Indeed, through the analysis of all peptide-mediated interactions of known high-resolution three-dimensional (3D) structure, we found that the structure of the peptide may be as characteristic as the consensus motif, and help identify target peptides even though they do not match the established patterns. Our analyses of the structural features of known motifs reveal that they tend to have a particular stretched and elongated structure, unlike most other peptides of the same length. Accordingly, we have implemented a strategy based on a Support Vector Machine that uses this features, along with other structure-encoded information about binding interfaces, to search the set of protein interactions of known 3D structure and to identify unnoticed peptide-mediated interactions among them. We have also derived consensus patterns for these interactions, whenever enough information was available, and compared our results with established linear motif patterns and their binding domains. Finally, to cross-validate our identification strategy, we scanned interactome networks from four model organisms with our newly derived patterns to see if any of them occurred more often than expected. Indeed, we found significant over-representations for 64 domain-motif interactions, 46 of which had not been described before, involving over 6,000 interactions in total for which we could suggest the molecular details determining the binding.Many biological responses to intra- and extracellular stimuli are regulated through complex networks of transient protein interactions where a globular domain in one protein recognizes a linear peptide from another, creating a relatively small contact interface. These peptide stretches are often found in unstructured regions of proteins, and contain a consensus motif complementary to the interaction surface displayed by their binding partners. While most current methods for the  Protein-protein interactions are paramount in any aspect of the cellular life. Some proteins form large macromolecular complexes that execute core functionalities of the cell, while others transmit information in signalling networks to co-ordinate these processes. The latter type, of more transient nature, often occurs through the recognition of a small linear sequence motif in one protein by a specialized globular domain in the other. These peptide stretches often contain a consensus pattern complementary to the interaction surface displayed by their binding partners, and adopt a well-defined structure upon binding. Information that is currently available only from high-resolution three-dimensional (3D) structures, and that can be as characteristic as the consensus motif itself. In this manuscript, we present a strategy to identify novel domain-motif interactions (DMIs) among the set of protein complexes of known 3D structures, which provides information on the consensus motif and binding domain and also allows ready identification of the key interacting residues. A detailed knowledge of the interface is critical to plan further functional studies and for the development of interfering elements, be it drug-like compounds or novel engineered binding proteins or peptides. The small interfaces typical for DMIs make them interesting candidates for all these applications. Proteins are key players in all aspects of cellular life. They seldom act alone, but rather in combination with other molecules. Some proteins form parts of large macromolecular complexes that execute core functionalities of the cell, while others transmit information in signalling networks to co-ordinate these processes. To disentangle the complex network of protein interactions, both complex membership and binary interactions are currently being studied in large-scale experiments in several model organisms de novo discovery of motifs from sets of sequences assumed to share a feature that e.g. explains interaction with a common partner de novo procedures for motif discovery flanking regions or context, increases the sensitivity of these methods The peptides involved in DMIs are characterised by a consensus motif with specific conserved residues that are recognised by the binding domain. Some positions of such motifs are restricted to particular amino acids, while others may allow a set of similar residues, or even arbitrary ones. Consensus motifs are often given as regular expressions. For example, the Src-homology-3 (SH3) domain binds proline-rich peptides, and several variants of the PxxP (‘x’ or ‘.’ denote arbitrary positions) pattern have been observed, including [RKY]xxPxxP  and PxxPx[KR] (class II) in vivoet al.Despite the small size of their binding interfaces, domain-motif interactions are known to be highly specific de novo discovery of motifs exploit the fact that they tend to occur in disordered regions, our work here focuses on another observation: upon binding to the domain, motifs adopt a well-defined structure  peptides in 41,224 structures were accepted by the SVM. Next they were filtered for contacts to neighbouring domains in order to find putative domain-peptide interactions. We intended to identify all peptide-domain interactions, regardless of whether they appear within or between proteins. Therefore, for each candidate peptide that had been accepted by the SVM, we checked for contacts with domains in the vicinity, independent of whether they are part of the same protein or of another. We did not find enough contacts for 2,890,451 peptides  and, upon visual inspection, many did not seem to be functional. These were often cases in which the domain and putative peptide, while in contact, had no extended binding surface, but rather protruding side chains touching each other, such that it was not clear whether this contact was biological or whether it might be an artifact that arose e.g. due to crystallization or buffer conditions. Other instances arose because of domain definitions that did not perfectly match the structure, in other words, when the Pfam domain assignments did not fully cover the structural units (folds), so that remaining elements (single strands or helices) were identified as binding peptides. Therefore we used domains in the protein structure classification CATH, which are defined on 3D structures 2, which holds for over 90% of the known DMIs , and found that, since the clusters are broader and cover more instances, the number of interaction types with sufficient non-redundant information to derive significant patterns (see subsequent paragraph) dropped from 224 to 96.The topological clustering procedure we developed focuses on the residues forming the interface. We computed the fraction of shared peptide-binding residues between each pair of domains from a family, mapping corresponding residues via alignment to Pfam's HMM profile for that family see Methods.enerated . A sequeenerated . Then thFor each topological cluster with at least 3 non-redundant sequences, we attempted to derive a consensus motif using SLiMFinder For 152 of the interaction types, covering 111 domains, we found at least one significant motif, and for 96 of these, significant motifs were found for all topological clusters (interaction types). The 46 remaining domains did not yield any significant motif, although 3 or more non-redundant sequences were available for pattern derivation. As a curiosity, 36 of the interaction types with significant motifs (31 domains) involved helical peptides, and 20 (18 domains) required at least one modified residue in the pattern. In total, 5,316 interactions in 3D structures are covered by these 152 interaction types for which we could derive a significant motif, including 4,202 inter- and 1,114 intra-chain interactions, respectively. However, 16 clusters among 15 domains contained only intrachain interactions that, upon visual inspection, did not seem to be functional peptide-mediated interactions and were thus excluded. In addition, 8 putative DMI interaction types are always found between proteins that also have a domain-domain interface (DDI), which presumably is more reliable. Our assumption here is that if the DMI is functional, it should also occur independent of a DDI. Hence we modified our method not to accept clusters unless there are interchain instances, and DMI that appear without a DDI in the same protein pair. It is interesting to note that only 18 of the 94 domains for which we find significant patterns in the full dataset are described in ELM. It should be noted though that ELM does not always provide Pfam domain names and thus the overlap could be slightly larger and we are just not able to detect it.leave-one-domain-out fashion, i.e., we removed all peptides binding to a given domain from the training set, and tested the recovery of the corresponding interactions and the detection of its consensus motif using the resulting SVM. For example, in one instance we left out all SH2-binding peptides (the test set), then re-ran the full motif discovery pipeline as described above and finally tested how many of them were rediscovered by the SVM trained on the remaining, non-SH2-binding peptides (the training set). If a peptide overlapping in at least 3 positions with a test set peptide was accepted and a significant pattern for its domain and topological cluster could be derived using SLiMFinder, the case was classified as “positive”, otherwise (peptide not accepted or non-significant pattern) as “negative”. For known DMIs, we also tested whether the known consensus motif given in ELM scored significantly using SLiMSearch To assess the performance of our method in detecting peptide-mediated interactions and discarding non-functional peptides or interfaces in the PDB, we created a benchmark set of 631 known DMIs et al.After applying the described procedure, we could automatically rediscover 423 of the 631 known DMIs interaction types, which correspond to a sensitivity of 68%. In terms of domains, we correctly recovered cases for 20 out of the 30 domains, i.e., the domain-based sensitivity (67%), very similar to that based on individual cases. Our method did not accept any of the negative cases from the benchmark, indicating that it is highly specific. Analyses on the 208 known DMIs that we could not recover showed that almost half of them (42%) can be explained by the fact that they are covered by domains and thus never considered as peptide candidates by our method, while manually curated sets like ELM and our previous study R[SFYW].S.P, but the number is reduced to only 206 out of the 7215 (2.8%) of the proteins not interacting with any 14-3-3-containing protein. This corresponds to an enrichment factor of 2.57, which is statistically significant . We used interactome data for selected model organisms with relatively good coverage . We only tested motifs binding to domains of which a structure was solved in this species, to make sure that there is a functional occurrence of it in the species in question, so this validation procedure was limited to 64 domains among the 111 for which we suggest binding motifs. To avoid false positive hits, we required pattern matches be outside of globular domains, as do many sequence-based tools for motif detection To assess the validity of each motif derived by SLiMFinder and confirm that it could indeed occur in different protein interactions, we checked whether it was over-represented in proteins known to interact with a partner that contains the respective binding domain see Methods.S..FP; where the S is always found phosphorylated. The ELM annotation also describes a phosphorylated S, along with two similar patterns (.S..F and .S..F.K).Although it is the general trends that conform the main message of this work, it is always illustrative to look at some specific examples to understand the nature of our results. Among the novel DMIs identified by our approach there are two that, in the meantime, have been included in ELM, but were not listed when our training set was built. These can be considered “blind tests”, since neither the SVM nor other filtering parameters were selected using the information in these domains and peptides. One case is the BRCA1 C Terminus (BRCT) domain, which forms dimers that bind a phosphopeptide DE.F  while the ELM pattern is a much longer one: [ED].{0,2}[ED].{0,2}[EDQ].{0,1}[YF]$. ELM contains the additional information that the peptide is always C-terminal, indicated by the $ symbol. In our approach we do not try to establish whether a pattern occurs at one of the termini, because peptides in 3D structures are often truncated, so that what appears to be a terminus won't necessarily be one in vivo. In this case, however, neither the ELM CAP_GLY pattern nor the one we derived is significantly enriched in proteins interacting with those containing the domain.The second DMI from our results that has been added to ELM is cytoskeleton-associated proteins domain (CAP_GLY), which is involved in the regulation of microtubules L..I[AG]D.[ILV], with the large hydrophobic residues pointing into the binding groove. Two other, very similar motifs scored significantly (LR.I.D.[LV] and R.I[AG]D.