BNIP-2 and Cdc42GAP Homology (BCH) protein domain, which shares a low sequence homology with the CRAL_TRIO domain, can serve as a regulatory scaffold that binds to Rho, RhoGEFs and RhoGAPs to control various cell signalling processes. In this work, we investigate 175 BCH domain-containing proteins from a wide range of different organisms. A phylogenetic analysis with ∼100 CRAL_TRIO and similar domains from eight representative species indicates a clear distinction of BCH-containing proteins as a novel subclass within the CRAL_TRIO/Sec14 superfamily. BCH-containing proteins contain a hallmark sequence motif R(R/K)h(R/K)(R/K)NL(R/K)xhhhhHPs  and can be further subdivided into three unique subtypes associated with BNIP-2-N, macro- and RhoGAP-type protein domains. A previously unknown group of genes encoding ‘BCH-only’ domains is also identified in plants and arthropod species. Based on an analysis of their gene-structure and their protein domain context we hypothesize that BCH domain-containing genes evolved through gene duplication, intron insertions and domain swapping events. Furthermore, we explore the point of divergence between BCH and CRAL-TRIO proteins in relation to their ability to bind small GTPases, GAPs and GEFs and lipid ligands. Our study suggests a need for a more extensive analysis of previously uncharacterized BCH, ‘BCH-like’ and CRAL_TRIO-containing proteins and their significance in regulating signaling events involving small GTPases.The CRAL_TRIO protein domain, which is unique to the Sec14 protein superfamily, binds to a diverse set of small lipophilic ligands. Similar domains are found in a range of different proteins including neurofibromatosis type-1, a Ras GTPase-activating Protein (RasGAP) and Rho guanine nucleotide exchange factors (RhoGEFs). Proteins containing this structural protein domain exhibit a low sequence similarity and ligand specificity while maintaining an overall characteristic three-dimensional structure. We have previously demonstrated that the  The functional complexity of living organisms is not only reflected by the number of genes or their protein products, but also by the cross-talk between them. This is signified by the fact that there are 1195 classes of known protein domain folds  belonging to 38221 Protein Data Bank entries of experimentally solved structures, indicating that multiple proteins tend to fold in a similar three dimensional structure. The ability of a protein module to interact with multiple proteinaceous binding partners potentially directs it to multiple cellular pathways and functions and thus makes it more versatile. A second level of complexity is added by the binding of non-protein molecules, which can modulate the three dimensional conformation of the protein domain and thus its cellular functions. The ‘Sec14 superfamily’ is one such large superfamily of protein modules The lipophilic domain of Sec14p is also designated as a CRAL_TRIO domain , which was first identified in cellular retinaldehyde binding protein (CRALBP) and Trio, a guanine nucleotide exchange factor (GEF). Other proteins such as tyrosine phosphatase (PTP) BNIP-2 and Cdc42GAP Homology (BCH) domain was initially recognized as a region of high protein sequence homology between BNIP-2 (BCL2/adenovirus E1B 19kDa interacting protein-2) and Cdc42GAP/p50RhoGAP Saccharomyces cerevisiae Sec14p protein. It is approximately 150 amino-acid in size and is known to be involved in the control of diverse aspects of cell dynamics such as apoptosis The This article attempts to highlight the unique sequence and structural features of BCH domains and outlines, which distinguish them from the CRAL_TRIO domains of the Sec14 superfamily. We have identified a large number of BCH domains from multiple organisms and a large dataset has been used to describe the potential evolutionary relationship between the BCH and CRAL_TRIO domains. BCH domains can be assigned to three distinct subgroups and we further investigate the divergence of these subgroups from their ancestral precursor genes, leading to a wider functional specialization. A possible pathway of BCH domain evolution is being proposed and we identified the most likely point of divergence from CRAL_TRIO-like proteins. In addition, we present 3-dimensional structural models for all three subgroups of BCH domains. Based on the discussed features, it will now be possible to distinctly identify BCH and CRAL_TRIO domains within different proteins.The protein Blast search was carried out in the GenBank database of NCBI to identify proteins containing putative BCH domains. The BCH domain of Human BNIP-2 protein  as defined in our earlier published work Dictyostelium discoideum, Drosophila melanogaster, Danio rerio, Xenopus laevis, Arabidopsis thaliana, Caenorhabditis elegans, Saccharomyces cerevisae and Homo sapiens. The PSI-BLAST search using yeast sec14p (NCBI accession: NP_013796) query identified multiple members belonging to Sec14 superfamily. A large number of protein homologs were identified in Arabidopsis thaliana (>30) and Drosophila melanogaster (>20). In order to avoid a data bias, only few representative sequences were selected from these organisms. This was based on clustering in phylogenetic trees, which were produced separately for each species. Outliers (defined as sequences with no CRAL_TRIO domain identified by the Pfam database) and shorter hits of length <150 amino-acids were excluded from further analysis. Multiple isoforms belonging to the same protein were also discarded. Separate searches were carried out for identifying members of the NF1 and RhoGEF subfamilies using human NF1 (accession: AAB59558) and Trio (accession: NP_009049) as query. An additional set of 98 proteins from the Sec14 superfamily was generated for constructing phylogenetic trees and for further comparative studies.Similar searches were carried out to identify CRAL_TRIO domain containing proteins. However, we restricted this search to the NCBI's RefSeq database http://itol.embl.de/) http://weblogo.berkeley.edu/). All sequence identities are calculated by MegAlign tool of Lasergene suit from DNASTAR Incorporation (http://www.dnastar.com/).The CRAL_TRIO and BCH domain sequences were aligned using the Clustal v2.0 multiple sequence alignment algorithm Iterative threading assembly refinement) De Novo automated protein structure predictions. Both methods have performed well in CASP experiments ab initio modeling and further refinement. The multiple threading alignments in I-TASSER were created by LOMETS algorithm to identify the structure fragments from a library, which was assembled by replica exchange Monte Carlo simulation methods. The predicted models were simulated by TASSER iterations for optimization to remove steric clashes between atoms and refining side-chain rotamer conformations. The ROBETTA server is part of the Rosetta folding program. It uses a Ginzu protocol No clear template with significant sequence homology was identified for modeling BCH domains. Thus, structures were predicted using the I-TASSER (The structures of the BCH domains (length 173 amino-acid) from human BNIP-2 (NCBI accession: NP_004321), RhoGAP (NCBI accession: BAG60756), GDAP (NCBI accession: NP_060156) proteins were predicted using I-TASSER with no specified templates and restrains. Only the core BCH domain of BNIP-2 was predicted using the ROBETTA server. The I-TASSER found the highest sequence identity (17%) among all available structures with the domain of human neurofibromatosis type 1 protein (PDB ID: 2D4Q) Dictyostelium discoideum (a slime mold) as query sequences and an e-value cut-off of >2. This analysis yielded no significant hit and therefore suggests the absence of BCH domains in the bacteria and the Archaea. This large dataset was taken as a resource for characterizing and classifying the BCH domain within the Sec14 superfamily. In addition, we also identified 98 proteins from eight representative organisms that belong to other sub-groups within the Sec14 superfamily. These were aligned with the dataset of 175 BCH domains, which based on their alignment with CRAL_TRIO domains were isolated from their full-length proteins. This set of BCH domains has longer amino-acid sequences as these sequences include N-terminal amino-acid extensions than previously reported Our search of the entire non-redundant GenBank database identified 175 proteins containing a putative BCH domain. BCH domains were identified in a wide range of eukaryotic species, including slime molds, fungi, animals and plants. In contrast, no BCH domain-containing proteins were identified from bacterial species. To confirm the absence of BCH domains in prokaryotes, we carried out Blast searches of all completed Archaea and bacterial whole genome sequences using four BCH domain sequences of Drosophila melanogaster and Caenorhabditis elegans. In contrast, only a single BCH domain protein is encoded in the genome of the nematode Caenorhabditis elegans.A phylogenetic tree including 175 BCH domains and 98 CRAL_TRIO domains was constructed as described in the methods section . The treDictyostelium), coanoflagellate (Monosiga), alveolates , green alga (Chlamydomonas) and yeast. Similarly, CRAL_TRIO domains were identified in many lower species of alveolates including Babesia (XP_001612272), Tetrahymena (XP_001018732), Paramecium  and the diatom Phaeodactylum (XP_002182927). This indicates that BCH domains evolved from their ancestors more than 1500 Mya ago with the appearance of Protists Similar to CRAL_TRIO, the BCH domain is completely absent in prokaryotes. The most primitive BCH domain was identified from slime mold , BNIP-XL (Extra Long proteins) and BNIP-H . All these proteins are involved in Rho GTPase regulation. Their distinct clustering into four subgroups in vertebrates has been observed for many gene families Nematostella) appear to contain only one BNIP-2-type gene which does not cluster with any of the four sub-groups (see Nematostella) and all four isoforms appeared first in teleosts (Danio). Ciona, a tunicate has two group-I BCH isoforms suggesting the divergence could have occurred from Ciona-like ancestors. Interestingly, no group-I genes were identified in more primitive invertebrate species, such as nematodes. However, such species have other BCH domain encoding genes, such as group-III BCH proteins. Group-II and III appears to be older than group-I as they were identified in more primitive forms. The sequence similarities within the members of group-I BCH domains is higher (64.8%) compared to group II (50.3%) and III (49.8%). Group-I BCH domain-containing proteins show unique associations with other protein domains also, among other examples, Spo7 (Pfam accession: PF03907) domains at the C-termini of BNIP-S proteins in a few mammalian species and DHH and DHHA2 domains at the N-termini in a few BNIP-H and BNIP-XL proteins.oups