Most biological processes 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. Although sufficient to ensure binding, these linear motifs alone are usually too short to achieve the high specificity observed, and additional contacts are often encoded in the residues surrounding the motif (i.e. the context). Here, we systematically identified all instances of peptide-mediated protein interactions of known three-dimensional structure and used them to investigate the individual contribution of motif and context to the global binding energy. We found that, on average, the context is responsible for roughly 20% of the binding and plays a crucial role in determining interaction specificity, by either improving the affinity with the native partner or impeding non-native interactions. We also studied and quantified the topological and energetic variability of interaction interfaces, finding a much higher heterogeneity in the context residues than in the consensus binding motifs. Our analysis partially reveals the molecular mechanisms responsible for the dynamic nature of peptide-mediated interactions, and suggests a global evolutionary mechanism to maximise the binding specificity. Finally, we investigated the viability of non-native interactions and highlight cases of potential cross-reaction that might compensate for individual protein failure and establish backup circuits to increase the robustness of cell networks. Proteins are key players in virtually all biological events that take place within and between cells. And yet, proteins seldom act in isolation and often accomplish their function as part of large molecular machines, whose action is co-ordinated through intricate regulatory networks of transient protein-protein interactions. Consequently, much effort has been devoted to unveiling protein interrelationships in a high-throughput manner, and recent years have witnessed the consecution of the first interactome drafts for several model organisms, including human 2However, high-throughput interaction discovery experiments indicate only that two proteins interact, but do not provide information about the molecular details or the mechanism of the interaction. Currently, this atomic level of detail can come only from high resolution three-dimensional (3D) structures, where the residue-contacts are resolved and the protein interaction interfaces characterised x’ or a ’.’). For example, Src-homology-3 (SH3) domains bind proline-rich peptides, and several variants of the PxxP pattern have been observed, like [RKY]xxPxxP  and PxxPx[KR] (class II) Linear motifs are short patterns of around 10 residues with a common function (i.e. binding to a globular domain) that occur in otherwise unrelated proteins. In isolation these motifs bind their target proteins with sufficient strength to establish a functional interaction. They are frequently found in disordered or unstructured regions, which are now known to be not simply loops or linkers, but serve a variety of functions Recently, large-scale experiments for the determination of peptide recognition profiles of interaction domains, and derivation of the corresponding patterns, have been developed in vivo. For instance, Lim and co-workers showed that the Pbs2 peptide is recognised only by the SH3 domain of Sho1  and does not cross-react with any of the other 26 SH3 domains in yeast et al. have also shown that the binding specificity of PDZ domains is optimised across the 157 domains contained in the mouse proteome in vivo. It is thus, as happens in phosphorylation events context which ultimately determines interaction specificity.Although the binding is mediated by a small number of contacts formed by the residues in linear motifs, this type of interaction is extremely specific in vitro actually meet in vivo and thus evolve into niches of molecular recognition that allow them to bind only the desired target domain. For instance, in T-cells, the SH3 domain of Fyn does not compete with the GYF domain of CD2BP2 for the proline-rich motif in the cytoplasmic tail of CD2, because Fyn is located in the lipid rafts while CD2BP2 occurs in the detergent-soluble membrane fraction, although the interaction would be possible in in vitro assays context refers to those residues in the protein containing the linear peptide that interact with a globular domain in another protein but lie outside the motif, as defined in ELM  and SH2 (2 new motifs). Like the known motifs, they bound the appropriate pocket in the domain and contained amino acids similar to key residues in the described patterns. We thus included them in our analysis, thereby extending our set to 390 interactions of known structure .in silico alanine scanning analysis showed that truncation of side chains in the motif almost always impaired binding, while a truncation of side chains or residues replacements in the context improved the overall interaction energy in about 20% of our cases. Motifs found within globular domains, such as those binding Metallophos/PP1 or Adaptin_N, showed an extremely high contextual contribution. Very low contextual contributions were observed only in structures with little context; considering only cases with a reasonable amount of context – at least as much as the motif – indicated that the contextual contribution to binding was at least 5%. It is important to note that, to avoid flexibility problems during crystallisation, some of the 3D structures of domain-motif interactions were solved with only a fragment of the motif-containing interaction partner or synthetic peptides to study binding properties, and thus the contribution of the context may be artificially limited. Individual contributions for each family are given in We found that, as expected, the strongest contribution to the interaction came from contacts involving residues in the motif, responsible, on average, for 79% of the global binding energy, ranging from 12% to 99.7% in the different types of interaction . Neverthet al. have shown that residues both N- and C-terminal of the pT are crucial for specificity et al. showed that while the pT is crucial, the surrounding region of 44 residues is required for tight binding to the FHA domain of Ki67, while short peptides were not sufficient to establish an interaction et al., who studied the interaction between FHA in Rad53 and a 10-residue peptide from Mdt1 in detail, and pointed out that peptide library screens can only provide leads for specificity of  domains because ionic interactions appear more important in such small peptides then they might be in full-size proteins We also identified several cases of unusual contextual contribution. For instance, the current motif definition for binding to the forkhead-associated domain (FHA) is 4 residues long and starts with pT, which is known to be crucial for the interaction. However, our data shows that 2–4 residues N-terminal of the pT also contributed strongly to the interaction. Experiments by Durocher The interaction between the EF hand domain and the IQ ligand, while also described as a domain-motif interaction, differed in several ways from other domain-motif interactions studied here: it involved the simultaneous binding of at least two domains, which have different orientations towards the peptide. In additionally, with up to 20 residues the motif is unusually long, while the EFhand domain has only about 30 residues. This atypical combination of size and stoichiometry may explain why the behaviour of this interaction type differs. It is also possible that the combination of several domains helps to achieve high specificity.To analyse whether the method of structure determination introduces any bias in context contribution, we separated our curated set of domain-motif interactions by methods and computed the contextual contribution to binding for each of them. The vast majority of interactions (over 500) were determined by X-ray crystallography, about 100 by NMR and 150 by other methods. However, we did not observe significant differences between these sets.We sought for a relationship between contribution to the global binding energy of each residue and their level of sequence conservation within their respective domain families. Among the globular domains involved in peptide-mediated interactions, we found no correlation between sequence variability and energy contribution. We observed a weak trend indicating that domain residues contacting mostly residues in the linear motif were more conserved than those contacting the context. This effect was present in the sequences derived from our dataset, but disappeared when we extended the alignment to other homologous sequences. Therefore it is difficult to unequivocally determine whether this increase in conservation is a true feature of motif-binding residues. The situation differed however when we considered residues in the linear motif. Here we found that the contribution of motif positions with fixed residues to the binding energy was higher than that of restricted positions, which in turn was higher that that of arbitrary positions, with each residue contributing 25%, 21% and 13% of the total energy, respectively . HoweverThe results above show that interaction contacts involving residues in the motifs were nearly optimal, with respect to maximising binding affinity, whereas changes in the context can improve it. This observation suggests that the context has not been selected to increase the strength of the binding, but to prevent non-native peptide-mediated interactions within an organism, to maximise binding specificity. To further test this hypothesis, we studied the topological and energetic compatibility of linear motifs and context residues in the peptide-mediated protein interaction pairs identified here.−16. Please, note that being closely related proteins, all members of each globular domain families in this work present very similar structural scaffolds, as defined in the Structural Classification Of Proteins (SCOP) database To analyse the topological binding variation within each interaction type in more detail, we optimally superimposed the equivalent globular domains  and used the obtained rotation and translation matrix to calculate the root mean square deviation (RMSD) between motif and context residues in the partner proteins. The placement and orientation of all motifs interacting with a given family was very similar, with an average RMSD of 2.5±3.2Å. The situation differed when we considered the context: We found several distinct topologies for each type of interaction, with contextual contacts being widely spread around the motif-binding groove, resulting in a larger average RMSD of 4.2±4.4Å between the equivalent contacting residues in the partner proteins . Since mYet the level of conservation of motif and context binding topologies varied considerably among families. Peptide ligands of Hormone_recep form α-helices which are structurally very similar, in the motif (1.9±2.1Å) as well as in the context (3.2±3.2Å). SH3 domains are special in this regard, because the different classes of peptides they bind have opposite directions with respect to the common poly-proline motif . The posWe also conducted a peptide exchange experiment in which we used the above structural domain superimpositions to assess the fit of each individual peptide onto every domain within the same family. We included all those interaction types with more than 10 distinct human domain-peptide pairs and, at least, one peptide of 10 or more residues, namely Cyclin_N, Hormone_recep, MATH, PDZ, SH2, SH3_1 class I and SH3_1 class II. We omitted EF hand because of its peculiar stoichiometry described above. We built the 6,738 non-native interaction pairs resulting from the combination of the above domains and interaction partners within each species, and computed both global binding energies as well as the contribution of individual positions to the interactions.