The majority of mitochondrial proteins are targeted to mitochondria by N-terminal presequences and use the TIM23 complex for their translocation across the mitochondrial inner membrane. During import, translocation through the channel in the inner membrane is coupled to the ATP-dependent action of an Hsp70-based import motor at the matrix face. How these two processes are coordinated remained unclear. We show here that the two domain structure of Tim44 plays a central role in this process. The N-terminal domain of Tim44 interacts with the components of the import motor, whereas its C-terminal domain interacts with the translocation channel and is in contact with translocating proteins. Our data suggest that the translocation channel and the import motor of the TIM23 complex communicate through rearrangements of the two domains of Tim44 that are stimulated by translocating proteins.DOI:http://dx.doi.org/10.7554/eLife.11897.001 Human, yeast and other eukaryotic cells contain compartments called mitochondria. These compartments are surrounded by two membranes and are most famous for their essential role in supplying the cell with energy. While mitochondria can make a few of their own proteins, the vast majority of mitochondrial proteins are produced elsewhere in the cell and are subsequently imported into mitochondria. During the import process, most proteins need to cross both mitochondrial membranes.Many mitochondrial proteins are transported across the inner mitochondrial membrane by a molecular machine called the TIM23 complex. The complex forms a channel in the inner membrane and contains an import motor that drives the movement of mitochondrial proteins across the membrane. However, it is not clear how the channel and import motor are coupled together. There is some evidence that a protein within the TIM23 complex called Tim44 – which is made of two sections called the N-terminal domain and the C-terminal domain – is responsible for this coupling. It has been suggested that mainly the N-terminal domain of Tim44 is required for this role.Banerjee et al. used biochemical techniques to study the role of Tim44 in yeast. The experiments show that both the N-terminal and C-terminal domains are essential for its role in transporting mitochondrial proteins. The N-terminal domain interacts with the import motor, whereas the C-terminal domain interacts with the channel and the mitochondrial proteins that are being moved.Banerjee et al. propose a model of how the TIM23 complex works, in which the import of proteins into mitochondria is driven by rearrangements in the two domains of Tim44. A future challenge is to understand the nature of these rearrangements and how they are influenced by other components of the TIM23 complex.DOI:http://dx.doi.org/10.7554/eLife.11897.002 Mitochondria perform a number of essential cellular functions ranging from production of ATP and diverse other metabolic intermediates to initiation of apoptosis. It is thus not very surprising that disturbances in mitochondrial function are associated with a number of human diseases, including neurodegenerative disorders, diabetes, and various forms of cancer . An esseThe TIM23 complex mediates translocation of presequence-containing precursor proteins into the matrix as well as their lateral insertion into the inner membrane. The latter process requires the presence of an additional, lateral insertion signal. After initial recognition on the intermembrane space side of the inner membrane by the receptors of the TIM23 complex, Tim50 and Tim23, precursor proteins are transferred to the translocation channel in the inner membrane in a membrane-potential dependent step . The traThe translocation channel and the import motor of the TIM23 complex are thought to be coupled by Tim44, a peripheral inner membrane protein exposed to the matrix . Like otTim44 binds to the Tim17-Tim23 core of the translocation channel . Tim44 aTim44 is composed of two domains, depicted as N- and C-terminal domains . Recent in trans, can support, although poorly, growth of yeast cells, giving us a tool to dissect the role of the C-terminal domain in vivo. We identify the C-terminal domain of Tim44 as the domain of Tim44 that is in contact with translocating proteins and that directly interacts with Tim17, a component of the translocation channel. Our data suggest that intricate rearrangements of the two domains of Tim44 are required during transfer of translocating precursor proteins from the channel in the inner membrane to the ATP-dependent motor at the matrix face.We report here that the function of the full-length Tim44 cannot be rescued by its N-terminal domain extended to include membrane-recruitment helices of the C-terminal domain, demonstrating an unexpected essential function of the core of the C-terminal domain. Surprisingly, we observed that the two domains of Tim44, when expressed URA plasmid carrying the wild-type, full-length copy of Tim44, no viable cells were obtained  with that of the strain having two Tim44 domains, both containing A1 and A2 helices, expressed and 30°C . Still, We isolated mitochondria from FL and N+C strains grown on fermentable medium and compared their mitochondrial protein profiles. Immunostaining with antibodies raised against full-length Tim44 detected no full-length protein in N+C mitochondria but rather two faster migrating bands . Based oWe conclude that Tim44 can be split into its two domains that are sufficient to support the function of the full-length protein, although only poorly.in vivo △DHFR, and pSu9(1–69)DHFR, were drastically reduced in N+C mitochondria when compared to wild type. Import of presequence-containing precursor of Oxa1 that contains multiple transmembrane segments was similarly impaired. Likewise, precursor proteins that are laterally inserted into the inner membrane by the TIM23 complex, such as pDLD1 and pcytb2, were imported with reduced efficiency into N+C mitochondria. In agreement with the established role of Tim44 in import of precursors of a number of components of respiratory chain complexes and their assembly factors, we observed a slightly reduced membrane potential in N+C mitochondria as compared to wild type △DHFR as a TOM-TIM23-spanning intermediate. Briefly, this precursor protein consists of the first 167 residues of yeast cytochrome b2, with a 19 residue deletion in its lateral insertion signal, fused to the passenger protein dihydrofolate reductase. In the presence of methotrexate, that stabilizes folded DHFR, the b2 part reaches the matrix, whereas the DHFR moiety remains on the mitochondrial surface resulting in an intermediate that spans both TOM and TIM23 complexes. The association of Tim44 and its domains with the arrested precursor protein was analyzed by chemical crosslinking followed by immunoprecipitation with antibodies to full-length Tim44 and its individual domains. In wild-type mitochondria, all three antibodies precipitated a crosslinking adduct of Tim44 to the arrested precursor protein, demonstrating that they are all able to immunoprecipitate the respective antigens , suggesting that the yeast system is more robust. This observation is in agreement with the notion that mutations that would severely affect the function of the TIM23 complex would likely be embryonically lethal in humans. Still, the disease caused by a mutation in the C-terminal domain of human Tim44 speaks for an important role of this domain in the function of the entire TIM23 complex. Furthermore, the mutation maps to the short loop between A3 and A4 helices in the C-terminal domain of Tim44. Based on the crystal structure of Tim44, it was previously suggested that the mutation could affect the conformational flexibility of the A1 and A2 helices  containing a full-length TIM44 followed by replacement of the chromosomal copy of TIM44 with a HIS3 cassette by homologous recombination. For complementation analyzes, endogenous promoter, mitochondrial presequence (residues 1–42) and the 3’-untranslated region of TIM44 were cloned into centromeric yeast plasmids pRS315 (LEU marker) and pRS314 (TRP marker) and obtained plasmids subsequently used for cloning of various Tim44 constructs. The following constructs were used in the analyzes: Tim44(43–209), Tim44(43–262), Tim44(264–431), and Tim44(210–431). The constructs encompassing the N- and the C-terminal domains of Tim44 were cloned into pRS315 and pRS314 plasmids, respectively. Plasmids carrying the full-length copy of TIM44 were used as positive controls and empty plasmids as negative ones. A Tim44 plasmid shuffling yeast strain was transformed with two plasmids simultaneously and selected on selective glucose medium lacking respective markers. Cells that lost the wild-type copy of Tim44 on the URA plasmid were selected on medium containing 5-fluoroorotic acid at 30°C.Wild-type haploid yeast strain YPH499 was used for all genetic manipulations. A Tim44 plasmid shuffling yeast strain was made by transforming YPH499 cells with a pVT-102U plasmid  , Tim44(43–209) (referred to as N in E. coli BL21(DE3) at 37°C and purified using affinity chromatography on NiNTA-agarose beads  followed by gel filtration on Superdex 75 column . Unless otherwise indicated, the His6-tags were removed by incubation with the TEV protease. The purified proteins were stored at -80oC in 20 mM HEPES/KOH, 200 mM KCl, 5 mM MgCl2, pH 7.5, until use.DNA sequences coding for various segments of Tim44 were cloned into bacterial expression vector pET-Duet1 introducing a TEV cleavage site between the HisoC. After three washing steps, specifically bound proteins were eluted with Laemmli buffer. Samples were analyzed by SDS–PAGE and immunoblotting.Purified proteins were coupled to CNBr-Sepharose beads  according to manufacturer's instructions and stored at 4°C. The beads were used for purification of domain-specific antibodies from the serum raised in rabbits against recombinantly expressed full-length Tim44. For direct binding analysis, mitochondria isolated from wild-type yeast cells were solubilized with 0.5% Triton X-100 in 20 mM Tris/HCl, pH 8.0, 80 mM KCl, 10% glycerol at 1 mg/mL and incubated with Tim44 constructs coupled to CNBr-Sepharose beads for 30 min at 4Thermal stabilities of wild type and P282Q mutant form of Tim44 were analyzed by fluorescence thermal shift assay . RecombiPreviously published procedures were used for protein import into isolated mitochondria, crosslinking, coimmunoprecipitations and arrest of mitochondrial precursor proteins as TOM-TIM23 spanning intermediates followed by crosslinking and immunoprecipitation under denaturing conditions . review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.eLife posts the editorial decision letter and author response on a selection of the published articles . An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent  and three reviewers, one of whom, Klaus Pfanner, is a member of our Board of Reviewing Editors. The reviewers have discussed the reviews with one another and the Reviewing editor has drafted this decision to help you prepare a revised submission.Thank you for submitting your work entitled "Rearrangements of the two domains of Tim44 drive protein translocation into mitochondria" for consideration by Summary:Mitochondrial import of matrix proteins requires the mitochondrial import motor to drive protein translocation. The manuscript by Banerjee et al. investigates the structure function relation of Tim44, an essential subunit of the mitochondrial import motor. To dissect the N- and C-terminal domains functionally, the authors expressed these domains in trans and find that they suffice to confer viability to yeast cells lacking TIM44. Their analyses reveal that mitochondria with "split" Tim44 display transport defects along the presequence import route. Their analyses suggest that the N- and C-terminal domains engage with different modules of the presequence translocase. In comparison to full length Tim44, the split protein causes reorganization within the presequence translocase, implicating Tim44 in translocase/motor communication.This is an interesting analysis that provides new and unexpected insight