hierarchy. SCOP and CATH therefore define their sets of domains on every level of the hierarchies and for many cases very differently and a large number of unexpected mappings . Those fold classes also build clusters of similar folds which are further discussed in the context of interfold similarities below.. An interesting example is the pair d1bbxd_ and d1rhpa_. The domains are classified to belong to two different classes in SCOP  and are indeed very different on the structure level, but belong to the same homology level according to CATH (2.40.50.40). A second example is the pair d1ku7a_ and d1j9ia_ (classified as a.6.1.5 and a.4.13.2). The two domains are indeed structurally similar (though they have a different number of helices). They are classified as different folds SCOP but belong to the same homology level in CATH (1.10.10.10). More examples and structural superpositions of such pairs can be found in Additional file Nevertheless, a large number of inconsistencies can not be explained by these well known superfolds. All inconsistent pairs can be interactively explored on All inconsistencies will lead to problems when benchmarking automatic structure classification methods. Also, they may lead to learning wrong concepts in the training phase of machine learning methods for protein structure classification for two reasons: 1.) decision criteria are only learned with respect to one classification and 2.) criteria are ignored in the learning phase because of inconsistencies.The pairwise comparison also allows us to extract sets of domain pairs which are consistently defined across the hierarchies and which may be used as novel benchmark sets to train and evaluate structure comparison methods. In particular, we extracted two sets of domain pairs:• domains which are consistently defined as being similar in both hierarchies (in the following denoted as the SCOP-CATH set) corresponding to the consistent fold, superfamily and family pairs in Table • non-similar, negative domain pairs, i.e. domains in the same class, which are consistently classified into different folds.. We also provide additional data, i.e. the details of the clustering process, which allows users to define their own benchmark sets using different sequence identity cutoffs in case that other sequence identity thresholds are appropriate for the specific application.Also, to avoid an overrepresentation of very similar domains in the dataset, we clustered the domains according to their sequence similarity. All domains with a pairwise sequence identity of more than 50% were clustered together. For each cluster we retained only one representative domain in the final benchmark set (SCOP-CATH50 set). The sets can be obtained at Redfern et al. also used a consistent set between SCOP and CATH in benchmarking their CATHEDRAL method . Our appOur dataset can directly be employed for training and benchmarking novel methods developed in the field on different levels of the hierarchies and therefore different levels of structural similarity. In the following, we show that this novel benchmark set allows for a much more consistent evaluation of structure comparison methods which is not biased by inconsistencies in the different gold standards.In the following, we discuss the results of two applications of our detailed SCOP-CATH comparison.For benchmarking purposes, and as an examplary structure comparison method, we used the TM-align method which computes a structural alignment optimizing the TM-Score . The TM-For our analysis, we compare the performance of TM-align on the complete benchmark set with the performance on the novel benchmark set proposed in this paper. The only difference between the two sets are the pairs being evaluated. While all pairs which are similar according to SCOP are evaluated in the original setting, our novel benchmark set contains only those pairs which are consistently defined to be similar or different in both SCOP and CATH. Therefore, while the domains contained in the sets are the same, the number of pairs being compared is much smaller in our novel benchmark set than in the original set (16% of the positive pairs have been removed).In the following we will discuss the plots shown in Figure (a) and (b) show the distribution of TM-Scores of domain pairs within the same class/fold/superfamily/family. The distributions of the scores are very similar between both sets indicating that the main properties of the benchmark sets are similar. There is no apparent bias in the benchmark set proposed here towards domains which are easier to classify and both sets appear to be equally difficult regarding their similarity relationships.Plots (c) and (d) in row two as well as (e) and (f) in row three introduce a novel type of plot to benchmark the performance of structure comparison methods. The plots can be used for any structure comparison method to evaluate in detail the classification performance and in particular the errors made by a method. Especially, they allow to estimate the performance of a method given a template database where members of the family and superfamily are missing and analyze in detail the number of domains for which problems occur in a set of domains and also quantify the dimension of the problem. Plots (c) and (d) show the number of domains for which we observe problems according to the structural similarity detected by TM-Align. For every query domain, we show how many domains from a different fold have a higher similarity score than the