Creating new protein domain arrangements is a frequent mechanism of evolutionary innovation. While some domains always form the same combinations, others form many different arrangements. This ability, which is often referred to as versatility or promiscuity of domains, its a random evolutionary model in which a domain's promiscuity is based on its relative frequency of domains.DV I ("domain versatility index"), which eliminates the effect of domain frequency. We explore links between a domain's versatility, when unlinked from abundance, and its biological properties.We show that there is a clear relationship across genomes between the promiscuity of a given domain and its frequency. However, the strength of this relationship differs for different domains. We thus redefine domain promiscuity by defining a new index, DV I. This is consistent with previous observations that the evolution of domain re-arrangements is primarily driven by fusion of pre-existing arrangements and single domains as well as loss of domains at protein termini. Furthermore, we studied the link between domain age, defined as the first appearance of a domain in the species tree, and the DV I. Contrary to previous studies based on domain promiscuity, it seems as if the DV I is age independent. Finally, we find that contrary to previously reported findings, versatility is lower in Eukaryotes. In summary, our measure of domain versatility indicates that a random attachment process is sufficient to explain the observed distribution of domain arrangements and that several views on domain promiscuity need to be revised.Our results indicate that domains occurring as single domain proteins and domains appearing frequently at protein termini have a higher  A unique triplet is composed of three domains – the domain of interest and its N- and C-terminal neighbours.Several measures of domain promiscuity exist see Tab. . One couNCO) . HoweverCO) [NN) . Tordai CO) [NN)  used yetDomains and their ability to form different combinations can be represented as a graph, allowing the usage of network-analysis tools. A co-occurrence network can be constructed as follows: nodes represent domains and edges between two domains are drawn if both domains occur at least once in the same protein. Since most domains occur in only one combination, but few domains form a large number of combinations, the network is scale free ,27. Sever = -0.15) between domain size and NTRP, meaning that small domains tend to be more promiscuous. However, whether domain size is correlated with abundance remains to be tested. Furthermore, they have shown that certain classes of domains are more versatile than others. For example, extracellular proteins and proteins located on type 1-1 exons are found to form a higher number of triplets (NTRP).Few factors have been proposed to influence a domain's likelihood to form many combinations. First, a domain is less likely to form combinations if it does not have a robust, autonomous fold . Secondldomain versatility index.Therefore, an important question arises: are all domains equally promiscuous given a certain number of occurrences? What observations concerning the causes of versatility are still supported if domain abundance is corrected for? Is the model of evolution completely random? In this study we show that, while taking a simple measure of domain promiscuity (such as the number of co-occurrences) is misleading, there is a property of domains that (i) is not simply correlated with the frequency of a domain, and (ii) corresponds to a domain's tendency to form combinations. We propose that a measure, which will be defined in the following, should be used that disentangles a domain's promiscuity from its frequency. We call this measure NN), number of domains with which a given domain occurs in the same arrangement (NCO) and number of triplets (NTRP) (Tab. First we asked whether different measures for asserting domain promiscuity produce similar results. We compared the following methods: number of different direct neighbours of a given domain (RP) Tab. . Thus, mNN) as a preliminary measurement for promiscuity. However, the results are similar if other measures are selected for computation .The data presented further were obtained using the number of neighbours  of a certain domain in different genomes. For each genome, we compare N with the number of neighbours this domain has in the given genome. We wanted to know if there is a correlation, in a given genome, between the number of occurrences of a domain and the number of its neighbours. First, we divided the SwissPfam database in sets, each corresponding to one organism only. Secondly, we compiled a list of all domains found in SwissPfam. For each of these domains, we analysed each of the genome files separately. Given the domain and organism, we counted the number of occurrences (N) of this domain as well as the number of its neighbours (NN). For each domain, we analysed the relationship between NN and N. A comparison of this relationship for two domains and sample plots for selected domains are displayed in Fig. To understand the relationship between the number of occurrences of a domain and its promiscuity, we investigated the following question in more detail. If the number of observed neighbours correlates with number of occurrences, does, for a given domain, the number of its neighbours N and NN. In fact, the relationship between NN and N is almost perfectly log-linear for many domains  are found only in one or two proteins in the SwissPfam data set. 77,629 domains (66%) have only two or fewer different neighbors. For Pfam A domains the corresponding numbers are 1624 (26%) and 1265 (20%), respectively. Therefore, no regression can be calculated for these domains. However, for the domains for which a correlation could be reliably calculated, all of them show a significant correlation between N and NN, domain promiscuity based on NN does not give substantially more information than its abundance. To understand how domains differ in their inherent properties to form new combinations, these two factors – number of abundance and promiscuity – must be unlinked. This prompted us to search for a new measure of promiscuity that would not be correlated with domain abundance. Using this measure, it should be possible to find out which properties of domains correlate with their abundance, and which show the intrinsic ability of a domain to form new combinations.According to the previously proposed random model of evolution , the proN and NN differs for different domains. Fig. NN). However, given the same N, the methyltransferase domain has many more neighbours than the Sushi domain.We observed that the strengh of the relationship between NN on N) for different domains. We argue that this measure reflects the concept of versatility better than other measures which are correlated with domain abundance.Although, as shown above, several measures of promiscuity are actually measures of their abundance, we see varying dependency of all these measures on the number of occurrences of the domain  and forms four combinations in which it has altogether three different neighbours (NN = 3). In the same genome, another domain B has three occurrences but only one neighbour . If we take NN as the measure of promiscuity, B will appear to be less promiscuous than A. Next, let us consider the situation in a second genome II  as the strength of the relationship between the number of occurrences of a domain and the number of its neighbours. More precisely, we calculate the logarithmic regression of NN over N, and take the linear coefficient as DV I.Let us consider the following theoretical example. Let domain urs Fig. . In the Fig. N =  and formWe observe that different domains exhibit different versatility. In essence, the DVI measures how likely a domain is to gain new combinations given a certain abundance in a certain genome. We measure the DVI by comparing abundances and domain combinations in different genomes. Each data point shows how this relationship evolved in a given context of genome evolution. Genomes evolve, among others, by genome and gene duplications during which the abundance of domains increases. How many new combinations are formed after such events through modular rearrangements depends on the ability of a domain to gain these new combinations. Thus, if we observe a strong linear relationship across genomes and even kingdoms, we conclude that there are intrinsic, domain-specific constraints that act on the evolution of domain combinations throughout the whole evolutionary history. It is therefore important to calculate the DVI of domains in many genomes to investigate how domain arrangements evolve.DV I can be done using different approaches. For example, one can include or exclude Pfam B domains. Furthermore, as only few data points exist for many domains, some of the DV I values are loaded with large regression error. Thus, it seems reasonable to select a cut-off for the percentage error. We only considered domains, for which the regression coefficient error was smaller than 10%.Calculating the N. Thus, for computational purposes, it is reasonable to select only those domains for calculation that occur in at least one genome with at least a given number of neighbours. We tested several approaches. For example, we tested regression models more complex than