into how the import motor constituent Tim44 engages with the presequence translocase. Moreover, the data provides evidence for a link between motor dynamics and translocase organization. Thus, the manuscript addresses a long standing question on how the dynamic of the import motor is linked to translocase function.Essential revisions:1) Since rearrangements of the two domains of Tim44 are not directly demonstrated, the title should be modified to better reflect the main message of the paper.2) The observed import defects of sorted and matrix targeted precursors is surprising. Why are sorted proteins affected that should not depend on the ATP driven motor and thus not on Tim44 function? The authors should assess the membrane potential directly to exclude indirect effects. Since the membrane potential-dependence of precursors varies a lot, AAC import alone might be misleading. In addition, carrier transport should be analyzed also by BN PAGE.3) Ting et al. (2014) reported that the N-terminal domain of Tim44 interacts with Tim23 as well as motor components by using crosslinking in intact mitochondria. Using purified Tim44 domains and mitochondria lysed with detergent, the authors confirmed the interaction of the N-terminal domain with motor components, however, not with Tim23. They speculate that crosslinking of the N-terminal Tim44 domain to Tim23 requires a specific conformation of Tim23 only adopted when Tim23 is bound to Tim17 in the inner membrane (i.e. in intact mitochondria). Since the authors demonstrate the interaction of the C-terminal domain of Tim44 with Tim17 by using the purified domains and lysed mitochondria, but use crosslinking in intact mitochondria for showing the interaction with precursor proteins, the two approaches should be compared more directly. A suitable approach would be to analyze the interaction of the C-terminal domain of Tim44 with Tim17 in intact mitochondria by crosslinking. They may use radiolabeled Tim17 imported into mitochondria and their domain-specific Tim44 antibodies to address this topic.4) The purification experiments shown in 5) 6) The conclusion that the TIM23 complex adopts an "altered conformation" in N+C mitochondria is based on crosslinking experiments. It would be good to support this conclusion by an independent approach, such as native gel electrophoresis or differential protease sensitivity of the TIM23 components.Moreover, the term "altered conformational flexibility" is not fully fitting to the results presented here. The authors rather look at an altered subunit composition and not protein conformations. The text should be changed.7) The part on the human patient mutation mimicked in yeast Tim44 is interesting, but not required for the rest of the story. Does the mutation in Tim44 affect the TIM23 complex or protein import in yeast or humans? As it stands it is not very informative. The authors should either remove that part or perform a functional analysis, i.e. test mutant cells/mitochondria for the functionality of the Tim44 C-terminal domain in the binding of Tim17 and precursor binding during mitochondrial import. 1) Since rearrangements of the two domains of Tim44 are not directly demonstrated, the title should be modified to better reflect the main message of the paper.The title was modified to now read “Two domains of Tim44 have different roles in the presequence translocase during transport of proteins into mitochondria”. We agree that the title may have been somewhat misleading as we have indeed not directly demonstrated rearrangements of the two domains of Tim44.2) The observed import defects of sorted and matrix targeted precursors is surprising. Why are sorted proteins affected that should not depend on the ATP driven motor and thus not on Tim44 function? The authors should assess the membrane potential directly to exclude indirect effects. Since the membrane potential-dependence of precursors varies a lot, AAC import alone might be misleading. In addition, carrier transport should be analyzed also by BN PAGE.We would not agree that the import defects of TIM23 substrates we observed in N+C mitochondria are in general surprising. Tim44 has been identified over twenty years ago as an essential component of the TIM23 complex required for transport of presequence-containing precursors into mitochondria. Over the years, several temperature-sensitive mutants of Tim44 have been identified that specifically affected transport along the TIM23 pathway. We would thus rather argue that it would be surprising if in N+C mitochondria, which contain only a split version of this protein, no import defect was observed. We were certainly surprised to see that the N+C cells are viable at all. The point that we observed no difference between sorted and matrix targeted precursors may potentially be seen as surprising. However, it should also be said that the role of Tim44 in the differential sorting of proteins by the TIM23 complex has not been carefully analyzed, at least to the best of our knowledge, in contrast to mtHsp70 and the J proteins Tim14/Pam18 and Tim16/Pam16. While the latter proteins are very likely directly involved in the ATP-dependent steps of protein transport, the exact role of Tim44 is far less clear. Indeed the data presented in this manuscript would suggest a role of Tim44 also at an earlier stage of protein transport, which could affect both sorted as well as matrix-targeted precursors. Also, we have previously seen that Tim44 can be crosslinked to laterally sorted precursors , even when their transport does not depend on the ATPase activity of the import motor. However, as differential sorting of precursors by the TIM23 complex is not a focus of this manuscript, we removed this point from the text to simplify it for non-specialists.We measured the membrane potential in FL and N+C mitochondria and, not surprisingly, observed its slight reduction in N+C mitochondria, in agreement with the very strong growth defect of N+C cells and with the established essential role of Tim44 in transport of a number of components of respiratory chain complexes and their assembly factors into mitochondria. In this respect, it is also worth noting that even in glucose medium at either 24°C or 30°C, N+C cells grow about three times slower than FL. In addition, rather than analyzing import of AAC by another method, we analyzed import of another TIM23-independent substrate, Tim23 itself. Like with AAC, we also observed no major difference in its import efficiency in FL and N+C mitochondria. These data are now included in 3) Ting et al. (2014) reported that the N-terminal domain of Tim44 interacts with Tim23 as well as motor components by using crosslinking in intact mitochondria. Using purified Tim44 domains and mitochondria lysed with detergent, the authors confirmed the interaction of the N-terminal domain with motor components, however, not with Tim23. They speculate that crosslinking of the N-terminal Tim44 domain to Tim23 requires a specific conformation of Tim23 only adopted when Tim23 is bound to Tim17 in the inner membrane (i.e. in intact mitochondria). Since the authors demonstrate the interaction of the C-terminal domain of Tim44 with Tim17 by using the purified domains and lysed mitochondria, but use crosslinking in intact mitochondria for showing the interaction with precursor proteins, the two approaches should be compared more directly. A suitable approach would be to analyze the interaction of the C-terminal domain of Tim44 with Tim17 in intact mitochondria by crosslinking. They may use radiolabeled Tim17 imported into mitochondria and their domain-specific Tim44 antibodies to address this topic.We agree that it would be excellent to demonstrate a direct Tim17-Tim44 interaction also by another assay such as crosslinking. However, to the best of our knowledge, no crosslinks of Tim17 to any other component of the TIM23 complex have ever been seen in wild type mitochondria, radiolabeled or not, not even to Tim23, even though their interaction was demonstrated by several different assays. The only crosslinks to Tim17 observed so far were upon a very comprehensive cysteine-scanning mutagenesis of Tim23 followed by cysteine-specific crosslinking  and the above mentioned paper by Ting et al. that used site-specific UV crosslinking to demonstrate an interaction between Tim17 and Pam17. Since the surfaces of either Tim17 or Tim44 involved in this interaction remain currently unknown, we feel that undertaking such a comprehensive site-specific approach would be beyond the scope of this manuscript.4) The purification experiments shown in  All the experiments presented in this manuscript are representatives of experiments repeated at least three times. In addition, all the critical experiments were not only performed with at least three biological replicates but were also repeated by at least two different researchers to make sure that all the observed effects are reproducible.Following the suggestion of the reviewers, we included a panel containing quantifications of binding of mtHsp70, as a representative of the import motor components, and of Tim17 to the various beads that were obtained in three different experiments. In addition, we added to the Figure legend the information that input contains 4.5% of the material used for binding. This information was unfortunately missing in the original submission.The original 5) The new 6) The conclusion that the TIM23 complex adopts an "altered conformation" in N+C mitochondria is based on crosslinking experiments. It would be good to support this conclusion by an independent approach, such as native gel electrophoresis or differential protease sensitivity of the TIM23 components.Moreover, the term "altered conformational flexibility" is not fully fitting to the results presented here. The authors rather look at an altered subunit composition and not protein conformations. The text should be changed.We greatly appreciate this suggestion. We analyzed the TIM23 complex by blue native PAGE and indeed observed a changed running behavior of both Tim17 and Tim23 in N+C mitochondria as compared to FL. Since this alteration mainly affected the 90 kDa complex whose only known components are Tim17 and Tim23, this finding provides further support for our notion that the conformation, rather than the composition, of the TIM23 complex is affected in N+C mitochondria. This new finding is now included in As also discussed above, since the coimmunoprecipitation experiments revealed no major changes in the composition of the complex and both crosslinking and BN-PAGE support the notion of the changed conformation of the complex, we think that the term “altered conformational flexibility” is fitting our results and is supported by them.7) The part on the human patient mutation mimicked in yeast Tim44 is interesting, but not required for the rest of the story. Does the mutation in Tim44 affect the TIM23 complex or protein import in yeast or humans? As it stands it is not very informative. The authors should either remove that part or perform a functional analysis, i.e. test mutant cells/mitochondria for the functionality of the Tim44 C-terminal domain in the binding of Tim17 and precursor binding during mitochondrial import.We agree that the patient mutation mimicked in yeast Tim44 is not required to understand the rest of the story and have therefore removed this finding from the Abstract. As we also previously stated in the Discussion, we have performed initial analyses of yeast cells carrying this mutation and we observed neither an obvious growth nor an obvious in vivo import defect in these cells. Both these observations are in agreement with the relatively mild effect seen in the affected patients . We did however observe that the mutation partially destabilizes the protein and we feel that this is an important observation as it may provide a clue as to what goes wrong in the affected patients and may stimulate studies with patient cell lines, which are clearly beyond the scope of this manuscript. We would thus prefer to keep this finding in the manuscript but are happy to leave the final decision to the editors.