see . Plants Group-II BCH: Group-II BCH domains were identified in plants, animals and also in multiple lower organisms. The domains from plants and animals form two distinct clusters in the phylogenetic tree and were designated as group-IIA  and group-IIB (plant's group-II BCH) (see Populus encodes 6 BCH domain genes Dictyostelium), moss (Physcomitrella) and green algae (Ostreococcus). Similar to group-I proteins, the mammalian group-II BCH domain proteins exhibit a high sequence similarity amongst themselves (>95%), indicating a more recent evolutionary split.BCH) see . SimilarGroup-III BCH: Group-III BCH domains are unique as they are located at the N-terminus of proteins. As they are associated with a RhoGAP domain at their C-terminus, they are referred as RhoGAP-type BCH domains. They are more divergent in mammalian species and other higher species and express two group-III BCH isoforms, which are associated with RhoGAP and BPGAP  Brugia, Caenorhabditis) have only one group-III BCH domain genes. Trichoplax, a Placozoan, has group-II and group-III BCH proteins, but no group-I BCH domain protein. No BCH domain is associated with a RhoGAP domain in plants. It is interesting to point out that in plants, RhoGAPs of REN family contain a Pleckstrin homology (PH) domain Rho of plants) and are associated with a Cdc42/Rac interactive binding (CRIB) motif at their N-terminus Dictyostelium discoideum. We therefore speculate that BCH might have diverged from an NF1-like ancestor, which had a RasGAP domain. Later, they probably associated with RhoGAP and macro domains through chromosomal recombination.Based on overall protein domain architecture and phylogenetic clustering, we hypothesize that the co-evolution of associated domains with BCH domains resulted in an additional functional divergence and complexity of the gene family. As CRAL_TRIO proteins diverged to bind multiple different hydrophobic molecules, we speculate that BCH proteins also have evolved to bind multiple ligands, many of which still need to be identified. The BCH family is the most distantly related subgroup within the Sec14 superfamily and is most closely related to NF1. Interestingly, similar to NF1 the slime mold BCH domain is also associated with a RasGAP domain (XP_645456). None of the group-I BCH is found in Multiple BCH domain sequences are included in the Pfam (release: 25) entry of CRAL_TRIO domains (Pfam accession: PF00650) and used for constructing a common domain profile. However, we postulate that CRAL_TRIO domains have distinct features from BCH domains. This hypothesis is supported by our finding that the HMM profile for CRAL_TRIO in the Pfam (release: 25) database fails to identify CRAL_TRIO domains in 4 RhoGEFs, 12 NF1 and 20 out of 175 BCH proteins (indicated by orange rectangles in Dictyostelium discoideum RasGAP protein (XP_645456: 509–711) was previously classified as a CRAL_TRIO domain The BCH sequence logo reveals a clear pattern of the characteristic residues that are conserved within the BCH domain and are missing in CRAL_TRIO domains. Examples are: P176, P221 and H248 see . AdditioThe conserved residues within CRAL_TRIO domains have structural and functional implications and the absence of these residues in BCH domains suggests their distinct nature. For example, position E152 (corresponding to E141K mutation in αTTP) is associated with the disease ‘Ataxia with Vitamin E deficiency’ (AVED) Many new genes diverge from their preexisting ancestors by gene duplication events and often acquire unique functional capabilities. These changes are usually reflected in their amino-acid sequences. As discussed, BCH domains exhibit functional features, which are not found in other members of Sec14 superfamily, such as GTPases binding activity, homo and heterophillic interactions Dictyostelium) indicates that the ancestral BCH sequence did not contain introns and different BCH domains evolved from their ancestors through intron insertion. This is similar to that observed by Qiu et al. in their study of 677 eukaryotic protein coding genes from 10 families Culex quinquefasciatus (accession: XP_001847511). It has two exons (amino acid 89 to 186), which are identical to two other exons (amino acid 187 to 284). A tandem duplication event has been reported in the NF1 gene, which results in Watson and Noonan's syndrome The number and position of introns revealed additional clues about the divergence of BCH domain-encoding genes . The absWe also observed insertions at the C-termini of BCH domains through alternative splicing . The conSo far, no molecular structure for any BCH domain has been experimentally determined and published. The three dimensional structures for NF1, CRALBP and Sec14p are highly similar for their core region, which excludes the N and C terminal regions. In the absence of clearly defined sequence homologies, the molecular structures for all three subgroups of BCH domains i.e. group-I (HsBNIP-2: NP_004321), group-II (HsGDAP: NP_060156), group-III (HsRhoGAP: BAG60756) were calculated and predicted using I-TASSER server The predicted three-dimensional structures of BCH domains will help us to identify and better understand the functional importance of specific residues that are uniquely conserved within the BCH domain. It also revealed putative residue interactions, which might be involved in important functional aspects. The conserved HP-motif in BCH domains coincides with the NC-motif in NF1. The NC motif is proposed to cover the ligand entry site BCH domains have also been shown to directly interact with small GTPase RhoA proteins and to be crucial for regulating the GAP activity in p50RhoGAP Similar structural features within proteins often indicate a common evolutionary origin. Subsequently, changes and a divergence of the primary sequence lead to functional specializations. Unlike conventional CRAL_TRIO domains, BCH domains are usually implicated in controlling cell dynamics by modulating the activity of small GTPases and their regulator proteins. These activities appear to be independent of interactions with hydrophobic ligands. Here we show that BCH domain-containing proteins diverged from the CRAL_TRIO gene family and acquired unique sequence features, which might contribute to their ability of binding ligands other than lipids. This divergence occurred during evolution as early as the appearance of protists. Among the three BCH subgroups, group-III (RhoGAP-type) appeared first and all three groups further diverged by intron insertion, domain swapping and gene duplication events. A possible evolutionary path for all three subgroups of BCH domain genes is depicted in Figure S1Taxonomic distribution of BCH domains across species. The numbers shown in this figure do not include alternative splice protein isoforms. Grey bars indicate the number of genomes represented in that group, while blue bars indicate the number of BCH domain-containing proteins, which were identified by database searches. Overall, mammalian genomes encode the highest number of BCH domain proteins, while lower organisms have only one or two BCH genes.(TIFF)Click here for additional data file.Figure S2Neighbor-Joining trees of BCH groups. The group-I, IIA, IIB and III BCH subgroups have 68, 30, 33 and 41 members respectively. In order to maintain clarity, the percent bootstrap values are shown for all branches except smaller branches. Accession numbers are displayed only if more than one BCH domain sequence was identified from one organism. The protein accession numbers for others can be found in the Danio rerio, Tn: Tetraodon nigroviridis, Ci: Ciona intestinalis, Dd: Dictyostelium discoideum, At: Arabidopsis thaliana, Rc: Ricinus communis, Pt: Populus trichocarpa, Gm: Glycine max, Mt: Medicago truncatula, Ps: Picea sitchensis, Zm: Zea mays, Os: Oryza sativa.(DOC)Click here for additional data file.Figure S3Alternative splicing within BCH domains. We investigated the alternative splicing at the C-termini of all three major groups of BCH domain proteins. Most of these isoforms were identified in mammalian species and only one from Xenopus and Danio suggesting functional complexity in higher organisms. A number of isoforms were identified in group-II BCH domains of Pan troglodytes (Chimpanzee). The functional implication of splice isoforms has been demonstrated for the BCH domain of the HsBNIP-S protein. The isoform BNIP-Sα containing a complete BCH domain mediates the pro-apoptotic effect, whereas the alternatively spliced isoform BNIP-Sβ is lacking such a domain  and functionality (DOC)Click here for additional data file.Table S1The table contains NCBI Accessions of 175 BCH domain-containing proteins and 98 other proteins representing the BCH-like and CRAL_TRIO groups of the Sec14 superfamily.(XLS)Click here for additional data file.Table S2The file contains multiple sequence alignments of 175 BCH domains and 98 BCH-like and CRAL_TRIO domains.(DOC)Click here for additional data file.


The amyloid precursor protein (APP) is a type I transmembrane protein that is expressed in a wide number of different cell types. Proteolytic processing by beta- and gamma-secretases releases 38-43 amino acid long peptides, so called Aβ amyloid peptides that accumulate within the plaques in the brain of Alzheimer´s disease patients. Alternatively, initiation of the proteolysis cascade by alpha-secretase prevents the development of these toxic peptides ,2.In spite of intense research regarding the involvement of APP in Alzheimer´s disease, The three-dimensional structure of the entire protein, its physiologic function and the regulation of its proteolytic processing remain, however, largely unclear to date .E. coli. Using limited proteolysis followed by mass spectrometry and Edman degradation as well as analytical gel permeation chromatography coupled static light scattering, we experimentally analyzed the structural domain boundaries. Using, pull-down assays and analytical gel filtration experiments we analyzed whether different domains interact with each other.To gain a deeper understanding about it, we cloned and recombinantly expressed different constructs of APP in We experimentally determined that the large ectodomain of APP consists exactly of two rigidly folded domains – the E1- and the E2-domain. The acidic domain, connecting E1 and E2, as well as the juxtamembrane region, connecting E2 to the single transmembrane helix, are highly flexible and extended. In addition, we demonstrated that the E1-domain does not tightly interact with the E2-domain, both in the presence and in the absence of heparin.APP hence forms an extended molecule that is flexibly tethered to the membrane. Its multi-domain architecture enables together with the many known functionalities the concomitant performance of several, independent functions, which might be regulated by cellular, compartment specific pH-changes.