−16), which supports the working hypothesis that context contacts are more specific than those found within binding motifs and thus play a key role in preventing potential cases of cross-reaction. Finally, it is worth noting that there is no correlation between unfavourable contextual interactions and large topological variations or with the length of the context.We saw that, 1536 out of the 6432 (24%) of the artificial (i.e. non-native) interactions tested have binding energies below the average of the native global energies for each type of peptide-mediated interactions. Moreover, we also observed that 1552 (24%) of them would bind better, with a lower energy, than the corresponding native pairs, indicating that interactions between these protein pairs would be possible, at least from a biophysical point of view. However, these numbers are likely to be overestimates as the vast majority peptide-mediated interactions that have been structurally analysed only contain truncated pieces of the full proteins involved in the interaction, which reduces the amount of context and thus its influence on the binding energy. Nevertheless, the most striking results arise from the differences observed between contacts involving residues in the motif vs the context. Here, we compared all the 6432 instances of artificial interaction pairs that showed a similar topological orientation of the contextual residues, as assessed by the motif RMSD. We found that, according to our energy calculations, one third (32.64%) of the linear motifs tested be compatible between different protein pairs, whereas this percentage drops to 16% when only the context contacts are considered. These two average values and, indeed, their corresponding distributions, are significantly different on a one-sided Fisher's exact test  necessary to perform their signalling functions ds). However, these sub-optimal contacts have a much stronger effect in non-native interactions, where they completely disrupt the potential interfaces, preventing thus cases of potential cross-reaction.However, the most striking results come from the quantification of the binding energy contributions and their implications. Our results convincingly show that the contribution of motif residues to global binding energy is paramount to ensure interaction, while the contextual contacts are most likely used to achieve high specificity. These observations suggest that contacting residues involved in peptide-mediated interactions have been selected through evolution on two different bases: those in the motif to ensure binding with high affinities, and those in the context to maximise specificity against other potential binders and prevent cross-reaction with homologous proteins. The “negative selection” evolutionary model for context residues was previously proposed by Lim and co-workers based on SH3 domains in yeast and a 10-residue peptide matching the SH3 class I-motif Concerning the interaction topology, we show that the position of the motif is conserved in the structure, while more flexibility is allowed for the placement of the context. It should, however, be taken into consideration that many structures in our dataset were determined using only a fraction of the actual binding partner, allowing for more flexibility in the protein's termini, which would be restrained in the full protein. This effect is especially pronounced in structures solved by NMR. Furthermore, it should be considered that interactions between full-length proteins are likely to show a stronger effect of the context, because the interaction surfaces will increase. The FHA-motif interactions discussed above give an example of how a larger context can influence the interaction.in vivo, and it has been suggested that, while affinity and specificity for single interactions are not high, the trimerization amplifies both aspects and can thus lead to highly specific interactions In some interaction types, such as those involving SH2 domains, the binding consensus motif is too short and degenerated to draw any significant conclusion. Besides, these interactions often depend on phosphorylation events and thus specificity is very likely to come from other biological context Comparing the contribution of context to binding and its length as determined in the structure, we observed an increase in the contribution of longer peptides, which was not related to the size of the source protein from which the peptide was taken. We therefore assume that the true contextual contribution may well be higher than what we observed here, due to a bias towards short peptides in current structures of transient interactions.Our peptide-exchange experiment showed that some non-native peptide-mediated interactions are energetically possible, but may have weaker binding energies than native protein pairs. It is worth remembering that, in this experiment, we tested only possible cases of potential cross-reaction between protein pairs of known 3D structure within species, regardless of other key aspects in biological regulation such as sub-cellular localization or expression times. And yet, we identified several cases where cross-reactions within the same sub-cellular compartments seem to be possible. These instances could represent potential backup circuits to increase the robustness of protein-protein interaction networks, since they could compensate individual protein failures in vivo-effect and thus binding patterns do not evolve against. All together, we conclude that other factors such as the hormone ligand and the tertiary structure of the NR are crucial players in cofactor binding, and that the contextual interactions are not as important as for other families.The occurrence of (seemingly) energetically feasible non-native pairings was observed across all families studied here, but we found that non-native pairings with low energies are quite common between nuclear receptors (NR) and cofactor peptides. Nuclear receptors have 4 interaction interfaces: a ligand-binding pocket that holds lipophilic compounds , the cofactor binding groove, a dimerization interface, and a DNA-binding site binding the HRE (hormone response element) for its activity as a transcription factor. Our studies here addressed only the cofactor peptide. While specificity for the hormone ligand is usually high The identification of potential non-native interactions and putative backup circuits is paramount to understand how cell networks work as a whole, in what it is known as systems biology. Given that we did not observe a clear correlation between sequence identity and binding for any of the cases studied, we conclude that sequence information alone is insufficient to make predictions on domain-peptide interactions. Thus knowledge or modelling of the interacting structures is required in order to successfully predict whether a given domain-peptide-pair will bind or not; similar approaches for the prediction of domain-domain interactions have proven successful Knowledge of the atomic details as to how transient protein interactions occur and the ability to predict peptide-mediated protein interactions are crucial for understanding and modelling regulatory processes, for the design of new cellular circuits in synthetic biology and the discovery of drugs that target such interactions To detect all cases of peptide-mediated protein interactions of known 3D structure, we first parsed the PDB (02/2007) and identified all those entries containing two or more interacting proteins. We extracted all the information regarding the different 66 types of ligand involved in peptide-mediated interactions from the Eukaryotic Linear Motifs (ELM) database (03/2007) and assigned Pfam families To avoid composition biases, we created a non-redundant set of interactions by clustering those pairs sharing a 100% sequence identity on both the domain and the peptide.in silico alanine scanning experiments. FoldX is an empirical force field which combines physical descriptions of interactions between residues with experimentally determined results. It takes into account solvent exposure, hydrogen bonds, electrostatics, van der Waals energies and clashes, water bridges, and backbone and side-chain entropy. The FoldX force field was initially trained on a set of over 300 mutants and tested on a set of over 600 mutants as well as on 82 protein-protein interaction interfaces, with a reported correlation between calculated and experimentally determined folding and binding energies of 0.8. SH3-peptide interactions, which we also studied in this work, were among the set of protein-protein interfaces. Furthermore the force field was shown to account for both stabilizing and destabilizing mutations with a high correlation (0.89) with experimental results To quantify the contribution of motif and context to the interaction, we used the FoldX package To test for a potential correlation between position specific sequence conservation and individual contribution to the binding energy, we computed the entropy of each residue in a multiple sequence alignment To analyse whether the contribution of motif- and context-binding residues to the binding energy in the globular domain differs, we computed the “context binding fraction” for each domain position, that is, the ratio of context contacts vs. all interchain (i.e. motif plus context) contacts observed for this residue. To study differences in binding contribution in the peptide, we split the ELM pattern into its positions and grouped them on the basis of stringency: “fixed”, “restricted”, and “arbitrary”, if only one, a small set, or any amino acid is allowed in this position of the motif, respectively. We then compared the alanine scanning results for motif positions as classified above.in silico alanine scanning experiments, as described above.We performed a peptide exchange experiment for each non-redundant set of interaction pairs with 10 or more representative 3D structures, and where at least one peptide was sufficiently long for studies of contextual effects (≥10 residues). For each pair of interactions , we structurally superimposed the globular domains  using STAMP We predicted potential non-native interactions when the binding energy of the constructed pair was either lower than that of the native peptide, or below the average of all native interactions of a particular interaction type (domain-peptide pair). We defined cross-talk as unidirectional when only one of the constructed interactions (d1:p2 or d2:p1) had a sufficiently low binding energy, or bidirectional, when both constructed interactions bound strongly enough. Pairwise domain sequence identity and peptide similarity were determined via Needleman-Wunsch global alignment Table S1List of all domain-motif interactions of 3d structure(0.10 MB TXT)Click here for additional data file.Figure S1Percentage of fixed, restricted, and arbitrary motif positions observed in our structural data and their corresponding binding contribution, determined via glycine scanning. Contribution between 0 and 2 is frequently observed for all three types, but fixed positions show a contribution between 2 and 4 much more often than arbitrary residues.(0.02 MB EPS)Click here for additional data file.Figure S2Distribution of the RMSDs observed for motif (solid lines) and context (dashed lines) among families studied in the peptide exchange. The inset shows a zoom to the RMSD range , where the majority of both motif and context differences are found. Motif RMSDs above 5 Å are rare, and only domains binding large ligands  show a few cases with contextual RMSDs above 20 Å.(0.04 MB EPS)Click here for additional data file.Figure S3Contribution of each relative position in the motif in binding of peptides to PDZ domains, native (n) and artificial (a) interactions as constructed in the peptide exchange see . Red thr(5.08 MB TIF)Click here for additional data file.Figure S4Exchangeability across all families studied in the peptide exchange. If both non-native protein pairs have a binding energy below the average of the native cases of the corresponding family, we predict bidirectional exchange (magenta squares). If only one of the non-native combinations is below this threshold, we predict unilateral exchange (blue circles). If none of the artifical pairs has a binding energy below the average of the native cases, we predict no cross-talk (cyan diamonds).(4.73 MB TIF)Click here for additional data file.