[LV]); both also contain the large hydrophobic residues in the appropriate spacing pattern. Structurally, the peptide is always helical, so one might consider replacing the arbitrary positions (.) by anything but proline [ ^P], because of the helix-breaking properties of this aminoacid. This restriction is also found in other motifs, such as ligands of hormone receptors (Hormone_recep), another all-alpha protein that binds small helical peptide ligands A very interesting example is that of the Bcl-2 protein family, which is crucial in the regulation of apoptosis and has both pro-apoptotic and pro-survival members Finally, in contrast to the three examples above, we could not identify a significant motif for Clp protease (CLP_protease), although sufficient non-redundant sequences were available. Given that Clp proteases degrade peptides with little sequence specificity leave-one-domain-out benchmark on the 3D structures of known ELMs de novo motif discovery can provide such details for a novel DMI. Indeed, few other methods exploit the information encoded in 3D structures, although the importance of the 3D structure of motifs and their flanking regions for functional analysis has recently been highlighted et al.The identification and correct classification of domain-motif interactions is a key issue to understand the biophysical principles governing interactome networks, such as the relationship between protein-binding domains and the consensus motifs they recognize. Accordingly, we have presented a method to indentify unnoticed domain-motif interactions (DMIs) among high-resolution 3D structures, which not only provides information on the consensus motif and binding domain, but also allows ready identification of the key residues on both the motif and the domain side. Applying this methodology to all currently available 3D structures has revealed 152 DMIs, 127 of which have not been described previously. Moreover, 64 of the motifs have been found to be significantly enriched in proteins interacting with those containing the respective binding domain. In a A different issue with the sequence-based quest for motifs is that the “shared feature” may be too loosely defined. For example, there are many kinases that phosphorylate [ST] or Y residues, yet it would not be possible to derive a meaningful motif from the set of phosphorylated sequences alone. Our method instead focuses on the atomic interface between peptide and domain, which includes a concise definition of the environment in which the motif is bound. This, in turn, ensures that all peptides in one group for pattern derivation use the same interface, which is a rather strict definition of “shared feature”. An additional advantage is the direct possibility of visual inspection of the suggested DMIs. The main bottleneck is the availability of domain-peptide interactions in 3D structures in large enough numbers and with sufficient diversity to allow for the derivation of a consensus motif. However, if this information is available, the results are highly specific and contain a level of detail that cannot be provided by other techniques. Furthermore, our method successfully detected helical peptides or those acting through beta-sheet addition, which present difficult cases for the other 3D-structure-based method for DMI interface detection We chose strict thresholds on contacts and interface size to limit the occurrence of false positives, which are often problematic when dealing with so few key residues as in motif-mediated interactions. While our high precision shows the advantages of those strict thresholds, we do miss some true motifs as described in ELM, in particular due to the exclusion of regions assigned to Pfam domains, which is responsible for almost half of the true motifs we do not recover. Yet the inclusion of these regions would disproportionately increase the computation time as well as the risk of false positives, since it has been shown that motifs usually occur outside of domains in vivo and which do not in vivo specificity of any given protein. Nevertheless, consensus motifs can be very helpful in studying commonalities among and differences between peptide-binding domains. A side effect of the large-scale derivation of motifs is that peptides are always given together with their binding domain in a way that can easily be transferred to other sorts of data, which is not always the case for manually curated DMIs. For example, by matching the pattern in one sequence and the binding domain in another may explain the mechanism behind some of the many protein-protein interactions which are currently being discovered in high-throughput interaction discovery experiments. This, in turn, may make them amenable to tinkering with this part of the network, either by designed peptides that might have fine-tuned affinity and specificity for the binding domain It should also be noted that our final list of DMIs  only incThe information we exploit here to identify novel DMIs among the set of interactions of known 3D structure (i.e. well-defined structure upon binding), is of different nature than the one used by more traditional sequence and disorder-based methods applied by many other tools The control data is based on the Structural Classification of Proteins (SCOP) Elongation or “length” of a peptide is the distance between the Linearity is computed by constructing a line through the first and last secondary structure assignments to peptides from the set of known DMI and from the SCOP background. The assignment is done on a single protein, after removing other chains in the structure but before extraction of the peptides, as DSSP does not always perform well on small fragments. Each peptide is assigned the DSSP class most frequently found among its residues. Note that because SVMs work on numerical data, including a more detailed description of the secondary structure of a peptide, such as the order of DSSP classifications, would require a much more complex model. We also used accessibility data computed by DSSP, and assigned the average accessibility of its residues to each peptide.The We used the implementation “SVM-light” To form a DMI, each peptide accepted by the SVM was also required to interact with a nearby domain, which may be part of the same protein or of another. Linear motifs usually form one connected interface with their binding domain, thus we excluded peptides in which there was a region of more than 4 residues that did not contact the domain, as well as those cases in which less than 60% of the peptide residues made contact with the domain.N, where N is the number of domains involved in the interaction. If domains do not contribute roughly equally to an interaction or, more exactly, if any domain contributes less than N/0.5, they are removed in a filtering step. Since this changes the number of domains involved in the putative DMI and hence the minimum interface size, we repeated the filtering until each domain-peptide interface contributed appropriately for its stoichiometry and no domain was removed any more in the given step; in other words, we repeated the procedure until convergence. Note that, while all domain-peptide-interfaces have to contribute at least 0.5/N, they do not necessarily contribute equally.We used NACCESS We first aligned all sequences for a given domain to the corresponding Pfam HMM profile Each continuous part of a protein that was covered by one or more peptides was designated as a peptide-containing region. These regions are non-overlapping by definition, and they represent parts of the protein that contain one or more peptides structurally similar to those found in known DMIs. The main motivation for this was that each pattern match in a given structure should only be counted once for “support” of that pattern, independent of how many accepted peptides include it cf. .ij, where i and j are candidate domain-peptide interactions. Like for the topological clustering, we applied complete linkage hierarchical clustering For the sequence-based clustering, domain and peptide alignments are computed individually, and then the pairwise similarities are combined into one score, which is then used for clustering. For domains, all sequences of a given family are aligned to the Pfam HMM profile, and the sequence identity is computed from this alignment, yielding a pairwise domain sequence identity score We used SLiMFinder We created interactomes for human, fly, worm and yeast by integrating protein-protein interaction data from the databases MINT, IntAct and HPRD p is the total number of proteins in the interactome. The p-value was computed using Fisher's exact test on the contingency table, as implemented in R To calculate motif enrichments in the interactome networks, we assigned Pfam domains, via HMM profiles, to all proteins in the respective interactomes, and tested for motif hits by regular expression pattern matching, only considering regions outside domains as described above. We then created a contingency table for each motif and species stating how many proteins contained at least one motif match, and how many interact with a protein containing the motif's binding domain. The enrichment factor was computed as Figure S1Linearity and Elongation of known motifs in comparison to a background sampling. (a) Linearity, (b) Elongation, (c) Linearity for 7-residue-peptides, split by DSSP-classification, (d) Elongation for 7-residue-peptides, split by DSSP-classification. The distribution of values varies greatly across and within classes of secondary structure.(0.14 MB EPS)Click here for additional data file.Figure S2(a) Interface size for known domain-peptide interactions. (b) Ratio of the interface between domain and peptide to the full protein-protein interface for the known DMI. Both are computed as described in the (0.77 MB EPS)Click here for additional data file.Table S1All DMI candidates derived from high-resolution 3D structures, specifying the binding domain, topology or interaction type ID, the consensus motif, enrichment and p-value in the interactome cross-validation, if applicable, and the ELM pattern and name, if available. For each interaction type, up to 3 top-ranked motifs are provided.(0.05 MB XLS)Click here for additional data file.


PDZ domains are abundant protein interaction modules that often recognize short amino acid motifs at the C-termini of target proteins. They regulate multiple biological processes such as transport, ion channel signaling, and other signal transduction systems. This review discusses the structural characterization of PDZ domains and the use of recently emerging technologies such as proteomic arrays and peptide libraries to study the binding properties of PDZ-mediated interactions. Regulatory mechanisms responsible for PDZ-mediated interactions, such as phosphorylation in the PDZ ligands or PDZ domains, are also discussed. A better understanding of PDZ protein-protein interaction networks and regulatory mechanisms will improve our knowledge of many cellular and biological processes. Diverse biological activities are regulated through the dynamic interactions of modular protein domains  and their corresponding binding partners . ElucidaPDZ domains are abundant protein-protein interaction modules found in various species Figure  3-6]. I-6. I3-6]PDZ domains are small and often modular entities consisting of 5 or 6 β-stranded and 2 or 3 α-helical structures . PDZ domAt present, more than 200 structures of PDZ domains - either the PDZ domains alone, their complexes with binding partners, or PDZ-PDZ dimers - have been determined by NMR and X-ray crystallography . Small-aPDZ domains are usually 80-100 amino acid residues long and adopt a similar topology: structural studies have revealed that canonical PDZ domains are usually composed of 6 β-strands (βA ~ βF), a short α-helix (αA) and a long α-helix (αB) Figure  21,46,4,446,47. Similar to canonical PDZ domains, the HtrA family, including HtrA (or DegP), DegS, and DegQ, adopt a PDZ-like fold consisting of 5 β-strands (β1-β5) capped by 2 α-helices (α2 and α3) and also 2 short β-strands at the N and C termini (βN and βC). The well-defined α-helix (α1) is formed in the region between the β1 and β2 loop of the PDZ-like domain Figure  17,41].,41.17,41in vitro remains unclear, because there is no evidence that the full length Shank1 and GRIP1 proteins form functional dimers in vivo.Although most PDZ domains that have been studied in isolation are found to be monomers ,20,47-49In vivo, the presence of the ZO-1 dimer or higher order oligomers was confirmed by the co-immunoprecipitation of fluorescent-labeled ZO-1 (GFP-ZO-1) and endogenous ZO-1  in PDZ-like domains is located before the β1 strand Figure , and theS) or extended (ε) conformation regions -X-S-D-V); 1b (Ω-[R/K]-E-T-[S/T/R/K]-ϕ); 1c (ϕ-ϕ-E-T-X-L); 1d (E-T-X-V); 1e (T-W-ψ); 1f (Ω-Ω-T-W-ψ); 1g ; 2b(W-X-Ω-D-ψ); 2c (W-Ω-ϕ-D-ψ); 2d (ϕ-ϕ-X-[E/D]-ϕ-ϕ-ϕ); 2e (ϕ-ϕ-ϕ-ϕ); 2f ([D/E]-ϕ-Ω-ϕ); 3a (Wx[S/T]-D-W-ψ); 4a (Ω-ϕ-G-W-F); ϕ, hydrophobic ; Ω, aromatic ; ψ, aliphatic ; and X, nonspecific ..2-adreneSome PDZ-containing proteins have a PDZ-binding motif at their C-terminal tail. The binding site of the PDZ domain in these proteins can be occupied by their own C-terminal sequences, thereby inhibiting the binding of the PDZ ligand ,136-141.et al. (2009) used small-angle neutron scattering (SANS) to demonstrate that the C-terminal EB region binds to ezrin, which induces conformational changes in two region of NHERF-1: the region linking PDZ2 and the C-terminal EB region and also that linking the PDZ1 and PDZ2 domains ,13645,13 domains . The aut domains .X11α protein, involved in regulating neuronal signaling, trafficking and plasticity[The lasticity, containlasticity. The autTamalin, which is also called the GRP1-associated protein [et al. (2007) have reported the crystal structure of the autoinhibitory PDZ domain of tamalin [ protein , contain protein -146. Sug tamalin . In the  tamalin .et al. (2007) investigated the binding preferences of the 5 PDZ domains in protein tyrosine phosphatase PTP-BL by using a random C-terminal peptide lambda phage display library [Recent studies provide evidence that protein-protein interactions influence the changes in the time scale and amplitude of protein motion within a domain as well as long-range coupled motions between protein domains ,147-149. library . They foet al. (2009) reported that the binding of ezrin to NHERF1 increases the binding capabilities of both PDZ domains -related Protein 4; CaMKII: Ca2+/calmodulin-dependent protein kinase II; HtrA: High-Temperature Requirement A; SH3: Src homology 3; GK: guanylate kinase-like; Y2H: yeast two-hybrid.AR: Alanine Rich region; LZ: Leucine-Zipper; GPCR: G Protein Coupled Receptor; NHERF-1: Na+/H+ Exchanger Regulatory Factor; β2-AR: βThe authors declare that they have no competing interests.Both authors wrote the manuscript and approved its final version.