Orthomyxovirus Influenza A virus (IAV) heterotrimeric polymerase performs transcription of viral mRNAs by cap-snatching, which involves generation of capped primers by host pre-mRNA binding via the PB2 subunit cap-binding site and cleavage 10–13 nucleotides from the 5′ cap by the PA subunit endonuclease. Thogotoviruses, tick-borne orthomyxoviruses that includes Thogoto (THOV), Dhori (DHOV) and Jos (JOSV) viruses, are thought to perform cap-snatching by cleaving directly after the cap and thus have no heterogeneous, host-derived sequences at the 5′ extremity of their mRNAs. Based on recent work identifying the cap-binding and endonuclease domains in IAV polymerase, we determined the crystal structures of two THOV PB2 domains, the putative cap-binding and the so-called ‘627-domain’, and the structures of the putative endonuclease domains (PA-Nter) of THOV and DHOV. Despite low sequence similarity, corresponding domains have the same fold confirming the overall architectural similarity of orthomyxovirus polymerases. However the putative Thogotovirus cap-snatching domains in PA and PB2 have non-conservative substitutions of key active site residues. Biochemical analysis confirms that, unlike the IAV domains, the THOV and DHOV PA-Nter domains do not bind divalent cations and have no endonuclease activity and the THOV central PB2 domain does not bind cap analogues. On the other hand, sequence analysis suggests that other, non-influenza, orthomyxoviruses, such as salmon anemia virus (isavirus) and Quaranfil virus likely conserve active cap-snatching domains correlating with the reported occurrence of heterogeneous, host-derived sequences at the 5′ end of the mRNAs of these viruses. These results highlight the unusual nature of transcription initiation by Thogotoviruses. Rhipicephalus sp. ticks collected from cattle in the Thogoto forest in Kenya Hyalomma dromedarriRhipicephalus sp. ticks in central Africa Orthomyxoviruses are a family of negative strand RNA viruses with 6–8 genomic segments. The best known genus is Influenza A virus (IAV), which infects mainly water and domestic fowl although some strains cause disease in mammals such as pigs, horses, seals and humans. Other genera of orthomyxoviruses include Influenza B virus, Influenza C virus, Isavirus , Thogotovirus and Quarjavirus Efficient translation of viral mRNAs in eukaryotic cells requires that they possess either a 5′ cap structure or special RNA structures called internal ribosome entry sites, which bypass the need for the cap. Many viruses that use capped mRNAs code for the required capping enzymes -RIL (Stratagene) from a pProEXHTb expression vector (Invitrogen). THOV PB2 627 (543–701), THOV PA-Nter (1–169), THOV PA-Nter D86A (1–169) and DHOV PA-Nter (1–171) domains were expressed as 6xHis fusion protein from a pET-M11 vector (EMBL Heidelberg) using the same bacterial strain. Proteins were first purified by affinity chromatography using Ni-sepharose resin , then cleaved with Tobacco Etch Virus (TEV) protease, further purified on a second Ni-sepharose column to remove His-tagged TEV and finally loaded on a Superdex 200 column . After TEV cleavage, additional non-native N-terminal residues were left: THOV PB2 627 domain (GAM), THOV PA-Nter (GMGSGMA), DHOV PA-Nter (GA) and THOV central PB2 domain (GAMA).THOV central PB2 domain (residues 323–486) was expressed as a 6xHis fusion in 2 and 22% PEG3350. For data collection at 100 K, crystals were flash-frozen in liquid nitrogen with a solution containing mother liquor and 30% (v/v) glycerol. SeMet labelled PB2 323–486 was purified and crystallized in a similar way.THOV PB2 323–486 was concentrated to 9 mg/ml in a buffer containing 20 mM Tris pH 7.0, 200 mM NaCl and 5 mM β-mercaptoethanol. The best-diffracting crystals grew within 2 days at 20°C in a solution containing 100 mM Bis-Tris pH 6.0, 100 mM MgCl2, 0.1 M MES pH 6.5 and 22% PEG 4000. Crystals were cryo-protected with 20–25% glycerol before data collection. DHOV PA 1–171 protein was concentrated to 20 mg/mL in the same buffer as THOV PA-Nter domain. Crystals were grown at 20°C in 3.2 M Na formate and 0.1 M Hepes pH 7.5 and cryo-protected with 25% glycerol before flash-freezing. SeMet labelled THOV and DHOV PA-Nter domains were purified and crystallized in a similar way.THOV PA 1–169 was concentrated up to 15 mg/mL in a buffer containing 100 mM NaCl, 20 mM Tris-HCl pH 8.0 and 10 mM β-mercaptoethanol. First crystals were obtained at 20°C in 20% PEG 4000, 0.1 M Mg acetate at pH 6–6.5 and the conditions were then refined to 0.1 M MgCl2 for 1 h for use as a heavy atom derivative. Crystals were cryo-protected with 20% glycerol before flash-freezing in liquid nitrogen.THOV PB2 543–701 was concentrated to 2.3 mg/mL in 250 mM NaCl, 50 mM Tris pH 8.0 and 2 mM DTT and crystals were grown in 0.1 M Hepes pH 7.0, 0.2 M ammonium sulphate and 22% PEG3350. Some of these crystals were further soaked in 5 mM HgClData collection and refinement statistic are given in I4 with one molecule per asymmetric unit and diffracted to 1.8 Å resolution. A second crystal form was of space-group P4212 with two molecules per asymmetric unit and diffracted to 3 Å resolution. The structure was solved by the single anomalous dispersion (SAD) method using 2.7 Å resolution data collected on SeMet labelled crystals at the Se K-edge. Three selenium sites were identified, refined and used for phasing with autoSHARP The best crystals of THOV PB2 323–486 belong to the space group P21 with two molecules in the asymmetric unit and diffracted to 1.3 Å resolution. The structure was solved by the SAD method, as described above, using 1.7 Å resolution data from SeMet labelled crystals measured at the Se K-edge. The native structure was refined to a final R-free/R-work of 22.0/17.3%. The DHOV PA-Nter model contains residues 1–168.Crystals of DHOV PA-Nter were of space-group C2 space-group and either two (2.7 Å resolution) or six (2.77 Å resolution) molecules in the asymmetric unit. The structure could not be solved by molecular replacement using PA-Nter due to the low sequence homology. Attempts to de novo phase by Se-Met labelling failed due to the too weak signal from the low number of methionines and even after making point mutations to introduce more methionines. The structure was finally solved by molecular replacement using the structure of DHOV PA-Nter using PHASER C2 cell model was refined to an R-free/R-work of 27.6/19.9%. The large C2 cell model was refined to an R-free/R-work of 27.1/21.0%. The THOV PA-Nter model contains residues 2–166.Two crystal forms of THOV PA-Nter were obtained, both of P43212 with one molecule in the asymmetric unit and diffracted to 2.4 Å resolution. The structure was solved by the SAD method using 2.8 Å resolution data from HgCl2 soaked crystals. One Hg site was sufficient to solve the structure. The native structure was refined to a final R-free/R-work of 23.9/20.8%. The THOV 627 domain model contains residues 547–681.Crystals of the THOV PB2 627 domain were of space-group 7GTP sepharose resin , bound for 3 h at 4°C, washed extensively with buffer containing 200 mM NaCl, 50 mM Tris-HCl pH 8.0 and 2 mM DTT, and eluted in the presence of 1 mM m7GTP. Input, washed and eluted fractions were analyzed on SDS-PAGE 15% acrylamide gel stained with Coomassie blue.IAV cap-binding domain , short flu panhandle RNA (36 nucleotides structured RNA) and poly(U) RNA (51 nucleotides unstructured RNA) as described The nuclease activity of IAV PA-Nter domain  as described 7GTP ligand in a cell containing PB2 domains. IAV and THOV central PB2 domains concentrated to about 75 µM were extensively dialysed in buffer containing 200 mM NaCl, 50 mM Tris pH 7.5 and 5 mM β-mercaptoethanol before measurements. m7GTP was injected at 0.6 mM for the IAV and 4 mM for the THOV experiments. Data were analysed with Origin7 soft .Isothermal titration calorimetry (ITC) was performed at 25°C with an ITC200 micro-calorimeter  and consisted of 25 injections of 1.5 µL mThe cDNAs encoding the the viral polymerase subunits and NP of THOV were cloned into eukaryotic expression vector pCAGGS. For detection of the recombinant proteins that contain single amino acid exchanges in PB2(R344A) and PA(D86A) by Western blot, cDNAs encoding wild-type and mutant proteins were fused to a C-terminal Flag-tag for PB2 or HA-tag for PA, respectively.Minireplicon assays were performed by transfecting 293T cells in 12 well format with 250 ng of pCAGGS-expression plasmids encoding PB2, PB1 and PA, 500 ng of NP as well as 100 ng of the viral minigenome construct pHH21-vNP-FF-Luc, containing firefly luciferase (FF-luc) cDNA in antisense orientation flanked by the noncoding regions of THOV segment 5 as described de novo phase by seleno-methionine labelling failed due to the too weak signal from the low number of methionines. To overcome this problem, we produced the homologous domain from DHOV PA , which readily crystallized and the structure was solved by the Single wavelength Anomalous Dispersion (SAD) method using seleno-methionine labelled protein. The resultant model was then used to solve the THOV PA-Nter structure by molecular replacement. Crystallographic details are given in Based on a sequence alignment with the IAV PA, a first construct (residues 1 to 185) was designed for the N-terminal domain of THOV PA subunit and the corresponding protein was expressed in bacteria and purified. Limited proteolysis with papain followed by mass spectrometry revealed a shorter, protease resistant fragment. The corresponding construct (residues 1 to 169) was cloned, expressed, purified and crystallized. The structure could not be solved by molecular replacement using that of IAV PA-Nter The structure of THOV PA-Nter is shown in 2+, Ca2+ or Mg 2+ ions, the IAV domain is thermally stabilised by 13, 8 or 6°C respectively, consistent with metal binding 2+ or Mg2+ ions destabilises the protein by 3–6°C  is sandwiched between aromatic residues His357 and Phe404, with Glu361 and Lys376 making base-specific interactions. The THOV domain conserves very few of these features which are characteristic of a cap binding site . Finally we made this same mutation in the context of the complete PB2 subunit and tested the effect in the THOV mini-replicon system. We found that the PB2 R344A mutation had no effect on the expression level of the PB2 subunit  nor on polymerase activity (detected by luciferase activity)  .de novo using anomalous scattering from a mercury derivative and refined to 2.4 Å resolution  makes alignments uncertain, without structural information. In addition, the PA and PB1 subunits are considerably shorter (respectively 622 and 710 compared to 736 and 757 for IAV), whereas the THOV PB2 subunit is slightly longer (769 compared to 759 in IAV). Nevertheless we were able to express, purify and determine the crystal structure of the three THOV polymerase domains corresponding to the PA endonuclease and PB2 cap-binding and 627-domains, previously structurally and functionally characterised for IAV polymerase , notably the shorter linker between the small N- and large C-terminal domains of THOV PA. The fact that the two THOV cap-snatching domains are apparently inactive in vitro could then be attributed to the need for activation by other parts of the polymerase or the vRNA as suggested previously 7GpppAmNN…. as substrate and cleave after Am, more akin to a decapping activity. This is plausible as the IAV endonuclease active site cleft is presumed to bind a minimum of about 5 nucleotides 7Gppp moiety and hence might be expected