The human innate immune system uses a system of extracellular Toll-like receptors (TLRs) and intracellular Nod-like receptors (NLRs) to match the appropriate level of immune response to the level of threat from the current environment. Almost all NLRs and TLRs have a domain consisting of multiple leucine-rich repeats (LRRs), which is believed to be involved in ligand binding. LRRs, found also in thousands of other proteins, form a well-defined "horseshoe"-shaped structural scaffold that can be used for a variety of functions, from binding specific ligands to performing a general structural role. The specific functional roles of LRR domains in NLRs and TLRs are thus defined by their detailed surface features. While experimental crystal structures of four human TLRs have been solved, no structure data are available for NLRs.predicted three-dimensional structures for NLRs. Specifically, we calculated amino acid hydrophobicity, charge, and glycosylation distributions within LRR domain surfaces and assessed their similarity by clustering. Despite differences in structural and genomic organization, comparison of LRR surface features in NLRs and TLRs allowed us to hypothesize about their possible functional similarities. We find agreement between predicted surface similarities and similar functional roles in NLRs and TLRs with known agonists, and suggest possible binding partners for uncharacterized NLRs.We report a quantitative, comparative analysis of the surface features of LRR domains in human NLRs and TLRs, using Despite its low resolution, our approach permits comparison of molecular surface features in the absence of crystal structure data. Our results illustrate diversity of surface features of innate immunity receptors and provide hints for function of NLRs whose specific role in innate immunity is yet unknown. The innate immune system provides a first level of defense against invading agents such as microbial pathogens and toxins and plays a role in maintaining a stable, healthy composition of the commensal microbiome. In mammals, the important role of continuously surveying the environment is played by members of two protein families: extracellular Toll-like receptors (TLRs) and intracellular Nod-like receptors (NLRs), also referred to as nucleotide-binding domain- and leucine-rich repeat-containing proteins. , the nucleotide-binding oligomerization (NACHT) domain, and the N-terminal effector domain. Several different effector domains can be found in human NLRs, including the caspase recruitment (CARD) domain ; the pyrin/PAAD (PYD/PAAD) domain in NALP1-14); the baculovirus "inhibitor of apoptosis" (BIR) domain (in NAIP); and an uncharacterized domain (in NLRX1), thereby partitioning NLRs into at least four subgroups. The variety of effector domains in other organisms, specifically invertebrates, is much larger . Followi; the bacAfter almost a decade since their discovery, information on ligand specificities of the NLR family proteins remains elusive. NOD1 and NOD2 are known to sense bacterial cell wall-derived peptidoglycans γ-D-glutamyl-meso-diaminopimelic acid (iE-DAP)  and muraThe main subjects of this work, the ligand-binding domains of NLRs and TLRs, both consist of multiple leucine-rich repeat (LRR) motifs. LRRs are extensively used by nature as building blocks for assembling scaffolds of protein interfaces, designed primarily for specific protein-protein interactions. About 500 LRR-containing proteins with diverse (and often unknown) functions have been identified in the human genome. Due to the importance of LRR proteins, they have been a subject of intensive experimental work, with the first crystal structure of a ribonuclease inhibitor ,29 proviWhile it is generally believed that LRR domains of most NLRs and TLRs are involved in direct binding of agonist molecules, for NLRs there is little experimental proof of this. Some evidence of direct agonist binding by NALP1 LRRs is given in . When reSince the overall shape of LRR domains from different proteins is relatively conserved, the functional similarities and differences between them must be defined by specific features of their surfaces. This would be true regardless of whether LRR domains interact with immunity "danger signals" directly or by means of intermediate molecules , or the functionality is conferred by LRR interactions with other (e.g. NACHT or PYR) domains of NLRs. The key point is that if LRR surface properties within a given NLR/TLR pair are similar, they are likely to bind the broadly similar type of molecules.In this manuscript, we investigate surface features of the twenty-two human NLRs, and by comparing them to those of TLRs, attempt to predict likely similarities between their functions. First, we have built homology models for LRR domains of twenty NLRs. Second, we have investigated distributions of amino acid charge and hydrophobicity across LRR domain surfaces and by means of clustering analysis have quantitatively assessed their similarities. Additionally, we have analyzed distributions of consensus N-linked glycosylation sites within TLRs, in order to support application of our approach to (presumably non-glycosylated) NLRs. While the simplicity of our analyses requires care when interpreting the results, we believe they may be helpful in designing future experiments to shed light on the role of NLR-coding genes in human innate immunity.Multiple alignments and phylogenetic trees  was used as a template. The two proteins for which the models were not built are NOD4 and NALP10. NOD4 LRR domain consists of 43 LRRs, and all LRR-containing proteins with available structures are too small to be used as a template. The second protein, NALP10, lacks an LRR domain.In order to visualize structures and surface features of LRR domains from human NLRs .In order to conduct a quantitative comparative analysis of LRR domain surface features in the absence of experimental structure data, we mapped physicochemical features of residues on the surface using existing knowledge about LRR sequence-to structure-mapping -33.The typical LRR sequence contains a conserved motif LxxLxLxxNxL, where L is leucine, isoleucine, valine, or phenylalanine, and N is asparagine, threonine, serine, or cysteine. In NLRs there is a striking correlation between their gene organization and amino acid sequence. Specifically, in NALPs, two consecutive LRR motifs XLaaNaLaaaaaaaaaaaaaaaaaLaaLXLaaNaLaaaaaaaaaaaaaaaaaaIaaL of length 28 + 29 = 57 residues are encoded by a single exon , while wFinally, from the available structures of other LRR-containing proteins it is known that the aaLXLaa pattern corresponds to a beta-strand in the inner concave surface, which is then followed by a C-terminal side loop, by an alpha-helix on the outside convex surface, and by the N-terminal side loop. This property is very well conserved in numerous LRR-containing proteins, and we have utilized it to improve mapping between LRR sequence and structure. The residue X in the aaLXLaa motif was used as reference residue to define positions of all other residues on the LRR surface Figure . The nexAfter we performed the sequence-to-structure mapping as described above, we were able to partition the surface into four parts and analyze distributions of amino acid properties such as charge and hydrophobicity within these parts separately. Distribution of potential glycosylation sites was also examined for extracellular TLRs.Amino acid charge and hydrophobicity distributions within LRR domains were calculated for each of the four surfaces shown in Figure i, we calculated average charge (qi) and hydropathy (hi) separately for each of the four surface regions. For an LRR domain with n motifs, this produced 4 n values for charge and the same for hydropathy. Next, in order to characterize the distribution of these values within each surface, we have calculated the first four moments, mk, (k = 0...3) of qi and hi distributions:Within a given protein, for each LRR motif fi is either qi or hi, andwhere m0 is simply the average value, and the moment m2 is related to the square of the standard deviation. The moments m1 and m3 reflect additional details of the distributions, in particular the directional trends (N-terminus being more/less hydrophobic or charged that the C-terminus).With these definitions, the moment rS . The similarity between distributions was then defined as (1 - rS). Clustering analysis was then performed by a complete linkage method utilizing Java Treeview software [The distributions of charge and hydropathy within one protein are thus each characterized by a sequence of 4 surface × moments = 16 values. In order to quantify similarities of distributions within different proteins, we used Spearman's rank-order correlation coefficient, software .Consensus N-linked glycosylation sites were predicted by searching for patterns NxxS and NxxT in TLR sequences. In globular proteins, such predictions greatly overestimate the number of glycosylation sites, assuming that all sites matching the pattern above are accessible to oligosaccharyltransferases in order for glycosylation to occur, while in reality most of these sites are buried inside protein structure. In our case, however, such predictions can be much more accurate because we could predict which of the potential glycosylation sites were exposed to solvent.As a first step, we have performed a phylogenetic analysis of LRR domains in NLRs and TLRs. Extreme sequence length diversity among LRRs from these families makes conducting such an analysis in a meaningful way nontrivial. This is further complicated by the fact that full length multiple alignment of complete sequences contains numerous large gap regions, which makes its truncation difficult. To circumvent the latter problem, we built a multiple alignment using a 10-fold increased gap extension penalty and truncated it from N- and C-terminal ends to make its length similar to that of the shortest sequence  NALPs and RI; (ii) CARD-containing proteins and NLRX1; and (iii) TLRs.  