highest scoring member of the domain's own family (red cross), superfamily (green x) or fold (blue star). On the x-axis we show all query domains for which we observe problems, while on the y-axis, the number of problematic cases for a query (i.e. the number of domains from different a different fold ranked higher than the own family/superfamily/fold) is plotted. For example if there are ten domains from a different fold scoring better than the most similar member from the domain's own family a red cross ) would be plotted. Similarly a blue dot is plotted if wrong proteins score better than a member of the query superfamily and a green x is plotted in the case of wrong domains scoring better than the own fold. Also, domains in columns which contain blue dots would not be assigned to their correct folds in the case of missing family and superfamily members since the best hit comes from a different fold.Plots (e)-(f) in row three are similar to panels (c)-(d), but instead of displaying the number of domains, they show the number of distinct folds (different from the domains own fold) which score better than the respective own family, superfamily or fold.Panels Comparing the plots that are computed based on the complete set of domain pairs (left column) with the plots computed on the benchmark set of consistent domain pairs (right column) we find that TM-Score/TM-align produces errors for only half of the domains tested and the dimension of the errors (i.e. the number of domains/folds which score better) also strongly decreases.The only difference between the two sets tested is the removal of pairs which are inconsistently defined between SCOP and CATH. While we remove about 16% of the positive pairs (in SCOP) to obtain the consistent set, the number of errors observed is reduced by 53% (compared to 16% error reduction which would be expected when removing arbitrary pairs). Therefore, the removal of pairs which are inconsistently defined in SCOP and CATH allows to over-proportionally reduce the number of errors. Our conclusions are twofold: many errors reported for protein structure classification methods originate from pairs of domains which are similar to one another, but are classified differently by SCOP or CATH. A different set of errors results from pairs that are e.g. classified in the same family but not similar enough to be distinguished from random pairs by a structure-based comparison method. Using only pairs of domains consistently defined in SCOP and CATH allows to reduce the amount of errors significantly and to separate erroneous behaviour of a method (e.g. errors in the similarity model for protein structures implemented in a method) from problems arrising due to pairs of domains for which even gold standards and experts disagree in their classification.(g) and (h). To compute them, we sort the results obtained for every query domain according to their similarity scores. Then, we count for every member of the query fold, how many distinct other folds score better than the respective fold member (please note that every fold is counted only once even if multiple domains from a fold lead to errors). The boxplot in (g)-(h) shows the errors for five specific fold members: for the best and worst scoring fold members, as well as for the fold member placed at the 25%, median and 75% positions in the sorted list. As, unfortunately, correct fold members score quite differently, this allows to assess the overall performance of fold members by showing how often wrong members score better than the selected five fold member representatives. The boxplot now simply summarizes these numbers for all queries. Thus the boxplots give a summarized overview of the observed errors. By comparing the two boxplots for the comprehensive and the consensus sets we again find a substantial reduction of errors in the consensus set. While the number of errors for the best scoring fold member is generally small, the errors for the low-scoring fold members quickly increase in both sets, but much more drastically in the comprehensive set as compared to the consensus set. For example, if we look at the fold members scored in the lower quarter (75%) of the fold members, we find 10 different random folds before a correct domain in the original dataset and only one fold in the novel benchmark set. This again indicates, that the number of errors as well as their quantity are significantly lower in the novel benchmark set compared to the original benchmark set.This figure is completed and summarized by the plots Overall, the novel benchmark set proposed here is much more consistent than the original pairwise relationships defined by SCOP. It results in a much smaller number of errors . Due to the largely reduced inconsistencies the set should also be well suited for training novel machine learning algorithms for protein structure classification, since it may allow for learning more consistent concepts from the input data.Furthermore, we expect that the new benchmark set and also the new type of plots allow for a more instructive and objective evaluation of other structure comparison methods as well. The benchmark set has already been applied to measure the performance of PPM, Vorolign and TM-align in .f1 defined in SCOP which map to a topology level in CATH t while this topology level in CATH also maps to a second fold f2 in SCOP  in CATH but are splitted into 38 different folds in SCOP. Another large cluster comprises α-hairpin proteins in SCOP which are clustered