to be more basic, as indeed observed (see above). However if the THOV, DHOV and JOSV PA-Nter domains really did have this very specific and novel activity one would expect them to show much more conservation than observed e.g. metal binding and catalytic residues. A further possibility is that both THOV domains are indeed inactive and that cap binding and cleavage are both performed directly in the polymerase active site. Finally it is possible that THOV does not actually do cap-snatching at all but that host encoded activities generate short capped RNAs. One scenario could be that viral transcription is primed with a single A and then capping is performed post-initiation by the cellular capping enzymes. Another scenario would be that free m7GpppA caps are generated by the host and are used by the viral polymerase for transcription priming. Indeed, cellular decapping enzymes are known to be present in the nucleus and could provide cap structures although they are described to produce m7GDP or GpppN rather than m7GpppN in vitro cap-snatching activity of vRNA depleted THOV vRNPs, although not with highly purified, recombinant proteins.Previous work on THOV replication has shown that THOV mRNAs are capped but lack heterogeneous sequences at the 5′ end Unfortunately our attempts to test point mutants in the two domains in minireplicon assays were inconclusive, since mutation of a conserved arginine in the putative ligand binding pocket of the central PB2 domain had little effect on polymerase activity whilst mutation of a conserved aspartate in the PA-Nter domain caused a loss of activity possibly by misfolding of the domain. Thus at this stage we are unable to elucidate the exact mechanism of cap-snatching by THOV polymerase. Further studies should focus on the reconstitution of the recombinant trimeric polymerase complex of THOV followed by more detailed mechanistic studies of its putative cap-snatching activity.It is interesting to look at the putative cap-binding and endonuclease domains of orthomyxoviruses other than influenza or thogotoviruses in relation to their reported cap-snatching activity. For the case of the isavirus, infectious salmon anaemia virus, heterogeneous sequences of 8–18 nts length have been detected at the 5′ end of the viral mRNAs In conclusion, our data show that despite overall structural similarity, the putative cap-snatching domains of thogotoviruses have very different biochemical properties than their counterparts in other orthomyxoviruses, notably IAV, and casts serious doubt on whether they are in fact active in cap-snatching. Thogotoviruses seem to have evolved a different strategy to gain access to the cap structure required for their mRNAs, possibly reflecting their adaptation to a different host species. Further work is clearly required to resolve the enigma of how Thogotoviruses initiate transcription.Structure factors and co-ordinates have been deposited in the wwPDB as follows:DHOV PA-Nter 4CGSTHOV PA-Nter form 1 4CGXTHOV PA-Nter form 2 4CHCTHOV central PB2 domain form 1 4CHETHOV central PB2 domain form 2 4CHFTHOV PB2 627 domain 4CHDFile S1Combined file of supporting information.(PDF)Click here for additional data file.


Protein domains are globular structures of independently folded polypeptides that exert catalytic or binding activities. Their sequences are recognized as evolutionary units that, through genome recombination, constitute protein repertoires of linkage patterns. Via mutations, domains acquire modified functions that contribute to the fitness of cells and organisms. Recent studies have addressed the evolutionary selection that may have shaped the functions of individual domains and the emergence of particular domain combinations, which led to new cellular functions in multi-cellular animals. This study focuses on modeling domain linkage globally and investigates evolutionary implications that may be revealed by novel computational analysis.A survey of 77 completely sequenced eukaryotic genomes implies a potential hierarchical and modular organization of biological functions in most living organisms. Domains in a genome or multiple genomes are modeled as a network of hetero-duplex covalent linkages, termed bigrams. A novel computational technique is introduced to decompose such networks, whereby the notion of domain "networking versatility" is derived and measured. The most and least "versatile" domains  are examined both computationally via sequence conservation measures and experimentally using selected domains. Our study suggests that such a versatility measure extracted from the bigram networks correlates with the adaptivity of domains during evolution, where the network core domains are highly adaptive, significantly contrasting the network peripheral domains.Domain recombination has played a major part in the evolution of eukaryotes attributing to genome complexity. From a system point of view, as the results of selection and constant refinement, networks of domain linkage are structured in a hierarchical modular fashion. Domains with high degree of networking versatility appear to be evolutionary adaptive, potentially through functional innovations. Domain bigram networks are informative as a model of biological functions. The networking versatility indices extracted from such networks for individual domains reflect the strength of evolutionary selection that the domains have experienced. The majority of proteins are composed of one or more domains, with the exception of certain unstructured polypeptides. It has been widely recognized, for example, in the Structural Classification of Proteins (SCOP) database, that domains are also evolutionary units which have undergone duplication and recombination . Domain ractions .On the other hand, focusing on understanding how new cellular systems arise, our recent large-scale cross-genomes study followed the evolutionary trajectories of domains . In partThese findings underlie the dynamic nature of domain evolutionary cycles between abrupt punctuation (domain shuffling) and equilibrium (domain modifications). As such, the network depicting the complex linkages among the domains in the modern-day genomes may be viewed as a consequence of such evolutionary cycling, in which both robustness against genetic perturbation and adaptivity to the micro-environmental changes are essential. At present, the biological mechanism for cells to acquire both robustness and adaptivity remains largely mysterious. Nevertheless one naturally expects that the mystery involves not only the biochemistry of each individual domain, if domains are considered as the evolutionary units, but also the domains' global organization and the inter-connectedness in the genome.k-core decomposition" analysis [In the present study, we set out, on one hand, to establish a computational framework that may adequately reveal the biological mechanisms underlying cellular evolutionary cycles and, on the other hand, to probe such mechanisms. Via a survey of 77 eukaryotic genomes, we rationalize the use of domain bigram networks as a computational model for the study of evolution and our analysis indicates a hierarchical modularity phenomenon existing in domain bigram networks. Based on "analysis , we intrA survey of 77 eukaryotic genomes  for each of the 77 bigram networks. The clustering coefficient of a network measures, on average, how well all neighbors of any vertex in the network are connected whereas the average distance measures how far the nodes in the network are separated apart. It has been studied that when the clustering coefficient is significantly higher than that of a randomly generated graph with the same size and edge density, the network exhibits a "modularity" . Such moOverall, we observe that the average clustering coefficient of most species is significantly higher than those of the random networks  and thac(k) ~ k-γ, with γ = 1.63 ± 0.46. As established by Ravasz and Barabasi [The co-existence of scale-freeness, indicated by the degree power-law relationships, and modularity, indicated by high clustering coefficients deserves particular attention, since folk knowledge typically equates "scale-free" with "random", and "modular" with "structural". In fact, it has been observed that scale-freeness and modularity are not necessarily mutually exclusive; for example, they co-exist in some complex networks such as the metabolic network ,23, protBarabasi , the depFrom this perspective, our results suggest that modules in domain bigram network are organized by such a hierarchical principle. As argued in Ravasz et al. , such a k-core decomposition" [k such that the domain is contained in the k-core of the bigram network. In a sense, domains with high versatility index are situated in the "core" of the network, whereas those with low versatility index are situated in the network "periphery". This shares certain similarity with the notion of local and global centrality of interacting proteins in protein interaction network [The existence of hierarchical modularity of bigram networks necessarily implies that some domains in the network appear more "versatile" than others and serve to connect the otherwise distant modules with rather distinct functions. We note that the term "versatility" here not only describes a domain's tendency to join other domains but also captures the domain's contribution to the global connectivity of the network. For this purpose, we introduce a notion of versatility, which we call "networking versatility", based on a computational technique known as "osition" . More sp network .We note that other notions of domain versatility or promiscuity have been studied previously ,13,28, fk-core decomposition to each kingdom bigram network and obtain the networking versatility index for each domain therein. There are 10 nested cores in metazoa network . The contrast of functions between the core domains and the network periphery domains justifies the term "networking versatility" in the sense that this notion of versatility indeed captures a domain's ability to act across different functional subsystems.The distinct networking versatilities of domains observed above are arguably consequences of evolutionary selection. As such, a domain's versatility is expected to correlate, to some extent, to its evolutionary adaptivity, which may be reflected via its level of conservation sequence-wise or structure-wise.We use amino-acid identity to measure the degree of sequence conservation within a domain family. Specifically, multiple-sequence alignment of all members within each human domain superfamily was conducted and the amino-acid identity percentage for every pairing of two domains was obtained. We plotted the mean and standard deviation of the identity percentage for all core domains and for all network peripheral domains Figure . The resAs is shown above, conservation at the overall amino-acid sequence level may be treated as an "adaptivity score" for individual domain families, accumulated through their evolutionary history. Further assessing and measuring such adaptivity at functional levels are arguably more relevant. This is because it is conceivably the underlying functions of domains that have been more directly selected by the evolutionary pressure and via their constituent proteins. From a functional perspective and with respect to the network core domains and network peripheral domains, a question naturally arises as to whether these two classes of domains exhibit distinct adaptivity level.We designed