Surprisingly, the sequence similarity-based phylogeny of the C-terminal LRR domain well agrees with the classification of all these proteins based on the N-terminal effector domain. Within NALPs, there are three distinct subgroups: (a) NALP2, NALP5, NALP7, NALP8, NALP14; (b) NALP4, NALP9, NALP11, NALP13; (c) NALP3, NALP12, and RI. It is interesting to note that IPAF and CIITA, as well as NLRX1 whose effector domain lacks definitive classification, group together with CARD-containing NODs. This suggests that NLRX1 effector domain might be CARD-like. This analysis supports known phylogenetic relationships between NLR and TLR families, but it does not permit us to draw any conclusions regarding similarities of surface features between TLRs with known ligands and NLRs.The sequences group into three distinct clusters: , and found that they contain 0-2 consensus glycosylation sites per surface, with the only exception being an N-terminal surface of NOD3, which interestingly contains 10 such sites.These findings indicate that while glycosylation of some TLRs certainly modulates their surface properties, in most cases the ligand binding sites are located in non-glycosylated regions and, in addition, the overall surface area affected by glycosylation is relatively small, so that global surface comparisons such as those performed here are not affected.The two physico-chemical amino acid properties that critically affect ligand binding and whose distributions we have thus analyzed are charge and hydrophobicity. Figure Figure i) In NALP2, which is known to sense MDP, the concave and side surfaces have both hydropathy and charge distributions that are the most similar to NAPL3, which is known to bind a diverse set of ligands, including MDP. (ii) NALP4 and NALP14 have similar hydropathy distributions, but dissimilar charge distributions. (iii) NALP5 and NALP8 have hydropathy and charge distributions similar to NOD1 (except over the outer convex surface) that binds iE-DAP. (iv) NALP6 has the shortest LRR domain, containing only 6 LRRs. Its surface properties are similar to those of TLR3, which contains 25 LRRs. Because the hydropathy and charge distribution moments are normalized by the number of LRRs in a given domain, similarity of distributions still allows to hypothesize that NALP6 and TLR3 may bind the same type of ligands, RNA motifs. (v) NALP7 charge and hydropathy are similar to NOD2 and NALP3 both binding MDP, suggesting small molecules as probable ligands. (vi) The high similarity of NALP8 hydropathy distribution and size to those of NOD2 and also NOD1 (whose convex surface hydropathy is different) suggests that they may share the same type of ligands--peptidoglycans. This is also supported by similarities in their charge distributions, with the difference being a more positively charged convex surface of NALP8. (vii) The hydropathy and charge of NALP9 are similar to RI, which binds RNase, thus suggesting that the NALP9 ligand may be protein-like. (viii) NALP11 shows hydropathy similarity with TLR7 that binds ssRNA, most likely by one of its side surfaces. NALP11 charge distribution is similar to that of TLR7 and also TLR3 (which binds dsRNA at its side surface) over its side surfaces, suggesting that ss/dsRNA is likely to be among possible NALP11 ligands. (ix) NALP12 surface properties do not exhibit any obvious similarity to others. (x) The hydropathy distribution of NALP13 is similar to that of NALP7 and NOD4, but neither of them has known ligands. (xi) NALP14 charge distribution on its concave surface is almost identical to that of IPAF, and distributions over other surfaces are similar to IPAF. The hydropathy distributions of these two NLRs are similar within only its concave side. Assuming that IPAF binds flagellin similar to TLR5, i.e., on its concave surface [xii) NOD3 hydropathy has some similarity to NALP3, but charge distribution is unlike that in any other domain. (xiii) NOD4 concave and side surfaces have hydrophobicity similar to NALP12, but the charge distribution is different. Also, NOD4 appears to have the largest ligand-binding domain of all NLRs and TLRs: it contains as many as 43 LRRs. (xiv) NLRX1 surface features do not exhibit similarity with any other domain. (xv) NAIP hydropathy distribution is similar to RI, while NAIP charge distribution is similar to TLR8.In order to gain further insight into similarities of LRR surface features between NLRs and TLRs, we conducted a careful manual inspection of similarity patterns across individual biologically relevant surfaces, also taking into account results of our TLR glycosylation analysis , show vThese correlations suggest that surface similarities between NLRs and TLRs found here may reflect possible but as yet unknown functional similarities. For most NLR genes the binding specificities are unknown, and only indirect functional information, such as from RNA and protein expression profiles, is available . In the Specific cases of similarities found here include predicted functional similarity between NALP4 and NALP14, but this doesn't allow for any inferences as the ligands for both are unknown; it is only known that NALP14 plays a role in spermatogenesis and its expression is restricted to testis . The simin vitro [An interesting observation is that NALP7, which is implicated in recurrent hydatidiform moles and reproductive wastage in humans and is known to inhibit IL-1beta upon stimulation with such small molecules as LPS in vitro , appearsin vitro , but itsin vitro ; this hoi) in-plane circular shape with very large radius ; (ii) nonplanar, torroid-like shape with regular radius ; (iii) structure consists of two or more connected circular parts each having regular radius/curvature. The actual three-dimensional shape of this domain thus represents a puzzle. Lastly, NAIP surface properties resemble those of RI and TLR8 binding ribonuclease and RNA, respectively. Murine Naip5  was shown to be required for intracellular response to bacterial flagellin in mice [Surface features of NOD3, NOD4, and NLRX1 did not provide us with an insight to make functional inferences. Two observations however need to be mentioned here. First, NOD3 contains unusually large number (as many as 10) of potential N-linked glycosylation sites on its N-terminal side, unlike in any other NLR and TLR. This however likely has little relevance to possibility of actual NOD3 glycosylation, as currently there are no reports of it being secreted. Second, as mentioned above, NOD4 appears to have extremely large number of LRRs (as many as 43) making its LRR domain the largest among all NLRs and TLRs. This implies that the NOD4 LRR domain structure may possess some of the following properties untypical for NLRs: . We believe that this could be another possible explanation of the diversity of LRR domain surface properties within NLRs, which in turn points to the possibility of diverse ligand-induced inflammasome activation mechanisms.in vitro. Because of the low resolution of our approach, our predictions should be more reliable in cases where TLR/NLR known ligands are macromolecular fragments, such as flagellin, ds/ssRNA, or DNA motifs. Improving the reliability of predictions and providing quantitative estimates for relative ligand binding affinities requires combined application of explicit structure-based approaches such as homology modeling and molecular docking. Work in this direction is in progress and will be reported elsewhere.We have built homology models and performed quantitative analyses of charge, hydrophobicity, and glycosylation distributions of LRR domains in almost all human NLRs and TLRs known today. These analyses, while conducted without experimental knowledge of NLR structures, provide a low-resolution comparison of LRR surface features between the two protein families. The validity of our approach is supported by several cases when receptors that bind similar ligands were found to have similar charge and hydrophobicity distributions. Comparison of NLRs with unknown functions to NLRs/TLRs that have known ligands allowed us to infer possible functional similarities, which can not be derived from conventional phylogenetic analyses. We hope that our predictions will be helpful in design of future experiments to decipher roles of previously uncharacterized NLRs in human innate immunity. We propose an experimental study to test whether the NLR macromolecular ligands we predict indeed modulate the activity of caspase-1 in the presence and absence of the corresponding NLRs AYI performed the calculations, analyzed the results, and drafted the manuscript. AG helped to formulate and coordinated the study, and edited the manuscript. Both authors read and approved the final manuscript.Supplementary materials. A multiple sequence alignment used for phylogenetic analysis in Figure Click here for file


Yersinia YadA, Haemophilus Hia and Hsf, Moraxella UspA1 and A2, and Neisseria NadA. TAAs also occur in symbiotic and environmental species and presumably represent a general solution to the problem of adhesion in proteobacteria. The general structure of TAAs follows a head-stalk-anchor architecture, where the heads are the primary mediators of attachment and autoagglutination. In the major adhesin of Bartonella henselae, BadA, the head consists of three domains, the N-terminal of which shows strong sequence similarity to the head of Yersinia YadA. The two other domains were not recognizably similar to any protein of known structure. We therefore determined their crystal structure to a resolution of 1.1 Å. Both domains are β-prisms, the N-terminal one formed by interleaved, five-stranded β-meanders parallel to the trimer axis and the C-terminal one by five-stranded β-meanders orthogonal to the axis. Despite the absence of statistically significant sequence similarity, the two domains are structurally similar to domains from Haemophilus Hia, albeit in permuted order. Thus, the BadA head appears to be a chimera of domains seen in two other TAAs, YadA and Hia, highlighting the combinatorial evolutionary strategy taken by pathogens.Trimeric autotransporter adhesins (TAAs) are a major class of proteins by which pathogenic proteobacteria adhere to their hosts. Prominent examples include  Bartonella henselae. This pathogen causes cat scratch disease in humans, but can lead to much more severe disease in immunosuppressed patients, e.g., during chemotherapy or after HIV infection. Surprisingly, domains previously seen in other TAA heads are combined in a novel assembly, illustrating how pathogens rearrange available building blocks to create new adhesive surface molecules.The ability to adhere is an important aspect of the interaction between bacteria and their environment. Adhesion allows them to aggregate into colonies, form biofilms with other species, and colonize surfaces. Where the surfaces are provided by other organisms, adhesion can lead to a wide range of outcomes, from symbiosis to pathogenicity. In Proteobacteria, colonization of the host depends on a wide range of adhesive surface molecules, among which Trimeric Autotransporter Adhesins (TAAs) represent a major class. In electron micrographs, TAAs resemble lollipops projecting from the bacterial surface, and in all investigated cases, the adhesive properties reside in their heads. We have determined the head structure of BadA, the major adhesin of  Yersinia enterocolitica, a species causing enteritis, mesenteric lymphadenitis, and reactive arthritis in humans Neisseria meningitidisMoraxella catarrhalisHaemophilus influenzaeBartonella henselaeBartonella henselae has also emerged as the agent of bacillary angiomatosis, an uncontrolled proliferation of blood vessels resulting in tumor-like masses of cells in patients with impaired immune systems.Adherence to the host is a key event in bacterial pathogenesis. The mediators of this process, called adhesins, form a heterogenous group that vary in architecture, domain content and mechanism of binding. Trimeric autotransporter adhesins, also referred to as OCAs for oligomeric coiled-coil adhesins, form a new class of adhesins recently defined from pathogenic proteobacteria All TAAs follow a head-stalk-anchor architecture in the direction from amino- to carboxy-terminus of the protein Bartonella henselae BadA is the longest representative, at over 3000 residues, and extends approx. 240 nm from the bacterial cell surface  is considerably larger than other well-studied TAAs, such as YadA (455 res.), Hia (1098 res.), UspA1 (863 res.) or NadA (364 res.). Although very long . To assay the stability of the trimers, we subjected the protein to proteolytic treatment with trypsin and chymotrypsin. In both cases we obtained fragments of approx. 