The PBC and the hinge were included in the previously reported allosteric model, whereas the definition of the β2,3 loop and the N3A-motif as conserved elements is novel. The N3A-motif is found in all cis-regulated CNB domains, and we present a model for an allosteric mechanism in these domains. Catabolite gene activator protein (CAP) represents a trans-regulated CNB domain family: it does not contain the N3A-motif, and its long range allosteric interactions are substantially different from the cis-regulated CNB domains.Cyclic nucleotides (cAMP and cGMP) regulate multiple intracellular processes and are thus of a great general interest for molecular and structural biologists. To study the allosteric mechanism of different cyclic nucleotide binding (CNB) domains, we compared cAMP-bound and cAMP-free structures  using a new bioinformatics method: local spatial pattern alignment. Our analysis highlights four major conserved structural motifs: 1) the phosphate binding cassette (PBC), which binds the cAMP ribose-phosphate, 2) the “hinge,” a flexible helix, which contacts the PBC, 3) the β Cyclic nucleotides are small regulatory molecules which transmit signal from receptors positioned on a living cell membrane into the cell interior and regulate multiple biological processes ranging from bacteria to humans. Such regulation occurs through binding of the cyclic nucleotides to the corresponding proteins. All such proteins contain a relatively small domain responsible for the cyclic nucleotide binding. The most important task is to understand how cyclic nucleotide binding (CNB) domains translate the signal into a biological response. In this work, we studied changes in different CNB domains induced by the cyclic nucleotides using a new method for comparison of protein structures: local spatial patterns alignment. This novel method compares protein molecules and detects conserved spatial patterns comprised of similar amino acid residues. Moreover, it ranks the detected residues with respect to their functional or structural importance. Our results show that there are at least two different families of CNB domains. The first family has four structural elements which perform the signal translation. Two of these elements were known previously and two are novel. The second family can translate the signal using only three elements, but they have to work in pairs to provide interaction between the functional elements. Cyclic adenosine monophosphate (cAMP) is an important second messenger, which regulates a large variety of cellular processes, including metabolism, cell shape transformation, gene transcription, photoreception and chemosensation The β-subdomain contains a highly conserved element, the Phosphate Binding Cassette (PBC), which is 14-residues long and contains a short flexible α-helix (B'-helix). Ribose-phosphate of cAMP, protected by the β-sandwich from the outside solution, forms six strong hydrogen bonds to the PBC. Due to these interactions, the B'-helix moves towards the cAMP molecule and adopts a compact conformation . Such moThe other part of the cAMP molecule, the adenine ring, acts as a hydrophobic moiety, which stacks against a “capping residue” in all known CNB domain structures 2 and β3 strands. A series of publications demonstrated, that it is an important element of allosteric mechanism in the PKA RIα∶A-domain A recent review summarizes this information into a general model for the CNB domain allosteric mechanism for PKA, Rap guanine nucleotide-exchange factor (Epac) and hyperpolarization-activated cyclic-nucleotide-modulated channel (HCN) To elucidate the cAMP induced allosteric mechanism that is conserved in different CNB domains, we used a recently developed method for protein structure comparison: Local Spatial Patterns (LSP) alignment that is capable of detecting similar patterns made up by amino acid residues in space. It is fast and does not require preliminary sequence or structural alignment of the compared proteins. Earlier we used it for comparison of protein surfaces of several CNB domains in the B-form and detected a conserved set of hydrophobic residues protecting the cAMP ribose-phosphate 2,3-loop and the “N3A-motif”. The latter consists of the A-helix, a preceding eight residue loop and a short N-terminal helix. The loop contains a set of 310-turns, and is termed “the 310-loop”. Based on these results, we propose a general model for the allosteric mechanism in CNB domains, which we called cis-regulated domains. In CAP the N3A-motif is reduced to a single A-helix. The difference between CAP and other CNB domains is discussed.Our analysis has shown that there are four elements conserved in all known CNB domains, with the exception of the CAP: the PBC, the hinge, the βThe LSP alignment is a new method to compare protein molecules. It is based on a graph-theoretical representation of protein structure, and the result of this alignment is a pair of isomorphic graphs. Vertices of the graphs correspond to the residues which form similar spatial patterns in both proteins. Each vertex/residue is connected to the rest of the graph by several edges. They indicate the residue neighbors whose positions and orientation in space are conserved with respect to this residue. As we have shown earlier α-atoms) any large scale rearrangements in the protein do not change the score significantly. Therefore, residues which form rigid structures inside the protein and maintain their relative positions will have a high level of IS. In contrast, those residues located in points of protein flexibility will have low IS, reflecting the loss of similarity between the two protein structures. One can speculate that the residues with the lowest IS can play an important role in the allosteric mechanism, as such elements like “hinges” or “switches” have to accept two distinctive “on” and “off” conformations.Earlier we used the LSP alignment for comparison of different proteins having similar functions. In this work we present an alternative way of using the LSP alignment program. Our purpose was to quantify cAMP-induced structural rearrangements in different CNB domains. This was made by aligning two different conformations of the same protein. As the IS reflects only local structural similarities  N-terminal part of the α-subdomain. These results are in good correspondence with the NMR studies of the RIα A-domain 2,3-loop in B-domain was less prominent. Similar results were obtained for the A-domain of PKA (RIIβ) and the potassium channel MloK1 , but similar to the B-domain of RIα. This is in a good agreement with the earlier observation that B-domains of PKA are more similar to the rest of CNB domains, than the A-domains el MloK1 . The dec10-helix. In the H-form of RIα∶A it contains a set of 3-turns, which do not form the classical 310-helix and is considered to be a loop, while the B-domain retains its 310-helix configuration. In H-form of PKA RIIα∶A and RIIβ∶A this element qualifies as an α-helix, but in the B-form of RIIβ∶A and RIIβ∶B it is a 310-helix. However, a close look at the middle part of the bundle shows that the geometry of its backbone in different CNB domains (both H- and B-forms), is rather conserved   . Such rearagine) , a featu10-loop residues positioned between the X–X pairs are usually involved in important interactions. For example, in RIa∶A-domain V134, L135 and H138 stack against a large hydrophobic cluster formed by the C-subunit and the PBC . Here we suggest a new boundary between the A and B domains, which will reflect the conservation of the N3A-motif. It is known that the RIα-(94-244) construct retains its functionality and is capable of both binding to cAMP and regulating PKA 244 and E245. An identical kink exists in the cAMP-bound RIIβ (between Y265 and E266). It seems logical to suggest that this kink indicates the border between the A and B domains, therefore defining E245 as the beginning of N3A-motif for the B-domain of RIα. Such a definition supports the observation made earlier by Huang and Taylor that RIα “residues 245–260 at the end of cAMP binding domain A are structurally more a part of domain B than domain A” The suggested conservation of the N3A-motif in different CNB domains raises a question about the definition of A and B domains in PKA-R. Until now, the beginning of the B domain was associated with the first residue in it's a-helix  and #5 (D167) (166) which makes a conserved hydrogen bond to the PBC-arginine carbonyl. The reason for strict conservation of the glycine is evident as its dihedral angles are ruled out for any other type of residue (φ = 89.4° and ψ = −26.8° for RIα∶A). Another conserved hydrogen bond is formed between the PBC-arginine amide and carbonyl of the residue #5. The third polar contact, which can be found in all CNB structures, is formed between the PBC-arginine guanidinium group and the carbonyl of residue #9 (N171). The hydrogen bond between the arginine and the side chain of residue #8 (D170), which was found to be important for RIα∶A, is not conserved: it can be seen only in the A domains of RIα and RIIβ. This residue, however, often binds to the side chain of residue #9 and provides communication between the β2,3-loop and the “hinge” as its carbonyl is always bound to the amide group of the first residue in the B-helix. Besides polar interactions with the PBC-arginine, residue #1 (I163) makes a conserved hydrophobic contact to the arginine side chain. This residue is a member of a conserved hydrophobic core formed by the highly scored residues: V162, I163, Y173, F198 and V213 (173 (one of the three highest scores obtained) – makes a conserved hydrogen bond to the residue #9 (N171), thus closing the circle around the PBC arginine.The second major region with highly scored residues was in the strands β5 (D167) . It contand V213 . The imp202), which in its turn also has a hydrophobic contact to the PBC-arginine in the cyclic nucleotide bound configurations.Glycine is the predominant residue in position #7, except for the B domains of RIIα and RIIβ, where it is substituted by alanine. Any increase of the side chain would lead to a steric clash with highly conserved alanine residues from the PBC  had a substantial chemical shift, in response to cAMP binding and are a part of the allosteric mechanism. Our results support this conclusion and demonstrate conservation of the hydrophobic interface through different CNB domains. Another highly scored residue positioned between B-helix and β8-strand (M151) is also a part of this interface.The hinge, which is a well known element of the allosteric mechanism, demonstrated medium levels of IS and a strong dependence on the presence of cAMP. The preceding β10-loop in RIα∶A-domain have an elevated level of hydrogen-deuterium exchange As we showed earlier the N3A-motifs of different CNB domains have very similar geometry, and conserved sequence motifs. However, this region, except for the C-terminus of A-helix, demonstrated low levels of IS. This indicates that the residues, conserved in the sequence, do not form a rigid spatial motif. This conclusion is supported by the fact that the N-helix and 34 and β5-strands received zero level of IS (4–5-loop involved in the conserved anchoring of the β2–3-loop via its #3 residue (Q165)   and 6A. cis-regulated CNB domains while defining CAP CNB domain as a trans-regulated CNB domain.We deliberately excluded the CAP CNB domain from the current analysis as it does not contain the classic N3A-motif. There are several distinctive features that distinguish the CAP CNB domain from those discussed above. The major difference is that in functionally active CAP the two identical CNB domains form a homodimer with the interface being formed mainly by their C-helices. Regulatory subunits of PKA also contain two CNB domains, but their mutual interaction is rather limited: e.g. A-helix of B-domain contacts cAMP and the hinge from the A-domain. In contrast, in CAP the interaction between the two monomers is the most important allosteric contact between the PBC and the hinge. 