in the same fold according to CATH (1.10.287). Part 2c shows a more complicated fold graph clustering proteins of immunoglobulin (CATH 2.60.40) and jelly-roll topologies (CATH 2.60.120) in a subgraph which also shows that those graphs do not necessarily form a clique.Two further interesting examples are shown in Figure Protein structure classification is an essential step towards a deeper understanding into the interplay of protein structure and protein sequence evolution. Here, we have carried out a detailed study of the similarities and differences between the two most prominent databases, namely SCOP and CATH, which have become gold standards in the field and are used in various machine learning approaches and assessments of structure prediction and classification like the CASP experiments.We find that there are essential differences between the two classification schemes due to their way of partitioning proteins into domains . SCOP tFor the remaining set of about 20'000 proteins we then tested how well their classifications agreed with the classification in the respective other hierarchy. For this comparison we have used the F-measure to determine the similarity of two sets of domains on a specific level of two hierarchies. We find that both hierarchies show significant differences and often disagree in their way to partition the protein structure space also in cases of nearly identical domain definitions.Given those findings and our mapping of SCOP and CATH hierarchy nodes, we extract a novel benchmark set of protein domain pairs which are defined consistently across both hierarchies. We show that benchmarking TM-align (as an examplary structure classification method) on the novel benchmark set leads to a largely improved performance in comparison to the original set where the similarities as defined with respect to SCOP. This is due to the fact that errors (proteins which are similar according to one hierarchy but separated into different classes in the other one) which occur due to inconsistencies are removed from the novel benchmark set. Therefore, the benchmark set proposed here allows for a more objective evaluation of the performance of protein structure comparison methods as the remaining errors observed are more likely to be due to the method itself. Furthermore, this set should have advantages for both, training and testing all kinds of prediction methods, especially machine learning approaches to protein structure classification since more consistent concepts may be learned in the training phase.Finally, the mapping between SCOP and CATH provides interesting, orthogonal knowledge on the topology of the protein structure space which allows to identify non-trivial links between different folds in e.g. SCOP via their connection observed in CATH. There are some very interesting and large (up to more than 30 folds) sets which may be clustered together in SCOP according to CATH. Among them are some known clusters of folds like the Rossmann fold topology. But there are also several other clusters of folds which may be interesting starting points for a further analysis of their sequence-structure properties and may help to further understand the interplay of protein sequence and protein structure evolution, also in the context of alternative splicing , as diffD1 and D2 and the hierarchy trees themselves as H1 and H2. We further define Hi =  where Di ⊆ Vi are the leaves of the tree. Since domain definitions in different hierarchies also may be different, we have to map the domains defined by SCOP (D1) and CATH (D2) in a first step. In a second step we will define and compute a mapping between inner nodes of the hierarchies.In the following, we will handle the two hierarchies (SCOP and CATH) as labeled trees, where the leaves correspond to the domains classified in the corresponding hierarchy. Inner nodes represent sets of protein domains which are clustered together on a specific level of the hierarchy. For SCOP, inner nodes represent classes, folds, superfamiles or families. For CATH, inner nodes correspond to classes, architectures, topologies and homologous superfamilies. We denote the underlying sets of domains of the two hierarchies with RP(d) of residue positions for all segments of domain d and define the similarity of domains via the intersection of their RP sets. Such a mapping is not necessarily unique, i.e. it is possible that a domain in D1 maps to more than one domain in D2 or, more generally, that n domains in D1 correspond to m domains in D2. In such cases the definitions of the domains may be very different and we exclude domains from D1, D2 if their overlap o (see below) is smaller than a specified threshold To. For two domains d1 and d2 from D1 or D2, respectively, we define the overlap o of two domains as:A protein domain is defined as a set of segments within one protein, where a segment is defined as a consecutive part of one chain of the protein. Note that this definition also allows to define discontinuous domains and domains spanning different chains of a protein. In order to compare different domain assignments of the same protein we have to compare sets of segments. To do so, we use the sets To > 0.5 the mapping will be unique (but not necessarily complete).If we use a threshold To exceeds a given threshold, i.e. for S1 ⊆ D1  the image of S1 is defined as follows:While the mapping of domains is more or less trivial (except for cases where the domain definitions differ to a large extent), mapping inner nodes