Our results show that β elements are not highly represented in DFBSs compared to α and γ elements. At the DDI level, all classes of binding sites tend to preferentially bind to the same class of binding sites and α/β contacts are significantly disfavored. Very few DFBSs are promiscuous: 80% of them interact with just one Pfam domain. About 50% of our Pfam domains bear only one single-partner DFBS and are therefore monogamous in their interactions with other domains. Conversely, promiscuous Pfam domains bear several DFBSs among which one or two are promiscuous, thereby multiplying the promiscuity of the concerned protein.While the number of solved 3D protein structures continues to grow rapidly, the structural rules that distinguish protein-protein interactions between different structural families are still not clear. Here, we classify and analyse the secondary structural features and promiscuity of a comprehensive non-redundant set of domain family binding sites (DFBSs) and hetero domain-domain interactions (DDIs) extracted from our updated KBDOCK resource. We have partitioned 4001 DFBSs into five classes using their propensities for three types of secondary structural elements (“ Protein-protein interactions (PPIs) are central to many biological processes. At the three-dimensional (3D) structural level, proteins often perform their function by interacting with other proteins to form protein-protein complexes. These complexes may consist of homo-dimers or higher order homo-multimers, or they may involve heteromeric interactions between different protein chains. While homo-interactions are observed relatively often in crystal structures, most processes of biological interest involve hetero interactions, and the corresponding structures are normally much more difficult to determine experimentally and to predict computationally . Consequet al. (2008) .Given a binding site DFBSf,bQ(s).Similarly, Equation (2) shows how we calculate the SSE propensity of a whole domain surface, s on the whole domain surface of member m ∈ f,bD.This calculation uses the same set of DDIs as above but It can be noted that by construction we haveα, β and γ. The three SSE propensities were then calculated as described in the Methods section. We initially attempted to define SSE classes automatically using various clustering methods . However, no well separated clusters ever appeared in the data and the calculated log-likelihood values were not significant. Furthermore, data visualization suggested a quasi-continuous distribution of the data over the three SSE classes.The secondary structural features of DFBSs and domain surfaces were estimated after annotating each amino acid residue with the DSSP program . The eighttp://mypage.iu.edu/tthomps/programs/html/tnttriplot.htm). In order to do this, the three SSE propensities were plotted in percent units on the ternary diagram, for each binding site and for each domain surface, as shown in Consequently, we decided to analyse the overall distribution of the three SSE propensities over DFBSs and domain surfaces using ternary diagrams (P(α) = 70% and P(α) = 30% on the one hand, and P(γ) = 70% and P(γ) = 30% on the other hand. Numerical details of the resulting 5 SSE classes are given in As can be seen from the ternary diagrams in α-rich”, and class 2 as “γ-rich”, because α and γ SSEs clearly predominate in these two classes, respectively. Similarly, it is natural to name class 3 as “α + γ”, class 4 as “β + γ”, and class 5 as “α + β + γ”, because this last class always has a significant proportion of each SSE type. It is worth noting that because the number of DFBSs and domain surfaces having P(β) ≥ 70%  is too low to be considered even as a subclass of the poorly populated α + β + γ class , whereas whole domain surfaces in this class contain mainly α SSEs .The means and standard deviations (SDs) of the SSE propensities calculated for the 5 classes for both DFBSs and domain surfaces are shown in  γ class , our claα-rich and γ-rich classes .Because each DFBS constitutes part of a domain surface, we first wished to investigate whether the SSE propensities of a DFBS are related to the overall SSE propensities of its complete domain’s accessible surface. α”, “all-β”, “all-γ” , and “no-α”, “no-β”, “no-γ” . The histogram in α-rich, γ-rich, and α+β+γ classes are more frequent among DFBSs than among whole domain surfaces, especially for the γ-rich class, whereas α + γ and β + γ are more frequent in whole domains than in DFBSs. It can also be seen that it is much more frequent for the “extreme” SSE compositions to be observed in DFBSs than in whole domain surfaces. More precisely, 16.3% of DFBSs fall within an “all” category, compared to only 1.3% of domain surfaces, and 89.2% of DFBSs belong to a “none” category compared to only 39.5% of domain surfaces. Within the particular “all” and “none” categories, the partition between α, β, and γ is roughly the same for both DFBSs and domain surfaces, with a majority of “all-γ” and very low “all-β” on the one hand , and with a majority of “no-β” and very low “no-γ” on the other hand. More than half of the DFBSs considered in this study lack any β-strand residues (56.4%).We also counted the numbers of DFBSs and domains having certain “extreme” SSE compositions such as “all-γ) are very common in protein-protein binding sites, especially in hetero-complexes. In addition, our results show that β-type SSEs are generally under-represented both on whole domain surfaces and in binding sites, going to a complete absence of β elements in more than 50% of DFBSs. Importantly, our analysis also shows that the SSE composition of a DFBS is largely determined by the overall SSE composition of the domain to which it belongs  DFBS pair is equal to 20.9%2 = 4.4%, and the expected probability for the  pair is equal to 2 × 20.9% × 24.9% = 10.4%  or ). These random pair-wise probabilities were multiplied by the total number of DFIs in our dataset (3084) to obtain the numbers of expected DFIs for random binding . This represents a strong confirmation, on a large dataset, of previous hypotheses that certain SSE classes of binding sites favorably or unfavorably interact with other SSE classes. More precisely, individual comparisons for each type of SSE class pair were performed using a z-test. For twelve out of the fifteen types of SSE class pairs, the observed numbers were found to be significantly higher or lower than the random ones (p < 0.001). The observed numbers were converted into percentages values and reported in α-rich, α + γ),  and .Thanks to our large dataset, all numbers are greater than 5, and we can use a γ-rich and β + γ classes of DFBSs and between α + γ and α + β + γ classes of DFBSs. Other pairs show lower percentages than random. The most striking pair corresponds to the binding between α + γ and β + γ classes (72 observed instead of 337 expected) suggesting it is physically difficult to form interfaces between binding sites containing α-helices on one hand and β-strand on the other hand. The lower percentages observed for the  and  pairs confirm this statement.It can be seen from Because our set of DDI is grouped by Pfam domains and DFBSs, it is possible to classify DFBSs according to their number of distinct Pfam partners. We therefore wished to ask whether there are any particular differences between DFBSs having just one Pfam interaction partner, and those having two or more Pfam partners. χ2 test, p < 0.001) only when the number of partners is greater than 3. In this row, individual comparisons for each SSE class (z-test) yields a significant difference only for the α-rich  and β + γ  classes. This observation suggests that, to accommodate large numbers of partners, the binding sites tend to involve decreased α and increased β secondary structure.Statistical comparison of the distribution of DFBSs over the five SSE classes in each row with the global DFBS distribution (last row) reveals that this distribution is significantly different . A similar trends towards a decrease in the number of residues involved at the interface is observed when compared with single-partner DFBSs .Finally, we used DSSP to calculate the solvent accessible surface (SAS) of each DFBS . KBDOCK allows to monitor multiple binding capacities at the levels of both individual DFBSs and Pfam domains. In particular, it is quite natural to think that Pfam domains having the highest number of Pfam partners also have the highest number of DFBSs. This led us to analyse the distribution of Pfam domains depending on the number of DFBSs per domain, as shown in In fact only 9.5% (205/2153) of Pfam domains harbour four or more DFBSs, with a maximum of nine for Actin domain (PF00022). The final promiscuity of these domains then depends on the promiscuity of each harboured DFBS. More concretely, versus 21). This is consistent with the promiscuity of individual DFBSs on these two domains, which is up to 49 for Ras, but only 10 for actin. Thus, promiscuity of DFBSs can strongly expand the promiscuity of Pfam domains, especially for domains that are part of hub proteins. Kinase domains, for example, are frequently observed in PPI network hubs, with some 400 hubs having some kind of kinase activity [It is interesting to see that the Actin domain currently has the highest number of DFBSs but not the highest number of Pfam partners, which is held by the Ras domain (56 activity . It can y coordinate) versus their total number of partners at the Pfam domain level (x coordinate). A clear correlation is visible between the two variables, showing that to ensure very high promiscuity, Pfam domains preferentially harbour DFBSs with high promiscuity.We further analysed the correlation between promiscuity at the DFBS and Pfam domain levels as shown in versus hetero, transient versus permanent, obligate versus non-obligate, biological versus non-biological contact, date hub versus party hub, sociable versus non-sociable, “singlish” interface versus multi-interface). Such a variety of language highlights the need for a well-defined terminology and methodology with which to describe, classify, and analyse the structural nature of PPIs and