14 kDa , which cThe CD spectra of the digested and undigested forms indicated well-folded proteins consisting primarily of β-sheets . Thermalmax of 320 nm, which is typical for buried Trp residues in folded proteins, does not change significantly in the trypsinized and chymotrypsinized fragments, but the intensity of the emission signal increases substantially (The single tryptophan residue close to the amino-terminus (Trp387) allowed us to perform fluorescence measurements. The λantially . ProteolThe undigested protein was crystallized under a variety of conditions. The crystals typically grew to 300×200 µm in size, were well ordered, and diffracted up to a resolution of 1.1 Å. All crystals tested, although of different shape and from different crystallization conditions, belonged to space group P1 with cell constants of a = 29.87, b = 51.14, c = 58.62, α = 65.87°, β = 76.6°, γ = 82.08°. In order to solve the structure of this fragment, a variety of heavy atom derivatives were prepared and data were collected, but none of the crystals showed binding. This was not unexpected, as the protein does not contain cysteine or methionine residues, which commonly bind heavy metal compounds. For the same reason, we could not use selenomethionine-based MAD-phasing.Haemophilus Hia (PDB code 1s7m). The two BadA domains each gave a separate match – the first domain to the C-terminal part of Hia and the second to the N-terminal part. These matches were intriguing, as Hia is also a TAA and the conserved GW and GIN motifs, which we had identified as key signatures of these domains The continued failure to determine the structure experimentally led us to re-explore the protein with bioinformatic tools. We had failed to identify potential homologs through either sequence comparisons or fold recognition, but a new method for detecting distant sequence similarity by comparing profile Hidden Markov Models with each other had just been developed in our department see . The twoWe therefore attempted to solve the BadA structure by molecular replacement with homology models based on the Hia structure. To this end, we built full-atom models for each domain, as described in the 2 from each chain, corresponding to half of its total surface area of 10040 Å2, are buried in the trimer.The overall structure of the construct is rod-like, with a length of 10 nm and an approximate diameter of 2.5 nm. Superposition of the three protein chains shows root mean square deviations (r.m.s.d.) of ∼1.2 Å, with the main differences in the termini and in the loops. Although the structural variability near the termini is probably an artifact of expressing a truncated construct, the overall r.m.s.d suggest an intrinsic flexibility of the three protein chains while the B-factors are low and equally distributed all over the protein chain except for the coiled-coil part. The three chains are tightly intertwined and each can only assume its structure in the context of the other two. 72 of 114 residues from each chain (63%) are involved in intersubunit contacts, including 16 residues in the hydrophobic core of the trimer . More thThe structure consists of four distinct elements, as anticipated from the sequence analysis , 3 and 4I of the next chain and the β4II- β5II hairpin of the last chain, as viewed clockwise from the N-terminus; the strand order is β2- β1- β3I- β5II- β4II , again as viewed clockwise from the N-terminus (The GIN domain (residues 435–466) also forms a β-prism of 5-stranded β-meanders, albeit not interleaved and perpendicular to the trimer axis. Its five β-strands (β7- β11) are extended N-terminally by the region connecting GIN with the Trp-ring domain in the next chain (β6)terminus .The neck sequence serves as a connector, which makes the transition from the wide diameter of the β-prisms to the narrower diameter of the coiled-coil stalk. Although being largely devoid of regular secondary structure, the neck forms an extended network of hydrogen bonds . The coiComparison of the BadA structure with the two previously determined TAA head structures from YadA All three proteins contain necks, which are structurally nearly identical . The simThe part of the BadA head which is not included in our construct shows extensive sequence similarity to the YadA head, allowing us to model it by homology see . The β-rThe similarity between YadA and BadA not only encompasses the left-handed β-roll domain, but also the neck connector and a short coiled-coil segment. Thus, even though the coiled-coil segment N-terminal to the Trp-ring domain was not resolved in our BadA construct, we could merge the model to the structure without gaps by aligning the registers of the coiled coils and modeling the missing part with parametric equations Bartonella henselae adhesin BadA. Surprisingly, these domains are structurally nearly identical to two domains from the Haemophilus adhesin Hia, despite their similarity being essentially undetectable by sequence comparisons. This is due to the short length of the individual domains, to two large inserts in Hia, and to the seemingly reversed order of the domains in the two proteins. The reversed order is due to the way in which the Hia fragment was constructed; in fact, Trp-ring and GIN domains also occur in tandem in Hia, albeit in multiple copy, and the Hia fragment combines the C-terminal GIN domain of one tandem with the N-terminal Trp-ring domain of the next. The near-identity of the structures is underscored by our ability to solve the BadA structure by molecular replacement, using the Hia structure as a modeling template. From this we conclude that TAA domains retain their structure closely, irrespective of their molecular context, and that their strongly interleaved nature prevents structural divergence, even after considerable sequence divergence has occurred. These findings support our previous proposal that the structure of TAAs can be elucidated by a “dictionary approach”: domains are identified by bioinformatics, compared to a database containing the structures of representative exemplars for each domain, modeled and assembled into complete fibers using the nearly invariant structure of connectors such as the necks and coiled-coil segments We have determined the structure of two domains from the head of the Bartonella quintana, which only have the YadA-like part of the head and lack the domains described here, only bind to collagen et al., submitted). We note in this context that the binding affinity of individual heads could be quite low, given their high density on the cell surface. A conspicuous structural feature of the Trp-ring domain are the many open hydrogen bond donor and acceptor groups at the edges of the three β-sheets forming the prism. In the only complex between a bacterial adhesin and fibronectin known to atomic resolution  and buffer B (CH3CN/0.05% TFA) with a gradient from 20–80% buffer B at a flow of 250 µl/min.For protease resistance assays, the BadA fragment (0.5 mg/ml) was incubated at room temperature in 20 mM MOPS/KOH pH 7.2, 150 mM NaCl with 10 µg/ml of either trypsin or chymotrypsin for 10 min. Reactions were stopped by addition of 1 mM PMSF, and samples were subsequently analyzed by SDS-PAGE and mass spectrometry. In preparation for MS analysis, the proteolytically treated protein was re-purified by ion-exchange and gel-size exclusion chromatography. LC HR MS measurements were performed with an Agilent 1100 series HPLC with a Waters Symmetry C4 3.5 µm column (2.1×100 mm), coupled to a micrOTOFLC mass spectrometer . Protein was eluted from the HPLC column using buffer A (HCircular dichroism (CD) spectra of proteins (12 µM) were recorded in PBS at 200–240 nm with a J-810 Spectropolarimeter (Jasco), using 1 mm cuvettes. The signal output was converted into molar ellipticity. Thermal stability was monitored by CD spectroscopy using a Peltier-controlled sample holder unit. Temperature profiles at 210 nm were recorded in 1°C increments with 0.2° pitch from 25°C to 100°C. In all cases a temperature probe connected to the cuvette was used to provide an accurate temperature record. The fraction of protein in the unfolded conformation, fU, was calculated as fU = (yF−y)/((yF−yU), where yF and yU represent the values corresponding to folded and unfolded states, respectively, and y being the observed value.ex = 293 nm and λem = 300–400 nm.Tryptophan fluorescence was measured at room temperature in PBS buffer at protein concentrations of 30 µM in a FP-6500 spectrofluorometer (Jasco) with λ+ and BadA− strains grown on blood agar Bacterial colonies of BadA2. The samples were sputter-coated with 8 nm gold-palladium and examined at 20 kV accelerating voltage in a Hitachi S-800 field emission scanning electron microscope.For scanning electron microscopy, colonies were postfixed with 1% osmium tetroxide in 100 mM Phosphate buffer pH 7.2 for 1 h on ice, dehydrated in ethanol and critical-point-dried from COFor transmission electron microscopy, glutaraldehyde-fixed cells were covered with 2% agarose and blocks containing single colonies were cut out. After postfixation with 1% osmium tetroxide in 100 mM Phosphate buffer pH 7.2 for 1 h on ice, these blocks were rinsed with aqua bidest, treated with 1% aqueous uranyl acetate for 1 hr at 4°C, dehydrated through a graded series of ethanol and embedded in Epon. Ultrathin sections were stained with uranyl acetate and lead citrate and viewed in a Philips CM10 electron microscope.For on-section immunolabeling, cells were fixed with 2.5% glutaraldehyde in PBS, dehydrated in a graded series of ethanol at progressive lower temperature from 0°C down to −40°C, infiltrated with Lowicryl HM20 and UV-polymerized at −40°C. Unspecific binding sites on ultrathin sections were blocked with 0.5% bovine serum albumin and 0.2% gelatine in PBS. Ultrathin sections were then incubated with a BadA specific rabbit IgG antibody . Data were indexed, integrated and scaled with the XDS program package The homology of the N-terminal part of the BadA head with YadA was found using PSI-BLAST http://wiki.c2b2.columbia.edu/honiglab_public/index.php/Software:Jackal). Each chain was modeled separately, then all chains were combined together and sterical clashes were removed with the profix program, again from the Jackal package.Sequences of corresponding domains were manually aligned with respect to secondary structure arrangement and conserved residues. Homology models based on the structures of YadA and Hia were built with the nest program from the Jackal package  for which we do not have a structural template.The coiled-coil segment preceding the Trp-ring domain has a periodicity of 11 and was constructed with BeammotifCC The model of the full head of BadA was constructed using the solved structure described here and the two models mentioned above. The necessary structural superimpositions were done with VMD Haemophilus Hia (1s7m). Two subdomains of this model were independently placed using the program MOLREP free-factor of 0.156/0.184 was achieved. Model superposition was performed by the programs top3d or LSQ included in the CCP4 program package http://molbio.info.nih.gov/structbio/basic.html). Figures were prepared using the programs DINO (http://www.dino3d.org/) and Rasmol (http://www.openrasmol.org/).The structure of the BadA head fragment was solved by molecular replacement using models based on the PDB coordinates of the partial head of http://protevo.eb.tuebingen.mpg.de/coordinates/.The x-ray structure was deposited in the Protein Data Bank . The model of the full BadA head can be downloaded from Figure S1Mass Spectrometry Analysis, Supplement to (2.60 MB PDF)Click here for additional data file.