To understand how these receptors have evolved different physiological requirements we have investigated the molecular evolution of glutamate gated receptors and ion channels, in particular the N-methyl-D-aspartate (NMDA) receptor, which is essential for higher cognitive function. Studies of rodent NMDA receptors show that the C-terminal intracellular domain forms a signalling complex with enzymes and scaffold proteins, which is important for neuronal and behavioural plasticityGlutamate gated postsynaptic receptors in the central nervous system (CNS) are essential for environmentally stimulated behaviours including learning and memory in both invertebrates and vertebrates. Though their genetics, biochemistry, physiology, and role in behaviour have been intensely studied The vertebrate NMDA receptor was found to have subunits with C-terminal domains up to 500 amino acids longer than invertebrates. This extension was specific to the NR2 subunit and occurred before the duplication and subsequent divergence of NR2 in the vertebrate lineage. The shorter invertebrate C-terminus lacked vertebrate protein interaction motifs involved with forming a signaling complex although the terminal PDZ interaction domain was conserved. The vertebrate NR2 C-terminal domain was predicted to be intrinsically disordered but with a conserved secondary structure.in silico methods we find that evolution has shaped the NMDA receptor C-terminus into an unstructured but modular intracellular domain that parallels the expansion in complexity of an NMDA receptor signalling complex in the vertebrate lineage. We propose the NR2 C-terminus has evolved to be a natively unstructured yet flexible hub organising postsynaptic signalling. The evolution of the NR2 C-terminus and its associated signalling complex may contribute to species differences in behaviour and in particular cognitive function.We highlight an evolutionary adaptation specific to vertebrate NMDA receptor NR2 subunits. Using  One of the most striking evolutionary adaptations of mammals is their capacity for learning and memory . Such hiN-methyl-D-aspartate (NMDA) receptor is a class of glutamate gated transmembrane ion channel that functions at the postsynaptic membrane of excitatory synapses . Su. Su28]. We used bioinformatic tools to examine the evolutionary origins of the NR2 intracellular domain. Though it has previously been observed that the four NR2 paralogues found in vertebrates differ in size due to variation in the length of their C-terminal domains, the overall level of conservation between them has not been addressed . We founBased on multiple species alignments of NR2 orthologues we show that vertebrate NR2s are consistently longer than invertebrate NR2s, and that this is due to difference in C-terminal domain length. We next asked if this vertebrate specific intracellular extension was a feature of the NR1 subunits or other glutamate receptors including ionotropic AMPA  and metabotropic receptors . The C-termini of all the other mentioned glutamate receptors do not exhibit comparable extensions between invertebrates and vertebrates. Thus the vertebrate/invertebrate contrast is specific to NR2. All of these glutamate receptor subtypes are involved with synaptic plasticity and are linked via C-terminal domains to intracellular adaptor proteins.D. melanogaster NR2 C-terminus bears some similarity (42%) to the terminal exon of mouse NR2B suggesting that NR2 may have been internally truncated due to intron gain in a common ancestor of invertebrates. Alternatively the similarity seen in the D. melanogaster intron may imply a vertebrate specific elongation perhaps due to the intron becoming coding, but this may be less likely due to the presence of in-frame stop codons in an intron. The ancestral state of NR2 may be revealed by further genome sequencing of more diverse and complex invertebrates. If Lophotrochozoans were found with the elongated NR2 version, then a C-terminal truncation in the Ecdysozoan clade would be the most parsimonious explanation . ,35. 8,35The expansion of the NR2 gene family has been utilised in the mammalian brain to produce distinct functional roles. Studies of synaptic plasticity at the CA3-CA1 synapses of the hippocampus, using mice lacking these subunits or with subunit specific pharmacological antagonists, reveal differences in synaptic plasticity . As ment+, H+, and glutamate [The analysis of the C-terminus structure suggests a dynamic switching function of NR2 where the domain alternates between unfolded and folded states, and that this modulates interactions with other proteins. The observed high composition of both serine and proline is a characteristic of intrinsically disordered proteins , which alutamate , and havlutamate . By bindThis 'structural switch' model implies an evolutionary selection on the unfolded domain. An alternative explanation is that there is no structural constraint and therefore nucleotides are free to be substituted rapidly by neutral evolution without any structural implications. We consider this scenario unlikely for the following reasons. Amino acid sequences for the C-terminal domain in each particular NR2 paralogues are well conserved between vertebrates, implying that since divergence of the NR2 paralogues each subunit has acquired structures that underwent purifying selection in vertebrates. Furthermore, all (~10) known phosphorylation sites and protein-protein interaction sites when mapped onto sequence alignments for each of NR2A and NR2B were found to be conserved throughout mammals. These interaction sites would require a certain extent of structural constraint, and so their conservation implies a structural framework to the NR2 C-terminal domain. Finally there is no explanation why the predicted secondary structures for NR2A and NR2B C-terminal domains have remained structurally similar since they have diverged, given that they exhibit such low similarity at the primary sequence level. The above arguments taken together suggest that a functional adaptation occurred in vertebrate evolution that accounts for NR2 C-terminal structure. Within vertebrate evolution there is evidence of further evolutionary adaptation of NR2A, which was reported to be under significant positive selection in primates when compared to rodents .in vivo.To test these evolutionary models it will be necessary to characterise NMDA receptor associated proteins of an invertebrate model in a comparable manner to that achieved in mice ,35. DetaNMDA receptors display a vertebrate specific elongation of the intracellular C-terminus of the NR2 subunit. This extension is unique among ionotropic glutamate receptors to the NMDA receptor NR2 subunits. Significant diversity in the NR2 C-terminus exists at the sequence level between the four NR2 paralogues in vertebrates, though each individual paralogue is highly conserved amongst non-teleost vertebrates. In contrast to the extracellular and transmembrane domains, each NR2 C-terminus is predicted to be unfolded in its native state. NR2A and NR2B C-termini are predicted to be highly similar at the level of secondary structure though they are a poorly conserved at the primary sequence level. We postulate that evolution of this vertebrate specific C-terminal domain of NR2 has resulted in an unfolded but conserved modular structure that may have contributed to the evolution and organisation of postsynaptic signalling complexes in vertebrates.Schistosoma mansoni nucleotide sequences were obtained from the Wellcome Trust Sanger Institute Pathogen Sequencing Unit's datababse [We obtained protein sequences for all genes from Ensembl Version 36. Homologues of mouse sequences were obtained using Ensembl BioMart . Schistoatababse ,53. Multatababse . Alignmeatababse . Where matababse . For comatababse ,58.To generate Nucleotide alignments to find the best region of similarity, the Smith-Waterman algorithm Water with an EBLOSUM62 matrix was applied . PhlyogeProtein interaction data for each NR2 subunit in mammals were obtained from the Human Protein Reference Database .The nucleotide sequence of the intracellular tail of each mouse NR2 subunit were searched against non-redundant nucleotide collection, whole genome shotgun reads, and expression sequence tag databases in Genbank by BLASTn, tBLASTn, and discontiguous megablast similarity searches, with low complexity masking . To searProtein folding predictions were made using the FoldIndex graphic web server . PredictConceived and designed study: TJR, RDE, SGNG, NHK. Performed the analysis: TJR, RDE, NHK. Wrote the paper: TJR, RDE, SGNG, NHK. All authors read and approved of the final manuscript.Smith-Waterman Alignment of Mouse NR2B C-terminal Exon Nucleotide Sequence against D. melanogaster C-terminal 2857 Nucleotide Intron. Mouse exon sequence named 'NR2B', D. melanogaster intron sequence named 'intron'.Click here for fileFoldIndex Comparison of NR2 C-terminal Domain Folding Propensity. Plot of AA sequence of N-terminus (N) to C-terminus (C) of mouse NR2C and NR2D against probability of folding. Green areas are predicted to be intrinsically folded, red areas are predicted to be intrinsically unfolded.Click here for filePSIPRED Secondary Structure Predictions of Mouse NR2C & NR2D C-terminal Domains. Green barrels represent alpha helices while yellow arrows signify beta sheets. Prediction confidence represented by blue bars.Click here for fileUnrooted Phylogenetic Tree of NR2 Subunits. Human (Hs), mouse (Mm), rat (Rm), zebrafish (Dr), ciona (Ci), Drosophila (Dm), C. elegans (Ce). Bootstrap values are shown at branch points.Click here for file


Protein phosphorylation is responsible for a large portion of the regulatory functions of eukaryotic cells. Although the list of sequenced genomes of filamentous fungi has grown rapidly, the kinomes of recently sequenced species have not yet been studied in detail. The objective of this study is to apply a comparative analysis of the kinase distribution in different fungal phyla, and to explore its relevance to understanding the evolution of fungi and their taxonomic classification. We have analyzed in detail 12 subgroups of kinases and their distribution over 30 species, as well as their potential use as a classifier for members of the fungal kingdom.Laccaria bicolor. This unexpected finding underscores the need to continue to sequence fungal genomes, since many species or lineage-specific properties may remain to be discovered. Furthermore, we found that the domain organization significantly varies between the fungal species. Our results suggest that protein kinases and their functional domains strongly reflect fungal taxonomy.Our findings show that despite the similarity of the kinase distribution in all fungi, their domain distributions and kinome density can potentially be used to classify them and give insight into their evolutionary origin. In general, we found that the overall representation of kinase groups is similar across fungal genomes, the only exception being a large number of tyrosine kinase-like (TKL) kinases predicted in Comparison of the predicted kinomes of sequenced fungi suggests essential signaling functions common to all species, but also specific adaptations of the signal transduction networks to particular species. Neurospora and Aspergillus have figured prominently in cell biology and genetics [Filamentous fungi detect and respond to a variety of signals. As saprophytes