experiments on selected domain families to investigate the level of their functional conservation within the family. We focused on PH domains (Pleckstrin-homology domain superfamily) from the core, and Actin (Actin-like ATPase superfamily) and Histone (Nucleosome core histone superfamily) from the network peripheral domain category. For each of these domain types, we developed assays to compare the functions of two domains within the same type but from distant species, yeast and human in this case Figure .S cerevisiae as EGFP (enhanced green fluorescent protein) fusions in a mammalian expression vector. These constructs were individually transfected into NIH-3T3 mouse fibroblast cells. The expressions of the yeast Actin (scActin) and Histone (scH2B) were visualized in the confocal EGFP channel P2 and PtdInsP3 (PIP2/3) in mediating Phosphoinositide 3-kinase signaling . We therFinally, we employed a standard assay to compare phospholipid binding properties of this pair of PH domains, purified both PH domains as GST-recombinant proteins , which resembles the SH2-TyrK bigram in the animals. This makes one wonder if the equivalence of such bigrams had occurred in species of the pre-TyrK world, and if the first bona fide TyrK would have arisen from an ancestor STK? This hypothetical scenario of evolutionary history illustrates how a gateway domain (such as SH2) potentially leads to modular expansions that contribute to the transition and advancement of lineages  to elaborate on our point. An effective pTyr mechanism is comprised of a three-part system that requires the cooperation among the writers , the readers (such as Src Homology 2/SH2 domains that bind to phospho-tyrosines) and the erasers (tyrosine phosphatases -- PTP) . Such multi-component signaling platform is the hallmark of multi-cellular animals, and is suggested to result from stepwise evolution during the emergence of metazoan species ,32. FromHierarchical modularity, often observed in social networks, plays an important role in the network's robustness to embrace and absorb impacts, namely those chromosomal changes resulting in aberrant domain linkages or amino acid permutations, which alter biochemical properties of domains and proteins. We infer that such hierarchical modularity in domain bigram networks and their encoding genomes also provides the cells with flexibility against punctuated impacts. In other words, the network has been compounded under the selection pressure in a fashion that is suited to buffer perturbations during domain recombination.The scheme of k-core decomposition of bigram networks reveals that hierarchical modularity is associated with varying levels of networking versatilities of the domains in the networks, and that domains, during their evolutionary history, have diverse levels of adaptivity. The highly adaptive domains become capable of participating in distinct molecular contexts, acquiring new functions, and recombining with domains that are otherwise irrelevant. On the other hand, the non-adaptive domains conserve their specific functions during evolution. This perspective is demonstrated by the sequence conservation levels of core domains (the most versatile) and network peripheral domains (the least versatile). In particular, it is observed that the network peripheral domains are significantly more conserved than the core domains. In fact, the notion that some domains are adaptive (core) and some are robust (periphery) is analogous to the distinction between local and global notions of centrality of interacting proteins as pointed out in Wuchty and Almaas ,27. Our The coherence of these results on one hand justifies the usefulness of domain bigram networks as a computational framework for the study of cellular evolution, and on the other hand reveals, to a degree, the biological basis underlying evolutionary cycles. We observed that the domain bigram network exhibits hierarchical modularity, which may serve as the basis for various functional platforms. Careful overview of the hierarchy and in-depth examination of the modules in domain bigram networks shall shed lights on how cells compartmentalize their functions at various levels of granularity and how these functions collaborate and interact locally and globally. Networking versatility, which is indexed by k-core decomposition of the network, appears to be a notion properly reflecting the evolutionary adaptivity of domains, as measured by sequence and functional conservation.This study used domain definitions from the SCOP database. SCOP first organizes domains into families if domains have a common origin supported by observable sequence resemblance. Families without observed sequence-level similarity are then clustered together into superfamilies if there is structural or functional evidence strongly supporting that they originated from a common ancestor. The SCOP hierarchy considers folds and groups superfamilies not known to be related but having the same secondary structures. The notion of SCOP superfamily, instead of family or fold, is taken as the definition of "domain" in order not to risk being too broad or too narrow ,7. We noSeventy-seven eukaryotic organisms with completely sequenced genomes in the SUPERFAMILY  databasei, is defined as:The versatility of a domain in its local context can be parameterized by the degree of the vertex in the network, which is defined as the number of vertices to which it links . The clui ncounts the number of edges between neighboring vertices of vertex i i, kis its degree. The average clustering coefficient of a network is defined as the mean value of vertex clustering coefficient.where The average distance (i.e. the mean shortest distance) in a network is defined as ij dis the shortest distance from vertex i to vertex j [where vertex j .To explore the hierarchical topology of domain bigram network, we first computed the topological overlap matrix of the network according to the method in Ravasz et al. . We thenhttp://vlado.fmf.uni-lj.si/pub/networks/pajek/.Pajek, a program for large-network analysis, was used for the calculation and visualization of domain bigram networks. This program is available at k and a given network, we define the k-core of the network as the maximum induced subgraph with vertices having degree not lower than k [k-core of a network by recursively removing all vertices with degree less than k until no such vertex exists in the resulting graph. It is possible that vertices with degree larger than k in the original network not be present in k-core since their degree could be smaller than k after their neighboring vertices are deleted  serves as a non-parametric method to investigate whole-genome or multi-genome bigram networks.Following , for a gr than k . A greedhttp://simgene.com/ClustalW).Multiple sequence alignment and subsequent calculation of pair-wise amino acid conservation within individual domain superfamilies were executed using tools provided by ClustalW2  following manufactory instructions. PH domains were cloned from yeast Cla4 (amino acids 42-197) and human Akt1 (amino acids 1-127) using primer pairs: 5'-ATATGGCGCGCCACCAAACTTATGAGTCAACTGGATTTA and 5'-GCTGATCGTTAATTAATCAACCAACGTGAACTTTATGTGTGAAG, ATATGGCGCGCCATGAGCGACGTGGCTATTGTGAAGG and 5'-GCTGATCGTTAATTAATCAGTTGTCACTGGGTGAGCCCGACCG, respectively.Phalloidin-Texas-red and DAPI were from Invitrogen. NIH-3T3 cells were from ATCC. Recombinant PH domains were harvested from pGEX 4T-2 expressions following the standard GST purification protocol (BD Biosciences). PIP-Strip™ was from Echelon Biosciences Inc. Polyclonal Anti-GST antibody was from GE Healthcare Life Sciences.NIH-3T3 cells were maintained in DMEM with 10% FBS. The transfection experiments were mediated by Lipofectamine 2000 reagent from Invitrogen. Twenty-four hours after the transfection, the cells were fixed in 4% PFA, permeabilized in 0.1% triton, and subsequently stained with phalloidin and DAPI. The confocal images were taken using an Olympus BX61 fluorescence microscope and processed by the software Volocity software from PerkinElmer.Purified GST-PH domain fusion proteins with concentration of 1 μg/mL were prepared in TBST buffer containing 2% of skim milk. PIP-strips were inoculated in the above solution for 16 hours at 4°C. The blots were subsequently processed following an anti-GST Far-Western protocol, and visualized by ECL.XYX, JJ and YYM designed the research. XYX performed the study. JJ contributed the experimental results. XYX, JJ and YYM analyzed the data and wrote the manuscript. All authors conceived the study and wrote, edited and approved the manuscript.supplementary results, tables and figures. supplementary results, tables  and figures Click here for fileThe survey information of domains in four kingdoms. common domains shared by all genomes (Table S1) and specific domains found in each kingdom .Click here for fileThe dependence of clustering coefficient on node degree in the 77 domain bigram networks. The log-log plots of clustering coefficient versus the degree of node in networks.Click here for file


Saccharomycetales has prompted the hypothesis of a modular organization of structural disorder, with most evolutionary conserved regions alternating with highly variable ones. The finding of a consensus sequence points to the conservation of a specific phosphorylation rhythm along with two disordered sequence motifs, probably acting as phosphorylation-dependent seeds in Whi5 folding/unfolding. Thus, the widely disordered Whi5 appears to act as a hierarchical, “date hub” that has evolutionary assayed an original way of modular organization before being supplanted by the globular, multi-domain structured Rb, more suitable to cover the role of a “party hub”.Cell growth and proliferation require a complex series of tight-regulated and well-orchestrated events. Accordingly, proteins governing such events are evolutionary conserved, even among distant organisms. By contrast, it is more singular the case of “core functions” exerted by functional analogous proteins that are not homologous and do not share any kind of structural similarity. This is the case of proteins regulating the G1/S transition in higher eukaryotes–i.e., the retinoblastoma (Rb) tumor suppressor Rb—and budding yeast, i.e., Whi5. The interaction landscape of Rb and Whi5 is quite large, with more than one hundred proteins interacting either genetically or physically with each protein. The Whi5 interactome has been used to construct a concept map of Whi5 function and regulation. Comparison of physical and genetic interactors of Rb and Whi5 allows highlighting a significant core of conserved, common functionalities associated with the interactors indicating that structure and function of the network—rather than individual proteins—are conserved during evolution. A combined bioinformatics and biochemical approach has shown that the whole Whi5 protein is highly disordered, except for a small region containing the protein family signature. The comparison with Whi5 homologs from  Saccharomyces cerevisiae, such a regulatory step takes place in the unbudded, G1 phase of the cell cycle, at a regulatory area termed START  with cyclins and inhibitors Sherr, . The majIn mammalian cells, the functional homolog of Whi5 is the multi-domain tumor-suppressor protein retinoblastoma (Rb)  or Whi5-derived peptides and analysis of interactome data of both Whi5 and Rb. We show that Whi5 is a largely disordered protein with features resembling those of date hubs, while Rb and its paralog pocket proteins