of the hierarchy appears to be more complicated. As already mentioned inner nodes represent sets of domains. The image of a set of domains in one hierarchy is the set of domains in the other hierarchy where S1 ⊆ img(D2) ⊆ D1 and S2 ⊆ img(D1) ⊆ D2 on the restricted hierarchies as:Further, we define the sensitivity, specificity and the F-measure of a domain mapping of two sets S1 from hierarchy H1 and S2 from hierarchy H2 where F-measure > 0, i.e. there needs to be at least one domain which occurs in both sets. The F-measure is especially useful as it accounts for a tradeoff between sensitivity and specificity. This is necessary since, obviously, the most sensitive mapping will be always the root, the most specific one the direct parent nodes of two mappable domains.In order to map sets of domains, we search for all inner nodes H2 which match best to a given node n1 in H1. From each path from the root to n2 in H2, only one node (the best one according to the F-measure) will be used in the mapping. Nevertheless, there may be different paths in H2 containing nodes mapped to the query node from H1. In those cases matches from different paths are also sorted according to their F-measures.Given the F-measure for every pair of nodes which have at least one mappable domain in common, we identify the nodes in n1 in H1 a sorted (by their F-measures) set MS(n1) consisting of all best matching non-leaf nodes for every path mapped to n1 in H2. These sets can be explored interactively via the browser at .Based on those definitions we calculate for each non-leaf node GC wrote the software. GC, FB, and RZ designed research and wrote the manuscript. All authors read and approved this manuscript.Examples of inconsistencies between CATH and SCOP. The file shows several interesting examples of differing classifications of protein domains in SCOP and in CATH. It contains 15 figures, each showing a structural superposition of two domains which are inconsistently classified.Click here for file


Studies for liquid-based Papanicolaou (Pap) tests reveal that liquid-based cytology (LBC) is a safe and effective alternative to the conventional Pap smear. Although there is research on ThinPrep and SurePath systems, information is lacking to evaluate the efficiency and effectiveness of systems based on cytocentrifugation. This study is designed to determine the sensitivity and specificity of the Shandon PapSpin  liquid-based gynecological system. We used split-sample and direct-to-vial study design.2,945 women referred to prophylactic check-up were enrolled in this study. Split sample design was used in 1,500 women and residual cervical cytology specimen from all these cases was placed in fluid for PapSpin preparation after performing conventional smear. The direct-to-vial study was carried out in another cohort of 1,445 women in whom the entire cervical material was investigated using only the PapSpin technique. Follow up histological diagnoses for 141 women were obtained from both study arms following 189 abnormal cytology cases. 80 LBC cases from the split sample group and 61 LBC cases in the direct-to-vial group were correlated with the histology results. The sensitivity and secificity of the conventional smear and PapSpin tests in both study arms were compared.vs 10 (0.7%) in PapSpin (P = 0.001). A higher proportion of unsatisfactory samples was found in the conventional smear group: 25 (1.7%) vs 6 (0.4%) cases (P = 0.001). In the split sample group, the sensitivity of the conventional and PapSpin tests was 68.7% vs 78.1%, and the specificity 93.8% vs 91.8%, respectively. In the direct to vial group PapSpin sensitivity was 75.9% and specificity 96.5%. The differences in sensitivity and specificity were not significant. The positive predictive values for the conventional and PapSpin methods were not different in the split sample group: 88.0% vs 86.2% and 95.7% in the direct-to-vial group. Also, no differences were found for negative predictive value .In the split sample group, conventional smears showed a higher proportion of ASC-US : 31 (2.1%) PapSpin showed good qualitative results in both study arms, even after the material splitting in the first study arm, and is a good alternative to the conventional Pap smear. Additionally, the PapSpin method offers several advantages such as the opportunity to prepare duplicate slides, option for HPV DNA testing and cell block preparations from residual material. Microscopic evaluation of thinner cell preparations is less time consuming than the conventional Pap smears. Cervical cancer is still an important health problem. Worldwide, cervical cancer incidence is over 471,000 with mortality rate averaging 230,000 deaths per year is second only to breast cancer.