2,3-loop and the N-terminal helical structure. Based on our analysis the latter is now recognized as a new conserved element of cis-regulated CNB domains – the N3A-motif. It is not present in at least one trans-regulated CNB domain – CAP; however, it can be found in another transcriptional regulator, Crp/Fnr family from Porphyromonas gingivalis trans-regulated CNB domains needs to be elucidated, but the fact that the allosteric interface between α and β-subdomains is significantly different  and #5 (D167). Spatial orientation of the turn is secured by another conserved interaction of residue #3 (Q165) with the β4,5-loop. The apparent reason for such rigidity is a correct positioning of the PBC arginine main chain, which is locked by two hydrogen bonds to residues #4 (G166) and #5 (D167). The tip of the arginine side chain is always bound to the #9 residue main chain (N171). A-domains of PKA regulatory subunits RIβ and RIIβ have an additional hydrogen bond to the side chain of residue #8 (D170). This interaction, however, is not conserved in other CNB domains. Alternatively, a polar bond between the eighth residue main chain and N-terminus of the B-helix was found in all studied structures. Thus, the possible role of the residue is to communicate between the PBC arginine and the hinge. The direct binding of residue #8 to the arginine, which is present in the PKA-R A-domains can substantially reinforce such communication.The most unexpected result was a very high scoring in the βine R209. The gua2,3-loop. This model is supported by the observation that the PBC-arginine side chain is not totally buried in the CNB domain, but partially exposed to the outer solution. According to the DSSP calculation 2 in the potassium channel to 30 Å2 in RIα∶A.These finding lead us to a suggestion that immobilization of the PBC arginine side chain has to be an important feature of the allosteric mechanism. Definitely, without a stable guanidinium group, one hardly can expect a stable bond with the ribose phosphate moiety of cAMP. Here we propose a general model describing the cyclic nucleotide related allostery . The mod2,3-loop, the hinge and the N3A-motif. Almost all elements interact with each other, leading to a rather complex allosteric mechanism. The primary function of the β2,3-loop is to position all parts of the arginine including the backbone, the hydrophobic part of the side chain and the guanidinium moiety. It also interacts with the hinge (through the residue #8) and the PBC (via hydrophobic contact between residue #7 and A202). The PBC serves, first of all, as a cyclic nucleotide molecule stabilizer, but it also plays an important role in immobilization of the arginine side chain (via A202). In addition, it interacts with the hinge and the N3A-motif, which usually forms protein-protein interface, providing the transition of the allosteric signal to the molecule, which contains the CNB domain.According to the proposed model stable cyclic nucleotide binding can be achieved only by interaction between all the major elements of the CNB domain: the PBC, the PBC-arginine, β2,3-loop. These elements were found in all studied cis-regulated CNB domains. The N3A motif is not present in CAP, which represents a trans-regulated CNB domain family. We propose a generalized allosteric mechanism for cis-regulated domains as follows: a) The PBC is a primary element, which binds sugar-phosphate moiety of cAMP. b) The β2,3-loop regulates the cAMP binding to the PBC via the conserved PBC-arginine. c) Both the PBC and the β2,3-loop communicate with the Hinge, which transfers the allosteric signal further to the N3A motif. d) The N3A-motif is the most malleable element of a CNB domain as it provides communication to the host protein.The LSP-alignment of H- and B-forms of different CNB domains revealed four conserved structural motifs: the PBC, the Hinge, the N3A-motif and the βα–Cβ vectors. The maximum distance between Cα atoms was 12 Å. Tolerance for Cα–Cα distance was 0.4 Å. Tolerance for Cα–Cβ distance was 0.75 Å. Tolerance for the dihedral angle between the vectors was 30°. Residues with the BLOSUM62 score greater than or equal to 1 were considered to be similar. Calculations were made on a personal computer  under Linux OS. Molecular graphics were prepared using PyMOL .The following structures were used in the current work: PKA∶RIα B-form Figure S1Study of the cAMP-induced conformational changes. Changes in (A) PKA∶RIIβ (A-domain) and (B) potassium channel (MloK1) by LSP-alignment.(0.09 MB DOC)Click here for additional data file.


Pathogenic bacteria adhere to the host cell surface using a family of outer membrane proteins called Trimeric Autotransporter Adhesins (TAAs). Although TAAs are highly divergent in sequence and domain structure, they are all conceptually comprised of a C-terminal membrane anchoring domain and an N-terminal passenger domain. Passenger domains consist of a secretion sequence, a head region that facilitates binding to the host cell surface, and a stalk region.Burkholderia contain an overabundance of TAAs, some of which have been shown to elicit an immune response in the host. To understand the structural basis for host cell adhesion, we solved a 1.35 Å resolution crystal structure of a BpaA TAA head domain from Burkholderia pseudomallei, the pathogen that causes melioidosis. The structure reveals a novel fold of an intricately intertwined trimer. The BpaA head is composed of structural elements that have been observed in other TAA head structures as well as several elements of previously unknown structure predicted from low sequence homology between TAAs. These elements are typically up to 40 amino acids long and are not domains, but rather modular structural elements that may be duplicated or omitted through evolution, creating molecular diversity among TAAs.Pathogenic species of The modular nature of BpaA, as demonstrated by its head domain crystal structure, and of TAAs in general provides insights into evolution of pathogen-host adhesion and may provide an avenue for diagnostics. Burkholderia pseudomallei and Burkholderia mallei are closely related gram-negative bacteria that are the causative agents of melioidosis and glanders, respectively. These organisms are considered biothreat agents and are classified by the NIAID as class B pathogens. Using bacteriophage-mediated immunoscreening, Tiyawisutsri et al. identified four Trimeric Autotransporter Adhesins (TAAs) in B. mallei that were expressed during glanders infectionet al. have homologs in B. pseudomallei. In total six TAAs were identified in B. mallei and nine in B. pseudomallei, indicating that these proteins are in relative abundance in comparison with other bacteria. TAAs have also been identified in Burkholderia cenocepacia, a respiratory pathogen associated with cystic fibrosis Yersinia enterocoliticaHaemophilus influenzaBartonella henselaeSalmonella entericaMoraxella catarrhalisPseudomonas aeruginosa provides additional insights into autotransporter function The earliest known and most well characterized TAA is YadA from a priorIn general, TAAs are highly variable in sequence and length, making them difficult to identify and define their domain boundaries B. pseudomallei are considerably larger and more complex. Several B. pseudomallei TAAs contain multiple head regions, regions of low complexity, and are up to 2800 amino acids long B. pseudomallei binds to its host cell surface, we have investigated several B. pseudomallei TAAs using a structural genomics approach B. pseudomallei trimeric autotransporter adhesin BpaA at 1.35 Å resolution. The BpaA head structure exhibits a novel fold of an intricately interwoven trimer that contains modular structural elements from other trimeric autotransporter proteins. Our work expands the foundation for understanding the structural basis for the adherence of infectious disease organisms to their hosts via TAAs.In comparison with YadA, the nine TAAs from B. pseudomallei contains a gene annotated as xadA or XadA-like protein based on sequence similarity to the TAA XadA from Xanthomonas oryzaeX. oryzae, we have adopted the term BpaA for the Burkholderia pseudomallei adhesion A protein investigated here. In B. pseudomallei, BpaA TAA is expressed as a 2757 amino acid long protein containing an N-terminal secretion peptide sequence, numerous head domains, a short coiled-coil domain, and a C-terminal membrane anchor domain  matched reasonably well with the domain boundaries predicted by the daTAA server despite differences in the presence and number of TAA sequence motifs  using selenomethionine or covalent heavy metal derivatization via mercury or platinum. Therefore, we attempted iodide ion soaking for SAD experimental phasing Attempts at molecular replacement using elements from YadA, Hia and Dex49a that have weak sequence similarity to BpaA as search models were unsuccessful. Therefore, we attempted The structure of the head domain of BpaA exhibits a tightly woven trimeric quaternary structure . No stru2 and a buried surface area of 18,583 Å2. The interior of the protein along the 3-fold trimer axis is highly hydrophobic  spanning residues 2278 to 2455 was cloned into a pAVA0421 vector using ligation independent cloning (LIC) E. coli using BL21(DE3)R3 Rosetta cells and autoinduction media 2, 3 mM β-mercaptoethanol, 25 units/ml of Benzonase® nuclease, and 0.05 mg/ml lysozyme). The resuspended cell pellet was disrupted on ice for 15 minutes with a Branson Digital Sonifier 450D . The cell debris was clarified by centrifugation on a Sorvall RC5 at 6,000 RPM for 60 min at 4°C. The protein was purified from the clarified cell lysate by immobilized metal affinity chromatography binding on Ni Sepharose High Performance resin  equilibrated with Binding Buffer . The recombinant protein was eluted with 500 mM imidazole and was further resolved by size-exclusion gel chromatography . Pure fractions collected in SEC Buffer  as a single peak were pooled. The protein was concentrated, flash frozen, and stored in −80°C. The affinity tag was not removed prior to crystallization trials.The third head domain of BpaA from Crystals were grown using the sitting drop vapor diffusion method at 16 °C using either the JCSG+ or PACT sparse matrix screens from Emerald BioSystems A 1.35 Å resolution native data set was collected remotely at the Advanced Light Source in Berkeley, CA USA using beamline 5.0.3 which has an ADSC Q315 detector. The data were reduced with HKL2000 The structure was solved by the single wavelength anomalous dispersion (SAD) method using iodide ions as the heavy atoms. Four iodide sites were located using Phenix