protein binding sites.There is considerable interest in understanding how experimentally observed PPI networks can be explained in terms of 3D structural interactions ,23. HoweSeveral previous studies of structural PPIs have identified potentially interesting relationships between the shapes and physical properties of protein-protein interfaces, and most such studies have suggested that it might be possible to use such properties predictively. However, these earlier studies of PPIs have been somewhat limited by the relatively small numbers of hetero protein-protein complexes available, and by the problem of how to select a suitable sub-set of protein binding sites to work with. Because KBDOCK is built from a complete and recent (June 2013) snapshot of the PDB and the latest version of Pfam (v27.0), our SSE-based classification of binding sites may be considered as the most comprehensive one to date. Indeed, we believe the work described here represents the largest systematic and non-redundant study of protein domain interactions to have been described to date. Furthermore, we believe that the very large coverage and lack of redundancy of our dataset make our classification quite reliable compared to earlier studies which used sub-sets of the PDB. This analysis was possible thanks to the well-characterized and non-redundant set of binding sites that we identified and stored previously in KBDOCK using our spatial clustering algorithm . In thisγ-rich features in binding sites, our results also show that there seems to be nothing particularly special about the SSE composition of a DFBS when compared to the rest of its domain surface. From a global point of view however, our results also show that “extreme” SSE compositions are much more common in DFBSs than in whole domains. More than 25% DFBSs in our dataset completely lack α helices, and more than 50% completely lack β-strand residues.As well as supporting previous findings regarding the preference for α-rich or α + γ DFBS to a β + γ DFBS is significantly disfavored. Knowledge of these secondary structure pairing propensities could be useful for the prediction of unknown DDIs, especially if combined with other physical properties [Furthermore, our results show that there exist certain non-random pairing preferences between the different SSE classes. In particular, interactions involving the same two SSE classes are more frequent than expected from random binding. Conversely, binding of an operties .α-rich and a higher probability of being classified as β + γ compared to the overall frequency of these SSE classes. This contradicts somewhat the results of Keskin and Nussinov [α helices, but their study performed in 2007 involved fewer DDIs than ours. We also see a tendency for DFBSs having more than one Pfam interaction partner to have slightly smaller binding site surface areas than single-partner DFBSs. This confirms a previous observation by Keskin etal. [Additionally, we find that just over 80% of DFBSs in our dataset interact with just one type of Pfam domain, and only around 3.5% of them interact with more than three different Pfam domains. Our SSE propensity analysis indicates that DFBSs with three or more distinct Pfam partners have a lower probability of being classified as n etal.  on a larMore than 50% of Pfam domains in our dataset harbour only one DFBS and 91% of these DFBSs are single-partner. Thus almost half of Pfam domains are monogamous in their physical relationships with other domains, and this finding could have considerable implications for the interpretation of large-scale PPI networks and for drug targeting. Conversely, the “promiscuous” DFBSs are mostly found in domains containing more than one DFBS. In particular the 9.5% of Pfam domains harbouring four or more DFBSs all possess at least one multi-partner DFBS. This suggests there exists a kind of “two-level multiplication” in the number of different partners for these domains. This partner multiplicity could be further enhanced when multi-partner domains assemble in multi-domain architectures. The resulting combinatorial expansion of partners could explain the structural basis for the observed promiscuity of some hub proteins. It has been proposed that the presence of hub proteins in a PPI network is a natural consequence of a “scale-free” network, or one which follows a “power law” distribution in the degree of its network nodes . Anotherγ-rich features and that interactions involving α/β contacts are disfavored. We find that the SSE character of multi-partner binding sites can significantly differ from single-partner binding sites when the number of partners exceeds three. Moreover, multi-partner binding sites are found to have significantly smaller surface areas than single-partner binding sites. Our analysis successfully identifies known hub proteins as containing domains that carry several binding sites among which at least one multi-partner binding site. Nevertheless, we also find that about half of the domains in our study are primarily monogamous in their hetero relationships with other domains, and this finding could have considerable implications for the interpretation of large-scale PPI networks and for drug targeting. We believe it could be very interesting to use the domain family interactions in KBDOCK to reconstruct and analyse PPI networks at a structural level and to investigate in more detail the 3D architectures of hub proteins and the topologies of structural DDI networks.Our structural classification of DFBSs provides a useful way to classify and analyse the secondary structure propensities of DDIs, and it highlights some SSE pairing preferences which might be useful for the prediction of unknown DDIs. We have used this classification to analyse the structural interactions of a large set of 4001 domain family binding sites located on 2153 Pfam domain families, and involving 5139 non redundant DDIs. Our results show that many DDIs involve


We describe a method to predict protein-protein interactions (PPIs) formed between structured domains and short peptide motifs. We take an integrative approach based on consensus patterns of known motifs in databases, structures of domain-motif complexes from the PDB and various sources of non-structural evidence. We combine this set of clues using a Bayesian classifier that reports the likelihood of an interaction and obtain significantly improved prediction performance when compared to individual sources of evidence and to previously reported algorithms. Our Bayesian approach was integrated into PrePPI, a structure-based PPI prediction method that, so far, has been limited to interactions formed between two structured domains. Around 80,000 new domain-motif mediated interactions were predicted, thus enhancing PrePPI’s coverage of the human protein interactome. Complexes formed between a structured domain on one protein and an unstructured peptide on another are ubiquitous. However, they are often quite difficult to detect experimentally. The development of computational approaches to predict domain-motif interactions is therefore an important goal. We report a method to predict domain-motif interactions using a Bayesian approach to integrate evidence from a variety of sources, including three-dimensional structural and non-structural information. The method was applied to the entire human proteome and showed significant improvement over existing methods. The method was incorporated into PrePPI, a computational pipeline for the prediction of protein-protein interactions that relies heavily on structural information. Approximately 80,000 new interactions were detected. The new PrePPI database provides easy access to about 400,000 human protein-protein interactions and should thus constitute a valuable resource in a variety of biological applications including the characterization of molecular interaction networks and, more generally, in the study of interactions mediated by proteins in families that may not be extensively studied experimentally. Mapping the human protein interactome has important implications for understanding basic biology and human disease at the molecular level . High-thAn important class of PPIs involves interactions between a short peptide motif of one partner, and a structured peptide recognition domain (PRD) from another –18. DiscIn this study we report a computational framework to predict interactions mediated by domain-motif interfaces. The method uses a Bayesian approach to integrate knowledge from the ELM database, domain-peptide structures from the PDB, and non-structural information. We have incorporated the method into PrePPI  and founThe performance of the PRD/motif predictor in rediscovering known human-human interactions was assessed using 5-fold cross-validation on the true positive and negative sets see  and compDespite its broad scope, certain domain-motif interactions, especially those not belonging to well-studied families, may not be included in the ELM database. To expand our coverage, we used experimentally determined complexes taken from the PepX database  as templWe again used a Bayesian approach to estimate the likelihood of an interaction given the properties of the model. Sources of evidence integrated into our Bayesian scheme include the sequence similarity score between the candidate motif and the motif in the template, the structural similarity score between the candidate PRD and the PRD in the template, whether the candidate motif is located in predicted disordered regions, and whether sequences around the candidate motif are conserved evolutionarily see . The resWe combined PRD/motif-based and Struct-based LRs with non-structural (NS) evidence that has previously been used to infer PPIs . SpecifiTo obtain further validation of our approach, we compared our strong predictions to the 257 known human domain-motif interactions found in the ELM database. A total of 75 known interactions were included in our strong predictions (123 when using a LR cutoff of 100), while using only evidence from PRD/motif, Struct or NS alone recovered only 6; 0; and 6 interactions, respectively. Furthermore, when using the combined