or parasites, fungi need to monitor the nutrient status, presence of a host and host defenses, and avoid or respond to osmotic or oxidative stress, light and other environmental variables . Althouggenetics .Protein kinases have roles in every aspect of regulation and signal transduction, and provide new targets for drug development . Most euBased on the kinomer database , we deveWe have studied the distribution, domain content and kinase density among 30 species representing the Dikarya or higher fungi, including two phyla  group seems to be much more compact in terms of kinase number. This indicates, perhaps, a "tighter" evolution of this group with respect to their signaling pathways. For those fungi that have not undergone kinome duplication, the number of kinases found for each group is remarkably similar  in fungi and our results support this. Nevertheless, in our data the Basidiomycete Laccaria bicolor shows, exceptionally, a huge number of TKL protein kinase genes. Pending direct experimental evidence, of course, this suggests a large deviation of this fungus kinome towards the TKL group.One striking exception to the overall similarity in the  distribution of kinases among the major subfamilies is the TKL kinase family (25) predicted for Neurospora circadian clock, for example, [An expanded and/or diverse kinome may provide a more flexible signaling network. This implies that overall parameters like kinome size as compared to proteome size might not always follow classical (and molecular) taxonomy. A striking example comes from recent work on the genomes of myxobacteria, which are prokaryotic, but contain an unexpectedly high number of eukaryotic-like (serine/threonine and tyrosine) protein kinases . The proexample, ,17). Fig-4) confirmed that the Ascomycete and Hemiascomycete fungi could be successfully classified using their kinome density values. This difference in kinome density might represent the result of evolutionary pressure toward diversification of signal transduction pathways. Although this seems logical, there is really no obvious correlation between kinome density or diversity and the "lifestyle"  of the sequenced species that we have studied. S. pombe is the only exception within the Ascomycetes, having the largest kinome density of all fungi studied. S. pombe though is the only species belonging to the Taphrinomycotina subphylum within the Ascomycetes, which represents its own subphylum branch. Thus we cannot anticipate whether this is a unique difference or a trend of its subphylum  appears almost in every fungal species, Pkinase_Tyr and Alpha_kinase domains are much more rare (see below). Pkinase_Tyr is highly represented only in database .Figure Of the first 10 most common domains in the fungi studied here, 9 are also present in the human kinome, reflecting a highly conserved kinome functional milieu: Pkinase_C , PI3_PI4_kinase , FATC and FAT (Accessory domain for PI34K domains), FHA , HATPase_c , C1_1 (Phospholipid binding) and PBD (GTPase interaction). These functional domains have very broad and general functions, thus explaining why they appear in all types of fungi and also in the human kinome. The exception is BCDHK_Adom3; this domain is involved in the regulation of the dehydrogenase complex that breaks down branched-chain amino-acids and it is similar to the HATPase_c family . InteresWe believe that the kinome domain distribution, both in type and number, should be indicative of functional and evolutionary differences between fungi. In the next section, we show that this variability is enough to differentiate among different fungal phyla and subphyla.Taxonomic classification is not always a straightforward task. In this work we tested several criteria for clustering all 30 fungi studied here, based on the number and type of kinases and additional domains (see Methods). Following that, we compared the results with fungal taxonomy ,21. We fM. globosa and U. maydis both belong to the subphylum Ustilaginomycotina and are fairly close to each other (dots 20 and 22). Likewise, dots 18, 19 and 21, which represent members of the Agaricomycotina: C. neoformans, P. chrysosporium and Laccaria bicolor, are closely located. Assuming that S. pombe forms its own singleton cluster, 26 members of the fungi family in this study are clustered by the PCA into one of four groups according to their domain distribution. Four species fall outside these clusters. Of those, two do not reside in any group, and two are mixed: among the ascomycetes, one member of the Pezizomycota , clusters differently from the Pezizomycotina and Saccharomycotina groups. Indeed, when looking closely at the domain content, there are several examples in which S. pombe is quite different from its ascomycete relatives. For example, it has a much lower density of HATPase_c domains, a higher percentage of HEAT domains and a higher percentage of the Rapamycin_bind domains. It would be difficult to conclude whether this reflects any aspect of fission yeast lifestyle, or is simply a consequence of evolutionary distance among the species. The PCA results indicate clearly that the domain distribution includes different evolutionary information at the level of the subphyla.As illustrated in Figure A. nidulans has unique domains like CNH, FAD-oxidase_C, RCC1 and many more, making a total of 16 unique accessory domains, which suggests that A. nidulans has possibly acquired a diverse kinase-related functionality. Currently, based on its physiology, there is no obvious clue as to why this should be so, but since A. nidulans is one of the best-studied model genetic species there may be a good basis to understand this result in future studies.While the classification, based on the most common functional domains from the kinome, is very similar to the classical taxonomy it would also be interesting to try and understand the functional and evolutionary implications of the rare domains appearing only in a certain phyla or fungal species. For example, the ascomycete C. neoformans). Many HEAT repeat-containing proteins are involved in intracellular transport processes. Although we cannot fully understand how these differences directly impact the function of the different species, there is a clear correlation between the domain distributions and the taxonomic classification.From the most common domain distribution, there are some clear cases of variations between the subgroups. For example the POLO_box domain appears in all Basidiomycota and in the Saccharomycotina, while is quite rare within the filamentous Ascomycota. Polo boxes appear to mediate interaction with multiple proteins through protein-protein interactions. The HEAT domain, common in both Ascomycete subphyla Pezizomycotina and Saccharomycotina, is extremely rare in Basidiomycetes , harmful (Aspergillus nidulans), or pathogenic  members of this group, which have drawn much attention over the years. Schizosaccharomyces pombe has a very high kinome density much similar to the Saccharomycotina group. The predicted proteome of Laccaria bicolor has an extraordinary number of TKL kinases; further work can determine whether this is an anomaly, or a more general trait found in mycorrhizal symbionts [e.g. G-protein coupled receptors) in order to study their evolution and variability within each fungi phylum. These data can also be used to guide experimental work to elucidate the function of individual protein kinases and the signal transduction pathways they function in.The overall distribution of protein kinases within very different fungal phyla and subphyla seems to be very similar. The overall kinome density is in good agreement with taxonomy. The distribution of additional domains, which could have functional implications, does differ significantly between species, and seems able to provide a functional classification that overlaps with taxonomical classification. Although generally the classical phyla classification correlates with the kinome density and domain distribution, there are exceptions. Basidiomycota do not cluster by kinase number, but they have a similar kinome to proteome ratio. Ascomycetes are well clustered by all criteria, with two exceptions: ymbionts . FinallyWe have designed and implemented an automatic pipeline Figure  to extraIn order to extract the kinome we fed the HMMer software (version 2.3.2 ) with pr2 of the number of sequences in the target database is likely to be a true homologue. For our fungi proteomes, this rule-of-thumb number is on the order of 20 bits. In any case e-value and bit score should be strongly correlated, true homologues will have both a good bit score and a good E-value [We chose a fixed bit score cutoff, since it provides us with a unified criterion for all fungi, so we can compare the numbers based on the same scale. The bit score reflects whether the sequence is a good match to the HMM model. A score above log E-value .Once the kinase groups are populated, the pipeline is designed to extract functional and phylogenetic information from the list of putative kinases. The pipeline procedure and subsequent analysis (see below) is summarized in Figure Homology search - The pipeline runs Blastp   ,27. The .7 ,27. TThe pipeline is written in Perl scripting language and was tested on Fedora and Ubuntu operating systems.http://www.R-project.org, 2009).We classified the 30 fungi based on the percentage of each domain type found in each species, limiting the data to those domains present in at least half of the fungi  and the wilcox.test function with its non paired mode.The Wilcoxon-Mann-Whitney test was performed on the kinome density values of our three phylogenic groups using GNU R software .Click here for filePipeline for kinome analysis. This file contains the pipeline software and documentation.Click here for fileAspergillus nidulans kinomePhylogenetic tree of the . Taxa names are composed of the predicted kinase group followed by an underscore and the protein code as it appears in the original proteome. Simulated bootstrapped values are also shown.Click here for fileDomain percentage data used as input for the PCA analysis. The first column lists the species names, abbreviated according to Table Click here for file