[The screening Pap test remained unchanged for over half a century, until recently, when several new advances were developed. In 1996, the US Food and Drug Administration (FDA) approved ThinPrep  liquid-based cytology (LBC) an alternative to the conventional smear. This was followed three years later by the approval of the AutoCyte Prep . These LBC systems are now well established for cervical cancer screening.Ample research on ThinPrep and SurePath techniques has evaluated the efficiency of these cytological methods and their technical and economic impact on cytology laboratories and cervical cancer screening. However,Our study was designed to evaluate the diagnostic ability of the PapSpin gynecological cytology system for detecting premalignant cervical lesions. We used a split-sample and direct-to-vial design because there are no direct-to-vial studies evaluating the PapSpin system. The main goal of our study was to compare the qualitative results and diagnostic concordance of concurrently obtained conventional Pap smear and PapSpin preparations, with histology follow-up in the split-sample group. In the direct-to-vial PapSpin group we correlated histological results with abnormal PapSpin test results and determined the sensitivity and specificity of the test.A total of 2,945 women were enrolled during the study period from 2006 to 2008. The screening population consisted of non-pregnant, 30 to 60-year-old women, presenting for routine cervical cancer screening at three large outpatient clinics in the capital city of Vilnius, Lithuania. Gynecologists were trained to collect the samples before the study. Written informed consent was obtained from each patient and comprehensive information was explained to the patient about the tests, including possible risks and the advantages. The protocol for the study, informed consent/agreement form, and the form for the split-sample design with PapSpin microscopic evaluation were approved by the Lithuanian Bioethics Committee of the Ministry of Health Care .Routine history was recorded and physical exams were performed. All slides and vials were labeled with appropriate patient identification data. In the split-sample group , a cytologic sample was obtained according to a standard procedure using the Papette  brush and a conventional smear was prepared. Subsequently, the Wallach Papette white tip with residual material was removed from its handle and placed into a vial of PapSpin Collection Fluid preservative. In the direct-to-vial group  the Wallach Papette white tip with the cervical sample was removed from its handle and immediately placed into a PapSpin Collection Fluid vial. A conventional smear was not prepared. All specimens were identified with patient study number.in situ). Unsatisfactory slides were categorized according to the Bethesda system recommendations for specimen adequacy.All conventional smears and PapSpin vials were sent to the National Centre of Pathology for processing and for cytology screening. Routine quality control procedures (including the rescreening of 10% negative cases) were followed. In the split-sample group, the conventional Pap smear was processed routinely and separately from PapSpin vials. A study number was given to the PapSpin vial. CytoCheck densitometer was used to monitor the cell density. Standardized cell dilution using Shandon PapSpin preservative fluid was performed according to the manufacturers protocol . The disFor the split sample group, all PapSpin results were compared with the results of the corresponding conventional Pap smear. If more severe lesions were detected on PapSpin, these results were reported to the gynecologist for appropriate clinical management.in situ), SCC (squamous cell carcinoma).Cervical histology  or hysterectomy specimen) was sent to the laboratory for routine processing and interpretation. A histological diagnosis was reported according to the 2003 WHO Tumours of the Breast and Female Genital Organs Classification system: benign, CIN1 , CIN2 , CIN3/CIS . Kappa measure was calculated under a dichotomous (positive or negative) classification. Diagnostic sensitivity and specificity with positive and negative predictive values were calculated using CIN2 and above (CIN 2+) as the reference standard for cytological diagnosis of HSIL. A p-value less than 0.05 was considered statistically significant.A total of 2,945 women were examined during the study. All the women were grouped into six age groups with five year intervals: 30-34 in group I, 35-39 in group II, 40-44 in group III, 45-49 in group IV, 50-54 in group V, and 55-60 in group VI . The meaThe study found 189 (6.4%) women with abnormal cytology results. Cytological abnormalities were more often detected in 30-39 years old women (42.4%). Abnormalities were detected in 104 (6.8%) cases in the split-sample group and in 85 (5.9%) cases in the direct-to-vial group.P = 0.001). There was no difference in HSIL+ interpretations: 28 (1.9%) coventional vs 33 (2.2%) PapSpin (P = 0.33). Increased number of LSILs were observed with the PapSpin: 29 (1.9%) vs 20 (1.3%) by conventional method. However, this difference was not significant (P=0.12). Additionally, a higher proportion of unsatisfactory samples was reported in the conventional smear group: 25 (1.7%) vs 6 (0.4%) cases (P = 0.001) with the PapSpin. The main advantages of PapSpin test 10.5over conventional smears were better cell preservation with improved cell visualization, reducing the ASC-US and unsatisfactory rates. The microscopic evaluation of thinner cell preparations is less time consuming than conventional Pap smear. The agreement of conventional and PapSpin split samples is shown in The cytologic diagnosis according to the Bethesda system categories in both study arms is shown in P < 0.05).In the direct-to-vial group, 31 (2.1%) HSIL+ and 22 (1.5%) LSIL cases were detected. The frequency of ASC-US was 1.4% and ASC-H-0.7%. The number of unsatisfactory samples was eight cases (0.6%). We did not observe a statisticaly significant difference in HSIL+ detection between split-sample and the direct-to-vial groups  obtained in the split-sample group and 61  in the direct-to-vial group. The indication for surgical management was abnormal cervical cytology (94 cases), endometriosis of the