log-linear, simple linear regression without logarithmic transformations and different versatility measures (NTRP or NCO) than the number of neighbours (NN). All the different calculation procedures give similar results, and the obtained coefficients are highly correlated . The different DV I sets calculated using different approaches and the overall DV I distribution correlate (Pearson r correlation coefficient > 0.8).Additionally, the number of neighbours is limited by the number of occurrences. In any given protein a domain can have at most two neighbours. Thus, if a domain occurs once, it can only have two neighbours; if it occurs twice, it can only have at most four neighbours. More abundant domains rarely achieve the maximum possible number of neighbours. However, domains occurring with a low copy number in a genome tend to approach the limit, thus facilitating a steeper slope of the regression line for small DV I . One can explain this by the fact that most occurrences of these domains fall within a repeat stretch, while the expansion of protein repeats [DV I for Pfam domains that (i) occur in at least 3 genomes, (ii) have at least 20 neighbours in at least one genome and (iii) have a low error (error of the DV I < 10%). In this set, we were able to define a DV I for 358 Pfam A domains and 58 Pfam B domains. The distribution of the calculated DV I is roughly normal with mean 0.60 ± 0.14. If the constraints are relaxed, the results are similar; however, the error of the calculated coefficients is larger.Furthermore, our initial analyses have shown that domains that form repeats  have an unusually low  repeats  does not repeats . Here, wDV I does not correlate either with the number of occurrences, N, or with other promiscuity measures such as number of neighbours, NN  or leucine rich repeat (LRRNT), despite having corrected for the number of repeats. Also, other domains such as Sushi or the Receptor L domain which are very frequent have a low DV I. Domains with a high DV I include several transcription related genes such as Zinc finger, bZIP or basic helix-loop-helix domain (bHLH), but also the caspase recruitment domain (CARD), chaperone domain DnaJ and different transferase domains . Furthermore, it turns out that the ratio r = 0.7, p < 10-15; see supplementary data) and thus can be used as a proxy.We find low ins Tab. . In ordedatabase  to analyFinally, the low number of the DVIs obtained was partly due to the rigorous thresholds applied. Relaxing the thresholds we obtain a DVI for over 1,200 Pfam A domains. We have repeated the analyses with this data set and found that our findings remain unchanged . Thus, the relatively small coverage of Pfam A+B is not an inherent limitation, because it depends only on the amount of available sequence information. With the growing number of completely sequenced genomes, it will become possible to calculate this property for many more domains.A recently published method by Basu et al.  tries toR2 to the DVI (linear and logarithmic regression) of every domain and to a regression model that corresponds to the weighted bigram frequency index. We find that the logarithmic regression model outperforms the bigram frequency index in 95% of the cases and the linear model in 62% of the cases. The average difference between the DVI (defined by the logarithmic regression model) and the weighted bigram frequency index is 12%.To compare DVI with the weighted bigram frequency index presented by Basu et. al , we applDV I: domain age, length and pattern in forming domain arrangements. Furthermore, we tested the influence of genetic mechanisms on the DV I. In particular, we studied the influence of LTR retrotransposons on the DV I because of the clear diagnostic features they exhibit and the established methods available to detect them. Retrotransposons can occasionally move non-viral domains and could thus enhance domain recombination rates. We also wanted to know whether domains with a certain function have a higher DV I. It is conceivable that, depending on a function and particular selective pressures, some domains may be more "useful" for the organisms in different protein contexts than others. For example, domains that are responsible for protein-protein interactions or DNA binding should be more likely to occur in different combinations. Vogel et al. [NN), however they did not find any conclusive results.The hypothesis that some domains might be more versatile than others – irrespectively of whether they are more frequent – is intriguing. We wanted to know whether, given the same frequency of occurrence, some domains will propagate and form new combinations more easily than others. We attempted to elucidate whether a certain property of a domain influences its versatility. Therefore, we explored the following possible links to the l et al.  exploredWe examined whether domains that originated early in life history have a higher number of neighbours, as predicted by the random evolution model. To get a rough estimate of the relationship between the age of a domain and its properties, we have adopted a simplified version of the approach described by Wang et. al  and diviFor example, domain PF00001 (a transmembrane receptor from the rhodopsin family) is found in all domains of life, while the Kringle domain (PF00051) can be found only in the eukaryotes. While the former would be defined as 'old,' the latter would be defined as 'new' and nodes in-between as 'middle.' Indeed, domains that originated early have, on average, a higher number of neighbours. For example, domains that are found in all main three life forms  have on average 12.51 neighbours, while the respective values for Eukaryota and Metazoa are 5.72 and 3.69. This does not mean that all ancient domains are very widely spread or have a high number of neighbours. In fact, the variance of these properties also increases with domain age, and thus there are domains that probably are very ancient, but do not form many combinations. For example, the RNA polymerase domain PF04983 occurs in many clades altogether 89 times, but has only 9 different neighbours.p < 10-15 in a one-way ANOVA for three age categories). Therefore, older domains seem to be more promiscuous if measured using previously described units of promiscuity such as the NN or NCO. This is possibly due to the fact that these domains had more time to spread, in accordance with the model presented by Vogel et al. [However, we expect that truly versatile domains should have equal chance of arising during the course evolution. That is, a young domain that recently appeared can also be versatile, albeit in a limited clade. For example, a novel transcription related domain that recently evolved in mammals is not expected to be different in its ability to form new domain combinations from a domain shared throughout the tree of life, although it will not have yet as many connections formed. Given the random model, the number of connections is a function of time, however not necessarily dependent on an inherent property different in older and in younger domains. We observe a strong correlation between the age of a domain and its observed number of occurrences, as well as the number of neighbours . Since the domains that are specific to a clade are generally younger than domains spread throughout the tree of life, this finding shows that the average DV I does not depend on the age of a domain  we calculated the average DV I for bacterial, archeal and eukaryotic genomes were, respectively, 0.64 ± 0.0003, 0.65 ± 0.0012, and 0.58 ± 0.0002. Thus eukaryotic domains tend to be even slightly less versatile, although though they may form, on average, more connections in a domain co-occurrence network than a prokaryotic domain. This is in contrast with previously reported findings based on domain versatility measures correlated with domain abundance [DV I. On the same hand, due to the larger genomes and gene expansion in Eukaryotes, domains are more abundant and domain rearrangements more frequent in terms of absolute numbers. This makes the domains appear more versatile if one applies one of the measures of promiscuity that is correlated with domain abundance.We find a small, but statistically highly significant difference between prokaryotic  and eukaryotic genomes. The average bundance . Since iDV I . We further supported this finding as follows. For a given domain, we considered all proteins in SwissPfam that contain this domain, and calculated the fraction of single-domain proteins . The proportion of single-domain proteins is correlated with the DV I of the domains . Therefore, if a domain occurs frequently as a single domain protein, it is likely to have a high DV I.It has been shown that domain rearrangement events very often involve whole terminal fragments ,34,35. SDV I. To test whether domains that are included in retrotransposition events tend to have a higher DV I, we used the LTR detection method described by Rho et al. [Mus musculus for long terminal repeats, translated the nucleic acid sequence flanked by LTRs into all six reading frames and detected Pfam A domains by running HMMPFAM from the HMMER package [-5 and removing common viral domains such as Transposases, RVTs or Gag-related domains, ~180 mostly non-viral domains were obtained. We were able to calculate the DV I for 53 domains, but did not detect any significant difference between