The crystal structure of a YkfC ortholog from Bacillus cereus (BcYkfC) at 1.8 Å resolution revealed that it contains two N-terminal bacterial SH3 (SH3b) domains in addition to the C-terminal catalytic NlpC/P60 domain that is ubiquitous in the very large family of cell-wall-related cysteine peptidases. A bound reaction product (l-Ala-γ-d-Glu) enabled the identification of conserved sequence and structural signatures for recognition of l-Ala and γ-d-Glu and, therefore, provides a clear framework for understanding the substrate specificity observed in dipeptidyl-peptidase VI, YkfC and other NlpC/P60 domains in general. The first SH3b domain plays an important role in defining substrate specificity by contributing to the formation of the active site, such that only murein peptides with a free N-terminal alanine are allowed. A conserved tyrosine in the SH3b domain of the YkfC subfamily is correlated with the presence of a conserved acidic residue in the NlpC/P60 domain and both residues interact with the free amine group of the alanine. This structural feature allows the definition of a subfamily of NlpC/P60 enzymes with the same N-terminal substrate requirements, including a previously characterized cyanobacterial l-alanine-γ-d-glutamate endopeptidase that contains the two key components (an NlpC/P60 domain attached to an SH3b domain) for assembly of a YkfC-like active site.Dipeptidyl-peptidase VI from  Two fates are possible for the generated murein tripeptide l-Ala-γ-d-Glu-DAP. Under normal growth conditions, these tripeptides are recycled by Mpl ligase and returned to the peptidoglycan-biosynthetic pathway. During nutrient-limiting conditions, an additional pathway  components, has been reported in many bacteria, including ay Fig. 1 is likelE. coli, such as AmpG, AmpD, Mpl and MpaA, have no orthologs in B. subtilis  endopeptidases. While their biochemical function seems to be conserved, the physiological roles of NlpC/P60 proteins are diverse, including involvement in cell separation, expansion, differentiation, cell-wall turnover, cell lysis, protein secretion and virus infection . Thus, it is generally assumed that these auxiliary domains function as targeting domains which localize their proteins to the cell wall. The functional synergy between the NlpC/P60 domains and their auxiliary domains is currently not fully understood. We have previously determined the crystal structure of a γ-d-Glu-DAP endopeptidase from cyanobacteria  in complex with l-Ala-γ-d-Glu. BcYkfC shares 40% sequence identity with YkfC from B. subtilis, which has previously been biochemically characterized  were aligned using HMMALIGN  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 (MGSDKIHHHHHHENLYFQ/G), 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 I-PIPE/V-PIPE mixture and dispensed onto selective LB–agar plates. The cloning junctions were confirmed by DNA sequencing. Using the PIPE method, the gene segment encoding residues Met1–Ala23 was omitted as these residues were predicted to form a signal peptide. Expression was performed in a selenomethionine-containing medium with suppression of normal methionine synthesis. At the end of fermentation, lysozyme was added to the culture to a final concentration of 250 µg ml−1 and the cells were harvested and frozen. After one freeze–thaw cycle, the cells were sonicated 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 TEV protease per 15 mg of eluted protein. The protease-treated eluate was run over nickel-chelating resin  pre-equilibrated with HEPES crystallization buffer  and the resin was washed with the same buffer. The flowthrough and wash fractions were combined and concentrated to 18.8 mg ml−1 as determined using the Coomassie Plus Protein Assay Reagent (Pierce) by centrifugal ultrafiltration (Millipore) for crystallization trials. The oligomeric state of BcYkfC was determined using a 0.8 × 30 cm Shodex Protein KW-803 column (Thomson Instruments) pre-calibrated with gel-filtration standards (Bio-Rad).Clones were generated using the Polymerase Incomplete Primer Extension (PIPE) cloning method  crystallization protocols  PEG 200 and 0.1 M phosphate–citrate pH 4.2. A needle-shaped crystal of approximate dimensions 100 × 15 × 15 µm was harvested after 29 d at 277 K. No additional cryoprotectant was added to the crystal. Initial screening for diffraction was carried out using the Stanford Automated Mounting system . This script shepherds the structure-determination process, as summarized below, using preset rules through a decision-tree that mimics that used by an experienced crystallographer. The calculations are parallelized on a computer cluster such that initial maps and models can usually be obtained within 1 h of the completion of data collection. In summary, the MAD data were integrated and reduced using XDS and then scaled with the program XSCALE  unless specifically stated otherwise. Atomic coordinates and experimental structure factors for BcYkfC at 1.8 Å resolution have been deposited in the PDB with accession code 3h41.Multi-wavelength anomalous diffraction (MAD) data were collected at wavelengths corresponding to the peak, high-energy remote and inflection wavelengths of a selenium MAD experiment at 100 K using a MAR CCD 325 detector (Rayonix) on SSRL beamline 11-1. Processing of the diffraction data and initial structure solution were carried out using the automatic structure-solution script 2.5.et al., 2009Glide v.5.0 (Schrödinger LLC). The positions of the bound ligand were used as restraints such that the l-Ala-γ-d-Glu portion of the docked substrate adopted a similar conformation as seen in the crystal structure. In order to perform the docking studies, the tri-oxidized cysteine (OCS238) in the crystal structure was replaced with the reduced form, which is needed for reaction in the papain family of cysteine peptidases. Furthermore, only the side-chain conformer with higher occupancy in the crystal structure was considered in the docking experiments when multiple conformations were observed for an active-site residue.Molecular docking was performed using the same protocol as described previously (Xu 3.3.1.B. cereus ATCC 10987) contains 333 residues (molecular weight 37.3 kDa), the first 23 of which are predicted to be a signal peptide by the Phobius web server  does not contain a signal peptide, suggesting that the two proteins function at different cellular locations.Full-length BcYkfC  is adjacent to the ykfB                l-Ala-d-Glu epimerase gene (BCE_2879) in the B. cereus genome. The YkfB epimerases (sequence identity 50%) from both bacteria do not contain predicted signal peptides. The genome association of ykfB and ykfC is also observed in other bacteria . However, the genome context of ykfBC is different in B. subtilis compared with B. cereus . Both genes are predicted to encode periplasmic dipeptide-binding proteins.As in us Fig. 2. The YkfB. subtilis and B. cereus. In B. cereus, the murein peptides are likely to be broken down outside the cell, with the resulting dipeptide l-Ala-γ-d-Glu being imported into cytoplasm for further processing by YkfB, while the reactions catalyzed by both YkfC and YkfB are likely to occur in the cytoplasm in B. subtilis.Based on the presence/absence of the signal peptide on YkfC and YkfB, as well as their genomic contexts, we suggest that the strategy for metabolizing murein peptides is likely to differ between 3.2.et al., 2002E. coli with an N-terminal TEV-cleavable His tag and purified by metal-affinity chromatography. In order to improve the chance of obtaining crystals, the predicted N-terminal signal peptide (residues 1–23) was not included in the cloned construct. The data were indexed in space group C2 and the structure was determined at 1.79 Å resolution with one molecule per asymmetric unit using the MAD method . The electron density for the main chain was well defined throughout the entire molecule. The mean residual error of the coordinates was estimated to be 0.11 Å by the diffraction-component precision index (DPI) method  are also supported by clear electron density. The final model of BcYkfC contains residues 29–333, one dipeptide l-Ala-γ-d-Glu, one phosphate, six polyethylene glycol (PEG) fragments from the crystallization solution and 265 waters. The residual residue (Gly0) from the cleaved N-terminal purification tag, residues 24–28 and the side chains of Glu201 and Arg270 were disordered and were not included in the final model. Data-collection, refinement and model statistics are summarized in Table 1The crystal structure of BcYkfC was determined using the high-throughput structural genomics pipeline implemented at the JCSG (Lesley 3.3.a and 3b). The two SH3b domains are similar to each other, with an r.m.s.d. of 2.2 Å for 57 aligned Cα atoms (sequence identity of 13%). A structural similarity search using DALI  region appears to be a common and unique structural feature of SH3b domains compared with their eukaryotic counterparts . Prokaryotic SH3-like domains have also been implicated in polypeptide binding  and lack the β-hairpin in the RT-loop region when compared with SH3b domains. The C-terminal NlpC/P60 catalytic domain of BcYkfC is remotely related to the papain family of cysteine peptidases   and a C-terminal NlpC/P60 cysteine peptidase domain (residues 208–333)  Table 2.2 per domain) between the two SH3b domains is mostly hydrophobic and is centered on interactions between the βA–βA1 and βA2–βB loops of SH3b1 and the βA2 and βB strands and βD–βE loop of SH3b2. The SH3b1–NlpC/P60 domain interface (903 Å2 buried surface per domain) is mediated through α2–α3–βA1 and the βC–βD loop of SH3b1, and α1–α2–α3 and the β6–β7 loop of NlpC/P60. The active site is located at this SH3b1–NlpC/P60 interface, whereas the SH3b2 domain is distal to the active site. A multiple sequence alignment of full-length homologs of YkfC  indicates that most of the highly conserved residues are either buried inside the protein or clustered around the active site .The three domains of BcYkfC are arranged in a triangle such that each domain interacts with the two other domains. The interface (∼570 Å3.4.a). Since an oxidized cysteine can no longer function as a nucleophile in the reaction  in the crystal based on the electron density . As the dipeptide was not added during protein purification or crystallization, it was most likely to have been obtained during protein expression in E. coli. Since this dipeptide is one of the reaction products of BcYkfC .The dipeptide fC Fig. 2, it uneqb). The first conformer of His291 (occupancy modeled as 0.7), which facilitates hydrogen bonding to His303, is identical to the corresponding histidine of the catalytic dyad in papain and