sources of evidence, the LR scores for more than half of the interactions (40 out of 75) ranked among the top 20% of all strong predictions. These 75 interactions were not dominated by a particular ELM class as they spanned 33 out of the 57 classes that represent the 257 known human interactions. We also examined overlap of our predictions with 160 human domain-motif complexes in PepX. The overlap is only 42 for strong predictions but increases to 86 when using a lower LR cutoff at 100.As summarized above, we have previously developed PrePPI, a computational PPI prediction method that performs comparably to experimental HT approaches. PrePPI combines structural evidence with non-structural evidence using a Bayesian framework but currently lacks the capability to predict domain-motif mediated PPIs , To add PrePPI_PRD/motif+Struct yielded 78,898 additional strong predictions compared to PrePPI_orig. Although more than 40% of the predictions come from the 5 most prevalent PRD families , over 130 ELM classes and 150 clusters of PepX template structures contributed to our results. Together with the original 317,814 interactions reported from PrePPI_orig, the new PrePPI which includes domain-motif mediated interactions contains a total of 396,712 predicted human PPIs.In this study we developed methods to predict PPIs mediated by domain-motif interfaces using both expert knowledge of domain-motif interactions in the ELM database and structures of domain-motif complexes in the PDB. Although there is some overlap between predictions made with the ELM and the structure-based approach (PRD/motif and Struct), differences between them likely led to the observed improvements when two strategies were combined. For example, the Bcl-2 families have multiple structural representatives in the PDB but are not included in ELM. Moreover, the sequence similarity scoring approach of PrePPI_Struct allows the identification of motifs outside of the consensus provided by ELM. For example, the motif sequences for several SH3 and nuclear box receptor complexes in PepX could not be described by consensus patterns from any of their corresponding classes in ELM. Overall, among our new strong predictions, 13,988 are made using motifs that cannot be described by consensus patterns from the corresponding ELM class. On the other hand, the use of consensus patterns as in ELM (and hence PrePPI_PRD/motif) can be effective in capturing the variability of motifs for large, well-studied families even when no structural information is available. In terms of finding PRDs, the structured-based method in PrePPI_Struct applies a filtering criterion to ensure that the candidate PRD aligns well structurally to the template PRD at the binding interface, which is not accounted for by the sequence-based Pfam definition in PrePPI_PRD/motif.As in the original PrePPI, combining sources of evidence that on their own provide only weak clues has a major impact on overall performance. For example, the consensus sequence patterns used in the PrePPI_PRD/motif approach can be promiscuous as can the use of sequence similarity in PrePPI_Struct, potentially leading to reduced prediction specificity. This can be especially problematic for interactions between candidate PRDs and motifs that interact via similar interfaces. For example, for the structural modeling component in PrePPI_Struct, it is possible that modeled interfaces for many different pairs of proteins share the same sets of clues if they are derived from the same template structure. In this case, prediction specificity would come from non-structural evidence. Moreover, prediction coverage based on the individual sources of evidence can be low as shown in Results, which highlights the importance of combining different sources of orthogonal information implicit in the Bayesian approach.d, they frequently depend on cellular conditions, many of which will never be sampled in an HT experiment  and [0.3–0.65], for the Bayesian classifier.We obtained domain-motif structures from the PepX database   for an interaction between two proteins. The datasets for training the classifier consist of a true positive set that includes 7,409 interactions compiled from a set of 5 databases –52 and sci, of an interaction between protein x and y, the conditional probability that one will observe the clue given that the interaction is in the true positive set or the true negative set is calculated as P and P. The probability P is calculated as P = n/NTP, where n is simply the number of interactions in the true positive set with the clue ci, and NTP is the total number of interactions in the true positive set. P is calculated analogously for the true negative set. A LR value can be calculated by dividing these two probabilities, LR(ci) = P / P, to reflect how strongly the clue ci is indicative of a true interaction.For LR(match)); b) the identity of the matching ELM class (LR(class)); c) whether the motif is located in a predicted disordered region (LR(diso)); d) whether the motif is conserved locally in sequence relative to its surrounding regions (LR(consv)). The latter three clues can be assumed to be independent of one another, but they all depend on the first clue being true. Their LR values were therefore normalized by the LR for the first clue, and the final LR for the domain-motif interface is therefore:For the PRD/motif method based on the ELM database, a total of four clues were used for the domain-motif component: a) whether a domain-motif match from the same ELM class is present (LR(match)); b) The PSD score between the candidate domain and the PRD component in the template (LR(PSD)); c) the sequence similarity score between the candidate motif and the motif component in the template (LR(SIM)); d) whether the motif is located in a predicted disordered region (LR(diso)); e) whether the motif is conserved locally in sequence relative to its surrounding regions (LR(consv)). As above, LRs for the latter four clues were normalized by LR(match) and the final LR for the domain-motif interface is:For the Struct method based on the PepX database, a total of five clues were used for the domain-motif component: a) whether a domain-motif match from the same template structure is present . New predictions are also provided as a supplement Click here for additional data file.S2 FigSame as (TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.


The borderline between the two groups is not clear, and many proteins cannot be described as definitive buffers or sensors. Here, we describe two highly-conserved structural motifs found in all known different families of the EF-hand proteins. The two motifs provide a supporting scaffold for the DxDxDG calcium binding loop and contribute to the hydrophobic core of the EF hand domain. The motifs allow more precise identification of calcium buffers and calcium sensors. Based on the characteristics of the two motifs, we could classify individual EF-hand domains into five groups: (1) Open static; (2) Closed static; (3) Local dynamic; (4) Dynamic; and (5) Local static EF-hand domains.Proteins with EF-hand calcium-binding motifs are essential for many cellular processes, but are also associated with cancer, autism, cardiac arrhythmias, and Alzheimer's, skeletal muscle and neuronal diseases. Functionally, all EF-hand proteins are divided into two groups: (1) calcium sensors, which function to translate the signal to various responses; and (2) calcium buffers, which control the level of free Ca In contrast, intracellular calcium concentrations are at least 104 times lower, and require control mechanisms to be maintained at the appropriately low levels. Any failure of these control mechanisms may lead to sustained calcium overload and eventual cell and organ malfunction. The disparity in extracellular and cytoplasmic concentrations of Ca2+ ions supports the unique signaling and regulatory roles of calcium within the cell.Calcium is essential for life 2+ ions from these stores. Calcium can also act as an extracellular primary messenger, thus taking on the role of a near-universal signaling molecule recognized by a wide variety of calcium-binding proteins in eukaryotes, prokaryotes and even viruses Calcium regulates many important aspects of cell activity, beginning with fertilization and ending with the apoptotic suicide of cells at the end of their life cycle 2+ ions in resting muscle is about 0.05–0.1 µM, but increases by one to two orders of magnitude when an external signal stimulates the cell. These Ca2+ ions are released either from intracellular stores (i.e. from the sarcoplasmic reticulum) or pumped up through sarcolemma from the intercellular space. In addition, increases in intracellular calcium concentrations play a central role in the function of neurons, triggering neurotransmitter release 2+ ions are mediated by calcium-binding proteins that, in turn, interact with their target proteins. For example, regulation of transcription is coupled with numerous intracellular signaling processes often mediated by secondary messengers. Growing evidence points to the importance of calcium, as one of the most versatile second messengers, in activating or inhibiting gene transcription through actions frequently mediated by members of the EF-hand superfamily of calcium-binding proteins The scope of calcium ion functions extends to the regulation of contraction of all types of muscles, where binding of calcium to troponin C triggers the interaction of actin and myosin 2+ ions, and whose sequence-variation profiles are established within the PROSITE database (http://prosite.expasy.org) 2+-binding helix-loop-helix domain, where two antiparallel DxDxDG calcium-binding loops are flanked by interacting incoming and exiting α-helices Currently, there are eleven known calcium binding consensus motifs that proteins use to recognize Ca2+ ions to various responses; and (2) Calcium buffers, represented by parvalbumin, calbindin D9k, calbindin D28k and calretinin that serve to modulate calcium signals as they bind free Ca2+ ions Conventionally, all EF-hand -containing proteins are divided into two groups: (1) Calcium sensors that include