domains found within LTR boundaries and other 'non-LTR' domains.Some domains may be more prone to be rearranged by a specific genomic mechanism. For example, retrotransposons are abundant in many eukaryotic genomes and may play a condsiderable role in their evolution -40. It ho et al. . We scan package  against DV I for terminal domains is 0.61 as opposed to 0.49 for the non-terminal domains (p < 10-15 for a one-way ANOVA). This means that, on average, terminal domains are more versatile.Previously, we have shown that domains in terminal indels differ with respect to their number of neighbours from domains found on non-terminal positions. We now asked, whether domains found at the termini are more often partaking in fusion/fission events simply because they are more abundant, or whether they tend to be more versatile. We found that the latter is the case. The average DV I correlates with average length of a domain: longer domains have a higher DV I . This is in variance with previous investigations, which were based on other versatility measures such as the number of triplets (NTRP) formed [N). It is therefore possible that domain length primarily correlates with domain abundance. Indeed, we see a statistically significant, negative correlation between the number of occurrences of a domain and the domain length . We also explored the link between domain structure and versatility, but did not find any correlation.Furthermore, the ) formed . We inteDV I are very often repeats. Note that the combinatorial effect of the repeats  have been accounted for as described in the previous section.Next, we asked what the functional properties of domains with different DVI values are. A manual investigation of the domain functions has shown that domains with low DV I , collagen triple helix (Collagen) or the Ankyrin repeat (ANK). We wanted to know whether these observations can be quantified and if they are statistically significant.Among the domains with the highest DV I for different GO terms and compared them using analysis of variance (ANOVA). We were not able to find any significant differences in average DV I between the different GO terms.To quantitatively test the differences, we analysed the GO terms that are associated with different domains. A GO term was associated with a domain, if it was shared by all proteins containing the given domain, as defined in the Interpro database . We calcDV I is limited to domains that occur in multiple instances in several genomes, only a small fraction of Pfam A domains could be associated with a DV I. To alleviate this problem we calculated the DV I for the 263 Pfam clans. For example, instead of calculating the DV I separately for SH3_1, SH3_2, SH3_3, SH3_4 and SH3_5 domains, we treated each occurrence of such a domain as the occurrence of its respective clan ID "CL0010". In summary, we were able to calculate the DV I for 115 clans. Furthermore, we calculated the average DV I of Pfam domains that belong to a given Pfam clans and found that both measures are significantly correlated . While the latter (averaging DV I over members of Pfam clan) seems to be a more straightforward approach, the former (substituting domain names in SwissPfam by the respective Pfam clan IDs) allows a calculation of the DV I for a larger number of Pfam clans. We see that, for most of the Pfam clans, the correlation between N and NN is equally strong as for Pfam domains .Since the calculation of a DV I.We further repeated the GO analysis for Pfam clans. Again, we do not find a clear connection between the GO functions associated with a Pfam clan and the calculated DV I values, we were not able to show that domains that have either a low or a high DV I have a particular functional assignment, i.e. common GO annotations. Potentially, there could be a problem with the way GO annotations are assigned to proteins and protein domains; for example, automatically assigning GO terms to a domain by taking over the GO terms of the proteins that share the given domain may introduce a systematic error. On the other hand, there is maybe no general functional link between protein domain versatility and its function .While we could see that protein domains from the same Pfam clan do indeed have similar The idea that some domains can more easily form new domain combinations than others is very intriguing. It is consistent with a model of protein evolution in which proteins with new functionality arise by combining functional units. A frequently used metaphor is the one of Lego blocks: complex proteins can be created by combining existing blocks of a simpler function. Just like the idea that new genes can be invented by neo- or subfunctionalisation ,47, thisWe show that the previously used measures of promiscuity are coupled to the frequency of a domain in a genome. Therefore, to understand the principles of domain-wise modular evolution, one needs to disentangle these concepts and decouple the promiscuity of a domain from the frequency with which it occurs.DV I), for what can be truly considered the versatility of a domain and have shown that this measure does not correlate with domain abundance or age. We have shown that this measure has properties expected from a measure of domain versatility. We find that domains that form single domain proteins or that occur at protein termini (where rearrangements occur frequently) have all a higher than average DV I. We also show that domain versatility, as expected for an intrinsic property, is not related to domain age. Furthermore, contrary to findings based on abundance-related versatility measures, we could not observe that eukaryotic domains are significantly more versatile. Also, we could not correlate the versatility with protein function or retrotransposition. We suggest follow-up studies should further consider the issue of linking DVI to the phylogenetic history of proteins, explaining when the proteins acquire new domain combinations and how the new combinations formed relate to the DV I.We have found a new measure, the domain versatility index  and number of triplets . Triplets are defined as unique combinations of either: three domains, with the given domain in the middle, two domains and a letter if the domain occurs at either N- or C-terminus (e.g. N-PF00001–PF00002 means that the given domain PF00001 occurs at the N-terminus of the protein) or three letters, if the domain occurs in a single domain protein (e.g. N-PF00001-C). We calculated the number of triplets as the number of unique triplet combination in which a domain occurs. These calculations were done for all domains in the data set.We calculated the following previously described indices of domain versatility: es , numbers . For eaDV I) as follows. For each domain, we considered the number of occurrences (N) of this domain in a genome as well as the number of immediate neighbours (NN). We define DV I as the coefficient of logarithmic regression of NN over N with its respective error.We calculated the domain versatility index  + b for logarythmic regression and NN = aN + b for linear regresion . The DV I was then defined as the a coefficient from the regression model.Furthermore, we calculated and compared both, logarithmic and linear regression coefficient of l et al. . The DV To correct for domain expansion, we have removed domain repeats from the data set. Similarily, to correct for gene expansion, we have removed identical domain arrangements from the data set.r = 0.91, p < 10-15).To test whether the use of SwissPfam introduces a significant bias to the observed data, we have recalculated the DVI using proteins from 749 full Eukaryotic, Bacterial and Archeal genomes from the Integr8 database, release 84  = log(N); consequently, we define NN' = NN log(NN) and use the following regression model in R: NN' = a log(N) + b. We have calculated the average R2value for each of the three models applied.To compare different approaches for the DVI calculation and the index described in , we usedu et al.  correspo"OLD" – domains that are common to all three main branches of life ; "MID" – domains that are present in all taxons of one of these branches ; "NEW" – domains that are present only in one subgroup of one of these branches (e.g. domains that occur only in Metazoa). The parameters  were calculated for these three groups and the significance of differences analysed with a one-way ANOVA.We defined three classes of domains. DV I for Pfam clans using two methods. First, we calculated it as the average DV I of all the Pfam domains belonging to a clan for which a DV I could be obtained. Next, we modified the XDOM files by replacing each domain by its corresponding clan ID, and calculated the DV I as previously described for Pfam domains. Domains that do not have a CLANs assignment were retained.We calculated the r correlation coefficient and Spearman rank correlation coefficient were used where appropriate. Principal component analysis of the different measures of versatility was done using the princomp method from the R package.For statistical analysis, the R package  in the vJW designed the study, carried out the calculations and analysed the data. EBB supervised the study, evaluated the results and proposed additional experiments. RAM studied the influence of LTR retrotransposons, of the domain age and of the protein structure on the DVI. JW, EBB and RAM wrote the manuscript.