The membrane scission event that separates nascent enveloped virions from host cell membranes often requires the ESCRT pathway, which can be engaged through the action of peptide motifs, termed late (L-) domains, in viral proteins. Viral PTAP and YPDL-like L-domains bind directly to the ESCRT-I and ALIX components of the ESCRT pathway, while PPxY motifs bind Nedd4-like, HECT-domain containing, ubiquitin ligases (e.g. WWP1). It has been unclear precisely how ubiquitin ligase recruitment ultimately leads to particle release. Here, using a lysine-free viral Gag protein derived from the prototypic foamy virus (PFV), where attachment of ubiquitin to Gag can be controlled, we show that several different HECT domains can replace the WWP1 HECT domain in chimeric ubiquitin ligases and drive budding. Moreover, artificial recruitment of isolated HECT domains to Gag is sufficient to stimulate budding. Conversely, the HECT domain becomes dispensable if the other domains of WWP1 are directly fused to an ESCRT-1 protein. In each case where budding is driven by a HECT domain, its catalytic activity is essential, but Gag ubiquitination is dispensable, suggesting that ubiquitin ligation to trans-acting proteins drives budding. Paradoxically, however, we also demonstrate that direct fusion of a ubiquitin moiety to the C-terminus of PFV Gag can also promote budding, suggesting that ubiquitination of Gag can substitute for ubiquitination of trans-acting proteins. Depletion of Tsg101 and ALIX inhibits budding that is dependent on ubiquitin that is fused to Gag, or ligated to trans-acting proteins through the action of a PPxY motif. These studies underscore the flexibility in the ways that the ESCRT pathway can be engaged, and suggest a model in which the identity of the protein to which ubiquitin is attached is not critical for subsequent recruitment of ubiquitin-binding components of the ESCRT pathway and viral budding to proceed. The release of an enveloped virus particle from an infected cell requires the separation of the viral and cell membranes. Many enveloped viruses accomplish this by parasitizing a set of cellular proteins, termed the ESCRT pathway, that normally separates cellular membranes from each other. In some cases, viral structural proteins encode peptides motifs that bind directly to, and thereby recruit, the ESCRT machinery. Alternatively, viruses can recruit enzymes, termed ubiquitin ligases, that bind to other proteins, and catalyze the addition of ubiquitin to them. It has, heretofore, been somewhat unclear precisely how the recruitment of ubiquitin ligases leads to the engagement of the ESCRT machinery. We show that the simple recruitment of a fragment of a ubiquitin ligase that is responsible for the addition of ubiquitin to other proteins is sufficient to drive virus particle release, even when it is not possible to attach ubiquitin to viral proteins. Paradoxically, we also found that simple attachment of ubiquitin to the same viral protein can also drive particle release. These results show that there is flexibility in the ways in which the ESCRT machinery can be recruited and how ubiquitin can be co-opted to enable this. Ordinarily, the ESCRT pathway induces topologically equivalent cellular membrane scission events including the biogenesis of multivesicular bodies (MVBs) The membrane scission event that separates the lipid membrane of nascent enveloped virions from host cell membranes is, in many cases, an orchestrated event requiring the participation of the class E vacuolar protein sorting (VPS), or The ESCRT machinery is composed of ∼25 proteins, many of which participate in the formation of several multiprotein complexes, known as ESCRT-0, -I, -II, -III In contrast, it has remained somewhat unclear how PPxY motifs access the scission machinery. Overexpression of certain HECT ubiquitin ligases that bind directly to PPxY or other motifs can markedly stimulate budding, and the catalytic activity of the HECT domain is essential for this activity Other observations suggest that PPxY and ubiquitin ligase-dependent budding may involve mechanisms other than direct Gag ubiquitination. In particular, overexpression of wild-type WWP1 stimulates PPxY-dependent particle production by a lysine-free Gag protein In this study we investigated the role of PPxY motifs, HECT ubiquitin ligase domains and ubiquitin in viral budding, using a lysine-free viral protein from the prototypic foamy virus (PFV), in which the attachment of ubiquitin to Gag can be rather precisely controlled. We show that the catalytic activity of a variety of HECT domains, targeted to a PPxY motif in assembling particles via a common C2/WW domain fragment of WWP1, is essential for their ability to promote PPxY-dependent VLP release. In each case, however, Gag ubiquitination is dispensable for their activity. Rather, the ability of the chimeric ubiquitin ligases to promote budding correlated broadly, albeit imperfectly, with their ability to catalyze autoubiquitination, Moreover, we show that artificial recruitment of an isolated HECT domain can also stimulate budding, while a HECT domain becomes dispensable for PPxY motif dependent budding if the C2/WW domains of WWP1 are directly linked to the C-terminal domain of Tsg101, an ESCRT-I subunit. Finally, we demonstrate that direct fusion of a single ubiquitin moiety to the C-terminus of PFV Gag is also capable of promoting budding, in a manner that recapitulates the ESCRT protein requirement for budding induced by PPxY-dependent ubiquitin ligase recruitment in the absence of ubiquitin acceptors in Gag. These results support a model in which PPxY motif-induced HECT ubiquitin ligase recruitment leads to the deposition of ubiquitin at or near the site of viral budding. However, the identity of the protein to which ubiquitin is attached, be it Gag or a bystander protein, perhaps including the HECT ubiquitin ligase itself, does not appear to be critical in order for subsequent recruitment of ubiquitin-binding class E VPS proteins and viral budding to proceed.To ascertain what properties of HECT domains are important for stimulation of virus particle release, we compared the properties of a panel of HECT domains. Nine members of the Nedd4-like HECT ubiquitin ligase family have been described in humans and these have the same domain organization as a single prototype member of this family in yeast, namely Rsp5  stimulated PPxY-dependent budding of lysine-free Lck-Gag-PY . ConversTo assess the relative catalytic activities of the chimeric HECT ligases, and assess whether this correlated with their differential ability to promote budding, we compared their abilities to carry out autoubiquitination and to ubiquitinate a Gag substrate encoding three lysine residues in close proximity to a PPxY late domain  found in the Nedd4-like family of proteins was sufficient to stimulate particle budding. To accomplish this, we constructed hybrid L-domain cofactors in which the essential domains were split and linked to putatively complementing domains in another L-domain cofactor . SpecifiIn a reciprocal experiment, we asked whether the PPxY motif and the C2/WW domains of WWP1 could be functionally replaced in the context of HECT domain/ubiquitin dependent budding. In other words, we determined whether recruitment of a HECT domain is sufficient to stimulate particle release, in the absence of the other protein domains (C2 and WW) to which it would ordinarily be linked. Specifically, we attempted to redirect P(T/S)AP-dependent particle production through a HECT domain-dependent pathway, by constructing chimeric proteins, termed Tsg-WWP1, Tsg-Itch and Tsg-Nedd4L, that contained the N-terminal UEV domain (residues 1–157) of Tsg101 linked to one of the three respective HECT domains . To testOverexpression of Tsg-WWP1, Tsg-Itch or Tsg-Nedd4L, respectively) resulted in clear stimulation of Lck-Gag(ASAP) budding . Tsg101-Overall, the experiments in The aforementioned experiments demonstrated that the requirement for a catalytically active HECT domain could be obviated by direct recruitment of ESCRT-I to a viral protein (Lck-Gag-PY) whose budding would normally be dependent on such recruitment. We next asked whether the requirement for HECT domain recruitment could similarly be obviated, in the context of a nearly identical viral protein, by simply depositing ubiquitin at the site of particle assembly, in the absence of ubiquitin ligase recruitment. To mimic the deposition of ubiquitin at sites of virion assembly, in the absence of ubiquitin ligase recruitment, we expressed an Lck-Gag protein, lacking L-domains, with a single ubiquitin appended at its C-terminus  generated extracellular particles while those expressing the unfused, L-domain deficient counterpart Lck-Gag(L-) protein did not . DirectlNext we analyzed the effect of combining L-domains and ubiquitin on VLP release. To accomplish this, Lck-Gag proteins containing various combinations of the L-domains and C-terminally fused ubiquitin  were expWe next attempted to mimic a situation that is somewhat typical of retroviruses, where only a fraction of Gag expressed in cells carries ubiquitin. This was done by co-expressing ubiquitin-fused and unfused Lck-Gag proteins in varying proportions. When this was done in the context of a Lck-Gag proteins lacking a PSAP motif (by co-expressing Lck-Gag(L-) and Lck-Gag-Ub), particle production was most efficient when a large fraction of the total Lck-Gag protein carried ubiquitin, and no stimulation of particle production was detectable when less than 25% of the Gag protein carried fused ubiquitin (d>100µM), several class E factors form multiprotein complexes with several ubiquitin-binding surfaces, which could provide sufficient avidity for their retention at sites of virion assembly. Under such a scenario, efficient recruitment of ESCRT complexes might require deposition of relatively large numbers of ubiquitin molecules in the vicinity of the assembling particle, a notion that is consistent with the finding that a large fraction of Gag must carry ubiquitin to compensate for the absence of a late domain , ESCRT-I , ESCRT-II  ESCRT-III , as well as several ESCRT-associated proteins or proteins that are known to bind to components of the class E VPS pathway . Most of these proteins, including known ubiquitin binding factors , gave eiWe next determined the effect of siRNA mediated disruption of known ubiquitin-binding complexes, as well the additional ESCRT-associated factors that were positive in our yeast 2-hybrid survey (ALIX and UBPY), on PPxY-dependent and fused ubiquitin-dependent Lck-Gag budding. The core components of the known ubiquitin binding ESCRT complexes  were targeted using pools of four siRNAs directed to Hrs, Tsg101 and Eap45, respectively. The potency of the siRNA pools was estimated by cotransfecting them with plasmids expressing YFP-tagged target proteins, followed by quantitative western blotting. By these criteria the Tsg101, Eap45, ALIX and UBPY siRNAs appeared effective . HoweverUbiquitin-dependent (Lck-Gag-Ub) budding was modestly inhibited (∼3-fold) by depletion of either Tsg101 or ALIX but was barely affected by UBPY or Eap45 siRNAs , suggestBecause ESCRT-I and ALIX perturbation both affected ubiquitin and PPxY-dependent budding, we sought to determine whether their simultaneous depletion would exhibit a stronger inhibitory effect. Unfortunately, cotransfection of the two pools of siRNAs  rendered each somewhat less effective, perhaps due to dilution of the active siRNAs . SpecifiThe precise role of HECT ubiquitin ligases in promoting PPxY-dependent virion release has, heretofore, been somewhat unclear. Our previous studies suggest that their ubiquitin ligase activity is critical for their ability to stimulate budding We compared the activities of HECT domains from various Nedd4-like family HECT ubiquitin ligases by fusing them to the C2 and WW domains of WWP1. While this strategy does not illuminate which ubiquitin ligases are responsible for viral budding in the natural context, it does allow an assessment of HECT domain function in a uniform background. We found that HECT domains varied significantly in their ability to stimulate PPxY-dependent particle release in this context. This variability was evident when there were no ubiquitin acceptors in the Gag protein and correlated better with the ability of the HECT domains to drive autoubiquitination than with their ability to ubiquitinate a modified Gag substrate that contained lysines proximal to a PPxY motif. The correlation between autoubiquitination and budding was imperfect, however, and it is possible that variation among