The soluble guanylyl cyclase (sGC) is a heterodimeric enzyme that, upon activation by nitric oxide, stimulates the production of the second messenger cGMP. Each sGC subunit harbor four domains three of which are used for heterodimerization: H-NOXA/H-NOBA domain, coiled-coil domain (CC), and catalytic guanylyl cyclase domain. The CC domain has previously been postulated to be part of a larger CC family termed the signaling helix (S-helix) family. Homodimers of sGC have also been observed but are not functionally active yet are likely transient awaiting their intended heterodimeric partner.To investigate the structure of the CC S-helix region, we crystallized and determined the structure of the CC domain of the sGCβ1 subunit comprising residues 348-409. The crystal structure was refined to 2.15 Å resolution.The CC structure of sGCβ1 revealed a tetrameric arrangement comprised of a dimer of CC dimers. Each monomer is comprised of a long a-helix, a turn near residue P399, and a short second a-helix. The CC structure also offers insights as to how sGC homodimers are not as stable as  active heterodimers via a possible role for inter-helix salt-bridge formation. The structure also yielded insights into the residues involved in dimerization. In addition, the CC region is also known to harbor a number of congenital and man-made mutations in both membrane and soluble guanylyl cyclases and those function-affecting mutations have been mapped onto the CC structure. This mutant analysis indicated an importance for not only certain dimerization residue positions, but also an important role for other faces of the CC dimer which might perhaps interact with adjacent domains. Our results also extend beyond guanylyl cyclases as the CC structure is, to our knowledge, the first S-helix structure and serves as a model for all S-helix containing family members. Mammalian guanylyl cyclases are key signaling proteins that produce the second messenger cGMP thereby regulating a variety of different processes such as vasodilation, diuresis, vision, and bone growth . These cThe mammalian sGC is a heterodimer with an α and β subunits. Each subunit has two isoforms, α1 α2 β1 β2, yet the sGCα1β1 is the most abundant whereas the sGCα2β1 is more predominant in brain tissue . The preManduca sexta's β3 subunit 14], and  subunit  and the  subunit  with het subunit  and α1α1[in vivo . Underst[in vivo -21 which[in vivo ; the cor[in vivo . Recent [in vivo  . This reRattus norvegicus sGC 348-409 CC domain were chosen such that it starts after the PAS-like/H-NOXA domain and ends before the cyclase domain . This sGC CC monomer has a theoretical pI of 5.62 and a calculated molecular weight of 7712.86Da. We performed analytic gel filtration of the CC construct prior to crystallization experiments to estimate the oligomerization state of this construct  to 2.15 Å resolution using SeMet single-anomalous-dispersion (SAD)-phasing . The eight monomers are conformationally similar to each other, with root-mean-square deviation (r.m.s.d.) for Ca atoms ranging between 0.27 Å and 1.50 Å based on pair-wise structural comparisons  form an anti-parallel CC dimer Figure . The sidm SOCKET , is indim SOCKET  whereas m SOCKET . Note thm SOCKET  which prm SOCKET  yielded m SOCKET .The CC of sGC harbors known regions found to be critical for dimerization. These include the broadly defined sGCa1 367-462  and sGCβThe CC domain is found in both mGCs and sGC and other S-helix containing proteins . The seqThe observation that the CC dimeric arrangement is anti-parallel is somewhat unexpected since other groups had suggested a parallel arrangement for guanylyl cyclase CC domains after having carried out molecular modeling studies of a parallel CC dimer ,28. Our 1UIX) as template for superimposing the sGCβ1 CC using the a-d residue assignment as guide. This generated homodimeric CC model was used to make a heterodimeric parallel CC model via changing the residues of one of the monomers into the sGCa1 sequence using COOT. These 3 models of aA helix CC dimers were not refined using minimization methods as they merely serve the purpose of visualizing the possibilities of salt-bridge formation of residues at the e and g positions. The following salt-bridges are possible for CC homo-/heterodimers in either a parallel or anti-parallel CC orientation:Whereas the a-d positions of a CC are favored to have leucine-like residues to promote CC formation, the residues at the e and g positions can be found to make inter-helix salt-bridges ,33: para-Anti-parallel heterodimer g-g' or e-e' salt-bridges: 1 (sGCa1 K448-sGCβ1 E361)-Anti-parallel homodimer g-g' or e-e' salt-bridges: 4 -Parallel heterodimer g-e' salt-bridges: 2 .-Parallel homodimer g-e' salt-bridges: 0This analysis suggests that only in the parallel CC orientation is there a possible electrostatic attraction benefit of a1β1 heterodimer formation compared to β1β1 homodimer formation .d:La ratio for parallel was found to be 3.5 but was 1.2 for anti-parallel CCs [a:(V+I)d was 14.2 for parallel and only 1.8 for anti-parallel. This ratio is also in agreement with that the isoleucine residue was found to be difficult to be accommodated at the d position for parallel CCs [d:La = 32:23 (45:42), (V+I)a:(V+I)d = 17:0 (22:0), and Na:Nd = 1:0 (3:0). These results indicate that the a:d appearance of three of the four analyzed residues  suggest that the CC orientation could be parallel (the L is less conclusive).In addition to electrostatic attraction, the presence of leucine, valine, isoleucine, and asparagine residues at either a or d positions can yield clues regarding the parallel or anti-parallel nature of CCs. Based on the analyses of previous dimeric CC structures, the Lllel CCs . In addillel CCs . Finallyllel CCs . To invevs anti-parallel CCs can come from the fact that these different orientations would have different distances between their termini which have to still somehow connect to their adjacent domains via intervening residues. We therefore constructed partial composite models of the H-NOXA/CC/GC domains with in one case a parallel and the other an anti-parallel CC orientation  mutant . In add studies . Note tht Figure  suggestit Figure  and theiWe have determined the structure of the CC domain of sGCβ1 which revealed a long aA helix, a short turn, followed by a short aB helix. The oligomeric state at first inspection seems to reveal a tetrameric anti-parallel arrangement yet our detailed analysis suggests that the dimer-of-dimers tetrameric arrangement is non-physiological and likely due to the hydrophobic end regions of the CC normally preferring to interact with other sGC protein regions that are missing in our crystallized construct. These hydrophobic CC end regions ended up therefore packing against another CC dimer to form this non-physiological tetramer. Within the CC dimer structure, we argue that the observed anti-parallel nature of the CC arrangement in the sGCβ1β1 homodimer is likely different from that in full length heterodimeric sGC since its sGCa1 CC partner is missing. In addition to having analyzed the possibilities of salt-bridge formation, residue preference at a-d positions, and inter-domain constraints regarding parallel vs anti-parallel CC formation for the S-helix, additional analysis also suggests a parallel arrangement as will be discussed next.1W25[2ASW and 3D36). Anantharaman et al. had therefore previously suggested that the S-helix could perhaps merge/extend from the termini of these HAMP and DNp domains [The S-helix is found in other non-guanylyl cyclase domain containing proteins and its proximity to other domains could also yield insights into the orientation arrangement since the distance of the termini is very different for the two different orientations. For example, the S-helix is also found in front of a diguanylate cyclase domain . sGC has evolved this characteristic despite that many of the individual subunits are known to homo-dimerize by themselves. The dimerization Kectively . Furtherunstable ,17,18. Aunstable . The CCDespite the above emphasis of the CC in guanylyl cyclases possibly being a signaling module, it is likely that not all signaling of the upstream domains in mGCs and sGCs goes solely through the CC domain to reach the catalytic GC domain. This is evident from that the H-NOX domain can directly interact and inhibit the GC domain in sGC .In addition to possible conformational changes involving just the long aA helix, activation conformational changes could also involve interactions of other sGC regions interacting with the end regions of the CC since there are a number of residues with conserved hydrophobicity . These end regions could perhaps interact with either CC's own aB helix, as in our structure since aB folds back on aA, and/or the small helix predicted to be between the H-NOXA and CC domain The coding sequence (residues 348-409) for the CC region from rsGCβ1 was subcloned into the pET15b vector between Nde I and BamH I restriction