Partitioning of a protein into structural components, known as domains, is an important initial step in protein classification and for functional and evolutionary studies. While the systematic assignments of domains by human experts exist (CATH and SCOP), the introduction of high throughput technologies for structure determination threatens to overwhelm expert approaches. A variety of algorithmic methods have been developed to expedite this process, allowing almost instant structural decomposition into domains. The performance of algorithmic methods can approach 85% agreement on the number of domains with the consensus reached by experts. However, each algorithm takes a somewhat different conceptual approach, each with unique strengths and weaknesses. Currently there is no simple way to automatically compare assignments from different structure-based domain assignment methods, thereby providing a comprehensive understanding of possible structure partitioning as well as providing some insight into the tendencies of particular algorithms. Most importantly, a consensus assignment drawn from multiple assignment methods can provide a singular and presumably more accurate view.http://pdomains.sdsc.edu/dConsensus; a web resource that displays the results of calculations from multiple algorithmic methods and generates a domain assignment consensus with an associated reliability score. Domain assignments from seven structure-based algorithms - PDP, PUU, DomainParser2, NCBI method, DHcL, DDomains and Dodis are available for analysis and comparison alongside assignments made by expert methods. The assignments are available for all protein chains in the Protein Data Bank (PDB). A consensus domain assignment is built by either allowing each algorithm to contribute equally (simple approach) or by weighting the contribution of each method by its prior performance and observed tendencies. An analysis of secondary structure around domain and fragment boundaries is also available for display and further analysis.We introduce dConsensus dConsensus provides a comprehensive assignment of protein domains. For the first time, seven algorithmic methods are brought together with no need to access each method separately via a webserver or local copy of the software. This aggregation permits a consensus domain assignment to be computed. Comparison viewing of the consensus and choice methods provides the user with insights into the fundamental units of protein structure so important to the study of evolutionary and functional relationships. The process of partitioning a protein into structural components, known as domains, has been much studied since the partitioning concept was suggested over thirty years ago by Wetlaufer  and RossAlgorithms for partitioning tertiary structure into domains have been appearing steadily over the last thirty years or more . Use of To partially circumvent the issue of disagreement among algorithmic methods, it is suggested that the predictions from multiple algorithms be presented and compared, rather than relying on the prediction of a single algorithm. Further to this comparison, generation of a consensus assignment among a variety of methods has been suggested  to captudConsensus, a web resource for the presentation of results from automatic domain assignment algorithms. Through dConsensus, the domains assigned by seven methods are made for minimum chain length of all protein chains in the PDB  • DomainParser2 • PUU • DDomain • NCBI • DHcL • Dodis dConsensus also builds a consensus assignment based on the assignments by each method. Two types of approaches have been taken to generate consensus: the 'simple' approach lets each method contribute equally to the consensus Figure , while thttp://csbl.bmb.uga.edu/downloads/#domainparser2. The NCBI-Vast method [http://www.ncbi.nlm.nih.gov/Structure/mmdb/mmdbsrv.cgi?ShowOp=VastSum&uid=1HTB). Domain definitions for the expert methods CATH [http://www.cathdb.info/wiki/doku.php?id=data:index. The design of dConsensus is modular; new domain assignment methods can be added by simply providing a Java wrapper for running a new method and loading the results into the database. The intention is to keep the tool up to date with new developments in the field. An Automatic update is performed once a month; ensuring assignment of domains to new proteins from the RCSB PDB.Algorithms for six of the seven methods used in this study have been implemented locally. A Java wrapper was written to sequentially run each algorithm on the entire PDB dataset and to store the results in a MySQL database. Executables for the published algorithms DDomain , DHcL 1, and Dodt method  had no eods CATH  and SCOPods CATH  were accsimple consensus). The process begins with pair-wise comparison between two methods; the agreement between two domain assignment methods requires that the methods assign the same number of domains and fragments per domain (if a domain is fragmented). Furthermore, an 80% agreement on the placement of the boundaries is required for each domain and domain fragment, i.e., 80% of the residues should be the same for each pair of compared domains/domain fragments. If the two methods agree they form a group where one of the methods serves as a representative for the group and is used for further comparison. If methods disagree each forms its own group. Each of the remaining methods is then compared to the existing groups and is either accepted by an existing group or rejected by all, hence starting its own group. A reliability score is calculated for each group based on the percentage of contributing methods to each group. In the simplest case there is agreement among all methods and the reliability of assignment is 100%. In more complex cases several distinct competing assignments (groups) are formed. The minimum reliability score required for consensus is 40%. If none of the groups reaches that value, no consensus is reported. Should the highest score of the group be over 40%, and another group's score is less than 10% away, both groups are suggested as a potential consensus.Methods are grouped according to the agreement among themselves , as well as secondary structure elements around the domain and fragment boundaries. Domains in the protein chain are displayed using a horizontal stacked bar chart Figure . Each dohttp://pdomains.sdsc.edu/v2/consensusform.php query form. Users who wish to view assignments by the various methods should use the http://pdomains.sdsc.edu/v2/proteinform.php query form. In the latter case the assignments by individual algorithms, as chosen by the user, are displayed in a way that allows easy comparison among methods. Assignments by the expert methods CATH and SCOP are also provided, when available, for easy comparison  within the structure, by considering how many methods find the same boundary. In this case most prominent/certain partitions will be clearly assigned (having a significant reliability score), while the remaining boundaries will be less certain and left to the interpretation of the user. Providing both of the approaches to consensus will give the user a more comprehensive picture of possible partitioning.Comparison between the simple and weighted consensus approaches favours the simple approach if we only consider the sheer number of chains which reach consensus. However, in some of these cases the consensus does not agree with CATH and SCOP predictions. There are more such cases of disagreement in the simple consensus than in the weighted consensus. The weighted consensus is slightly better than the simple consensus if we only consider cases that are in agreement with expert consensus. In either case the differences in performance between the two consensus approaches are rather small. Figure  and 2D. To our knowledge the dConsensus resource is the first to aggregate and provide a consensus for structure based domain methods. Not only can the results of seven algorithmic methods be viewed side by side, but the consensus among the methods can be calculated to permit the user to quickly assess possible 3D domain structure partitioning for any protein chain in the PDB. The tool is easily extendable to include new algorithmic methods should these become available.dConsensus as included in the pDomains website should be of use to a broad audience, from students who are learning about principles of protein structure, to those who are considering improving existent methods for domain assignments, to investigators who would like a quick answer as to the possible partitioning of a new structure. The advantage of this approach is the lack of lag time typical of expert methods such as CATH and SCOP - within a month of the structure appearing in the PDB, the consensus domain assignment along with the assignments by seven algorithmic methods is available.Project name: dConsensus• Project home page: http://pdomains.sdsc.edu/dConsensus• Operating system(s): Platform independent• Programming language: Java and PHP• Other requirements: Java 1.3.1 or higher• License: Free for non-commercial use• PDB: Protein Data Bank; PDP: Protein Domain Parser; SCOP: Structural Classification of Proteins; CATH: Hierarchical classification of protein domain structures by Class (C), Architecture (A), Topology (T) and Homologous superfamily (H); The terms 'automatic' and 'algorithmic' domain assignments are used interchangeably in this work, also by algorithm we imply algorithm used by the automatic method.SV and PEB conceived the work. KA wrote the code in its entirety and performed evaluation of the methods with SV's guidance. All authors wrote the manuscript and gave final approval.