p107 and p130—in which structured domains are linked by disordered regions—more closely resemble party-hub proteins. Comparative analysis of the Whi5 and Rb interactome highlights a significant core of conserved common functionalities associated with the interactors. In order to link biological mechanism to interactome data, we propose a concept map for Whi5 that vastly extends previous models of its functionality. Such a multi-scale approach indicates that structure and function of the network—rather than individual proteins—are conserved during evolution.In this paper we present a multi-scale comparison of Whi5 and Rb. Our investigations combined the use of a large array of predictive bioinformatic tools with WHI5 gene amplified from genomic DNA of W303A S. cerevisiae strain with the oligonucleotides Whi.NdeI (TAAATCATATGAGTTTGAGAACGCCG) and Whi.XhoI (TAAATCTCGAGAGACGTCTCCACTTCGG), was cloned into the His6-tag expression vector pET21a using NdeI and XhoI restriction sites. The resulting vector, pET21[Whi5], contains the open reading frame for Whi5Sc C-terminally linked to an His6-tag by a three-amino acid linker, as confirmed by nucleotide sequencing. The vector was inserted into Escherichia coli BL21 Rosetta cells (Novagen); transformed cells were cultured in 1 L low-salt Luria–Bertani broth containing 100 mg/L ampicillin and 34 mg/L chloramphenicol at 37°C until OD600 ~0.5 was reached. Cells were induced for 2 h by 200 mM IPTG at 30°C, harvested by centrifugation and resuspended in 1/200 volume of lysis buffer  containing 10 mM imidazole and protease inhibitors cocktail . Cells were then either directly extracted or stored at −20°C. Protein extraction and IMAC purification on Ni2+/NTA beads were carried out as already described  were carried out according to the procedure described in , phosphorylated motif 1 , and motif 3  were chemically synthesized  and used in Surface Plasmon Resonance (SPR) experiments  maintaining a flow rate of 5 μl/min. At least four concentrations of each analyte were tested twice. Surface regeneration was accomplished by injecting 100 mM NaCl (30 sec contact) two or three times. Thermodynamic parameters of the interactions, such as KD, were derived by simultaneous fitting of binding curves obtained with different concentrations of analyte, using BIAevaluation 4.1 software.Peptides corresponding to Whi5All the bioinformatic tools used are readily accessible through the relevant websites, and listed in Table Sc was analyzed by the algorithms PSIPRED, JNET, and TRANSSEC from the server Proteus 2.0. The relative disorder was analyzed by the Composition Profiler  from the DisProt database /Corder, where CX is the content in a given amino acid of Whi5Sc (or of the set of IDPs) and Corder is the corresponding value in the set of ordered proteins. Negative fractional difference indicates depletion, while positive difference indicates enrichment in the corresponding amino acid. Amino acids are arranged on the x axis from the most rigid to the most flexible according to the Vihinen's flexibility scale . We predicted the structural disorder of Whi5 and Rb with different neural networks. PONDR-FIT is a meta-predictor that integrates outputs of six different disorder predictors  within long disordered regions .The disorder plots show a per-residue output where regions that exceed 0.5 on the p-distance   . More in detail, for human Rb, the predicted Cdk-phosphorylation pattern most similar to the experimental one was obtained combining the prediction for Cdk2, Cdk4, and Cdk6 at high threshold. Hence, the same setting was used for all Rb-related proteins. The prediction of Whi5Sc motif 1 (residues 136–162) and of motif 3 (residues 245–267) peptides were obtained by the generated_extended.inp module of the crystallography & NMR system (CNS) software .Electrostatic surface potentials were calculated by numerical solution of the Poisson-Boltzmann equation implemented in APBS  database  and rich of residues associated with disorder , , that is nearly coincident with the Whi5 domain recognized by Pfam . The anomalous migration of Whi5Sc on SDS-PAGE could result, as typically observed for IDPs, from amino acid composition and in particular from its lower content in hydrophobic residues and higher content of charged residues . The dependence of hydrodynamic radius log from molecular weight log, for different protein conformations  can be described by different, empirical equations of straight lines (Uversky's formulas)  noticeably larger than that calculated with Whi5Sc theoretical weight (~26 Å). It has been observed that a 15–20% increase of hydrodynamic radius is associated to the transition between native and molten globule state, and an even higher increase indicates a pre-molten globule  belong to the Ascomycota fungi, their sequences result widely heterogeneous. A BLASTP search launched with Whi5Sc against a non-redundant protein data bank retrieved putative homologs uniquely in the order of Saccharomycetales. Fifteen sequences  were obtained filtering for a maximum e-value of 0.0001, and a minimum match length of 23% with respect to the length of the query sequence (Table Although the 93 members of the Whi5-like family retrieved by Pfam (December 2012) are all from ce Table .PONDR-FIT predicts that the Whi5 homologs are almost completely disordered, the only exception being the region corresponding to the GTB motif Figure . Figure W. ciferrii, the only species not belonging to the family of Saccharomycetaceae, does not contain motif 1. The sequences of the three conserved motifs are shown in Figure Applying the alignment software MEME to the fifteen Whi5 yeast homologs and searching for the three most conserved domains, we found that the GTB motif, referred from now on as “motif 2,” is flanked by two motifs (motif 1 and motif 3) that fall in disordered regions. Motifs 1–3 were manually refined and their positions within the sequence of the Whi5 homologs are shown in Figure In motifs 1–3 we observed a relatively high frequency of prolines, as well as of aromatic and hydrophobic residues  Figure , as witnAlso charged amino acids are non-randomly distributed along the sequences of Whi5 homologs. With few exceptions, motifs 1 are remarkably more basic  than the entire protein (pI ranging from 6.28 to 9.56), while motifs 3 are acidic  Table .Sc, all the putative Cdk1 sites were found experimentally phosphorylated  cluster in motifs 1 and 3. More in detail, we observed that motif 1 contains the Cdk1 phosphosites 8–10 and also the site 7 . Motif 3, with the only exception of the homolog from K. naganishii contains the single phosphosite 12 (Ser262 in Whi5Sc). Motif 2 does not contain any phosphorylatable residue in all analyzed sequences. The 11th Cdk1 phosphosite of Whi5Sc (T215) is included in an “inter-motif” sequence between motifs 2 and 3, whose length and number of phosphorylatable sites is variable   Figure .Sc wild-type motif 1, its phospho-variant and wild-type motif 3, and we calculated for each of them the electrostatic potential surface . Cdk-phosphorylatable residues, analyzed by GPS2.1, gave a very similar probability score inside each sequence and among different proteins of our data set  is the least conserved and has no Cdk-phosphorylatable sites, while the third and the fourth blocks (roughly corresponding to RbPL linker and to the C-terminus) are the most conserved and most phosphorylated . This was even more evident when we restricted our analysis to the Rb proteins from five vertebrates  database.Genetic and physical protein interactors of budding yeast Whi5 were obtained as described in Material and Methods. Out of 144 Whi5 interactors, only 18 physically interact with Whi5, the remaining having been classified so far only as genetic interactors. With the exception of the histone deacetylases Hos 1, 3 and the protein kinase Pkp2, all Whi5 physical interactors have one or more physical interactors among the Whi5 genetic interactors. In turn, some of these second level interactors directly bind to third level interactors. Thus, the Whi5 interactome is organized hierarchically. Figure Protein interactors of human Rb, drawn up as described in Materials and Methods and listed in Table The largest supercluster in Revigo-generated treemap of Whi5 interactors is tagged as “protein phosphorylation.” It includes terms related to regulation of macromolecular biosynthesis and gene expression, whereas the other large supercluster includes terms related to regulation of cell cycle. The presence in the major supercluster of terms related to regulation of metabolic processes, together with the enrichment of terms related to “response to stress and stimuli and phosphorus metabolism,” suggests a previously un-noticed combination of Whi5 with internal and external signals, whose tight integration is required for proper regulation of the G1/S transition.The largest supercluster in Revigo-generated treemap of Rb interactors—tagged as “transcription from RNA polymerase II promoter”—includes GO terms related to metabolic processes and their regulation, such as “RNA metabolic process” and “macromolecule biosynthetic process,” gene expression and regulation of biological process, as well as terms related to post-translational modifications, including “protein modification process.” The supercluster tagged as “cellular response to stress” includes terms generically related to stress response as well as more specific response such as DNA damage, ions and estradiol and p53-mediated signal transduction events. The supercluster tagged as “regulation of cell cycle” includes GO terms related to regulation of apoptotic process, cell proliferation and differentiation, etc. The supercluster “chromosome organization” includes mostly terms related to chromatin organization and remodeling.Sc and human Rb interactors. As expected, side-by-side comparison of the three panels highlights terms related to the control of cell cycle and transcription, but also includes less expected terms such as metabolism, phosphorous metabolism and response to stress. Notably, cell death is enriched in Rb interactors, but is not present neither in Whi5 nor in the common terms. Strikingly, in the case of Rb the functions have been defined by a group of proteins that physically interact with Rb itself, while most Whi5 interactors have indirect connections to Whi5. Extension of analysis to genetic interactors results particularly useful in those case, such as S. cerevisiae, in which extensive, often genome-wide, genetic data sets are available.Comparison of GO term enrichment of Whi5 and Rb  and Kap60 (importin α) and recognizes the Nuclear Localization Sequence at the N terminal moiety of Whi5. Whi5 nuclear export is mediated by the karyopherin Msn5 and requires a Nuclear Export Sequence whose function is regulated by phosphorylation . Regulated phosphorylation by these kinases may link the functional state of Whi5 to different stimuli and/or cell fates. For instance Tpk1, one of the catalytic subunits of cAMP-dependent protein kinase, Ptk2 and Pho89/Pcl9 may link Whi5 to sensing of different nutrients such as carbon, nitrogen and inorganic phosphate. Some of the other kinases that phosphorylate Whi5 or its interactors may contribute to define yeast cell fate. These kinases include Agt1 (autophagy), Yck1 and Hsl1 (cell polarity/morphogenesis), Ime2 (meiosis), Rad53 (DNA damage-repair), while the KAT acetylating enzyme might convey information regarding the metabolic state. In the concept map reported in Figure While the role