sites using the following primer set: Forward primer: 5'-gga att cca tat ggc tac acg aga cct ggt cct ttt-3', backward primer: 5'-cgc gga tcc tca ctt gtg tct cag ctc att ggc aac-3'. The I371 M mutant was generated by site directed mutagenesis method, to facilitate structure determination by SeMet phasing (forward primer: 5'-ca caa gag ctg gaa atG ctc aca gac agg ctg c-3' and backward primer: 5'-g cag cct gtc tgt gag Cat ttc cag ctc ttg tg-3'). The resulting pET15b_rsGCβ1_348-409I371 M plasmid encodes the following polypeptides in the T7 expression region: mgsshhhhhhssglvpr/gshmATRDLVLLGEQFREEYKLTQELEE. coli BL21 (DE3) pLysS cells (Invitrogen). The bacteria were grown in 8L M9 minimal media containing 50 μg/mL ampicillin and 37 μg/mL chloramphenicol at 37°C until a cell density of 1.2-1.4 OD600 was reached. The protein expression was induced with 300 μM isopropyl-β-D-thiogalactopyranoside (IPTG) supplemented with essential amino acids and selenomethionine for 8 hours at 37°C. The cells were harvested by centrifugation at 6000 rpm, followed by freezing at -80°C. The pellet was thawed on ice, resuspended in buffer A containing 20 mM Tris-HCl, 100 mM NaCl, 2 mM β-mercaptoethanol and lysed by sonication. The crude lysate was clarified by centrifugation at 16,000 rpm for 15 minutes, incubated with 3 mL Ni-NTA (Qiagen) for 4 hours and washed with buffer A plus 15 mM imidazole extensively. The protein was eluted using 50 mM buffer A plus 350 mM imidazole and dialyzed against 4L buffer A overnight. Thrombin (Sigma) digestion was carried out afterwards at 20°C for 8 hours and monitored by SDS-PAGE. The untagged protein product was loaded onto a Hitrap Q-sepharose (Amersham-Pharmacia Biotech) column and eluted with NaCl gradient. Fractions containing protein of interest were pooled together, concentrated and frozen in aliquots at -80°C. Prior to crystallization trials, a gel filtration polishing step using Superdex75 (Amersham-Pharmacia Biotech) was applied and the final protein buffer is 10 mM Tris-HCl, 100 mM NaCl, 2 mM β-mercaptoethanol.The pET15b_rsGCβ1_348-409I371 M vectors were transformed into 3 were grown in the above condition. For data collection at, the crystals were stepwise transferred into mother liquor reservoir solution with 5%, 10%, and 15% glycerol prior to freezing the crystal for data collection.X-ray diffraction-quality crystals of the CC construct were obtained at 20°C with the sitting-drop method by mixing 2 μl of the protein solution with 1 μl of the precipitant solution. Drops were equilibrated with a well containing 300 μl of 0.1 M Bis-Tris pH 7.0, and 0.7 M Ammonium Sulfate. Crystals of average dimensions of 300*20*20 μmhttp://pymol.sourceforge.net/. Coordinates and structure factors for the sGCβ1 CC domain have been deposited with the PDB (PDB identifier 3HLS).A Single Anomalous Dispersion (SAD) dataset for the rsGCβ1_348-409-I371 M crystal was collected at NSLS X29 beamline at Se peak wavelengths to 2.15 Å resolution and processed with HKL2000 . The crysGC: soluble guanylyl cyclase; H-NOX: heme-nitric-oxide-and-oxygen binding domain (or H-NOB); H-NOXA: heme-nitric-oxide-and-oxygen binding associate domain (or H-NOBA); H-NOB: heme nitric oxide binding domain; H-NOBA: heme nitric oxide binding domain associated domain; CC: coiled-coil; GC: guanylyl cyclase; ECD: extra-cellular domain; TM: transmembrane helix; KHD: kinase-homology domain.XM designed and carried out the experiments and wrote the initial draft of the manuscript. AB helped plan the experiments and aided in the manuscript revisions. FVDA designed experiments, analyzed the data and helped write the manuscript. All authors read and approved the final manuscript.


Understanding the constituent domains of oncogenes, their origins and their fusions may shed new light about the initiation and the development of cancers.We have developed a computational pipeline for identification of functional domains of human genes, prediction of the origins of these domains and their major fusion events during evolution through integration of existing and new tools of our own. An application of the pipeline to 124 well-characterized human oncogenes has led to the identification of a collection of domains and domain pairs that occur substantially more frequently in oncogenes than in human genes on average. Most of these enriched domains and domain pairs are related to tyrosine kinase activities. In addition, our analyses indicate that a substantial portion of the domain-fusion events of oncogenes took place in metazoans during evolution.We expect that the computational pipeline for domain identification, domain origin and domain fusion prediction will prove to be useful for studying other groups of genes. The first oncogene, SRC, was discovered in a chicken retrovirus in 1970 [An n cancer . The nor in 1970 . Since t in 1970 .Protein domains are compact and semi-independent units of a protein, each of which may consist of one or more contiguous segments of a peptide chain and have its own biological function . They arBcr and Abl genes or their constituent domains [Recent studies on oncogenes and cancer pathology have pointed to the importance of individual domains and domain fusions in oncogenesis. It has been reported that genes containing domains from specific domain families may have particular relevance to human cancer -13. For In this study, we have developed an integrated computational pipeline for studying the domain composition, domain fusion and domain origin. Specifically, our computational pipeline includes the following key components: (1) identification of the origin of each component domain of known oncogenes and the relevant fusion events; (2) co-occurrence analysis of oncogene domains; (3) identification of the domains and domain pairs that appear more frequently in oncogenes than in the background, namely the collection of all human genes; and (4) functional analyses of the identified frequent domains and domain pairs. We then applied this pipeline to all well characterized human oncogenes, and had a number of new and interesting observations. To the best of our knowledge, this is the first comprehensive analysis specifically addressing the domain composition, origin and fusion of oncogenes.Material and Methods, we have carried out a detailed analysis of oncogene domains and co-occurring domains for their origins and functional analysis.Using the computational procedures outlined in i.e., the different alignments for a specific domain in one clan and using one domain ID to denote the corresponding subtypes. In our dataset, there exist two alignments SH3_2 and SH3_1 for the SH3 domain. The same holds for the SAM domain, where SAM_PNT is the entry for the SAM domain and two different alignments, SAM_1 and SAM_2, exist for this entry, respectively. Although they have different accession numbers in Pfam, we just use SH3 and SAM_PNT to denote these two types of domains, respectively. The distribution of these domains' origins across different cellular organisms is given in Figure subtractive searching results , to take potential HGT into consideration. Based on the literature search, we found that domain SWIB and non-enzymatic domains ig and SAM are likely to have arisen in eukaryote. Their homologs are identified in prokaryotes, likely resulted from HGTs from eukaryotes [103 distinct domains [see Additional file karyotes . Also thkaryotes .versus that of the other genes, we have compared our results with Lipika et al. [i.e., Bacteria_only, Archaea_only, Bacteria_archaea, low level Eukaryotes, Metazoan, Chordate, Mammalian, Homo sapiens) compared with the whole domain dataset in the human genes and their p-values. Our results indicate that the origin distribution of oncogene domains is largely consistent with that reported by Lipika et al. [In order to further analyze the statistical difference between the domain origin distribution of oncogenes a et al. , which pa et al.  for the We divided the oncogene domains into groups based on their GO annotation Table . These oFurther analyses suggest that domains with different functions might have come from different origins Figure ; Table 2Among the 103 identified oncogene domains, 38 are found to be present in viruses  and the others contain at least three different domains. Their initial appearance in cellular organisms and their presence/absence in viruses are given [see Additional file Among the 50 fused domains, 7 events have been identified in viruses. These 7 fused domains can be divided into 4 categories according to their functions: pkinase-related domain fusion ; platelet-derived growth factor domain fusion ; helicases-related domain fusion  and DNA/ligand-binding domain fusion . Interestingly, ~90% of the virus proteins harbouring these fused domains come from the Potyviridae family and the remaining almost all come from the Orthoretrovirinae family. Potyviridae is one of the largest and most important families of plant viruses. Although the relationship between retroviruses and cancer has been widely established -21, the We have also examined the origins of all the oncogenes as a whole. Our goal is to find out at what stage in evolution all component domains of an oncogene are fused together for the first time, considered as the origin of the oncogene [see Additional file Among the 24 oncogenes whose initial domain fusions occurred in prokaryotes, 20 have the same domain fusions in viruses  is known to be a growth factor. This gene encodes one of the two polypeptide chains that together constitute PDGF, a platelet-derived growth factor domain. Our analysis shows that the PDGF domain generally originated in metazoans or chordates, and the corresponding oncogene first came into being in chordates.Only one oncogene PDGFB  receptor [KIT and ROS [The receptor . Several and ROS . These o and ROS .Transcription factors are nuclear proteins that regulate the expression of their target genes. They typically belong to multi-gene families that share common DNA-binding domains such as zinc fingers. Our data shows that oncogenes acting as transcription factors mostly originated in chordates, and a few of them (25%) came from metazoans. It has been speculated that the pathologically activated form of these transcription factors no longer fulfils their physiological regulating functions but acts as a carcinogen ,24.JUN, which can bind tightly to other nuclear onco-proteins. In addition, a substantial portion of oncogenes in this category belongs to the myc gene family that is related to nuclear transcription and myeloblastosis. It