The integrin family of heterodimeric cell adhesion molecules exists in both low- and high-affinity states, and integrin activation requires binding of the talin FERM  domain to membrane-proximal sequences in the β-integrin cytoplasmic domain. However, it has recently become apparent that the kindlin family of FERM domain proteins is also essential for talin-induced integrin activation. FERM domains are typically composed of F1, F2, and F3 domains, but the talin FERM domain is atypical in that it contains a large insert in F1 and is preceded by a previously unrecognized domain, F0. Initial sequence alignments showed that the kindlin FERM domain was most similar to the talin FERM domain, but the homology appeared to be restricted to the F2 and F3 domains. Based on a detailed characterization of the talin FERM domain, we have reinvestigated the sequence relationship with kindlins and now show that kindlins do indeed contain the same domain structure as the talin FERM domain. However, the kindlin F1 domain contains an even larger insert than that in talin F1 that disrupts the sequence alignment. The insert, which varies in length between different kindlins, is not conserved and, as in talin, is largely unstructured. We have determined the structure of the kindlin-1 F0 domain by NMR, which shows that it adopts the same ubiquitin-like fold as the talin F0 and F1 domains. Comparison of the kindlin-1 and talin F0 domains identifies the probable interface with the kindlin-1 F1 domain. Potential sites of interaction of kindlin F0 with other proteins are discussed, including sites that differ between kindlin-1, kindlin-2, and kindlin-3. We also demonstrate that F0 is required for the ability of kindlin-1 to support talin-induced αIIbβ3 integrin activation and for the localization of kindlin-1 to focal adhesions. Cell adhesion to the extracellular matrix is fundamental to the development of multicellular organismset al., under revision). The F3 FERM domain has a phosphotyrosine-binding domain fold and selectively binds to the more membrane-proximal of the two NPxY-like motifs in the short cytoplasmic tails of β-integrin subunits.739WDTANNPLYDEA750 of the β3 integrin tail shows that the integrin peptide interacts predominantly with the hydrophobic surface on strand S5 of the F3 domain.There are two talin genes in vertebrates,Caenorhabditis elegans752T and NITY759 in β3 integrins); (ii) kindlins act synergistically with the talin head to activate β3 integrins; and (iii) kindlins are required for talin-mediated integrin activation.Studies on the UNC112 gene in Kindlins are most similar in sequence to the talin FERM domain, but initial alignments suggested that this similarity was limited to the F2 and F3 domains.et al. unpublished results; Protein Data Bank (PDB) ID: 2YS3]. However, the original sequence alignments with talin were much less clear-cut in the N-terminal half of the sequence. Our recent structural studies on talin  have led to the identification of (i) an ∼ 30-amino-acid insert in talin F1 and (ii) a new domain (F0) at the N-terminus of talin , and it varies in length between the kindlins  spectrum of the isolated kindlin-1 insert indicates a substantially disordered conformation , the closest structural homologues of kindlin F0 identified by a DALI database searchZ-score = 9.7; r.m.s.d. = 1.5 Å) , and the radixin F1 domain .We have used the revised sequence alignment to design a pet151 His-tagged construct encoding the N-terminal region (residues 1–96) of kindlin-1, that is, the predicted F0 domain. The expressed protein was soluble and stable; it was readily purified and the ein fold a. The si= 1.5 Å) d, interfThe position of conserved residues in kindlin F0 was analyzed using ConSurfet al., under revision) shows that these residues are important components of a well-defined, relatively rigid interface between F0 and F1. Therefore, W62 and W69 would be expected to be components of a similar interface between the F0 and F1 domains of kindlin-1. In support of this notion, the residues predicted to be important in the F0F1 interface, including W62 and W69, are conserved in all kindlins and across species  increase in αIIbβ3 activation  cultured in either LB for unlabeled protein or M9 minimal media for preparation of isotopically labeled samples for NMR. Recombinant His-tagged kindlin polypeptides were purified by nickel affinity chromatography following standard procedures. The His-tag was removed by cleavage with AcTEV protease (Invitrogen), and the proteins were further purified by anion-exchange chromatography. Protein concentrations were determined using extinction coefficients at 280 nm calculated from the aromatic amino acid content according to ProtParam− 1 cm− 1; kindlin F1: 20,065 M− 1 cm− 1; kindlin residues 145–244: 11,460 M− 1 cm− 1; F1 (Δ145–244): 8480 M− 1 cm− 1.The cDNAs encoding murine kindlin-1 residues 1–96 (F0), residues 141–249 (F1 insert), residues 94–275 (F1), and residues 94–275 Δ145–244 (F1 Δinsert) were synthesized by PCR using a mouse kindlin-1 cDNA as template and cloned into the expression vector pet-151TOPO (Invitrogen). Kindlin polypeptides were expressed in 2H2O. NMR spectra were obtained at 298 K using Bruker AVANCE DRX 600 or AVANCE DRX 800 spectrometers, both equipped with CryoProbes. Proton chemical shifts were referenced to external 2,2-dimethyl-2-silapentane-5-sulfonate, and 15N and 13C chemical shifts were referenced indirectly using recommended gyromagnetic ratios.α, and Cβ resonances as described previously.13C-resolved 3D nuclear Overhauser enhancement spectroscopy (NOESY)-HSQC. The resonance assignments of kindlin 1–96 have been deposited in the Biological Magnetic Resonance Data Bank16163.NMR experiments for the resonance assignment and structure determination of kindlin 1–96 were carried out with 0.5 mM protein in 20 mM sodium phosphate, pH 6.5, 50 mM NaCl, 2 mM DTT, and 10% (v/v) 15N-edited NOESY-HSQC , 13C-edited NOESY-HSQC , 13C-edited NOESY-HSQC  on aromatics, and 2D NOESY in 2-->H2O . All NOESY peaks were picked semiautomatically in Analysis with noise and artifact peaks removed manually. Cross-peak intensities were used to evaluate target distances. Dihedral restraints (φ/ψ) were obtained from chemical shifts using the TALOS database.1H-15N HSQC spectra collected in the range 15 –35 °C. Initial models were generated with CYANA using the CANDID2KMC.Distance restraints were obtained from the following experiments: 3D F − Fo)/(Fintegrin), where F is the geometric mean fluorescence intensity (MFI) of PAC1 binding, Fo is the MFI of PAC1 binding in the presence of ethylenediaminetetraacetic acid, and Fintegrin is the standardized ratio of D57 binding to transfected cells. Fintegrin expression ratio was defined for double