calmodulin, recoverin, and most of the other known EF-hand proteins, which function to translate the signal of a change in concentration of Ca2+-binding EF-hand domain fold During recent years, we have been examining the structural determinants that govern metal-controlled structural cooperativity, intrinsic disorder, dimer formation and various structural and chemical properties of parvalbumins and S100 proteins, which constitute the two largest sub-families of proteins with the Ca2+-binding supersecondary structure Nearly 40 years ago, Kretsinger and Nockolds (1973) described what came to be known as the canonical “EF-hand” Ca2+ ion binding loops and the β-strands forming the antiparallel β-sheet linker of the EF-hand domain  labeled the six residues that bind calcium in the carp muscle calcium-binding protein as X, Y, Z and -X, -Y, -Z When we examined the structural superposition of the eleven representatives of the EF-hand containing proteins we found conserved characteristic clusters of interacting residues located around positions X-4. Because the Odd and Even EF-hand motifs are antiparallel, there are two such clusters located on the opposite sides of the antiparallel β-sheet linker, which include interacting residues from equivalent positions X-4, -X+1 and -Z+1 . Cluster2+-loaded human grancalcin; PDB code: 1K94), clusters I and II are obviously identical, fully aromatic, and no connecting loops are present : 15 out of 15 at X-4, 11/15 at -X+1 and 15/15 at -Z+1  and calcium-unbound (apo) forms. Individual values for all compared proteins are shown in Tables S4 and S5 in In order to investigate the role of the two conserved but different clusters of amino acids stabilizing the structure of the EF-hand domain and the effect of calcium binding on the domain conformation, we have applied two different approaches: (1) For the eleven family representative structures, we calculated the total interacting surface areas among the amino acids within each cluster, as well as the interacting surface area between the two clusters within the domain ; (2) We 2.The interacting surface area data from the fold representative structures  show tha2 in grancalcin and 45.8 Å2 in the EF-hand multi-domain protein Cbl. Where interactions occur between clusters I and II, the amino acid at positions -Z+1 from both clusters are involved and the exiting α-helices of the two EF-hand motifs are consequently positioned closer to each other.The interacting area between clusters I and II varies from no contact in calmodulin to 34.2 Å2 and 84±11 Å2, respectively; RMSD for superpositioned Cα atoms of 0.6±0.2 Å; rows A in 2 less and in some cases the interacting surface area increases considerably on calcium binding. Cluster II can be divided into two groups. In one group, calcium binding produces little change in both the contact surface area within the cluster  available pairs of EF-hand domains whose three-dimensional structures are known with and without bound calcium, the interacting surface area  and RMSD2+ ions on the overall fold can be seen through the differences in the interacting surface areas between clusters I and II (column “B–G” in Table S4 in 2 (apo) → 2±2 Å2 (Ca2+) (row E in 2 (apo) → 28±7 Å2 (Ca2+) (row F in 2 (apo) → 3±3 Å2 (Ca2+)  is already open without bound CaClusters I and II join the incoming and exiting helices of the EF-hand domain structure and predominantly consist of non-polar and hydrophobic amino acids. The short β-sheet linker of the EF-hand domain is also mainly hydrophobic and is formed from two β-strands bound together by two hydrogen bonds , I62QTnCF29W at X-4 in cluster II (↓12%), and F78QTnCF29W at -Z+1 in cluster I (↓8%), and similar 6 to 7 -fold changes at I73, V80 and M81, but small effects on -X+1 (F75) in cluster I and -Z+1 (L42) in cluster II. Increases in the Kd were seen for V45QTnCF29W (↑19%) and L49QTnCF29W (↑19%) also located the exiting helix of the odd EF hand motif. The packing of the hydrophobic triad in the vicinity of cluster I of the C-terminal calmodulin-like domain  have shown that for the calcium buffer S100P an F15A mutation at X-4 in cluster I completely abolished dimerization required for activation EF-hand domains have been classified as buffers and as sensors The structural changes refer to the transition between “closed” and “open” protein conformation, which may or may not occur on calcium binding, and the resulting calcium-dependent/independent or calcium-sensitive/insensitive mechanism of protein function. Originally, it was shown that in calmodulin and troponin C, distances and angles between the flanking α-helices did change upon calcium binding, while in calbindin 9K they did not 2+ ions. As a key underlying component of the EF-hand domain structure, the three-residue clusters, I and II, are critical for the structure of the EF-hand domains they are found within and consequently the overall function of the protein. Cluster I is primarily aromatic and as a unit it appears to be the more static and stabile linchpin for the overall structure since it maintains higher mutual surface area contacts that do not rearrange dramatically on calcium binding in any of the available structures, varying between 80±11 Å2 and 89±12 Å2 .Type 2. Closed static EF-hand domains – where the domain conformation is closed, cluster II is compact, and clusters I and II are close to each other and interact with each other. These features are not affected by the presence or absence of calcium. These features are shared by the calcium-free structures of the Type 4 set (see below). Includes the buffers S100A16, Cbp40  and calpain .Type 3. Local dynamic EF-hand domains – where a closed-to-open transition takes place on calcium binding in which the cluster II becomes more compact and interactions between clusters I and II are eliminated. Includes the calcium sensors, such as S100A1, S100A4-A6 and S100B.Type 4. Dynamic EF-hand domains – where cluster II is compact in the absence of calcium but where the inter-cluster distance increases on calcium binding. Includes the classical calcium sensors such as calmodulin (N- and C- domains) and troponin C, and polcalcin.Type 5. Local static EF-hand domains – where the conformation of cluster II becomes more compact on calcium binding but the inter-cluster distance remains essentially unaltered. Includes the calcium sensors S100A13 and calpain (C-domain).http://www.uniprot.org) is shown in Table S7 in With the exception of the Type 2 domains, all other domains on calcium binding have clusters I and II that are compact and are separated from each other . The cleCurrently, there are eleven known calcium binding consensus motifs www.accelrys.com). Because of semi-manual all-against-all structural comparisons and analysis of local structural similarities, we stayed at the structural family level (11 structures), and did not go below, to the level of individual protein domains (51 structures). Beyond the 51 structures of individual protein domains, the rest of the 410 structures included either orthologs of the same proteins from the different species or structures of various mutants, which could be discarded from analysis. For the second and third tasks to study effects of calcium binding on the domain conformation, the creation of two different datasets was straightforward. We manually analyzed all 410 structures of EF-hand domains and the corresponding literature to select all double and single calcium binding EF-hand domains, whose three-dimensional structures are known with and without bound calcium and possibly other ligands.From 410 available X-ray and NMR structures of EF-hand domains, we had to create three different datasets: (1) a representative non-redundant dataset of different EF-hand domains to scan for structural motifs, i.e. clusters of amino acids and interactions, which are conserved across different structural families of EF-hand proteins; (2) a dataset to study effects of calcium binding on the domain conformation of double calcium binding EF-hand motifs, which would include all known pairs of EF-hand domains, whose three-dimensional structures are known with and without two bound calcium atoms; and (3) a similar dataset to study calcium binding effects on single calcium binding EF-hand motifs. For the first task of searching for conserved amino acid clusters and interactions, we had to manually choose a set of best resolution representative structures from the 11 different structural families of EF-hand proteins, given in SCOP www.accelrys.com).All types of structural superpositions and RMSD calculations based on back-bone and all atoms other than hydrogen, including (1) the superposition of the entire Odd and Even EF-hand motifs of EF-hand containing proteins; and (2) local structural superpositions of clusters I and II, were done using the SuperPose superposition server Calculation of atomic contacts and interacting surface areas was done using the Contacts of Structural Units (CSU) software, which is based on the surface complementarity approach developed by Sobolev et al. Hp-X projection distance must be ≤1.2 Å Geometric parameters to assign CH-π interactions were chosen to satisfy criteria given in Brandl et al. The criteria to assign weak hydrogen bonds were taken from Derewenda et al. All geometrical calculations  were made using the Accelrys Discovery Studio molecular modeling environment. Color figures in this manuscript were produced with MOLSCRIPT File S1Table S1. Assignment of interatomic contacts among the residues in clusters I and II according to the Interatomic Contacts software Table S2. Values of distances and angles for the CH-π interactions shown in Table S3. Values of distances and angles for the CH-O interactions shown in Table S4. Summarized areas of interacting surfaces among the amino acids within cluster I and cluster II . Table S6. Values of distances and angles for the interactions shown in Table S7. Five classes of EF-Hand domains, based on the characteristics and conformational changes within the Clusters I and II  and within the entire EF-hand domain .Contains Tables S1–S7. uster II , and the(DOC)Click here for additional data file.