has been reported that the Myc genes have been found in a wide variety of vertebrates, including mammals, birds, amphibians, and fish [Many oncogenes of this category have been identified in our dataset. One representative is and fish ,26. The BCL2 [The first oncogene shown to regulate programmed cell death is BCL2 . SeveralBCL2 ,27. ThisWe have constructed a domain co-occurrence graph for 124 oncogenes, which consists of 105 domains (nodes) and 141 co-occurring domain pairs (edges), as shown in Figure A large-scale analysis of co-occurrence networks of the protein domains collected from the ProDom, Pfam and Prosite domain databases was previously performed by S. Wuchty , which fWe have identified a number of domains and domain pairs that are highly frequent in oncogenes, compared with those in the background human genome. We consider such domains as significant if they show high occurring frequencies and high numbers of co-occurring domains in the oncogenes but not in the background set. These domains are Pkinase_Tyr, SH2, SH3, RhoGEF and fn3, with functions related to signal transduction, enzymatic activity, and cell surface binding. Moreover, pkinase_Tyr has protein-tyrosine kinase activities, and SH2 and SH3 mediate protein interactions. They are known to play a key role in diverse biological processes such as growth, differentiation, metabolism and apoptosis in response to external and internal stimuli.p-values more significant than 10-6. It should be noted that 10-6 is a rather significant cut-off based on our experience in identifying frequent domain pairs from the background.To find out which domain pairs occur more frequently in oncogenes than in the background genome, we have carried out enrichment analyses of the domain pairs. Table Myc gene family. bHLH uses a common mechanism for DNA binding and dimerization while the Myc boxes, on the another hand, appear to be unique to the Myc family and are involved in transcription activation and neoplastic transformation [We expect that domain fusions might have brought new functions to their host proteins. This type of functional transformation has been reported previously ,29-31. Formation . While tormation .Two significant triad domain fusions, {SH2, SH3, Pkinase_Tyr} and {Furin-like, Recep_L_domain, Pkinase_Tyr}, are found Figure . PkinasePhylogenetic profiling is a computational technique for functional analyses of domains and their fusions [Diverse origins of frequent domains and domain pairs are found in cellular organisms through our phylogenetic profile analyses, which provide complementary information to our earlier analysis of domains and domain pairs.  fusions . We haveOur data show that nearly all frequent individual domains originated in prokaryotes, and have a wide distribution across many genomes, while the frequent domain pairs almost all first emerged in metazoans Table . TherefoWe have analyzed the origins of component domains and domain fusions of oncogenes, and studied the unique characteristics of the oncogene domain pairs in comparison with those in the background human genome. Most of these domains and domain pairs are functionally related to protein tyrosine kinase activities, which are closely related to cancer pathophysiology. Our phylogenetic profile analysis provides additional evidence to support our observation that frequent domain pairs in oncogenes tend to originate in higher organisms. The knowledge gained from this computational study may provide useful insights about the complex processes of oncongenesis.Homo sapiens were collected from CNIO OncoChip project website  and the Cancer Genome Anatomy Project database  [see Additional file 124 proto-oncogenes of et al. recently conducted a census of human cancer genes on the basis of genetic evidence [A proto-oncogene only becomes an oncogene when mutations or over-expressions take place . Note thevidence , whose cTwo sets of genomes and their encoded proteins were used in our study, one including the whole set of proteins encoded in 495 sequenced genomes  from the Integr8 database (release 58)  and the Homo sapiens with Pfam domain annotation was downloaded from the Integr8 database, which contains 25,025 protein sequences without splicing isoforms. This dataset set served as the background for our statistical analyses.The complete set of proteins of et al.[It should be noted that currently there is no well-accepted benchmark dataset for oncogenes. Since our data were mainly selected from CNIO OncoChip project and Cancer Genome Anatomy Project database, a likely bias may exist when compared with other datasets. One future plan of our work is to investigate several other cancer gene datasets, including those identified by exon sequencing studies such as TCGA  dataset et al., to derisubtractive search procedure [Our computational pipeline for identification of the origins of oncogene domains and domain fusion events consists of three main steps Figure . The firrocedure , in conjHomo sapiens, namely metazoans, chordates and mammalian , bacteria_only (first hit only in bacteria but not in archaea) and archaea_bacteria (first hit in both archaea and bacteria).We have used three types of nodes of origin for bacteria and archaea depending on the presence of remote homologs, i.e. TaxDom is developed to execute the procedure outlined above and to facilitate visualization of the search results. The program is written in Perl and Java.A tool package i.e., from eukaryotes to bacteria or archaea, has been reported only anecdotally [subtractive searching procedure, to fix any false origination classification for proteins that have been reported in literature to have emerged in eukaryotes first and then were transferred to prokaryotes. We have corrected such false origination predictions by our above procedure, based on our extensive literature search results.Horizontal gene transfer (HGT) has played a substantial role in organismal and genome evolutions . Gene trcdotally . Althougdomain co-occurrence graph [We define a ce graph  as folloThe following defines the enrichment ratio  of domaiN = the number of proteins in the background set,ns = the number of proteins in the background that contain domain pair s,M = the number of proteins in the oncogene set, andms = the number of oncogene proteins that contain domain pair s.p-value, knowing that it follows a hypergeometric distribution [We use the following formula to calculate the enrichment ratio of proteins that contain a specific domain pair in oncogenes and its ribution :TaxDom is the computational tool that we developed for visualizing domain evolution and their fusion events presented in this study, and it is freely accessible at .QL carried out the design and implementation of the computational pipeline and drafted the manuscript. JH was responsible for the evolutionary analysis of the oncogenes. HL and PW participated in the preparation and analysis of oncogene data. YX and XY conceived the study and coordinated the involved data analyses as well as writing the manuscript. All authors read and approved the final manuscript.Supplementary S2. 103 domains encoded from oncogene proteins.Click here for fileSupplementary S3. 50 whole domain fusions of oncogenes.Click here for fileSupplementary S4. Proteome-wide patterns of origin nodes in oncogene proteins.Click here for fileSupplementary S1. oncogene list.Click here for file


Caenorhabditis elegans proteomes. The domains recognize features of the last seven ligand positions, and we find 16 distinct specificity classes conserved from worm to human, significantly extending the canonical two-class system based on position −2. Thus, most PDZ domains are not promiscuous, but rather are fine-tuned for specific interactions. Specificity profiling of 91 point mutants of a model PDZ domain reveals that the binding site is highly robust, as all mutants were able to recognize C-terminal peptides. However, many mutations altered specificity for ligand positions both close and far from the mutated position, suggesting that binding specificity can evolve rapidly under mutational pressure. Our specificity map enables the prediction and prioritization of natural protein interactions, which can be used to guide PDZ domain cell biology experiments. Using this approach, we predicted and validated several viral ligands for the PDZ domains of the SCRIB polarity protein. These findings indicate that many viruses produce PDZ ligands that disrupt host protein complexes for their own benefit, and that highly pathogenic strains target PDZ domains involved in cell polarity and growth.PDZ domains are protein–protein interaction modules that recognize specific C-terminal sequences to assemble protein complexes in multicellular organisms. By scanning billions of random peptides, we accurately map binding specificity for approximately half of the over 330 PDZ domains in the human and  Caenorhabditis elegans, we were able to show that PDZ domains are more specific than previously appreciated. PDZ domains also remain functional under high mutational pressure, and only a few of the vast number of possible PDZ domain specificities are utilized in nature. These PDZ domain specificities are conserved from human to worm, implying that the specificities evolved early and were reused over evolution instead of being reshaped. The specificity map generated here was used to predict and experimentally confirm new viral PDZ-binding motifs. We present evidence that pathogenic viruses, including avian influenza, bind host PDZ domains via these motifs, thereby competing with signaling by host complexes, which leads to disruption of growth and polarity of the host cells.The PDZ domain is a structural domain that functions as a protein–protein interaction module that recognizes specific C-terminal peptide sequences to assemble intracellular complexes important in signaling pathways of multicellular organisms. These modules are associated with human disease and are targets of viruses and other pathogens. By examining peptide specificity and substrate diversity of roughly one half of the PDZ domains known to exist in human and the nematode  A genome-scale specificity map for PDZ domains reveals how family members recognize ligands to assemble signaling complexes and also reveals how viruses target these domains to subvert host cell function. Modular protein–protein recognition domains are involved in the assembly of numerous intracellular complexes that mediate diverse cellular functions. Thousands of recognition domains are contained within the