of Cln1,2,3-Cdk1 kinase complexes in regulating Whi5 function and subcellular localization is known  , involved in acetylation of histone proteins and other proteins, and help to promote cell cycle progression. This protein also acetylates Whi5 and Swi4  likely required in transient binding interactions, does not impair the overall view of Rb as a multi-interface or party-hub protein.Overall, hubs evolve more slowly than proteins with few interaction partners  or catalytic  subunits of protein kinases. Among the 36 phosphorylation sites predicted by the NetPhosYeast 1.0 server in Whi5ed Table . Twelve ed Table —rather ted Table .Studies of network topology give interesting insights into the function of biological modules, but do not account for cellular dynamics. Molecular models are needed to increase our understanding of organization, regulation, and execution of the module under investigation, to identify principles of design and system-level properties and finally to achieve predictive ability on the behavior of the system and its components. As a first step to improve our understanding of Whi5, we step-wise added first-, second- and third-level interactors thereby obtaining a first “concept map” of Whi5 function and regulation. The concept map indicates that fine tuning of Whi5 activity is likely more complex than so far anticipated Figure –S9. ThusBy further focusing on molecular details of Whi5 structure and through biomolecular interaction experiments with model peptides, we have been able to integrate different aspects of Whi5 function as outlined in the model of Figure either on Whi5 or on Swi6 and mimics what can actually be observed in Swi64Ala or Whi64Ala mutants. In wild-type cells we may expect that both proteins get some phosphorylation and that dissociation is induced when the first protein gets four phosphate groups. The model is consistent with genetic data , coupled to selected experiments allows to integrate previous information on Whi5, highlighting the importance of a multi-scale approach for a full understanding of complex biological functions Kitano, . The impThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Vibrio cholerae, and Certhrax, an anthrax-like toxin from Bacillus cereus, are among six new bacterial protein toxins we identified and characterized using in silico and cell-based techniques. We also uncovered medically relevant toxins from Mycobacterium avium and Enterococcus faecalis. We found agriculturally relevant toxins in Photorhabdus luminescens and Vibrio splendidus. These toxins belong to the ADP-ribosyltransferase family that has conserved structure despite low sequence identity. Therefore, our search for new toxins combined fold recognition with rules for filtering sequences – including a primary sequence pattern – to reduce reliance on sequence identity and identify toxins using structure. We used computers to build models and analyzed each new toxin to understand features including: structure, secretion, cell entry, activation, NAD+ substrate binding, intracellular target binding and the reaction mechanism. We confirmed activity using a yeast growth test. In this era where an expanding protein structure library complements abundant protein sequence data – and we need high-throughput validation – our approach provides insight into the newest toxin ADP-ribosyltransferases.Chelt, a cholera-like toxin from  Computer tools helped us uncover and understand potent protein toxins that empower bacterial pathogens against plants, animals and man. These toxins are potential drug targets and researchers can use them to make vaccines. New toxin knowledge aids the long-term goal of finding alternatives to antibiotics, to which pathogens are becoming more resistant. The toxins share similar structure despite low sequence identity, so our search links sequence and structure features. We present a ranked list and computational characterization of six new toxins combined with cell-based tests. Sequence data from over 6,500 genome projects is available through the Genomes OnLine Database + and covalently transfer a single or poly ADP-ribose to a macromolecule target, usually protein, changing its activity. Many prokaryotic ADPRT toxins damage host cells by mono-ADP-ribosylating intracellular targets. G-proteins are common targets including: eukaryotic elongation factor 2 , elongation factor thermo unstable, Ras, Rho (ADP-ribosylation locks Rho GTPase in the GDP-bound state and disaggregates the actin cytoskeleton) and Gs-α . Other targets include actin (ADP-ribosylation inhibits actin polymerization) ADP-ribosylation is a post-translational modification that plays a role in many settings Researchers use ADPRT toxins to develop vaccines Pseudomonas aeruginosa exoenzyme S (ExoS), Vibrio cholerae cholera toxin (CT), Bordetella pertussis pertussis toxin (PT) and Corynebacterium diphtheriae diphtheria toxin (DT). Toxic ADPRTs are divided into the CT and DT groups to better organize the family. We focus on the CT group, which we divide into the ExoS-like, C2-like, C3-like and CT-PT-like toxins.ADPRTs occur in viruses, prokaryotes, archaea and eukaryotes. Genomes acquire them through horizontal gene transfer CT group primary sequences are related through a specific structure-linked pattern  and 2 [2et al. suggested the PN loop is flexible and implicated it in locking the nicotinamide in place during the reaction + conformation. The ARTT loop is plastic, having both “in” and “out” forms that might aid substrate recognition The toxin catalytic domain consists of several regions. We describe them here going from the N- to C-terminus using previously introduced nomenclature N2 mechanism N1 mechanism et al. recently devised a specific version of this mechanism for iota toxin, which we follow closely in this work 5D-PN bond rotates to relieve strain and forms a second ionic intermediate. (The electrophile and nucleophile might migrate by an unknown mechanism to further reduce the distance between them.) Finally, the target makes a nucleophilic attack on the second ionic intermediate. The SN1mechansim – believed widely applicable to CT group toxins – is a template for new toxins given the historical structure similarity and consistent NAD+ conformation in the active site as shown in Researchers have long debated the ADPRT reaction details. Some suggest an S5 (multiple polypeptides). C3-like toxins are A-only. ExoS-like toxins have toxic A-domains and are often paired with Rho GTPase activating protein (RhoGAP), which are not true B domains. C2-like toxins are AB toxins that contain B domains that are structural duplicates of the A domain. These B domains are not toxins; they bind proteins that are similar to anthrax protective antigen (PA) including Vip1, C2-II and Iota Ib 5 and have B domains that form a receptor-binding pentamer Quaternary structure for the toxins is wide-ranging. Several combinations exist for toxin domains (A) and receptor binding or membrane translocation domains (B). The B domains have diverse structures and functions and exist as fusions or separate polypeptides. Various formats include: A-only, two-domain AB (single polypeptide), three-domain AB (single polypeptide) and ABLow overall sequence identity hampers conventional sequence-based homology searches et al. used PSI-BLAST to identify new ADPRT toxins, including SpvB from Salmonella entericaMycoplasma pneumoniaStreptococcus pyogenesPseudomonas syringaeOtto et al. released the last detailed data mining results for the toxin family et al. proposed that a match between the conserved ADPRT pattern with corresponding secondary structure is one way to reduce dependence on sequence identity. The pattern helps ensure function and reduces the total sequence set to a smaller subset for screening; secondary structure prediction ensures that key active site parts are present PSI-BLAST is a classic way to expand protein families, but it has limits. For example, unrelated sequences often “capture” the search. Also, nearly a decade has passed since Pallen in silico approach to guide and complement – rather than replace – follow-up in vitro and in vivo studies. Here, we extract features and patterns from known ADPRT toxins and explain how they fit new toxins. We use in silico methods to probe structure, secretion, cell entry, activation, NAD+ substrate binding, intracellular target binding and reaction mechanism.Our contribution is to expand ADPRT toxin family using a new approach. The difference is that we use fold-recognition searches extensively rather than relying on PSI-BLAST or secondary structure prediction. Our genomic data mining combines pattern- and structure-based searches. A bioinformatics toolset allows us to discover new toxins, classify and rank them and assess their structure and function. Often, data mining studies simply present a table of hits with aligned sequences, but do not interpret or analyze those hits in detail. Our aim – rather than to explicitly confirm the roles of the six proteins, 15 domains, 18 loops and 120+ residues discussed – is to develop a theoretical framework for understanding new toxins, based on 100s–1000s of jobs per sequence. We intend our in vitro characterization. Also, ADPRTs are abundant within bacterial genomes and researchers make the sequences available faster than we can conduct biochemical studies. New toxins might play a role in current outbreaks and are also excellent drug targets against antibiotic resistance. Our new study design expands the family by ∼15% (from 36 to 42 toxins).A computer approach is fitting for several reasons. Such an environment is a safe way to study new toxins. Challenges in cloning, expressing, purifying and crystallizing often prevent in silico approach. We use Saccharomyces cerevisiae as a model host to study toxin effects. Increasingly, researchers are turning to yeast to study bacterial toxins. Yeast are easy to grow, have well-characterized genetics and are conserved with mammals in cellular processes including: DNA and RNA metabolism, signalling, cytoskeletal dynamics, vesicle trafficking, cell cycle control and programmed cell death in silico and cell-based studies helps identify and characterize new ADPRT toxins.Cell-based validation complements our et al. biochemically characterized during this writing We searched fold-recognition databases – including Pfam 24.0 We count 36 known ADPRT toxins see . The six3D-jury and catalytic domain novelty suggested by sequence identity to the nearest known toxin. 