the HECT domains in their ability to catalyze different lengths and types of ubiquitin chains (e.g. K48 versus K63-linked chains), or their ability to ubiquitinate other bystander proteins, could influence their ability to stimulate viral budding. In this regard it was notable that there was no correlation between the ability of the HECT domains to undergo autoubiquitination versus their ability to catalyze ubiquitin ligation to Lck-Gag-PY-3K. It was nonetheless true that the ability of the HECT domains to stimulate budding was, in every case, absolutely dependent on their ability to catalyze the ligation of ubiquitin to a substrate. This suggests that the proposed role of HECT domains as adaptors that bind directly to downstream factors is of secondary importance in stimulating budding, or that this adaptor function requires catalytic activity. This latter scenario could, conceivably, be operative as a result of HECT autoubiquitination.These studies underscore the remarkable flexibility in the ways that the ESCRT pathway can be engaged to achieve viral budding  Using a When HECT domains were used to promote budding, the requirement for catalytic activity was absolute, irrespective of how they were recruited to Gag and, importantly, in the absence of ubiquitin acceptors on the viral Gag protein. This finding suggests that ligation of ubiquitin to trans-acting factors, perhaps including the HECT domain itself (i.e. autoubiquitination), rather than Gag is important for viral budding. It is superficially paradoxical, therefore, that ubiquitin could promote budding of the very same Gag protein even when ubiquitin was not ligated to a trans-acting factor, but rather was directly fused to Gag. These findings suggest that the identity of the protein(s) to which ubiquitin is attached is not of critical importance, and ubiquitination substrates can, in principle, include Gag, the ubiquitin ligase itself, or other trans-acting proteins. The mere presence of ubiquitin at the site of particle assembly appears sufficient to engage the ESCRT pathway and stimulate budding.The intrinsic manipulability of L-domains, the proteins that bind to them  and the apparent lack of importance of the identity of ubiquitination substrate suggests that each serve simply as recruitment factors to engage the downstream machinery that mediates membrane fission and particle release. Since ubiquitin binds to the very same factors (ESCRT-I and ALIX) that are bound by PT/SAP and YPXL type L-domains, and depends on them to stimulate budding, then ubiquitin itself can be conceptually viewed, in the context of viral budding, as a transferable L-domain that acts in a position-independent manner. In essence, this notion is a simple extension of the concept originally demonstrated by Parent et al, who showed that conventional L-domains function in a position independent, transferable manner A finding that is consistent with the aforementioned arguments, is that budding that was dependent either on a PPxY motif or a ubiquitin fused directly to Gag exhibited similar dependence on particular components of the ESCRT pathway. Notably, perturbation of individual segments of the pathway (ESCRT-I and ALIX) caused partial inhibition of ubiquitin-dependent Lck-Gag-PY, Lck-Gag-Ub and MLV Gag particle release. Previous work has shown that Mason-Pfizer monkey virus particle release, which is dependent on a PPxY motif, is quite strongly inhibited by depletion of Tsg101 Since ubiquitin-binding class E VPS factors generally have a low affinity for individual ubiquitin molecules , the effPrevious studies have shown that direct ubiquitin fusion to RSV or EIAV Gag can alleviate a late budding defect imposed by proteasome inhibitors or functionally replace a YPDL L-domain pCAGGS-based expression plasmids encoding Lck-Gag(PSAP), Lck-Gag(L-), Lck-Gag-PY, and Lck-Gag-PY-3K plasmids have been described previously DNAs encoding the HECT domains from WWP1 (residues 543–922), WWP2 (491–870), Nedd4 (520–902), Nedd4L (593–975), Itch (483–862), Smurf1 (374–757), and Rsp5 (431–809) were amplified from plasmids encoding the full-length HECT ligases 5 293T cells in six-well plates were transfected using polyethylenimine (Polysciences) with 1 µg of pCAGGs/Gag-derived plasmids, alone or with 1 µg of pCR3.1/YFP, pCR3.1/YFP-WWP1/HECT, pCR3.1/YFP-Tsg-HECT, or pCR3.1/YFP-C2-WW-Tsg-C plasmids, or the indicated amounts of pCR3.1/YFP-Vps4 E228Q plasmid. For EIAV and MLV VLP release assays, 293T cells were transfected in the same format with 500ng of, pCR3.1/EIAVGag or pCR3.1/MLVGag-HA plasmids. VLPs were pelleted by ultracentrifugation of 2 ml of 0.22-µm-filtered culture supernatants, collected 48 hours after transfection, over a 2ml 20% sucrose cushion for 90 min at 115,000×g. VLP and cell lysates were analyzed by Western blotting.For Gag particle release assays, 5×105) in six-well plates were cotransfected with 1 µg of pCAGGs/Lck-Gag-PY-3K, 500 ng of pHA-ubiquitin, and 1 µg of the indicated chimeric pCR3.1-WWP1-HECT ligase. At 36h after transfection, cells were thoroughly lysed at room temperature in detergent-rich RIPA buffer  and 5mM N-ethylmaleimide to inhibit deubiquitination) and cleared of cellular debris by microcentrifugation. The lysates were then diluted 5-fold in the same buffer containing NP-40 rather than SDS, to adjust the concentration of SDS to 0.1% and NP-40 to 1.0%, and split into two fractions. From one fraction, Gag proteins were immunoprecipitated with αPFV serum and protein G-Sepharose beads. From the other fraction, YFP-HECT ligase proteins were immunoprecipitated with αGFP monoclonal antibody and protein G-Sepharose beads. Immunoprecipitates and unfractionated cell lysates were analyzed by Western blotting.293T cells (5×105) in six-well plates were transfected with siGENOME siRNAs targeting Luciferase, Tsg101, Hrs, Alix, UBPY, or Eap45 (Dharmacon) using Lipofectamine 2000 (Invitrogen). After 24h, cells were transfected with the same siRNAs and the indicated Gag expression plasmids. VLP and cell lysates were prepared 48 h after the second transfection. To assess knockdown efficiency, 293T cells were transfected once with YFP-Tsg101, -Hrs, -ALIX, -UBPY, or -Eap45 expression plasmids and corresponding siRNAs. GFP expression in cell lysates harvested 48 h after transfection was assayed by quantitative Western blotting.293T cells , anti-EIAV equine serum , anti-GFP (Roche), and anti-HA  anti-Tsg101  or anti-ALIX rabbit serum (a gift from Wes Sundquist). Subsequently, the blots were probed with species-specific peroxidase-conjugated secondary antibodies and chemiluminescent substrate reagents. Alternatively, for quantitative Western blotting, membranes were probed with species-specific antibodies conjugated to IRDye800CW, and fluorescent signals were detected and quantified using a LICOR Odyssey scanner.Yeast cells (Y190) were transformed with the pGBKT7- and pVP16-derived plasmids described above. Transformants were selected and protein-protein interactions were assayed by β-galactosidase reporter activity as previously described


S1) or state 2 (S2) Lec domain with respective bent (B) or extended (E) EGF orientation. Simulations illustrated that both S1 and S2 conformations were unable to switch from one to another directly. Instead, a novel S1' conformation was observed from S1 when crystallized B-S1 or reconstructed “E-S1” structure was employed, which was superposed well with that of equilibrated S1 Lec domain alone. It was also indicated that the corresponding allosteric pathway from S1 to S1' conformation started with the separation between residues Q30 and K67 and terminated with the release of residue N87 from residue C109. These results provided an insight into understanding the structural transition and the structure-function relationship of P-selectin allostery.Allostery of P-selectin lectin (Lec) domain followed by an epithelial growth factor (EGF)-like domain is essential for its biological functionality, but the underlying pathways have not been well understood. Here the molecular dynamics simulations were performed on the crystallized structures to visualize the dynamic conformational change for state 1 ( Selectin is characterized by an extracellular C-type lectin (Lec) domain, a single epidermal growth factor (EGF)-like domain, and a variable number of short consensus repeat (SCR) units homologous to complement regulatory proteins, a transmembrane region, and a cytoplasmic tail via presenting sufficient length and flexibility for a P-selectin molecule to bind to its counterpart ligand, P-selectin glycoprotein ligand 1 (PSGL-1) x-modified glycan at T616 Selectin-ligand interactions play a crucial role in inflammatory and immune responses X  complex make it possible to elucidate the structure-function relationship of P-selectin-PSGL-1 interactions at atomic level state 1 Lec domain (denoted as S1) followed by bent EGF domain (denoted as B) with a closed angle from EGF to Lec domain, and the other is state 2 Lec domain (denoted as S2) followed by extended EGF domain (denoted as E) with an open angle  involved directly in ligand binding site parallels the binding interface and points away from the major binding sites while that of S2 Lec domain orients vertically outwards the binding interface and points toward those sites. Noting that B-S1 conformation appears in the unliganded P-LE or liganded P-LE with sLex and E-S2 conformation is found in the liganded P-LE-SGP-3 complex, it is evident that the conformation of Lec domain is associated with the orientation of EGF domain and that the allosteric pathways of conformational transition is expected. The structural analysis is also crucial to the binding capacity of a selectin molecule to PSGL-1 ligand under blood flow, since the conformational change of Lec domain is usually accompanied with the action of mechanical force Orientation and conformation of Lec and EGF domains are important to possess their functions. Comparison of crystallized structures of unliganded P-LE domains with liganded P-LE-SGP-3 or P-LE-sLevia introducing a glycan wedge between Lec and EGF domains S1) to the high-affinity conformation (the same as S2) R2 and R3 regions in the current study; cf.S1 to S2 takes place directly and what are the underlying structural bases. Here we explored the allosteric dynamics of P-selectin Lec domain at atomic level to understand the structural evolution of P-selectin allostery. Impacts of Lec domain stability, EGF domain orientation, and SGP-3 ligation on conformational change of Lec domain were tested using molecular dynamic (MD) simulations.Allostery is a common feature for biomolecular function observed in such the proteins as FimH B-S1 structures of unliganded P-LE domains (PDB code: 1G1Q) and of sLeX-liganded E-LE domains (PDB code: 1G1T), and the E-S2 structure of SGP-3-liganded P-LE domains (PDB code: 1G1S) were employed as initial structures. Here a strontium ion Sr2+ was replaced by a calcium ion Ca2+ for the E-S2 structure. Every simulation system was built by solvating the target molecule into a rectangular water box and neutralized with ∼100 mM Na+ and Cl− ions added to mimic the physiological ionic concentration, and was then equilibrated no less than 5 nanosecond (ns) using a NAMD program fs) and the periodic boundary conditions were applied in the simulations. Prior to equilibration process, energy minimization was initiated with 10000 steps by fixing backbone atoms