expressing cells as follows: Fintegrin = (Ftrans)/(Funtrans), where Ftrans is the geometric MFI of D57 binding to double expressing cells and Funtrans is the MFI of D57 binding to untransfected cells. FACS data analysis was carried out using FlowJo FACS analysis software and statistical analysis using GraphPad Prism.The activation state of stably overexpressed αIIbβ3 integrin in CHO cells transiently expressing DsRed-tagged mouse talin head and/or GFP-human kindlin-1 fragments was assessed in three-color FACS assays using a modification of previously described methods.DsRed-tagged mouse talin head (amino acids 1–433) was generated as previously describedCHO cells stably expressing αIIbβ3 integrin were transiently transfected with 3 μg of indicated cDNAs using Lipofectamine™ (Invitrogen). Twenty-four hours after transfection, cells were detached and allowed to re-adhere and spread on fibrinogen-coated (10 μg/ml) coverslips. After 4 h of plating, cells were fixed, permeabilized, and stained with monoclonal anti-vinculin antibody (Sigma-Aldrich) and anti-mouse Alexa 568 (Invitrogen) as described previously.2KMC.Coordinates for mouse kindlin-1 F0 have been deposited in the PDB with accession number


Saccharomyces cerevisiae encodes two FF domain proteins, Prp40, a component of the U1 snRNP, and Ypr152c, a protein of unknown function. The structure of Prp40, its relationship to other proteins within the U1 snRNP, and its precise function remain little understood.The FF domain is conserved across all eukaryotes and usually acts as an adaptor module in RNA metabolism and transcription. Here we have investigated the essentiality and interaction properties of the FF domains of yeast Prp40. We show that the C-terminal two FF domains of Prp40 are dispensable. Deletion of additional FF domains is lethal. The first FF domain of Prp40 binds to U1 protein Luc7 in yeast two-hybrid and GST pulldown experiments. FF domains 2 and 3 bind to Snu71, another known U1 protein. Peptide array screens identified binding sites for FF1-2 within Snu71 (NDVHY) and for FF1 within Luc7 (ϕ[FHL] × [KR] × [GHL] with ϕ being a hydrophobic amino acid).Prp40, Luc7, and Snu71 appear to form a subcomplex within the yeast U1snRNP. Our data suggests that the N-terminal FF domains are critical for these interactions. Crystallization of Prp40, Luc7, and Snu71 have failed so far but co-crystallization of pairs or the whole tri-complex may facilitate crystallographic and further functional analysis. Spliceosome assembly in yeast occurs in a stepwise manner with U1, U2, U4/U6 and U5 snRNPs binding sequentially to the pre-mRNA and each other. The first defined step is the building of the commitment complex in yeast or the E complex in metazoans where the U1snRNP binds initially to the 5' splice site. The metazoan U1snRNP contains the proteins U1-A, U1-70K and U1-C. The yeast U1snRNP contains the homologs of these proteins, Mud1, Snp1 and Yhc1, as well as seven additional proteins .In yeast the essential U1snRNP component Prp40 plays an important role in bringing the 5' and 3' splice sites into spatial proximity so that the intron can be spliced out of the pre-mRNA  found PrPrp40 is a modular protein consisting of a pair of WW domains followed by a series of four FF domains Fig. . FF domaHere we investigated the role of the FF domains and their binding specificity. More specifically, our study aims to complement other data in order to define a consensus binding site for FF domains, such as those sites known for other domains such as the SH3 domain (whose consensus binding site is "proline-rich"). We addressed this problem by a combination of genome-wide yeast two-hybrid screens, in-vitro binding assays, peptide arrays, and genetic experiments.We show that only two of the four FF domains of Prp40 are essential and explain this behaviour by their interactions with two other components of the yeast U1 snRNP. The binding site we found significantly differs from previously published binding sites of FF domains and thus implicates that FF domains are interaction modules with a wide range of specificities, in stark contrast to other domains such as SH3 or PDZ domains.Saccharomyces cerevisiae as Gal4-activation domain fusions . These s . These experiments showed that the FF1 domain of Prp40 binds to a C-terminal fragment of Luc7 .These results encouraged us to revisit the protein topology of the U1 snRNP. We tested all U1snRNP associated proteins by systematic pairwise yeast two-hybrid tests using full-length bait and prey constructs but found only the previously detected interactions Prp40-Snu71 and Prp40-Luc7.E. coli and purified on glutathione sepharose beads. Surprisingly, protein constructs containing the FF3 domain consistently failed to be expressed in significant amounts. We conclude that the FF3 domain renders the FF3, FF2-3, FF1-3 and FF1-4 constructs somehow insoluble or unstable. Nevertheless, Snu71 and Luc7 were translated and 35S-labeled in vitro and incubated with the remaining GST-FF fusion proteins. These experiments clearly showed that Luc7 binds specifically to the FF1 and possibly FF2 domains of Prp40 although binding to FF2 was signficantly weaker than to FF1 , that appeared to be the primary interactor of the FF1 domain  in Snu71 which was obtained by the same approach  narrowed down by four overlapping peptides was detected when Snu71 II was synthesised on a CAPE-membrane as 20-mer overlapping peptides with 3 amino acid shifts and probed with GST-FF1-2.The two-hybrid mapping experiments did not provide any information about the precise binding sites of the Prp40 FF domains within Luc7 and Snu71. In order to map the binding sites of the Prp40 FF domains we synthesized overlapping 15-mer peptides of Luc7 and Snu71 on nitrocellulose membranes and probed them with GST-FF domain fusions Fig. . The pepin Figs. . Althougin Figs.  were carin Figs.  and subsin Figs.  to definin Figs. . Interesach Fig. . A consiAlthough the two-hybrid mapping experiments indicated that the first two FF domains of Prp40 are the most important ones, their physiological role remained unclear. To determine the physiological role of the four FF domains in Prp40 we deleted one or more FF domains starting from the C-terminus in vivo Fig. . We wereSaccharomyces cerevisiae intron-containing genes, DBP2 and ECM33, which are commonly used as splicing reporters. However, RT-PCR assays of the RNAs of these two genes revealed no splicing defect in the FFΔ3–4 mutant (data not shown).Because Prp40 is a well-established component of the U1 snRNP, we reasoned that its phenotype is probably due to a defect in splicing. To investigate this question, we tested splicing efficiency in two selected Although the composition of the the yeast U1 snRNP has