3D-jury accepts models from various servers and makes pair-wise comparisons. Each pair gets a similarity score that equals the total number of Cα atom pairs within 3.5Å after overlap. The final score is the sum of the similarity scores divided by the number of pairs considered plus one. A higher J3D-jury implies a stronger prediction. The closest toxin relative to a newly predicted toxin indicates the new toxin's novelty and aids function prediction. Identity to a known toxin ranges from 25% to 60%. We show predictions for the toxins in We ranked the toxin candidates by relevance signalled by ISI Web of Knowledge hits to the species name . As wellAligned sequences of known and new CT group toxins are critical to further studies  encodes Chelt. It is specific to this strain. Chelt GC content is 14% lower than the overall genome (34% vs. 48%); also, a transposase gene immediately follows the Chelt gene, indicating horizontal gene transfer typical of the ADPRT toxins. Chelt is a 601-residue, 69 kDa protein. It has a secretion signal (∼1–18), followed by toxin domain Ia (∼19–179) and Ib (∼180–240) and a presumed cell-binding domain II (∼241–601) (241–601)  and 5A.Psathyrella velutina lectin . Weaker similarities also exist to human integrin αVβ3 . Prokaryotic lectins allow differential eukaryotic cell recognition. Indeed bacterial lectins can mimic eukaryotic adhesion motifs 2+, or possibly Mg2+, binding and perhaps even integrin mimicry. Chelt is reminiscent of ricin toxin from the castor bean. Ricin is a two-domain toxin that contains both a lectin for binding the cell-surface galactosyl residues for cell-entry and a second domain that causes cell death Chelt is unusual in that it has a second domain attached to the catalytic domain  and our model against this template was also high quality. The Chelt catalytic domain adopts an α+β ADP-ribosylation fold consisting of anti-parallel β-sheets and having separate α and β regions.Domain I, the catalytic domain, is 60% identical to LT-A from Chelt must likely be activated by reduction of a disulfide bond between Chelt C205 and C220; cleavage at or near I215 (details are unclear due to a four amino acid deletion compared to LT-A between H214 and I215); and interaction with an ADP-ribosylating factor, perhaps ARF3, in the Chelt regions ∼45–57, ∼109–113, ∼134–141 and ∼167–182 , the α3 helical region (∼82–99) and the ARTT loop (∼104–129)  . The ARTechanism . First, Cell-based toxin expression in yeast, driven by the copper-inducible CUP1 promoter, shows cell death in the presence of the wild-type toxin. We observed mild growth restoration with the E128A mutant, dramatic growth restoration with the E130A mutant and near-complete growth restoration with the E128A/E130A double mutant . The wilB. cereus G9241. (A slightly larger relative exists in another B. cereus strain.) Most B. cereus strains are harmless or cause foodborne illness, but researchers have implicated this strain in several severe pneumonia cases Bacillus anthracis. The closest fold recognition match is to anthrax toxin lethal factor . So we modeled Certhrax against LF. Certhrax has two domains: domain I (∼1–241) presumed to bind PA and domain II (∼242–476) is the toxin domain (Certhrax (UniProt Q4MV79) is encoded in n domain  and 5B.B. cereus cells secrete this protein non-classically. Certhrax likely behaves similarly to LF in cell entry because of similarities in domain I, which is likely responsible for PA-binding. We describe a supposed model of Certhrax here using LF as a template B. cereus forms spores that humans inhale into lung alveoli. Spores that escape from macrophages enter the lymph system where B. cereus germinates. Here, B. cereus produces protective antigen  that may bind Certhrax and edema factor (UniProt Q4MKW0). Both Certhrax and LF have a PA binding domain; sequence identity over this domain is 36%, within the safe zone of homology. But, Certhrax lacks the catalytic zinc metalloprotease domain of LF that proteolyzes mitogen activated protein kinase kinase (MAPKK or MEK). It contains a functional ADPRT domain instead of the vestigial ADPRT domain of LF . Fold recognition and phylogenetic analysis suggest similarities to C3-like toxins. We propose a likely mode of NAD+ binding, target binding and ADP-ribosylation based on alignment data and our modeling experiments , the PN loop (∼390–402) and the ARTT loop (∼420–430) . The ARTechanism . First, B. antracis effects Toxin gene expression in yeast, driven by the CUP1 promoter, shows cell death in the presence of the wild-type toxin. We observed mild growth restoration with the Q429A and E431A mutants and near-complete growth restoration with the Q429A/E431A double mutant . The wilM. avium strain 104 is a predicted ADPRT with possible relevance to AIDS patients who face a high risk of M. avium infections M. avium subsp. paratuberculosis and M. avium subsp. avium ATCC 25291.) M. avium is both an environmental microbe and opportunistic pathogen causing chronic, pulmonary infections in immune-compromised individuals. Mav toxin is an 825-residue, 83 kDa protein with four putative domains: an ESAT6-like domain I (∼1–96), a predicted helical pore-forming domain II (∼97–439), a largely disordered domain III (∼440–674) and the toxin domain IV (∼675–825) (Mav toxin (UniProt A0QLI5) from 675–825)  and 5C.3d-jury = 65; 16% identity). Virulent mycobacteria need the ESX secretion system for pathogenesis: ESX-1 deletion weakens virulence in M. tuberculosis, M. bovis and M. marinumDomain I suggests secretion through the ESX (type VII) secretion system. This matches the non-classical secretion result. Fold recognition matches residues 1–95 to the 6 kDa early secreted antigenic target , a tropomyosin leucine zipper  and spectrin-like repeats . Domain III has slight propensity for forming β-sheets; but it is disordered. Its role is unknown, but it might recognize and bind cell-surface receptors. Combining domains II and III we found matches to the Cry insecticidal α-pore-forming toxins .Domain II is α-helical, especially from 134–348. It might be a multi-helical bundle of short and long helices poised to form pores for target cell entry. Fold recognition matches are to the soluble domain of bacterial chemoreceptors . Mav, compared with iota toxin, has an 18-residue deletion after region 1 between P735 and A736. Also, and possibly affecting targeting, it has a two-residue PN-loop insertion (S765–S766).Domain IV is the catalytic domain. Fold recognition suggests matches to Art2.2  – suggested as a virulence factor in E. faecalis strain, V583 Enterococcus faecalis CH188. EFV toxin itself is a 487-residue, 56 kDa protein and has a needle-like helical domain I (∼1–309) and catalytic domain II (∼310–487) (EFV toxin (UniProt Q838U8) is a medically relevant ADPRT candidate from a vancomycin-resistant 310–487)  and 5D.E. faecalisThe toxin is non-classically secreted . A type IV secretion system has been identified in 3d-jury = 49; a hard target for LOMETS, which provides this high-confidence match) and weaker matches to the pore-forming domain of colicin s4 . Also genetic context suggests similarities to the bacteriophage P22 needle implicated in cell-envelope penetration Domain I bears large sequence similarity to phage minor head region from 147–268 that suggests a possible phage origin. The phage head match is reminiscent of the dual role of Alt in bacteriophage T4 as both a phage head structure component and a RNA-polymerase targeting ADPRT Bacillus thuringiensis VIP2 over 166 residues. EFV toxin has C2-like character based on its phylogenetic branching. It also has a region 3 EXE sequence pattern that suggests an Arg target. Fold recognition suggests that its closest structure match is to C2-I .Domain II is 25% identical to + binding, target binding and ADP-ribosylation based on alignment data and our modeling experiments  is an ADPRT from 3D-jury = 71) and the Sro7 exocytosis regulator . These proteins are WD40 repeat-containing proteins . Also, we found matches to several tandem seven-bladed β-propeller xyloglucanase structures  that hydrolyze polysaccharides.TccC5 is 938-residue, 105 kDa protein and has four domains: domain I (∼1–341), domain II (∼342–675), domain III (∼676–738) and domain IV (∼739–938)  and 5E. 3d-jury = 123), outer surface protein OspA , Tyr-Val-Thr-Asn (YVTN) domain from an archaeal surface layer protein , lyases , among others. Function prediction suggests domain I contains two YD repeats possibly involved in binding carbohydrates and heparin. Also, domain I contains a lipocalin pattern, hinting at a connection to small-molecule transporters.Fold recognition on domain I, a hard target, produces matches to various β-propellers such as βγ-dimer of the heterotrimeric G-protein transducin , oxidoreductases , among others. The C-terminal end of domain II appears to have recombination hot spot (Rhs) repeats employed in other secreted bacterial insecticidal toxins and eukaryotic intercellular signalling proteins, and often involved in ligand binding. Rhs suggests horizontal transfer; it is related to YD repeats and also often contains VgrG, a type VI secretion protein. β-propellers are structurally conserved but functionally diverse, so it is difficult to pinpoint exact functions for domains I and II. While the exact role of these domains is unclear, a likely role is gaining cell entry. Domain III seems helical with unknown function.3d-jury = 92) throughout the catalytic domain. Recently, Lang et al. identified the cellular target as RhoA Q63 TccC5 domain IV best matches SpvB but identity is only 25% over the toxin core, making TccC5 among the most novel toxins discussed here. Fold recognition results suggest that TccC5 is similar to C3bot2 . Vis entry into target cells is unclear. It may travel through a transporter, be aided by other pore-forming toxins or be directly released into the cytosol after V. splendidus invasion.Vis (UniProt A3UNN4) is an ADPRT from a known pathogen, 8 and 19  and 5F. + binding, target binding and ADP-ribosylation based on alignment data and our modeling experiments. NAD+ binding . If no match to an ADPRT-related structure appeared, we removed them from the list. We checked ScanProsite matches against fold-recognition results, and adjusted them to ensure that we correctly identified the region 1 Arg, region 2 “STS” motif and region 3 ARTT motif.Fisher reviewed fold recognition servers ab initio methods and uses a PSI-BLAST generated profile to find templates. For significant matches it uses MODELLER for modeling and the protein domain parser (PDP) for domain parsing. If it does not find matches, it relies on neural networks or support vector machines (SVMs) The CASP7 competition compared domain prediction tools et al. compared modeling software Nayeem +-bound structures using MODELLER and alignments to an NAD+-bound template: C3bot1 (PDB 2A9K) +-binding patterns.We modeled NADab initio loop models. These elements are reassembled and refined to produce the final model. When I-TASSER failed to produce a result matching the sliding-window fold recognition data (four cases), we selected suitable templates from this fold recognition data. We docked the templates using HADDOCK 6). We refined loops automatically after model building and ranked them by Discrete Optimized Protein Energy (DOPE) statistical potentials to find the top model. We visualized the models using PyMol.We modeled full-length ADPRT structures using I-TASSER, the top-ranked program for fully-automated structure prediction in CASP7. It combines folds and supersecondary structures selected from the PDB with et al. reviewed model quality assessment programs (MQAPs) Laskowski + binding using crystal structures solved with NAD+ in the active site: C3bot1 (PDB 2A9K) + binding. We used the 3dLOGO + binding interactions in the known ADPRTs to identify the equivalent residues in the new ADPRTs. We also analyzed our NAD+-bound models and compared the ADPRTs modeled directly against the NAD+-bound templates using Modeller We assessed NADN1 alleviated-strain model, first proposed by Tsuge et al., that many believe is widely relevant to the entire family + binding we used 3DLOGO We developed the ADPRT toxin reaction mechanism for the new toxins using the SSaccharomyces cerevisiae strain W303  on yeast-peptone-dextrose or synthetic dextrose (SD) drop-out medium. We performed the yeast growth-defective phenotypic test and quantified growth as previously described We cultured Text S1Supplementary discussion, figures, tables and data.(4.63 MB PDF)Click here for additional data file.