of protein or heavy atoms of sugar followed by additional 10000 steps with all atoms free. System heating was then performed from 0 to 300 K at 30 K increment every 5 picosecond (ps). A smooth (10–12 Å) cutoff and the Particle Mesh Ewald (PME) method were employed to calculate van der Waals forces and full electrostatics, respectively. The 300 K heat bath was manipulated under Langevin thermostat, and the 1 atm pressure was controlled by Nosé-Hoover Langevin piston method.The crystallized B-S1 structure of P-LE domains was forced to unfold using cf-SMD algorithm with a constant force of 100 pN along the vector from the fixed C-terminal atom L116-Cα of Lec domain to the pulled C-terminal atom D158-Cα of EGF domain. P-LE-SGP-3 complexes with different conformations were forced to dissociate using cv-SMD algorithm where C-terminal atom P618-Cα of SGP-3 ligand peptide was pulled via a spring with a spring constant of 70 pN/Å at a constant speed of 0.01 Å/ps along the vector from fixed C-terminal atom D158-Cα of EGF domain to the pulled end. Every system was equilibrated no less than 2 ns before SMD simulations.Steered molecular dynamics (SMD) simulations were also conducted in some cases to test the impact of external force on conformational stability of Lec domain during P-LE unfolding or P-LE-SGP-3 complex dissociation. Here θ between two vectors connecting respectively the geometric center of heavy atoms of Lec-EGF interface hinge (residues A120 and S121) to that of Lec domain (residues W1 to T119) and to that of main EGF domain (residues C122 to T141)  of Lec and EGF interface involving two terminals of Lec domain (W1-Y3 and T119-A120), the loop region (denoted as R1) right ahead alpha helix 2 (I29-Q30), the anti-parallel beta sheets β4 and β5 and followed loop R54-N75 (denoted as R2), and the loop P81-D89 involving in ligand binding site (denoted as R3)  were equilibrated for 16 and 20 ns, respectively  or S2 (red). As exemplified in S1 structure exhibited a transition phase with ∼1.5 Å at <6 ns followed by ∼2.2 Å at >8 ns when aligning with reference S1 (black), while it yielded much high RMSD value ranging from ∼2.8 Å at <6 ns to ∼3.5 Å at >8 ns when aligning with alternative reference S2 (red). Averaged residue displacement calculated from last 2 ns equilibration presented the significant conformational change in the loop of R3 region, as exemplified by a maximal value of 10.1 and 17.4 Å for residue R85 (arrow), respectively (arrow) between anti-parallel β4 and β5 sheets of R2 region. These data suggested that S1 Lec domain alone is not stable.ructures . Here EGectively . To quanectively A, C andectively B, D viaectively B. The sS2 Lec domain to reference S1 or S2 for 20 ns simulation. Again, both RMSDs reached a high equilibrium value of ∼2.8 Å for last 12 ns  but high RMSD of ∼2.6 Å to reference S2 (red) (black line) than that for the latter  . Similarly, S2 Lec domain was also stabilized by SGP-3 ligation, as seen in an equilibrated RMSD of ∼2.4 Å and ∼1.3 Å to reference S1 (black) and S2 (red), respectively (S2 (red) and large value to reference S1 (black) , indicating these observations from the simulations were reliable.Next we tested the impact of presence of SGP-3 ligand on conformational difference of Lec domain. ulations  were donS2 (red) A. Combi Å; red) B, theseectively D. Corre (black) E. Notin complex C and th complex F. TheseB-S1 and E-S2 structures E-S1” and “B-S2” reconstructed by interchanging B-EGF domain with E-EGF domain between two P-LE structures were equilibrated for 10 ns with constrained backbone atoms of interchanged EGF domain  was intermediate  or “E-S1” (pink) with S1 (blue) and S2 (silver) references  was superposed well with that from S1 Lec domain alone (cyan)  imparted the confidence that this novel conformation is a stable structure. By contrast, the equilibrated conformation from “B-S2” with bent EGF domain was similar to that of S2 Lec domain except of loosing of R3 loop (data not shown). Thus, re-orientating from B- to E-EGF domain induced the conformational changes of S1 Lec domain on R3 and N-terminal of R2 but the re-orientating from E- to B-EGF just relaxed S2 Lec domain at the R3 and the C-terminal of R2.Moreover, the equilibrated structures from either profiles B, whichS1 alone A, cyan r “E-S1” B, pink ferences A–B. Thee (cyan) C and th from S1 A–B. PreS1' conformation from S1 induced by the deletion of EGF domain or re-orientation from B- to E-EGF domain proposed the possible allostery between S1 and S1'. To further test the universality of S1' conformation and to elucidate the allosteric pathway from S1 to S1' Lec domain, Set IV simulations were performed, combined with the interchanged EGF orientations, by tuning those key residues of the entrance residues W1, A28, I29, and E34 of Lec domain, the pivot residue W62 of R2, and the ending residue D89 of R3, which seemed to be crucial in the interconversion between B-S1 and E-S2 (7). One series of simulations were done for “E-S1” structure combined with key residues tuned to those of S2 conformation  or S1' conformation from equilibrated S1 Lec domain (open bars) was calculated from last 2 ns equilibration and the threshold of 3.0 Å was used to determine if the conformational change takes place or not. It was found that, as compared to the equilibrated S1 conformation from B-S1 or reconstructed “E-S1” structure (st or 2nd set of bars from the left1), the additional adjustment of residue W1 or D89 orientation tuned to that of S2 did not induce conformational change of S1 Lec domain . The combined tuning of W1/A28/E34 or W1/A28/E34/W62, however, resulted in the conformational change , resulting in the conformations similar with that of equilibrated S1 Lec domain   or S2 (open bars) conformation  . All theS1 to S1' conformation? Further analyses demonstrated that the allostery was initiated by the breakage of water bridge among Q30 of R1, K67 of R2, and E135 near H region and resulted in extending EGF domain (pink newcartoon and CPK residues in '). Originally the water bridge networks between Q30 and K67 and between Q30 and E135 in B-S1 structure (blue newcartoon and licorice residues in R1 and part of R2 (W62-K67) . It also weakened the interaction between R1 and R2 loop (W62-K67) and pushed rotationally R2 away from original location ('). The motion of R2 was then transmitted to the loop (H108-K113) through the β7 and β8 sheets via strong Hbond interaction between R54 of R2 and D89 of R3 and the disulfated bond between C90 and C109, which, in turn, pushed C109 residue away (') and disrupted the interaction of C109 backbone Hbond with N87 of R3. The release of N87 from C109 resulted in the R3 re-orientation and, finally, terminated the allostery of Lec domain ('). In short, the destroy of hinge water bridge induced the opening of cavity gate governed by Q30-K67 interaction and weakened the interaction between R1 and part of R2 (W62-K67), followed by the re-orientation of R2 transferred to C109 via the interaction of R2 N-terminal and R3 C-terminal as well as the rigid C90-C109 disulfated bond, which finally activated the conformational change of R3 through the breakage of N87-C109 Hbond. Such an allosteric pathway was further confirmed by the following correlations: The re-orientation of R2 (defined by RMSD of part of R2 (K55-W60)) was reinforced with width of cavity gate (defined by distance between two atoms of Q30OE1 and K67NZ) (R2 re-orientation (R3 (defined by RMSD of R3) correlated positively with N87-C109 Hbond disruption  B. The dolecules B'. It d K67NZ) G; The bentation H; And tsruption I.S1 Lec domain by deleting EGF domain  and then evolved into S1' around 25 ns, with the RMSD of R3 heavy atoms to S1 (black) and S1' (red) running up to ∼6.2 Å and down to ∼2.8 Å, respectively . EGF orientation also exhibited the multiple phases with more than three times of transition between bent (∼122°) and extended (∼138°) conformations . Here a 45 ns simulation for E-LE domains was performed to further test the universality of S1' allostery from S1 in different selectin members. The RMSD of R3 heavy atoms relative to P-selectin S1 (black) and S1' (red) conformation indicated that E-selectin Lec domain also experienced the spontaneous allostery to S1' after ∼15 ns fluctuation around S1, where the allostery happened sharply and the S1 and S1' conformations were ultimately distinct  overlapped partially to that of S1 conformation (black) in both P- (S1' (or S1) conformation. It was also found that the distribution shifted rightward from S1 to S1' conformation, implying that S1' conformation favors more extended EGF orientation and that the extension of EGF orientation promotes the possibility of allostery from S1 to S1'. Moreover, the slight  induced by forced allostery from its low-affinity conformation (S1) is favored to its ligation S1 and S2 takes place directly along the predicted pathway and what the dynamic pictures are. Thus, MD simulations of P- and E-LE were performed in the current study, attempting to visualize the dynamic pathways of structural allostery for Lec domain. By elucidating the impacts of the stability of Lec domain alone, the ligation of SGP-3 ligand, the orientation of EGF domain, and the stability of LE domains, our results indicated that the allostery of Lec domain existed and the orientation of EGF domain induced the conformational change of Lec domain. Three novel observations were found in the simulations: the first is that the interchanged transition between S1 and S2 was unable to take place directly but a stable, novel conformation (S1') was presented originating from S1, the next is that the conformation of equilibrated S1 Lec domain alone was similar with that of equilibrated S1' conformation when EGF domain for S1 was tuned from B to E orientation  of bacterial FimH was attributed to the conformational change of Lec domain induced by pilin domain entation  and 5, aentation .−1 and ∼10−2 s−1) or a wedge mutant that opens the interface between Lec and EGF domains (in the magnitude of ∼10−2 and ∼10−3 s−1) S1' structure or characterizing the high-affinity phase for P-LE wedge cleft can be proposed to elucidate the biological significance of the novel S1' conformation in future studies.At least three lines of evidence were proposed from experimental measurements for the co-existence of multiple conformations of P-Lec EGF domains. First, both the slow and fast phases concurrently exist in the dissociation kinetics for a wild-type P-selectin construct . Conclusively, the five sets of simulations  defined well the specificity and stability of S1' conformation and the allosteric analyses indicated that the novel S1' conformation takes place along a different pathway.Dynamic allosteric pathways found in the current study were also different from those proposed by comparing the conformational differences of crystallized S1 to S1' could happen universally, is it also possible to further induce the conformational change from S1' to S2? PSGL-1 ligation is one of potential pathways since the ligation of Lec domain makes it possible to alter the conformation of Lec domain. For example, the fluorescent intensity of labeled P-selectin was enhanced up to 10–13% when binding to PSGL-1 ligand S2 conformation was stabilized by the presence of SGP-3 ligand   A–B. NotS1' conformation favored the extended EGF orientation than that of S1 conformation Click here for additional data file.