other NlpC/P60 enzymes. The second rotamer points His291 towards the solvent. The side-chain isomerism in the active site is likely to be a consequence of the observed oxidized state of the catalytic Cys238, since the oxidized Cys238 makes multiple hydrogen-bond interactions with nearby side chains, including Tyr226, Ser257 and His291 .Two active-site residues, Ser257 and His291, display two discrete side-chain conformations . The average B value for the bound ligand is 26 Å2 , indicating that it is well ordered with almost full occupancy in the active site. The interface between the dipeptide and the protein buries a total surface area of 490 Å2. The dipeptide is stabilized by multiple hydrogen bonds. The free amine of l-Ala in the S2 pocket makes hydrogen bonds to Glu83 O and the side chains of Tyr118 (OH) and Asp256 (Oδ1), which are highly conserved in the YkfC subfamily of NlpC/P60 enzymes (see below). The α-NH group of γ-d-Glu in the S1 site forms a weak hydrogen bond to Asp237 Oδ2. The α-carboxyl of d-Glu is stabilized by hydrogen-bonding interactions with Ser239 and Ser257 . On the solvent-exposed side of the substrate, waters are also involved in the hydrogen-bond network with the substrate and the enzyme (not shown). The l-Ala methyl side chain points towards a hydrophobic pocket defined by the side chains of Trp228 and Ala229. Additionally, the aliphatic C atoms of d-Glu are involved in hydrophobic contacts with Trp228. As a result, Trp228 contributes to both the S2 and S1 binding sites.The active-site residues form a pocket that is highly complementary in shape and chemical properties to the ligand  dipeptidase that is found in the cytoplasm during sporulation , which is the only residue from SH3b1 whose side chain interacts with l-Ala. The residues from the catalytic domain that contribute to the S2 and S1 sites are identical in BcYkfC and DPP VI (except for an Ala/Ser mutation at position 257 of BcYkfC). Therefore, we conclude that both the B. subtilis and the B. cereus YkfCs are likely to have very similar substrate specificity for murein peptides that contain an N-terminal l-Ala. Enzymes with this specificity cannot cleave PG-biosynthesis precursors such as UDP-MurNAc-pentapeptide and thus are not likely to interfere with the PG-synthesis pathway.The active site of fC Fig. 5. Dipeption Fig. 5. Thus, Don Fig. 5. Further3.7.B. subtilis YkfC has previously been shown to process the tripeptide l-Ala-γ-d-Glu-l-Lys, the tetrapeptide l-Ala-γ-d-Glu-l-Lys-d-Ala and the pentapeptide l-Ala-γ-d-Glu-l-Lys-d-Ala-d-Ala . The Cδ carboxyl group of γ-d-Glu is displaced owing to the oxidation of Cys238 in the crystal structure. In the modeled structure, Tyr226 OH interacts with the carboxyl group of γ-d-Glu . This docked structure places the Cδ atom close to Cys238 SH (distance ∼3.6 Å) and could represent the chemically productive conformation. The electrostatic surface of the binding site is highly complementary to that of the substrate, indicating that the polar and charged interactions are likely to be important for substrate recognition. The basic His290 and Arg306 residues could interact with carboxyl groups. It has previously been reported that the catalytic efficiency of B. subtilis YkfC for l-Ala-γ-d-Glu-Lys is significantly less than that for tetrapeptides or pentapeptides .               fC Fig. 5, resulti3.8.et al., 2004NlpC/P60 cysteine peptidases are ubiquitous in bacteria, with ∼5000 NlpC/P60 domains in the current Pfam database when metagenomics data are included  do not contain the conserved tyrosine that is believed to be essential for catalysis. These proteins are likely to have the NlpC/P60 fold, but either have lost cysteine peptidase activity or are remote homologs that hydrolyze different substrates . The S sites (S2 and S1) are significantly more conserved than the S′ sites in these proteins. The most conserved site is S1, which is tailored to recognize γ-d-Glu . The three most highly conserved residues , aside from the catalytic dyad, are involved in hydrogen bonding with γ-d-Glu . At position 257, residues with small side chains (Ala/Ser/Thr) are observed, creating a cavity for the carboxyl group of γ-d-Glu. Trp228 contributes to both the S1 and S2 sites and a conserved hydrophobic residue is usually found at this position, which may facilitate interaction with the substrate Ala Cβ. Therefore, the conservation of active-site residues indicates that a significant percentage of the NlpC/P60 family is likely to be specific for γ-d-Glu. More than 1300 proteins which contain the strictly conserved Trp228, Asp237, Ser239 and Tyr226 residues  are most likely to bind X-l-Ala-γ-d-Glu moieties (where X is H or other moieties).The nonredundant (nr) database of protein sequences was searched using the 3.9.l-Ala-γ-d-Glu endopeptidases from the cyanobacteria A. variabilis (AvPCP) and N. punctiforme (NpPCP). These enzymes contain an N-terminal SH3b domain and a C-terminal NlpC/P60 catalytic domain . The full-length AvPCP can be superimposed onto BcYkfC with an r.m.s.d. of 2.3 Å and 29% sequence identity for 193 aligned Cα atoms (Table 2c). The striking similarity between the cyanobacterial enzymes and YkfC was not previously detected by sequence analysis owing to the presence of two insertions: the entire SH3b2 domain and the α1–α3 region of SH3b1.We have previously determined the crystal structures of two closely related NlpC/P60 s Table 2. Thus, td). Without these insertions to stabilize this domain interface, AvPCP uses a different strategy to achieve a similar purpose: a longer C-terminal loop in NlpC/P60 extends to interact with the SH3b domain.The βC–βD loop and the βA1–βA2 loop of SH3b1 are longer compared with SH3b of AvPCP. The βC–βD loop of YkfC is located upstream of the S2 site. The βA1–βA2 loop of YkfC contains three helices (α1–α3) and is located on the S′ side of the binding groove in the catalytic domain. This insertion packs against the surface of the NlpC/P60 domain and contributes to stabilizing the interface between the SH3b1 and NlpC/P60 domains. Furthermore, it is also defines the shape of the S′ binding site, which appears to be more restricted for BcYkfC than for AvPCP . Additionally, it sterically hinders the docking of a large moiety beyond the S2 site and directly contributes to specificity. This role constitutes a new function for SH3b domains.We previously proposed that the cyanobacterial enzymes have the same substrate requirement as DPP VI based on modeling studies  is essential for YkfC specificity by neutralizing the positive charge on the free amine of l-Ala. Among the 2599 sequences obtained above, 282 proteins were identified that contained an aspartate equivalent to Asp256 and a conserved catalytic dyad. In 239 of the 282 proteins (85%), the conserved tyrosine was maintained in their SH3b domains (Tyr118 of BcYkfC), while this tyrosine was not conserved in enzymes that lacked Asp256. Thus, the presence of an aspartate residue at position 256 and a conserved tyrosine in SH3b is highly correlated . Interestingly, NlpC/P60 domains with the conserved aspartate are also found in a few single-domain or multiple-domain proteins that do not contain any detectable SH3b domain. The biochemical properties of these proteins are currently unknown, but they could represent novel variants of a YkfC-type enzyme.BcYkfC and cyanobacterial γ-l-Ala, i.e. DPP VI, YkfC and AvPCP/NpPCP. The cyanobacterial enzymes contain one SH3b domain, while the homologs in other bacteria contain two SH3b domains. As expected, the SH3b domains are highly divergent. The βD strand, in which the conserved tyrosine is located, is more conserved . This strand is also responsible for the interface with the catalytic domain. BcYkfC is a member of a cluster of highly conserved homologs that are present in closely related species such as B. anthracis, B. thuringiensis and B. weihenstephanensis (sequence identity of ≥95%). All members of this group contain signal peptides. Signal peptides are also present in some members of the bacteroidetes group, but not in proteobacterial and cyanobacterial homologs.Nevertheless, the 239 proteins above with the conserved aspartate (Asp256) and tyrosine (Tyr118) are most likely to define a YkfC-like subfamily of NlpC/P60 proteins. These NlpC/P60 enzymes are predominately distributed in four phyla of bacteria: bacteroidetes, cyanobacteria, firmicutes and proteobacteria (α-proteobacteria). They include all currently known enzymes with the same requirement for a free N-terminal It is thus likely that the YkfC subfamily of highly specialized enzymes has evolved from a common ancestor, which was likely to have been a P60-like general-purpose enzyme with two or more SH3b domains connected to a C-terminal NlpC/P60 domain. SH3b domains may have lost the function of targeting domains and over time the nonessential SH3b domain would have been lost, as seen in the cyanobacterial enzymes.4.l-Ala-γ-d-Glu is the first structural representative of an NlpC/P60 enzyme with a bound ligand. This structure allowed us to identify the determinants of substrate specificity, which further led to the classification of a subfamily of highly specialized NlpC/P60 enzymes. The studies here also provide structural and functional insights into the NlpC/P60 family of enzymes. Additional information about BcYkfC is available from TOPSAN (Krishna et al., 2010http://www.topsan.org/explore?PDBid=3h41.The structure of BcYkfC in complex with l-Ala-γ-d-Glu complex, 3h41YkfC–            PDB reference:


In order to understand how biological systems function it is necessary to determine the interactions and associations between proteins. Gene fusion prediction is one approach to detection of such functional relationships. Its use is however known to be problematic in higher eukaryotic genomes due to the presence of large homologous domain families. Here we introduce CODA , a method to predict functional associations based on the gene fusion idiom.We apply a novel scoring scheme which takes account of the genome-specific size of homologous domain families involved in fusion to improve accuracy in predicting functional associations. We show that CODA is able to accurately predict functional similarities in human with comparison to state-of-the-art methods and show that different methods can be complementary. CODA is used to produce evidence that a currently uncharacterised human protein may be involved in pathways related to depression and that another is involved in DNA replication.ftp://ftp.biochem.ucl.ac.uk/pub/gene3d_data/v6.1.0/CODA/. Predictions are also available via web services from http://funcnet.eu/.The relative performance of different gene fusion methodologies has not previously been explored. We find that they are largely complementary, with different methods being more or less appropriate in different genomes. Our method is the only one currently available for download and can be run on an arbitrary dataset by the user. The CODA software and datasets are freely available from  In the post-genomic era it has become clear that the parts list of genomes is insufficient to explain organismal complexity. Research is shifting towards understanding organisms as systems of interacting parts. Many new approaches are being developed to identify the relationships between these parts in terms of interactions and functional associations. Gene or domain fusion is one of several genome context methods which can be used to predict functional associations between pairs of proteins Gene fusion is an evolutionary process whereby initially separate genes become fused into a single open reading frame, which is expressed as a multi-domain protein chain. By detecting these events it can be inferred that the unfused proteins are functionally related. Bioinformatic approaches which identify fusion events in order to predict functional associations use either whole protein sequence comparison or domain family assignments. These are known as gene fusion and domain fusion respectively. The two approaches are compatible and have previously been combined The principle problem in accurately detecting functional relationships with these methods is caused by large, promiscuous domain families. If a relative of domain family A is found fused to a relative from domain family B, all proteins containing domains from A are potentially associated to all those containing domains from B within any particular genome. If families A and B are large, then there are many possible functionally associated pairs. Large families tend to be functionally diverse and it is unlikely that all members will be involved in the same biological process Promiscuous domain families are found in many different proteins, fused to many different partner domain families Large families tend to be promiscuous and vice versa, therefore solving one problem solves the other. One approach is to simply exclude proteins containing highly promiscuous domains Our method, CODA, uses the domain fusion approach and implements a novel score to cope with the problem of large, promiscuous families. CODA is compared against two existing implementations of gene fusion and one of domain fusion. STRING-fusion is a gene fusion approach used for the STRING database by the Bork group We show that in several cases CODA can produce a greater number of hits than other methods while maintaining accuracy and more generally that gene/domain fusion approaches can succeed in higher eukaryotes to a similar degree as in lower eukaryotes. Furthermore we find that the different methods are complementary, with low overlap between the results they produce.CODA is available for download and can also be used through web services, allowing users to determine functional relationships in their genome of interest.Initially we show how different domain family classifications affect the performance of our method CODA  and how it copes with the problem of promiscuous domains. Subsequently CODA is compared to other gene/domain fusion approaches. Benchmarks were performed using a measure of similarity between the Gene Ontology (GO) terms of each functionally associated protein pair predicted in the query genome by each method. We use an ‘enrichment’ score where a score of 10 means that we have found 10 times as many functionally similar pairs of proteins as would be expected by chance.Saccharomyces cerevisiae (hereafter referred to as yeast) as the query genome as this genome has the most comprehensive functional annotation. The human genome is used as an example of a complex higher eukaryote and to provide an independent test of performance.CODA was developed using CODA uses domain pairs to identify fusions. In previous work ProDom and Pfam domains have been used to detect domain fusions for prediction of functional associations Domain fusion methods are liable to detect many false positives due to promiscuous domains and large families We wanted to determine the performance of CODA relative to comparable methods. To our knowledge the relative effectiveness of different gene/domain fusion methods has been unclear. While gene fusion has the potential to exploit all known genes, domain fusion only has access to those parts of genes classified into domain families. However, because domain families are composed using powerful methods for detecting homologues (e.g. profile Hidden Markov Models), domain fusion approaches can detect more distantly related fusion events. Sequence comparison approaches (e.g. Smith-Waterman and BLAST) used in gene fusion approaches cannot detect such distant relationships. Thus it is unclear whether one approach might provide more coverage than the other.Several resources provide functional associations derived from such methods. These include STRING Do fusion methods tend to find many links between few proteins, or few links between many proteins? In order to examine the number of links vs. proteins produced by the methods the first 500 top scoring hits from CODA were taken for comparison with the top 500 from STRING-fusion, as STRING-fusion only produced ∼500 hits  in the proteins identified as involved in fusion events . There wA contains domain x and protein B contains domain y. Both human and mouse genomes contain a gene with both x and y domains. If there are fewer than 10 pairs of genes in yeast with x or y domains, Truong's method will identify a functional linkage between A and B. In this case there is one gene with domain x and there are nine with domain y, making a total of nine possible linkages and Truong-fusion accepts this linkage. Truong does not take account of the number of copies in the target genome. In this case there are nine genes with domain x and 26 with domain y in mouse and even more in human. CODA therefore gives a score of 0.13 (1/(1+9)+1/(26+9)), which is not significant for functional similarity. CODA is designed to score the uncertainty about whether the fused domains and the domains being linked are orthologues; in this case it is clearly not certain. CODA and Truong often disagree because CODA takes into account the frequency of domains in the target genome (that containing the fusion gene), which Truong's method does not. The two methods agree on those linkages about which there is little or no uncertainty regarding orthology in the relationship between fused and unfused domains. They differ however when there is greater uncertainty.The reason for the low overlap between CODA and Truong-fusion is that, despite both using domains to identify fusions, they have rather different approaches for reducing false positives. For example, if in yeast, protein The overlap between CODA and STRING-fusion is apparently higher than for CODA and Truong-fusion. STRING-fusion is strict about orthology when calling a fusion event  and this is, in theory, more similar to a high-scoring CODA hit than a high-scoring Truong-fusion hit.Of the nine links identified by both Truong-fusion and CODA, eight are known from experiments to act in the same pathway. The ninth is either a novel result or a false positive (Thymidylate synthase and Protein VHS3). More generally, we found, using the full STRING database, that those links which overlapped between CODA and any of the other fusion methods tended to have evidence from other types of prediction methods, most often gene neighbourhood. This might be expected due to the similarity of gene/domain fusion and gene neighbourhood methods, compared to say co-expression, which leverages more distinct information. 70% of the CODA-Truong, 48% of the CODA-STRING and 50% of the CODA-Prolinks overlaps were also found by gene neighbourhood. This is compared to 30%, 17% and 25% for co-expression.In previous work the analysis of gene fusion for function prediction has been largely limited to prokaryotes and yeast. Detecting functional relationships between proteins using gene/domain fusion in higher eukaryotes is hampered by expanded gene/domain families. In order to examine this we have tested the performance of methods on the human genome.Prolinks-fusion performed less well on the human dataset than in yeast, even when allowing homologous pairs . In thisThe Truong-fusion results had been obtained using Swiss-Prot release 39 and TrEMBL release 17. These datasets were released in 2001 at which point the human genome was not complete. CODA requires complete genomes for accurate scoring and therefore it was not possible to compare against Truong-fusion for human. However Truong-fusion managed to find 235 associations between human proteins for an enrichment of 28.Annotation from the OMIM (Online Mendelian Inheritance in Man) database was mapped to all associations identified by CODA in human using Gene3D The uncharacterised human protein Q6NZ37 (UniProt accession) was found to be associated with several proteins involved in mental disorders. TPH2 (Q8IWU9) is known to be involved in major depressive disorder (MIM:608516) and is directly involved in the biosynthesis of serotonin from L-tryptophan. Another associate of Q6NZ37, TPH  has been shown to be involved in suicidal behaviour, thought to be related to depression We wanted to determine how much additional functional coverage of the human genome could be generated by CODA. To do this we considered those proteins between which CODA found high confidence functional associations and asked how many could, based on these associations, be assigned a GO term where before they were unannotated. CODA found 1453 high confidence (CODA score > = 0.56) associations between 900 human proteins using the Gene3D dataset. Of these 900 proteins, 664 could already be annotated with a GO biological process term using annotation from GOA  and allowing all evidence types. Of the remaining 236 unannotated proteins, 107 could be annotated by transferring high quality GO annotation  using the associations established by CODA. Although this is a small number of proteins in terms of the whole human genome, these proteins have not previously been annotated with GO terms. The annotations for these proteins are presented as Supplementary We present a new domain fusion method designed to be accurate in predicting functional associations between proteins in higher eukaryotes. The genomes of higher eukaryotes contain large protein domain families, which make the detection of functional associations by gene/domain fusion less reliable. To cope with