human proteome, and these have been classified into over 70 distinct families . The PDZX[T/S]XφCOOH) and class 2 (XφXφCOOH), where X is any residue and φ is a hydrophobe [X[ED]XφCOOH, were also identified . T–4. Tp = Our prioritization approach is useful because potential ligands can be considered for experimental follow-up in order of similarity to the phage-mapped specificity profile. For instance, when studying a particular protein of interest, it may be useful to expand the list of potential ligands to include additional lower-scoring ligands that may nonetheless be physiologically relevant. To illustrate the utility of this approach, we focused on DLG1, one of the first and best characterized PDZ-containing proteins  2,5,41],5,412,5,Pathogenic viruses and bacteria use short linear peptide motifs that target PDZ domains and other peptide-binding modules to perturb host signaling networks ,10–13. TTo further explore viral targeting of PDZ domain proteins, we focused on SCRIB, a protein known to be targeted by human papilloma virus (HPV) . SCRIB cSome of our predicted viral ligands are known, whereas many are novel. The HPV E6 protein (peptide 1) is known to disrupt SCRIB function and causes loss of epithelial cell polarization and concomitant hyperproliferation , and theCOOH) of the cell-surface receptor Crtam and SCRIB in T cells, which is critical to attenuate proliferation and maintain a late phase of T cell polarity [To further explore this hypothesis, we investigated whether the PDZ-binding motif alone is sufficient to induce effects associated with pathogenic viral infections. Based on our PDZ specificity map, we designed a synthetic peptide that binds to the SCRIB PDZ domains with high affinity (peptide 14). We recently described an interaction between the C terminus  BLAST , PFAM 3, and SMAS-transferase, as described [DNA fragments encoding PDZ domains of interest  and S2 wescribed ,28. All 10 unique members) were used to isolate ligands for PDZ domains using a series of iterative panning steps, as described [http://baderlab.org/Data/PDZ).C-terminal phage-displayed peptide libraries (>10escribed . Specifiescribed . For othSP for a given column (position) of a PWM was calculated as is done for the letter height in a sequence logo [SP value of one means the given PDZ domain is completely specific for a single amino acid at that position, and a value of zero means that there is no preferred amino acid at that position. As no domains exhibited specificity at position −9, the SP value for each position was corrected for bias in the peptide library by subtracting the specificity score at position −9 for the entire set of 3,066 unique PDZ domain ligands .For each PDZ domain, the set of peptide ligands was used to create a binding profile statistical model as a PWM. The nce logo , except D, was then calculated using the following distance metric:A specificity map  was consa = PWM A; b = PWM B; w = number of columns in the PWM ; ∑ = alphabet used in PWM = the number of rows in the PWM ; and L = a letter from the reduced alphabet.where http://baderlab.org/Software/LOLA. The tree was manually annotated by an expert (SSS) to define specificity classes.This distance metric is normalized such that zero represents perfectly similar PWMs and one represents perfectly dissimilar PWMs. Similarity is calculated as 1 − distance. The clustering results were visualized in standard fashion (as a tree with branch lengths corresponding to PWM pair distances). Leaf ordering for graphical tree display was optimized using the algorithm of Bar-Joseph et al. . ClusterD, described above, on a per PWM position basis (one position per heat map table cell). PWMs used 20 amino acids, instead of the reduced set described above, to provide a more fine-grained measure of PWM distance (which requires that the ∑ parameter in Equation 1 be the set of 20 amino acids). Resulting differences were then normalized across all 91 mutants per position . The linear color gradient represents the difference of the mutant specificity profile compared to wild type, from minimum (blue) to maximum (red) distance. Significant differences are greater than one standard deviation away from the mean and highlighted in green. The map was then manually annotated with relevant sequence logos and structures.Each row of the heat map depicted in The PWM representing the specificity profile for each of the 82 mapped PDZ domains was used to search for C-terminal matches in the RefSeq human proteome  and viral proteome  sets, downloaded May 21, 2007 . SCRIB vAdditional ligands can be found by choosing a more liberal score threshold. We chose not to optimize the score threshold to maximize overlap of predicted interactions to a benchmark, since we could find no suitable benchmark. Available interactions in which the PDZ domain involved in the interaction was known were too few, and remaining interactions involved full-length proteins without domain-level resolution. We did not complete this conservative prioritization for worm due to the absence of almost any known interactions for worm PDZ domain containing proteins and poor Gene Ontology (GO) annotation coverage for predicted ligands.p-value of 8.6 × 10−18. The p-value was computed by calculating the overlap of 1,000 random shufflings of our prioritized interactions with the UniHI benchmark. Our randomly shuffled prioritized interactions overlapped approximately 6.7 interactions in the benchmark on average, with a standard deviation of approximately 3.4 and with a normal distribution. The normal distribution was used to calculate the p-value.The overlap statistic was computed based on all protein interactions involving our mapped PDZ domains in the UniHI database . ApproxiFor predicted endogenous PDZ domain ligands, GO term enrichments were computed against all available GO annotation packaged with BiNGO on January 17, 2007, using the BiNGO Cytoscape plugin ,54 with http://baderlab.org/Software/LOLA and for searching sequence databases using a specificity profile to find potential protein interactions via a Cytoscape plugin from http://baderlab.org/Software/BRAIN.All computational analyses were performed using custom Java software built with BioJava 1.4 and JFreeChart. Free, open source Java software for visualizing and clustering specificity profiles is available from http://baderlab.org/Data/PDZ and have been submitted to the DOMINO and PDZBase (accession codes cpe_3 to cpe_176) databases.All peptide sequences are available from 50) values using competition ELISAs, as described [50 value was defined as the concentration of peptide that blocked 50% of PDZ domain binding to immobilized peptide.Peptides were synthesized with acetylated C termini. The binding affinities of peptides for PDZ domains were determined as 50% inhibition concentration  was added, and the plates were harvested 8 h later.Naïve escribed . On day Figure S1Each column heading shows the wild-type sequence at each position, which is labeled according to a structure-based nomenclature shown in (3.85 MB PDF)Click here for additional data file.Figure S2The relationship between overall PDZ domain sequence identity and specificity profile similarity. Each point represents a pair of PDZ domains from our mapped set. Red circles represent pairs assigned to the same class, as defined in our specificity map, and blue squares represent all other pairs.(267 KB PDF)Click here for additional data file.Figure S3p-value of the overrepresentation of the GO term in the set relative to the number of genes in the genome. White nodes are not significantly overrepresented, however they are included in order to illustrate the GO structure within the three different categories.Overrepresented terms for the human proteins in (A) GO biological process.(B) GO molecular function.(C) GO cellular localization.(850 KB PDF)Click here for additional data file.Figure S4Overrepresented GO biological process terms for the proteins in (270 KB PDF)Click here for additional data file.Table S1Escherichia coli. The listed amino acid ranges indicate the length of the constructs used in the analysis and not necessarily the PDZ domain boundaries defined by computational domain identification.The domains are colored as follows: green, purified and peptide-phage selections were successful; blue, purified but peptide-phage selections were unsuccessful; grey, not cloned or could not be purified in a soluble form from (43 KB PDF)Click here for additional data file.Table S2E. coli. The listed amino acid ranges indicate the length of the constructs used in the analysis and not necessarily the PDZ domain boundaries defined by computational domain identification.The domains are colored as follows: green, purified and peptide-phage selections were successful; blue, purified but peptide-phage selections were unsuccessful; grey, not cloned or could not be purified in a soluble form from (47 KB PDF)Click here for additional data file.Table S3C. elegans domains are highlighted in yellow, and values greater than or equal to 0.2 are highlighted in green.Values were only determined for 72 domains that had ten or more selected peptides. (41 KB PDF)Click here for additional data file.Table S4Specificity of unmapped domains is predicted to be highly similar (>0.83 profile similarity) to the mapped domains with greater than 70% sequence identity in the binding site. The species of origin is shown to the right of each domain.(9.07 MB PDF)Click here for additional data file.Table S5Click here for additional data file.Prioritized Endogenous and Viral Ligands for Human PDZ DomainsPDZ domains are listed in alphabetical order and prioritized ligands are listed in ascending order by interaction score. Lower interaction scores are better. For each domain, only those viral ligands with better scores than the best endogenous ligand are shown. Viral or known endogenous ligands are highlighted in magenta or yellow, respectively.(74 KB PDF)Table S6PDZ domains are listed in numerical order, and for each, the 50 ligands with the best prediction score are listed in ascending order by interaction score. Lower interaction scores are better. Known ligands reported in PDZBase for DLG1 or the other DLG homologs  are highlighted in yellow or blue, respectively.(41 KB PDF)Click here for additional data file.Table S7NA denotes ligands that were not predicted by the scoring algorithm.Known ligands reported in PDZBase are ordered in ascending order of interaction score for the highest scoring PDZ domain . Lower i(24 KB PDF)Click here for additional data file.