Single nucleotide polymorphisms (SNPs) constitute an important mode of genetic variations observed in the human genome. A small fraction of SNPs, about four thousand out of the ten million, has been associated with genetic disorders and complex diseases. The present study focuses on SNPs that fall on protein domains, 3D structures that facilitate connectivity of proteins in cell signaling and metabolic pathways. We scanned the human proteome using the PROSITE web tool and identified proteins with SNP containing domains. We showed that SNPs that fall on protein domains are highly statistically enriched among SNPs linked to hereditary disorders and complex diseases. Proteins whose domains are dramatically altered by the presence of an SNP are even more likely to be present among proteins linked to hereditary disorders. Proteins with domain-altering SNPs comprise highly connected nodes in cellular pathways such as the focal adhesion, the axon guidance pathway and the autoimmune disease pathways. Statistical enrichment of domain/motif signatures in interacting protein pairs indicates extensive loss of connectivity of cell signaling pathways due to domain-altering SNPs, potentially leading to hereditary disorders. Hereditary disorders are often linked to rare mutations in the form of single nucleotide polymorphisms (SNPs) More than ten million SNPs have been identified in the human genome Current methods for discovery of SNP-phenotype association include those focusing on non-synonymous SNPs that alter functional motifs such as binding sites This study focuses on a specific subset of human genotype-disease association, namely the annotation of SNPs that alter protein domains and thus potentially break bonds between interacting proteins in cell signaling pathways In this study, we screened the human proteome for domain annotation using the PROSITE web tool The human SNP database was downloaded from the NCBI dbSNP database  The PROSITE output for each sequence was in the form of a matrix with three columns, with the columns indicating (1) the ID number of the PROSITE domain, (2) the binary value identifying the presence or absence of a domain, and (3) the PROSITE matching score (MS) if the domain was expressed as a profile (position weight matrix) rather than expressed in the form of a pattern (regular expression). The second and the third columns of the output data allowed us to identify those SNPs that either removed a domain from the protein structure or drastically altered it when compared to sequences with and without the SNPs (DA-SNPs). If the PROSITE domain was defined as a regular expression, the second column was sufficient to identify whether the domain also existed in the presence of the SNP.For those domains expressed as a profile, we checked the third column for the value of the matching score parameter of the same protein with and without the SNP. We defined a domain distortion (DD) parameter as the ratio of the difference in the matching score (due to the presence of the SNP) to the matching score of the sequence without the SNP. In our scans, DD varied from 0 to 0.3, which was the maximum domain distortion observed in our computations. Domains with DA-SNPs were defined as the sets of domains for which the sequence with SNPs no longer fits the regular expression, plus a set of profile domains with a finite DD value cut off in the presence of the SNPs.Examples of structural diagrams of proteins with DA-SNPs were obtained using the Protein Data Bank  We used statistical enrichment to identify protein signatures  most likely to be found among binding partners of the proteins containing domain-altering SNPs. We created a score matrix with rows indicating domains that can be altered by an SNP and columns indicating domains and motifs found in binding partners of proteins with domain altering SNPs. Each element of the score matrix represented the number of times a domain with an SNP was found associated with a signature  on a binding partner. The web tool ELM (2010 version) Our computations show that proteins with SNPs in one or more of its domains are significantly more likely to be associated with human disorders. Out of the 63,899 SNPs in the coding regions of proteins, 1,782 SNPs are present in the Online Mendelian Inheritance in Man (OMIM) database version 2009 Among proteins with domain SNPs, those with domain-altering SNPs are even more likely to be associated with disorder/disease. Domain-altering SNPs discovered in our PROSITE screening method consists of two subsets. The first subset consists of SNPs the sequence no longer satisfies in the regular expression for the domain. The second subset is composed of domains defined as a profile above a prescribed domain distortion (DD) parameter cutoff. An example of a domain with DA-SNP is p53. We evaluated the statistical enrichment of the domain altering SNPs in the OMIM Database as a function of the DD cutoff, as shown in Proteins with domain-altering SNPs crowd GO molecular function categories involving calcium ion binding, adenyl ribonucleotide binding, protein kinase activity, and endopeptidase activity at DD >0.10. The DD cutoff of 0.10 corresponds to 598 domain-altering SNPs present in 505 proteins. Among these proteins, 242 had at least one known binding partner in the Human Protein Reference Database (HPRD) Shown in Next, we estimated the links broken in these pathways due to domain-altering SNPs. We have determined domain-domain and domain-motif pairs (signature pairs) statistically enhanced in protein-protein interactions involving proteins with domain-altering SNPs. The results show that only a very small fraction of possible signature pairs are statistically enriched in PPIs presented in HPRD We used statistically enriched signature pairs in the estimates of links broken due to a protein expressing a domain-altering SNP. A link (transient or stable) between two proteins is assumed broken due to a domain altering SNP if the opposing protein pair contains at least one signature enriched with the SNP containing domain in the PPIs in HPRD. Shown in The protein links we estimate to be broken between a protein containing a DA-SNP and its immediate neighbors are shown in The genome wide association studies seeking a correlation between genetic makeup and complex diseases, such as cardiovascular diseases This study explores the mechanisms by which SNPs that fall into protein domains in the human genome potentially contribute to disease. Protein domains are functional units closely aligned with post-transcriptional modification (as in phosphorylation) and play important roles in establishing the connectivity of cell signaling networks via binding to upstream and downstream proteins How does an SNP with domain altering properties affect the connectivity of pathways? The key to answering this question lies in the discovery of the set of proteins that bind to proteins under consideration via the domain containing the SNP. The grammar of protein-protein interactions in terms of primary sequence and/or 3D structure is yet to be fully understood. We used a statistical enrichment approach to identify protein domains (motifs) on the opposing protein most frequently associated with the SNP-containing domain under consideration. We then assumed a bond (transient or steady) was broken whenever we came across such a signature pair among the immediate partners of the protein with a domain altering SNP. Results shown in the present study for focal adhesion and the natural killer cell mediated cytotoxicity pathways indicate extensive loss of connectivity in these cellular pathways, caused by the presence of domain altering SNPs among the proteins in these pathways. Even when we reduced the estimates of bonds broken with the use of signature pairs already known to predict protein-protein interactions, the loss of connectivity persisted at multiple cell compartments. We obtained qualitatively similar results for axon guidance and neutrophin signaling pathways altered by the presence of a domain altering SNP (not shown).In conclusion, proteins with domain-altering SNPs are statistically enriched in the list of proteins known to be associated with disease. These proteins crowd pathways associated with immunological, neurological and cardiomyopathy disorders. Protein functional groups statistically enriched with proteins with domain altering SNPs include calcium ion binding, adenyl ribonucleotide binding, protein kinase activity, endopeptidase activity, serine-type peptidase activity, DNA binding, and GTPase binding proteins.Table S1List of proteins with domain altering SNPs (DD >0.1) for which both the SNP and the protein were previously associated in the literature with a hereditary disorder or complex disease. The columns in the table present the gene symbol, the SNP ID, the name of the domain with the SNP, whether is DD violation (yes) or violation of regular expression (no), and the associated disorder/disease.(0.02 MB XLS)Click here for additional data file.