this problem, previous methods have either considered only orthologues Gene/domain fusion methods in general have been thought to perform better in prokaryotes than eukaryotes as prokaryotes tend to have smaller families of homologous genes/domains These methods seem to occupy two distinct niches. The methods which can achieve the highest accuracy but which provide a relatively small number of hits (STRING-fusion and Truong-fusion) are useful for identifying high quality sets of associations. However for any particular protein it is unlikely that they will find an association. Methods such as Prolinks-fusion and CODA can provide less certain associations for a greater number of hits and therefore would be more appropriate where the other methods cannot provide associations.Interestingly there is little overlap between the methods in terms of the functional links they predict and even the proteins included in the links. This suggests that the particular implementation greatly affects the links obtained (e.g. using domains vs. whole proteins). Furthermore, much as different genome context methods have been combined to produce larger sets of confident predictions e.g. , using dFinally it was shown that CODA is able to identify putative disease-related proteins in humans. Furthermore, many previously unannotated human proteins were assigned GO terms using CODA suggesting that this approach will also be able to annotate previously unannotated proteins in many other genomes. These results are provided as a supplementary CODA is available for download and can be used to determine functional relationships between proteins in any genome of interest to the user. Pre-calculated results are also available via web services.Co-Occurrence of Domains Analysis (CODA) requires Multi-Domain Architectures (MDAs) of proteins for complete genomes. An MDA is a symbolic representation of the predicted domains for a protein. The order and frequency of domains in a protein is not considered by CODA and so discontinuous domains can be collapsed and repeats ignored.Several alternative MDA datasets were generated using domain assignments from Gene3D v5 ftp://ftp.ebi.ac.uk/pub/databases/swissprot/sw_old_releases), while TrEMBL release 17 was kindly provided by the PANDA group at the EBI.Only the results of the methods against which we compare CODA were available, rather than the algorithms themselves. Therefore, in order to compare CODA against these methods, it was necessary to run CODA on the datasets used to generate those results. For STRING and Prolinks, descriptions of the sequences used are available from the respective web-servers. The Truong dataset combined Swiss-Prot release 39 and TrEMBL release 17. The Swiss-Prot release was retrieved from the EBI FTP server  terms of proteins using the semantic similarity approach of Resnik et al. In this benchmark false positives could not be directly determined as many proteins were unannotated or annotated with relatively non-specific GO terms. Therefore, instead of precision we calculated enrichment, based on the number of positive hits expected by chance. Protein pairs identified by a method, which exceeded a GOSS score of 4, were considered true positive hits. Only ∼3% of GOSS scores were > = 4 see . For botCo-Occurrence of Domains Analysis (CODA) uses a Multi-Domain Architecture (MDA) representation of proteins in complete genomes (target genomes) to discover pairs of proteins involved in common biological processes within a complete genome of interest (the query genome). It is a novel approach in the domain fusion idiom using a new scoring method.i =  in a query genome g. P is the set of domains in protein p. aP denotes that protein p contains a domain of superfamily a. J is the set of domain pairs j =  where aP, bQ. In other words J consists of all the distinct pairs of domains between proteins p and q. It is also required that P∩Q = {}, as the two proteins must not share any domains of the same family.Here we consider how the method is implemented for a particular pair of proteins s where aS and bS, i.e. domains which are separated in the query genome are found fused in the target genome. The set T comprises those genomes other than g, which contain such proteins s. For a domain pair j in genome g, the fusion score jC is taken as a maximum over all genomes in T:T| is the number of elements of set T (i.e. the number of target genomes), ngA and ngB are the frequencies of domain A and domain B respectively in genome g and ntA and ntB are the frequencies of domains A and B respectively in genome t.To determine a fusion event we require that a target genome (one other than the query genome) contains a protein i in query genome g, the maximum jC is taken over all possible domain pairs j.J| is the number of elements in set J (i.e. distinct domain pairs). Thus iC is the CODA score for proteins p,q (pair i); the best (highest) score over all domain pairs between the proteins and over potential fusion proteins in all genomes (other than the query genome). The important novel aspect of this score is that it takes the maximum score over all the genomes whereas other methods do not consider target genomes individually. The score was chosen to reflect the uncertainty that fused domains and their unfused relatives are orthologues. The highest (best) possible score is 1. This occurs when there is only one example of each domain family in the query genome and one fused protein in a target genome, with no other domain homologues. In this case it is highly likely that the query protein domains are orthologous to the target protein. Several other scoring schemes were trialled, including that used by Marcotte & Marcotte For a particular protein pair Figure S1Comparative performance of Pfam, Pfam-CATH, CATH and CATH-Pfam MDA datasets on the yeast genome. Enrichment is the ratio of true positives achieved by CODA to the number expected by chance. Points are plotted at successive score cut-offs. At an enrichment of 10, Pfam-CATH performed best with 1791 hits; Pfam achieved 1663 hits, CATH-Pfam 792 and CATH 296. At higher enrichment (e.g. 15), the Pfam dataset outperforms all others and finds ∼500 hits. Datasets based principally on CATH domains (CATH-Pfam and CATH) performed less well than those based on Pfam domains. This may be because CATH superfamilies tend to be broader than Pfam families, including more functional subfamilies. This could result in generally reduced scores for hits involving these larger families. Additional analysis using CATH subfamilies did not improve performance however  and thus(0.60 MB EPS)Click here for additional data file.Figure S2E. coli which is a fusion of these two domains: A′B′. Let us say that A and A′ are in the same 50% cluster but not the same 60% cluster, i.e. they share 50% sequence identity. The counts for ngA in the CODA score (equation 1) then only include the number of members of the same 50% cluster that belong to yeast. ngB is the number of members of that 50% cluster which belong to E. coli. Likewise, if B and B′ are in the same 70% cluster but not the same 80% clusters, then the counts are taken from that 70% cluster. Using subfamilies slightly improves performance at high enrichment but only where there are few hits. We therefore concluded that the reduced performance of CATH relative to Pfam was related to a mixture of lower coverage of genomes and than the size and functional specificity of the families.Performance of CODA using CATH domains, with and without subfamilies. CATH domains showed lower performance than Pfam domains in detecting functional relationships between proteins using CODA. This could have been due to low coverage of CATH domains relative to Pfam or because CATH has larger families causing low scores for many hits. CATH superfamilies were clustered at varying sequence identity cut-offs  using an in-house implementation of directed multi-linkage clustering. The domain counts used in the CODA score were then adjusted using these clusters. Let us say that there are two proteins in yeast, each with one domain. The first protein contains domain A and the second domain B. A protein is found in (0.52 MB EPS)Click here for additional data file.Figure S3Relationship between number of links and proteins for first 500 novel links between CODA (blue) and STRING-fusion (red).(0.55 MB EPS)Click here for additional data file.Figure S4Relationship between number of links and proteins for first 1000 novel links between CODA (blue) and Prolinks-fusion (green).(0.57 MB EPS)Click here for additional data file.Figure S5Relationship between number of links and proteins for first links between CODA (blue) and Truong-fusion (orange).(0.56 MB EPS)Click here for additional data file.Figure S6Overlap criterion for combining CATH and Pfam domains into a single dataset. The datasets which included both CATH and Pfam domains were generated in two ways. The CATH-Pfam dataset had CATH domains assigned first, while Pfam-CATH had Pfam domains assigned first. Examples of the second type of domain were added if the overlap between them and the already assigned, primary domains was no greater than 30% in both directions see . The ini(0.14 MB TIF)Click here for additional data file.Figure S7Distribution of biological process GOSS scores between yeast proteins in the Gene3D dataset. Proteins without appropriate GO terms were excluded. GOSS score bins were bounded such that the bin labelled 2 contains values > = 2 and <2.5. The bars represent the frequency and the blue line represents the cumulative proportion of GOSS scores which have less than the stated value. We show here the distribution of GOSS scores in the yeast genome. Protein pairs which score 0 because they do not have comparable GO terms were ignored .(0.63 MB EPS)Click here for additional data file.Table S1Size of Gene3D datasets and genome coverage with different Multi-Domain Architecture (MDA) types. Coverage is calculated as the percentage of proteins which have at least one domain. The CATH-Pfam and Pfam-CATH datasets therefore appear identical, although their domain assignments are not.(0.03 MB DOC)Click here for additional data file.Table S2Coverage of STRING, Prolinks and Truong datasets with Pfam domains. Coverage is calculated as the percentage of proteins with at least one domain. Raw numbers are shown in brackets.(0.03 MB DOC)Click here for additional data file.Table S3Percentage of individual protein for each genome in each dataset which has at least one relevant GO term.(0.03 MB DOC)Click here for additional data file.Dataset S1Novel functional annotation assigned to human gene products using CODA.(0.33 MB XLS)Click here for additional data file.