cervix (1), leiomyoma of the uterus (6), endometrial carcinoma (3), borderline serous cystadenoma of the ovary (1), dysgerminoma (1), ovarian adenocarcinoma (2), abnormal colposcopy (15), unknown indications (17).vs 78.1% and the specificity was 93.8% vs 91.8% respectively. In the direct-to-vial group, the PapSpin sensitivity was 75.9% and specificity 96.5%. The differences in sensitivity and specificity were not significant.A comparison of cytological and histological diagnoses in both study groups is presented in Tables The positive predictive values for the conventional and PapSpin methods were not different in the split-sample group: 88.0% and 86.2%, but was higher with the direct-to-vial group-95.7%. Also, no differences were found for the negative predictive value: 82.1%, 86.8% and 80.0%, respectively.PapSpin cytology preparations in comparison to conventional protocols have optimal preparation quality. Homogenized cytology material was uniformly transferred. The background features including microorganizms were preserved with exception of blood elements. Dysplastic cells  and tissThe current high cost of the widely used ThinPrep and SurePath commercial systems for liquid-based cytology preparations encouraged us to evaluate a less expensive alternative cytopreparatory technique. Cytocentrifugation as PapSpin was considered due to the fact that the Shandon Cytospin  has been a routine component of most cytology laboratories for 30 years. More flexible regulations for cervical cancer screening tests in Eastern Europe allowed us to evaluate PapSpin technology for cervical screening purposes.In our study, we evaluated the efficiency of the PapSpin technique for cervical cancer screening in two different sampling settings: the split-sample design, where PapSpin technologies were potentially influenced by splitting the material, and direct-to-vial design, where the whole cervicalmaterial was tested with the PapSpin technique. As a reference standard for cytology, we used results on cervical biopsy, LEEP, and hysterectomy specimens.et al., Weynand et al., Garbar et al. and Rosenthal et al. have already been published in the literature.[et al.[Split-sample studies of the PapSpin systems effectiveness by Khalbuss terature.–8 The dae.[et al.vs 1.7%). Satisfactory results were obtained after the reprocessing in 2 out of 6 PapSpin cases in the split-sample group and in 3 out of 8 cases in the direct-to-vial group. The remaining samples had insufficient cytologic material. Because our baseline for conventional and PapSpin unsatisfactory rate was already low, the benefit of improving the unsatisfactory rate was limited due to the participation by the primary health care centers with expierenced physicians.One of the advantages of LBC is the low unsatisfactory rate due to optimal cell fixation and easy monitoring of preparation quality. Additionally, this approach allows reprocessing of unsatisfactory samples. Although reprocessing is associated with increased laboratory costs (reagents and labor), it increases the satisfactory rate. Our study’s unsatisfactory rate as compared to the previous studies, are lower for LBC as compared to conventional preparation (0.4% vs 2.1% . Data from The College of American Pathologists PAP program survey indicates a wide range (0.9% to 11%) of ASC-US reporting with ThinPrep and SurePath methods.[Currently, the gynecological practice is faced with challenges related to the equivocal ASC-US interpretation. HPV reflex testing, colposcopy, and biopsy are recommended to manage patients with ASC-US cytology. Since it methods.In our study, HSIL+ detection rate was higher than that reported by the College of American Pathologists PAP surveys (from 0.1 to 2.0%). This is Three HSIL+ cases were missed with conventional cytology in the split-sample test group. Retrospective review revealed that one was missed at the screening level, one was erroneusly categorized due to poor smear quality, and one metastatic gastric tumor was detected in PapSpin preparation with a lack of cells in the conventional test.vs 28 (1.9%) with PapSpin test. Similar observations have been reported by Berenstein et al. in their meta-analysis for ThinPrep.[We also noticed an increase in LSIL detection with PapSpin in the split sample group. We found 19 (1.3%) with the conventional test ThinPrep. These reet al.,[In the current study, the sensitivity of conventional cytology as compared to PapSpin was 68.7% and 78.1%. Specificity was 93.8% and 91.8% respectively. Split-sample results of our study are similar to those reported by Garbar et al., concludeWe thank ThermoFischer for provision of 3.000 vials, Pappette brooms, and a cytocentrifuge and gynecologysts Julia Musteikiene , Saule Murmiene  and Daiva Kersulyte (Karoliniskes outpatients Clinics) as well as her colleagues for collaboartion.Written consent for publication was obtained from the patient or their relatives.No competing interest to declare by any of the authors.http://www.icmje.org/#author.All authors of this article declare that we qualify for authorship as defined by ICMJE Each author has participated sufficiently in the work and take public responsibility for appropriate portions of the content of this article.Each author acknowledges that this final version was read and approved.This study was conducted with approval from Institutional Review Board (IRB)  of all the institutions associated with this study.Authors take responsibility to maintain relevant documentation in this respect.