been known for a decade ,13 the p421DVHY motif. In any case, our results confirm that different FF domains clearly have different binding specificities with the FF1-2 region being the business end of Prp40  methionine . PCR primers were as follows: Luc7p Forward: GGATCCTAATACGACTCACTATAG GGAAACAGCCACCATGTCAACTATGTCAACGCCT, Luc7p Reverse: CTA CAC AAA GCG TCT TCC GGG; Snu71p Forward: ATCCTAATACGACTCACTATAG GGAAACAGCCACCATGAGGGAT ATTGTATTTGTA, Snu71p Reverse: TCA GGT CCC CAA GCG AAA TTC.Modified primers for Luc7 and Snu71, containing a T7 promotor and a eukaryotic translation initiation site were used to generate PCR products for use with the T2; 0,1 mM EDTA; 1 mM DTT; 1 mM PMSF; 0,2% Triton-X-100; 100 mM NaCl) for 2 h at 4°C under rotation. Beads with bound proteins were washed six times (for 10 min under rotation at 4°C) with pulldown buffer and proteins harvested in SDS-sample buffer, separated by SDS-PAGE, and analyzed by autoradiography.GST-FF domain fusion proteins or GST alone were coupled to glutathione-Sepharose beads (Amersham Pharmacia Biotech) and incubated with 4 μl of in vitro-translated proteins in pulldown buffer , pH 8.0) and then incubated overnight at 4°C with 10 μg/ml purified GST fusion protein or GST control protein in blocking buffer. After washing three times with TBS the membranes were probed with anti-GST antibody  in blocking buffer with 0.2% Tween for 3 h. The membrane was washed three times with TBS and then incubated for 1.5 h with horseradish-peroxidase-coupled anti-mouse mAb  in blocking buffer with 0.2% Tween followed by washing three times with TBS. Analysis and quantification of peptide-bound GST fusion proteins were carried out using ECL . QRALAKDLIVPRRP is known to bind GST and was used as a positive control.in vivo PCR products were created containing a protein A/kanMX6 cassette from the pYM8-plasmid [Prp40ProA: Forward, GCG TCA AAA AAG AGG CAT TTA ACT CCG GCT GTG GAA TTG GAC TAT CGT ACG CTG CAG GTC GAC, Reverse, ATA ATT TAT ATA ATG ATT AAC AAG ATA GAG GTC GAC ACG TCA GAA ATC GAT GAA TTC GAG CTC G; ΔFF4ProA: Forward, AGA AAC GAA AAG ATA CAA CAG AAA CTC CAA AAT GAG CGT AGG ATA CGT ACG CTG CAG GTC GAC Reverse, ATA ATT TAT ATA ATG ATT AAC AAG ATA GAG GTC GAC ACG TCA GAA ATC GAT GAA TTC GAG CTC G; ΔFF3-4ProA: Forward, CTT CAA AAC AAA CTA AAT GAG CTC CGA CTG CGC AAT TAT ACC AGA CGT ACG CTG CAG GTC GAC; Reverse, ATA ATT TAT ATA ATG ATT AAC AAG ATA GAG GTC GAC ACG TCA GAA ATC GAT GAA TTC GAG CTC G; ΔFF2-4ProA: Forward, CTT TCC AAT AGA TCA GCC GAT CAA CTT CTT AAG GAA CAC AAT GAA CGT ACG CTG CAG GTC GAC, Reverse, ATA ATT TAT ATA ATG ATT AAC AAG ATA GAG GTC GAC ACG TCA GAA ATC GAT GAA TTC GAG CTC G. Control primers for the verification of successful recombination: Forward, GGA CGA ACT ATA AAC GAG (Prp40 specific bp 322–339); Reverse, GTC GAC CTG CAG CGT ACG (pYM8 specific S3R primer [In order to delete the FF domain -plasmid  which weR primer .600) were normalized and then measured hourly over 540 min.To determine the growth rate of the FF deletion strains cell densities (ODPU has conceived this study. PU and CE performed yeast two-hybrid screens. CE mapped interaction domains, carried out the pull-downs, the SPOT-synthesis and mapping experiments. CE also made the in vivo FF domain deletions and characterized their phenotype. CE and PU wrote the paper. All authors read and approved the final manuscript.


Intersectin-1L is a member of the Dbl homology (DH) domain guanine nucleotide exchange factors (GEF) which control Rho-family GTPase signaling. Intersectin-1L is a GEF that is specific for Cdc42. It plays an important role in endocytosis, and is regulated by several partners including the actin regulator N-WASP. Intact intersectin-1L shows low Cdc42 exchange activity, although the isolated catalytic DH domain shows high activity. This finding suggests that the molecule is autoinhibited. To investigate the mechanism of autoinhibition we have constructed a series of domain deletions. We find that the five SH3 domains of intersectin are important for autoinhibition, with the fifth domain (SH3(E)) being sufficient for the bulk of the autoinhibitory effect. This SH3 domain appears to primarily interact with the DH domain. We have determined the crystal structure of the SH3(E)-DH domain construct, which shows a domain swapped arrangement in which the SH3 from one monomer interacts with the DH domain of the other monomer. Analytical ultracentrifugation and gel filtration, however, show that under biochemical concentrations, the construct is fully monomeric. Thus we propose that the actual autoinhibited structure contains the related intramolecular SH3(E)-DH interaction. We propose a model in which this intramolecular interaction may block or distort the GTPase binding region of the DH domain. Rho family GTPases are the master regulators of the actin cytoskeleton. Consequently, they coordinate diverse cellular processes including motility, adhesion, cytokinesis, phagocytosis, and neurite extension and retraction Like many other modular signaling proteins, most GEFs contain a conserved catalytic domain (which acts on GTPases) embedded within a more complex set of other domains that act to regulate or localize this catalytic domain. The largest family of GEF proteins contain a catalytic Dbl-homology (DH) domain in vivo and in vitro, have greater nucleotide exchange activity than the respective DH domains alone The function of the PH domain in regulating nucleotide exchange is less understood, but its invariant positioning immediately C-terminal to the catalytic DH domain suggests an important role. The PH domain is present in many intracellular signaling proteins, and has been shown to bind both proteins and phosphoinositides Outside of the DH-PH domain cassette, Rho GEFs are highly diverse in sequence. Rho GEFs are generally large proteins (>1000 amino acids), and often contain several domains involved in their localization, association with other proteins, and regulation of nucleotide exchange activity Intersectin-1L is a large (∼190 kDa), modular, endocytic scaffolding protein that also has GEF activity for Cdc42. It is composed of two N-terminal Eps15-homology (EH) domains, a putative coiled-coiled domain, five SH3 domains related with a sequence identity of 30%–40% , followed by the DH and PH domains and a carboxyl-terminal C2 domain  [31]. InA number of studies have addressed the pathways downstream of the intersectin-1L GEF activity, which is highly specific for the Rho GTPase Cdc42 As seen for many GEFS, the ability for intersectin-1L to mediate nucleotide exchange is inhibited in the full-length protein in vivoin vitroHowever, activation by the proline-rich region of N-WASP has not been demonstrated with recombinant intersectin-1L fragments, suggesting