A rapid emergence of animal-specific domains was observed in animals, contributing to specific domain combinations and functional diversification, but no similar trends were observed in other clades of eukaryotes. In higher multicellular eukaryotes, complex protein domain combinations contribute to various cellular functions such as regulation of intercellular or intracellular signaling and interactions. To elucidate the characteristics and evolutionary mechanisms that underlie such domain combinations, it is essential to examine the different types of domains and their combinations among different groups of eukaryotes.We observed a large number of group-specific domain combinations in animals, especially in vertebrates. Examples include animal-specific combinations in tyrosine phosphorylation systems and vertebrate-specific combinations in complement and coagulation cascades. These systems apparently underwent extensive evolution in the ancestors of these groups. In extant animals, especially in vertebrates, animal-specific domains have greater connectivity than do other domains on average, and contribute to the varying number of combinations in each animal subgroup. In other groups, the connectivities of older domains were greater on average. To observe the global behavior of domain combinations during evolution, we traced the changes in domain combinations among animals and fungi in a network analysis. Our results indicate that there is a correlation between the differences in domain combinations among different phylogenetic groups and different global behaviors.Rapid emergence of animal-specific domains was observed in animals, contributing to specific domain combinations and functional diversification, but no such trends were observed in other clades of eukaryotes. We therefore suggest that the strategy for achieving complex multicellular systems in animals differs from that of other eukaryotes. Protein domains are the basic building blocks that determine the structure and function of proteins, and they may be considered the units of protein evolution. Furthermore, combinations of protein domains provide a broad spectrum for potential protein function -4. EukarIt is already known that the number of combinations involving a particular domain is quite varied, and that the distribution of the number of combination partners follows a power law distribution -10. PrefIn this work, we focus on the relationship of domain combinations and functional diversification in eukaryotes, with consideration of hierarchical classification based on their phylogenies. We also explore how domains and their combinations are distributed and conserved in each group of eukaryotes. In order to define specific domains and combinations for each phylogenetic group, we modified the method developed by Mirkin and coworkers , which eOur analysis uncovers differences in specific domains and their combinations among different phylogenetic groups of eukaryotes. We observe a large number of animal-specific and vertebrate-specific domain combinations. However, those domains having a large number of combination partners are different in animals and vertebrates, and their functions are strongly linked to their characteristic functions that evolved in the common ancestors of animals and vertebrates. Examples include animal-specific combinations in tyrosine phosphorylation systems and vertebrate-specific combinations in complement and coagulation cascades. In animals, especially in vertebrates, the average connectivity of animal-specific domains is markedly high. In contrast, the older domains tend to have greater average connectivity in other groups of eukaryotes. These observations suggest that the properties of domains are nonuniform in terms of generating domain combinations.Our findings also made it possible to reconstruct an evolutionary history of the domain combinations in each clade of eukaryotes and to observe changes of combinations based on a global network analysis. The global features of the reconstructed evolution of the network are consistent with the observed differences in properties of group-specific domains. Therefore, our analysis enables us to link local differences among group-specific domains with the global features of domain combination changes during evolution. From these observations, it is suggested that the strategy for achieving complex multicellular systems might be different, even among eukaryotes, in terms of the preference for generation of domain combinations.We used the domains defined in the Pfam database . Of 7,45Neurospora crassa and Magnaporthe grisea), land plants , and Dictyostelium discoideum . It should be noted that species with a large number of proteins do not always have a large number of domain combinations; for instance, Entamoeba histolytica and Trypanosoma cruzi have large numbers of proteins and few combinations.Domain combinations can be defined in several ways, such as by co-occurrence in a protein sequence. Here, in order to distinguish domain architectures possibly generated by individual evolutionary events, we defined a combination as two consecutively located domains Figure . We alsoWe first identified eukaryote-specific domains in the set of 4,315 domains found in 47 eukaryotes, among which 2,065 domains were also found in prokaryotes. Even if a domain is found in both prokaryotes and eukaryotes, it may still be considered a eukaryote-specific domain in the case of horizontal transfer from eukaryotes to prokaryotes. In order to discriminate those domains that presumably existed in the commonote, the common ancestor of eukaryotes and prokaryotes, we reconstructed the most parsimonious scenario of gains and losses of domains during prokaryotic evolution using the method proposed by Mirkin and coworkers . As a reWe next identified group-specific domains for each group of eukaryotes, where 47 eukaryotes were divided into 14 groups. We classified the groups hierarchically, based on their phylogenetic relationships . We considered two additional groups, namely deuterostomes (vertebrates plus ascidian) and opisthokonta , in the hierarchical classification. Because horizontal gene transfer among eukaryotes can be disregarded ,20, we aWe then examined group-specific domain combinations. In contrast to the case of group-specific domains, a group-specific combination cannot be defined by simply tracing the last common ancestor because identical combinations can arise independently in different groups. We again used the method proposed by Mirkin and coworkers  to reconHere we focus on the domains forming these animal-specific or deuterostome-specific combinations. The 875 animal-specific combinations consist of 558 domains, and the 610 deuterostome-specific combinations consist of 478 domains. Among them, 72 domains in animal-specific combinations and 50 domains in deuterostome-specific combinations have more than five partner domains, which we call hub domains. Although 36 domains were commonly found in both groups, the hub domains tend to have preferentially large numbers of combination partners in each group. For example, the protein kinase domain (Pfam ID: Pkinase) was found in 37 animal-specific combinations but only in eight deuterostome-specific combinations. In Tables These hub domains in group-specific combinations are presumably involved in different functions that have evolved in the common ancestors of respective groups. In animal-specific combinations, the protein kinase domain (Pkinase) was found to have the greatest number of partners. Other hub domains in animal-specific combinations include the SH2 domain, the protein-tyrosine phosphatase domain (Y_phosphatase), and the phosphotyrosine interaction domain (PID), which are all related to tyrosine phosphorylation signaling Table  21-24].-24.21-24On the other hand, domains involved in the complement and blood coagulation cascade were frequently found in deuterostome-specific combinations Table . In the Figure p(k) ∝ k-γ - where k is the number of combination partners (the degree of a node). The degree distributions of combination networks of all domains in Homo sapiens, Saccharomyces cerevisiae, A. thaliana, and T. cruzi are shown in Figure The mechanisms for generating domain combinations was subjected to global network analysis. The decreasing pattern for the nonanimal groups shown in Figure H. sapiens the γ value rapidly decreased after the divergence of animal and fungi, whereas in the lineage of S. cerevisiae the γ value gradually increased. In order to examine this difference, we defined the union domain combination network in each lineage of H. sapiens and S. cerevisiae. All nodes and all edges were accumulated in the union network along the phylogenetic hierarchy without considering the loss of domains or combinations. The γ values for the union networks are shown in dashed lines in Figure S. cerevisiae. Similar analyses were performed for all other lineages and the result is indicated by the dashed line in Figure Using this procedure we traced the changes of the γ value along the phylogenetic hierarchy for animals and fungi .Table For completeness, we further analyzed the affect of the definition of the domain combination networks on our results. In related work, domain combination networks were simply defined as the co-occurrence of two domains in a protein sequence without considering domain order. Using this definition, all trends in our results were conserved (data not shown).S. cerevisiae, Caenorhabditis elegans, and Drosophila melanogaster. Therefore, the results indicating high connectivity in late eukaryotes could not be generally claimed; high connectivity was actually found mostly in animals, and not necessarily in fungi and plants. In animals, we also found that the animal-specific domains have very high connectivity, which correlated well with their work. However, when considering group-specific domains in nonanimal groups, we observed a correlation between connectivity and age, in which the oldest domains inherited from the commonote had the greatest connectivity among nonanimal eukaryotes  divergence was about 300 million years, and that the lengths of the periods differed little from each other [By tracing and comparing the changes of domain combination networks together with the phylogenetic relationships between eukaryotes, we observed differences in the evolution of the combination networks in e Figure . In the ch other -36  on a protein sequence, we selected the domain with the most significant E value. We used precomputed HMMER results stored in KEGG Sequence Similarity Database (SSDB) with Pfam ver. 14 for protein sequences in KEGG GENES, and we computed the HMMER assignments for proteins obtained from KEGG DGENES and Ensembl with the same Pfam version as stored in KEGG SSDB.We used the proteomes of 47 eukaryotes and 223 prokaryotes obtained from the genome and draft genome sequences stored in the Kyoto Encyclopedia of Genes and Genomes (KEGG) GENES and DGENES databases  and the To define specific domains and combinations for each clade of eukaryotes that are hierarchically classified Figure , we consAlthough it is commonly believed that a new gene emerges only once in a single lineage during evolution, genes can also be gained through horizontal gene transfer . Mirkin i and τn as children of v  for the case when the node inherits a gene (ei(c)) and the case when the node does not inherit a gene (en(c)) using the method proposed by Mirkin and coworkers [Ci and Cn, based on comparison of ei(c) and en(c) according to the following condition:In step 1, for each child node oworkers . In stepc in Ci to τi and graft the child c in Cn to τn. In step 4, consider two cases - τi and τn inherit the gene  be a set of such species for all leaves of a subtree Tx Domain rooted at x as follows  denotes the last common ancestor of a set of species S, and ancestors denotes the set of all nodes in the path from the root to the parent of node s in the phylogenetic tree TSpecies . Then, the time when the subfamily diverged was estimated to be lca(S(c1), TSpecies). Because the domain sequences were hierarchically classified, subfamilies were defined hierarchically.Where The following additional data are available with the online version of this paper. Additional data file Provided is a figure showing detailed phylogenetic relationship among 47 eukaryotes.Click here for fileThis figure illustrates the difference in the number of combination partners among each group-specific domain in extant deuterostomes.Click here for fileThis figure illustrates the difference in the number of combination partners among each group-specific domain in extant invertebrates.Click here for fileThis figure illustrates the difference in the number of combination partners among each group-specific domain in extant fungi.Click here for fileThis figure illustrates the difference in the number of combination partners among each group-specific domain in extant protists.Click here for fileThis figure illustrates the difference in the number of combination partners among each group-specific domain in extant plants.Click here for fileProvided are tables showing the statistics of domain assignments for eukaryotes (Supplementary Table 1) and all results of history reconstruction (Supplementary Table 2).Click here for file