Superoxide dismutases (SOD) are ubiquitous metalloenzymes that catalyze the disproportion of superoxide to peroxide and molecular oxygen through alternate oxidation and reduction of their metal ions. In general, SODs are classified into four forms by their catalytic metals namely; FeSOD, MnSOD, Cu/ZnSOD and NiSOD. In addition, a cambialistic form that uses Fe/Mn in its active site also exists. Cyanobacteria, the oxygen evolving photosynthetic prokaryotes, produce reactive oxygen species that can damage cellular components leading to cell death. Thus, the co-evolution of an antioxidant system was necessary for the survival of photosynthetic organisms with SOD as the initial enzyme evolved to alleviate the toxic effect. Cyanobacteria represent the first oxygenic photoautotrophs and their SOD sequences available in the databases lack clear annotation. Hence, the present study focuses on structure and sequence pattern of subsets of cyanobacterial superoxide dismutases.Thermosynechococcus elongatus BP1) and MnSOD (Anabaena sp. PCC7120) reveal the sharing of N and C terminal domains. At the C terminal domain, the metal binding motif in cyanoprokaryotes is DVWEHAYY while it is D-X-[WF]-E-H-[STA]-[FY]-[FY] in other pro- and eukaryotes. The cyanobacterial FeSOD differs from MnSOD at least in three ways viz. (i) FeSOD has a metal specific signature F184X3A188Q189.......T280......F/Y303 while, in Mn it is R184X3G188G189......G280......W303, (ii) aspartate ligand forms a hydrogen bond from the active site with the outer sphere residue of W243 in Fe where as it is Q262 in MnSOD; and (iii) two unique lysine residues at positions 201 and 255 with a photosynthetic role, found only in FeSOD. Further, most of the cyanobacterial Mn metalloforms have a specific transmembrane hydrophobic pocket that distinguishes FeSOD from Mn isoform. Cyanobacterial Cu/ZnSOD has a copper domain and two different signatures G-F-H-[ILV]-H-x-[NGT]-[GPDA]-[SQK]-C and G-[GA]-G-G-[AEG]-R-[FIL]-[AG]-C-G, while Ni isoform has an nickel containing SOD domain containing a Ni-hook HCDGPCVYDPA.The sequence conservation and structural analysis of Fe (The present analysis unravels the ambiguity among cyanobacterial SOD isoforms. NiSOD is the only SOD found in lower forms; whereas, Fe and Mn occupy the higher orders of cyanobacteria. In conclusion, cyanobacteria harbor either Ni alone or a combination of Fe and Ni or Fe and Mn as their catalytic active metal while Cu/Zn is rare. As described by McCord and Fridovich ) for Cu/ZnSOD. In addition, the individual sequences of all the SOD metalloforms were also manually retrieved from public databases . Identical sequences from the same organism were removed manually. Intoto, 64 sequences representing 24 complete genomes and individual submissions obtained are listed in Table The non-redundant database of protein sequences  were retrieved using the PHI-BLAST  search tThe secondary structure consensus was carried out using nnPREDICT  and JPREThermosynechococcus elongates BP-1) and MnSOD (1gv3-Anabaena sp. PCC7120) with exception for NiSOD (1t6u-Streptomyces coelicolor) and Cu/ZnSOD  were retrieved from PDB. The 3D structures were analyzed using SWISS-PDB viewer [Representative crystal structures of available cyanobacterial FeSOD  [I and the dimer residues are represented by *.v.7.0.5) . The higClick here for file