The leucine-rich repeat-containing G protein-coupled receptors (LGRs) family consists of three groups: types A, B, and C and all contain a large extracellular domain (ECD) made up of the structural motif – the leucine-rich repeat (LRR). In the LGRs, the ECD binds the hormone or ligand, usually through the LRRs, that ultimately results in activation and signaling. Structures are available for the ECD of type A and B LGRs, but not the type C LGRs. This review discusses the structural features of LRR proteins, and describes the known structures of the type A and B LGRs and predictions that can be made for the type C LGRs. The mechanism of activation of the LGRs is discussed with a focus on the role of the low-density lipoprotein class A (LDLa) module, a unique feature of the type C LGRs. While the LDLa module is essential for activation of the type C LGRs, the molecular mechanism for this process is unknown. Experimental data for the potential interactions of the type C LGR ligands with the LRR domain, the transmembrane domain, and the LDLa module are summarized. GREAT gene (The receptors for the peptide hormones H2 relaxin and insulin-like peptide-3 (INSL3) are unique members of the leucine-rich repeat-containing G protein-coupled receptors (LGRs) family . LGRs arEAT gene , LGR8),EAT gene , 7. In 2,EAT geneEAT gene ] and LGREAT gene ]. INSL3 EAT gene , and theAlmost 15 years since the initial identification of RXFP1, the landscape of H2 relaxin research is diverse and complex. H2 relaxin is considered a pleiotropic hormone with many functions, including central roles in collagen turnover , 13 wounThe LGR family is classified as “Type A” rhodopsin-like GPCRs based on the similarity of its transmembrane (TM) domain. They are unified into this family based on their large ECDs containing leucine-rich repeats (LRRs). The first LRR-containing protein to be identified was leucine-rich α2-glycoprotein (LRG)  but sincDrosophila  module , 26. Curosophila  and are xLxLxxNxL, where the underlined residues form the β-strand, x is any amino acid and leucine may be substituted by valine, isoleucine or phenylalanine; and asparagine by cysteine, serine or threonine , showed a horseshoe-shaped structure . This cuhreonine , 39.10 helices, polyproline II helices, β-turn or β-strand  and C-terminal (LRRCT) capping motifs, respectively. Based on sequence analysis, LRRNT motifs have a consensus sequence of CPx(2-5)CxCx(6-19)Cx(6-8)Px(3)Px(5)LxL, where x indicates any residue , 45. TheBased on phylogenetic analysis and the number of cysteine residues present, there are four types of LRRCT motifs, CF1–4 . CF1 is The FSHr crystal structure is the best understood of the LGRs . The LRRUpon binding to ligand, there is a significant change to the hinge structure of FSHr, otherwise the LRR domain is similar to that of the ligand free . The binThe LRR domain and the ligand of FSHr and TSHr are similar, although the disulfide arrangement of the LRRNT differs . NeverthThe LRR domains in Type B LGRs are typically larger than those of Type A LGRs Table . The cry2)  , 60. The2) (2 . LGR5 bi2) (2 . In type2) (2 . Moreove2)  spectroscopy in an effort to understand the importance of specific residues , 93. WhiJoining the LDLa module to the LRR domain is a linker of variable length, 32 or 25 residues in human RXFP1 and RXFP2, respectively. This linker has been considered a simple tether with the function of intramolecular localization of the LDLa module to the TM domain for efficient activation. Swapping the LDLa module of RXFP2 onto RXFP1 resulted in loss of signaling, suggesting that the LDLa modules cannot be swapped; although in this study a large portion of the linker of RXFP2 was also swapped . HoweverThe LGR family has in common a LRR domain that serves as a ligand-binding site. From the point of view of mechanism, this is the only common feature of the three subtypes of receptors. At the extreme, the type B LGRs on binding ligand do not function through a canonical GPCR pathway by activation of either G-proteins or β-arrestin. When the ligand binds to type A LGRs, it undergoes a conformational change that enables an interaction between the ligand with the C-terminal hinge of the ECD, which is proposed to release the TM domain of the receptor from an inhibited state. Evidence presented for the type C LGRs, RXFP1 and RXFP2, shows that the true agonist of these receptors is the N-terminal LDLa module. Thus, it is hypothesized for these receptors that the binding of ligand results in a conformational change to the ECD to present the LDLa module to the TM domain for activation. Structures of the ECDs of type A and type B LGRs, free and in complex with ligand, suggest that conformational change of the LRR domain of the type C LGRs is unlikely. Given the size and structure of the ligands, H2 relaxin and INSL3, and the LDLa modules, it is difficult to envisage significant conformational changes to these molecules. Therefore, hypotheses of reorientation of the LDLa module or localization through modification of the structure of the linker that tethers the LDLa module to the LRR domain may be key to the activation process. Further research, including structure elucidation, is required to understand how the type C LGRs are activated.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


This modification regulates a number of biological processes including chromatin reorganization, DNA damage response (DDR), transcriptional regulation, apoptosis, and mitosis. PARP1, functioning as a DNA damage sensor, can be activated by DNA lesions, forming PAR chains that serve as a docking platform for DNA repair factors with high biochemical complexity. Here, we highlight molecular insights into PARylation recognition, the expanding role of PARylation in DDR pathways, and the functional interaction between PARylation and ubiquitination, which will offer us a better understanding of the biological roles of this unique post-translational modification.Protein  The process is DNA end resection-independent, and is also unrelated to sequence homology. Therefore, C-NHEJ occurs throughout the cell cycle, but predominantly in G0/G1 and G2 phase in vitro in vivo evidence as reported recently. A structural PARP1/DNA-PKcs/Ku molecular complex has been identified in which PARP1 elicits a major architectural rearrangement of the DNA-PKcs-mediated synapsis Several studies support an important role of PARP1 in C-NHEJ. Interaction between PARP1 and DNA-PKcs facilitates genomic integrity during V(D)J recombination and prevents tumor development As a new DSB repair signal pathway, alt-NHEJ has attracted much attention recently HR can be activated by single-stranded DNA (ssDNA) resection. The process produces a lagging strand gap or 3′overhang, which is the key step for HR To regulate numerous biological functions, PAR chains must be recognized by diverse proteins such as DDR factors. To date, several distinct classes of PAR-binding modules have been identified. These include the PAR-binding zinc finger (PBZ), the Macro domain, the WWE domain, the BRCT domain, the forkhead-associated (FHA) domain, the OB-fold domain, the PIN domain, and the RNA recognition motif (RRM) domain The recently-identified PBZ domains possess the consensus sequence [K/R]xxCx[F/Y]GxxCxbbxxxxHxxx[F/Y]xH O-glycosidic ribose-ribose is the signature of PAR chains The WWE domain is the most recently discovered PAR-binding domain, named after the three strictly conserved amino acid residues, tryptophan-tryptophan-glutamate (WWE) O-acetyl-ADPR The Macro domain, which consists of 130–190 amino acid residues, is evolutionarily conserved and widely spread throughout all kingdoms of organisms. This is distinct from the PBZ and WWE domains. It is estimated that more than 300 proteins, including 11 human proteins, with a diverse set of biological functions possess the Macro domain It is well known that FHA and BRCT domains can bind to phosphorylated proteins and modify protein–protein interactions The OB-fold is an ssDNA or ssRNA binding domain that has been found in proteins from all three kingdoms. OB-fold comprises 70–150 AA residues forming five-stranded beta-barrel with a terminating alpha-helix The PIN domain-containing proteins serve as nucleases that cleave ssDNA/ssRNA in a sequence-specific manner The RRM is one of the most abundant protein domains in eukaryotes, which can serve as a plastic RNA-binding platform to regulate post-transcriptional gene expression /TLS, EWS/EWSR1, TAF15, SAFB1, SAF-A, and hnRNPUL1/2, have been identified, and these proteins can be recruited to DNA damage sites via binding to PAR chain through RGG boxes It is reported that some RNA and DNA binding motifs can recognize PAR chains. Motifs enriched in arginines and glycines, which are termed glycine-arginine-rich (GAR) domains and/or RGG boxes, were identified several decades ago. RGG boxes are found in more than 1000 human proteins that are involved in numerous biological processes including transcription and DDR Ubiquitin is a small regulatory protein consisting of 76 amino acid residues, which has been found in almost all tissues of eukaryotic organisms. It can be covalently transferred to a Lys residue of an acceptor protein. This process is termed ubiquitination Recent studies have demonstrated that PARylation can serve as a signal for the ubiquitination and promote the degradation of PARsylated proteins PARP1 is a key facilitator of DDR and is implicated in tumorigenesis of several malignancies, particularly those associated with dysfunctional DNA repair pathways +-consuming enzymes, such as sirtuin 1, are thought to compete for NAD+ with PARPs Over the last decades, PARylation has been proved to be involved in numerous cellular functions including DDR. PAR serves as an initial sensor and mediates the early recruitment of DNA damage repair machineries. As a kind of protein modification, PARylation is tightly and dynamically regulated. PAR chain synthesis is mediated by several PARPs, whereas PARG mainly takes charge of PAR chain degradation. Great strides have been made in the past few decades to decipher the PARylation regulatory processes and the underlying molecular mechanisms. However, many questions remain to be answered. First, other NADThe authors declare that they have no competing financial interests.


N-ter autoinducer binding and C-ter helix-turn-helix (HTH) DNA binding domains. The autoinducer binding domain recognizes signaling molecules which are most often N-acyl homoserine lactones (AHLs) but could also be other novel and yet unidentified molecules. In this study we carried out a series of specific domain swapping and promoter activation experiments as a first step to engineer synthetic signaling modules, taking advantage of the modularity and the versatile/diverse signal specificities of LuxR proteins. In our experiments the N-ter domains from different LuxR homologs were either interchanged or placed in tandem followed by a C-ter domain. The rational design of the hybrid proteins was supported by a structure-based homology modeling studies of three members of the LuxR family  and of selected chimeras. Our results reveal that these LuxR homologs were able to activate promoter elements that were not their usual targets; we also show that hybrid LuxR proteins retained the ability to recognize the signal specific for their N- ter autoinducer binding domain. However, the activity of hybrid LuxR proteins containing two AHL binding domains in tandem appears to depend on the organization and nature of the introduced domains. This study represents advances in the understanding of the modularity of LuxR proteins and provides additional possibilities to use hybrid proteins in both basic and applied synthetic biology based research.A sub-group of LuxR family of proteins that plays important roles in quorum sensing, a process of cell-cell communication, is widespread in proteobacteria. These proteins have a typical modular structure consisting of  The LuxR homolog is the transcriptional regulator that recognizes and binds, at threshold concentration, to the autoinducer molecules [generally N-acyl homoserine lactones (AHLs)] synthesized by the LuxI homolog to regulate gene expression  DNA binding domain  is a system of bacterial communication which involves the synthesis and detection of chemical molecules called autoinducers to regulate community-specific traits in a cell density dependent manner  to bind target promoter regions usually activated by canonical LuxR and vice-versa . We also designed synthetic hybrid variants Ola, Lao, and RhLas by exchanging the Pseudomonas aeruginosa PUPa3 and Escherichia coli strains used in this study are listed in Table P. aeruginosa PUPa3 and its derivatives were routinely grown in Luria Bertani (LB) broth and when required antibiotics were added in the following concentrations: 100 μg ml−1 nitrofurantoin (Nif), 100 μg ml−1 ampicillin (Ap), 300 μg ml−1 kanamycin (Km), 100 μg ml−1 gentamycin (Gm), 100 μg ml−1 tetracycline (Tc) and 250 μg ml−1 chloramphenicol (Cm). E. coli DH5α and M15 were routinely grown in LB broth and antibiotics were added in the following concentrations when required: 50 μg ml−1 Km, 10 μg ml−1 Gm, 10 μg ml−1 Tc, and 100 μg ml−1 Cm. External addition of rice macerate in LB was performed as described by Gonzalez et al.  and the DNA-binding domain (approximately 230 bp) and the latter eluted and saved. The construct pRHLR was used as template to amplify the AHL-binding domain using the pair of primers T3/RhlRSmaI Rv and the amplicon was cloned in pGEM® T easy. It was then digested with HindIII/SmaI generating a fragment of approximately 520 bp. The AHL-binding domain of RhlR was then cloned HindIII/SacI in frame with the DNA-binding domain of LasR in pBBR1MCS2 generating the construct pRHLAS. All hybrid constructs are schematically represented in Figure Gene synthesis was performed by GeneArt®  or by GenScript  and all nucleotide sequences are listed in Table t-test. The growth curves of all strains were determined and no significant differences were observed . After this 23 μl of 3% Triton X-100 in GUS buffer and 23 μl of 3% sodium lauryl sarcosinate in GUS buffer were added to the samples, the preparations were incubated at 30°C for 10 min, and then 100 μl of 25 mM p-nitrophenyl-β-d-glucuronic acid (PNPG) (Sigma) were added. The reaction was stopped by adding 280 μl of 1 M Na2CO3 after sufficient yellow color had developed. Both the optical densities at 595 nm (OD595) of the bacterial cultures and the OD415 of the samples, measuring the intensity of the yellow color developed by the β-glucuronidase reaction (OD415PNPG), were determined. 1 Miller unit of β-glucuronidase activity was defined as follows: 1 Miller unit = 1000 × {[OD415PNPG − (1.75 × OD595)]/(t × v × OD595)}, where t is the time of the reaction (in minutes), v is the volume of the culture assayed (in milliliters), OD595 is the cell density just before the assay, and 1.75 is the correction factor. All measurements were done in triplicate. Statistical analysis was performed using Student's t-test.The β-glucuronidase activities of overnight cultures of Five web-based servers were exploited to build the 3D homology models and the top-score models generated were then ranked and validated by the protein model quality predictors AIDE  to activate promoter regions of lasI and rhlI in E. coli. As expected OryR activated reporter expression regulated by the promoter of the Xoo proline iminopeptidase gene  which is known to be its target gene in the presence of rice-plant macerate  . Reporter expression was not detected in the presence of any of the proteins tested  in the presence of the cognate signals of their N-ter domains . The hybrid synthetic LuxR proteins generated as part of this study are represented in Figure N-ter of the proteins and at the level of the linker regions of the autoinducer binding domains that are combined in these hybrids.Results from our experiments with hybrid LuxR proteins indicated that swapping modular domains of these proteins did not lead to loss of function and actually conferred specificity toward the cognate signal of the interchanged N-ter AHL-binding domain of RhlR was placed upstream the native LasR, we used C4-HSL alone, OC12-HSL alone or a mixture of both signal molecules. We observed that C4-HSL alone was not able to induce activation of the lasI promoter, whereas OC12-HSL induced reporter expression AG motif present both in the las and the pip boxes  but also on the sequential order of the domains. In fact, synergistic effect due to the presence of both cognate signal molecules was observed for one of the two hybrid proteins, i.e., Dahl2. These observations provide new information regarding the modularity and signal specificity of non-native hybrid LuxR proteins.LuxR homologs are important proteins that are involved in gene regulation in Gram-negative bacteria  did not result in N-ter autoinducer binding domain. Even swapping the autoinducer binding domain between a protein that responds to plant derived signal molecules  and a protein that responds to OC12-HSL  conferred the ability to the new hybrid protein to respond to the new cognate signal molecule but kept the specificity toward its native promoter region. The ability to control the molecules, to which the LuxR homolog proteins respond, could allow bacteria to respond not only to its cognate QS signal molecule, but also to an exogenous signal molecule.LuxR hybrid proteins that had their domains exchanged proved to be still functional when they were presented with the signal molecule known to activate the interchanged N-ter AHL-binding domain of RhlR added upstream, proved to be functional only when the LasR cognate signal molecule was exogenously provided . On the other hand the construct, in which the AHL-binding domain of RhlR was placed in between the AHL-binding and the HTH-DNA binding domain of LasR (Dahl2), showed full activity only in the presence of both signals. This latter result could pave the way to the construction of hybrid proteins which will respond not to one but to two different AHL signals triggering a second level of control or even leading to a complex regulation system, as for example by adding the PSM-binding domain of OryR in between the two domains of LasR. Our results also shows that by simply adding a second AHL-binding domain in a native LuxR homolog might not always confer a second level of control making clear that more studies are required to elucidate the importance of the N- and C- ter domains of LuxR homolog for their transcriptional activity. It is tempting to speculate that the outcome of this response is the likely result of the interplay between the protein folding, solubility and stability properties of the autoinducer binding domains in the absence of the cognate AHL that differs in the Dahl1 and Dahl2 hybrid variants. Indeed LasR is highly insoluble in the absence of OC12-AHL, while RhlR is known to be soluble, even if not active, in the absence of C4-AHL. Therefore, in both cases the solubility and the associated functionality of the two hybrids are likely to be strictly dependent on the presence of OC12-AHL, essential for the LasR AHL-binding domain to be soluble. However, in the Dahl1 hybrid the conformational change allowing a proper HTH DNA-binding domain orientation is likely to be only dependent on the LasR AHL-binding domain (due to its proximity to the HTH DNA binding domain) and therefore only on the presence of OC12-AHL. On the contrary in the Dahl2 variant the presence of OC12-AHL that is known to trigger the folding of the LasR AHL-binding domain might be suboptimal in conferring the proper HTH DNA-binding domain orientation due to the presence of the soluble RhlR AHL-binding domain just upstream of the HTH DNA binding domain. The presence instead of both AHLs might guarantee the overall proper folding and stabilization and therefore the optimal transcriptional activity.The addition of a second ligand binding domain upstream a native LuxR homolog proved to work differently depending on the constructed protein. Dahl1, that is comprised of native LasR with an lux-like boxes. It also shows that domains of different LuxR subfamilies which respond to different signal molecules can be exchanged indicating a most probable common ancestor. Lastly the results obtained here using tandem double AHL-binding domains could pave the way to the design of novel proteins which are functional when they respond to more than one type of AHLs. These results open new avenues for future research on the importance of the modularity of LuxR proteins.In summary, our study evidences that OryR and LasR possess promoter regions with highly similar The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.