that other, unidentified components present in cell extracts may be necessary for activation of DH domain nucleotide exchange activity To understand the mechanism of intersectin regulation, we have used deletion analysis to map the minimal autoinhibited fragment of the protein. A crystal structure of this fragment has been determined, which gives insight into the molecular mechanism of autoinhibition and activation of this protein.in vitro GEF activity of the adjacent DH domain in vitro. This activity was monitored by following the increase in relative fluorescence due to the loading of a fluorescent GDP into Cdc42. Relative to the isolated DHPH domain, the GEF activity of fragments SH3(ABCDE)-DH-PH, SH3(CDE)-DH-PH, SH3(DE)-DH-PH, and SH3(E)-DH-PH were repressed approximately 3 fold. This result demonstrates that the SH3(E) domain is both necessary and sufficient for DH-PH repression. In support of this observation, DH-PH constructs lacking the SH3(E) domain (SH3(ABC)-DH-PH, SH3(D)-DH-PH) were not significantly repressed. They exhibited GEF activity comparable to the DH-PH domain alone  ((Both SH3(E)-DH-PH and SH3(E)-DH fragments of intersectin-1L were subjected to extensive crystallization trials. We were unable to crystallize the SH3(E)-DH-PH fragment, but the SH3(E)-DH fragment (1151–1431) yielded well-formed, hexagonal-shaped crystals, belonging to space group P3 (3JV3) (. The SH3 (3JV3) ( [8]. Of  (3JV3) ( [8]. The (3JV3)  domain from one chain interacts with the DH domain of the other chain, an arrangement suggestive of a domain-swapped dimer . Domain 2) is buried upon dimerization between DH domain monomers and involves mostly contacts between polar and charged residues. The hydrophilic nature of the dimerization interface is consistent with that found in proteins that readily exchange binding partners. The interaction surface of the DH domain monomers has contributions from the central portion of α1 (CR1), the c-terminal portion of α3, α5 (CR3), and one face of the c-terminus of α6. This surface overlaps highly with the Cdc42 binding surface -DH fragment is exclusively monomeric at micromolar concentrations, more than 100 times greater than used in the in vitro nucleotide exchange activity assay . Consequ2 of surface area on the DH domain, and involves contacts with the first half of α2, the middle fragment of α3, and one face of the c-terminal region of α6 (with the other face of α6 interacting with the DH domain from the other chain). The first and second β-strands, and the small loop between the third and fourth β-strands of the SH3(E) domain are involved in the interactions with the DH domain (The SH3(E) domain from one monomer forms an intermolecular interaction with the C-terminal half of the DH domain from the second monomer. This interaction occurs side of the DH-domain opposite the Cdc42 GTPase binding site. It buries 534 ÅH domain . SpecifiThe intersectin-1L SH3(E)/DH domain interaction observed here is in contrast to that observed in the crystal structure of autoinhibited Asef, an SH3 domain containing GEF specific for Rac Phe-1169, Trp-1185, and Tyr-1201 of the intersectin-1L SH3(E) domain correspond to three aromatic residues that form a series of ridges and grooves on the domain surface, against which a polyproline type II helix (PPII) containing a PxxP motif can pack The probable “hinge-loop” region that would exist in a different conformation in the case of a monomeric SH3(E)-DH domain fragment must be long enough for the SH3(E) domain to fold back to the same peptide chain, a distance of approximately 50 Å. Although the linker between the SH3(E) domain and the DH domain is well ordered and forms multiple bridging contacts in crystallographic dimer, fully extended it could stretch up to 74 Å (assuming an average of 3.5 Å between α-carbons). Blockage or occlusion of the GTPase binding site by the extended linker in the SH3(E)-DH domain monomer could inhibit DH domain GEF activity.The GEF activity of intersectin-1L is controlled by a core autoinhibitory interaction involving the fifth SH3 domain (SH3(E)) and the catalytic DH domain The crystal structure of the SH3(E)-DH domain fragment presented here reveals a homodimer in a domain swapped arrangement with the SH3 from one monomer forming an intermolecular interaction with the DH domain of the other monomer. While SH3-induced dimerization of the DH domain would obscure the Cdc42 binding interface, it is unlikely that this is the mechanism by which intersectin-1L GEF activity is autoinhibited. Both analytical ultracentrifugation and size-exclusion chromatography indicate that the SH3(E)-DH fragment is fully monomeric at micromolar concentrations. Therefore, we propose that the actual autoinhibited structure involves the related intramolecular interaction between SH3(E) and the DH domain. In the biologically relevant, full-length intersectin the proposed positioning of the SH3(E) domain on the DH domain may require a shift of the PH domain to prevent a steric clash, which is directly C-terminal to the DH domain. Although the SH3(E)-DH-PH fragment is repressed , we were2 of buried surface area upon complex formation. Candidates in β2 include Asn-1776 and Leu-1778, which contribute 7% and 8% to the total buried surface area upon complex formation respectively, and contact residues in α3 and α6 in the DH domain. Finally Ser-1191 (between the third and fourth β-strands) contacts a couple of residues in α2 of the DH domain and contributes 12% of the total buried surface area upon complex formation. The effect on DH domain GEF activity of altering the length of the linker between the SH3(E) domain and the DH domain can also be investigated.The effect on GEF activity of mutating the DH binding site in the SH3(E) domain would shed more light on the biological relevance of this observed interaction. Candidate residues for mutation in the SH3(E) domain should contribute at least 5% to the buried surface area upon complex formation and should not be conserved among intersectin SH3 domains . Such reGenerally, autoinhibition can be either “direct” or “indirect” Several synthetic guanine nucleotide exchange factors utilizing the DH domain of intersectin-1L have been engineered using some of the basic principles of autoinhibition described above in vitroin vitro nucleotide exchange activity Our crystal structure indicates that the DH domain binding surface in SH3(E) does not overlap with the proline-peptide (PxxP) binding groove. This observation is consistent with data showing mutations in the PxxP binding groove in SH3(E) that disrupt SH3-PxxP binding have no affect on inhibition of exchange activity Although we have identified the core, minimal inhibitory fragment, it is likely that inhibition of the biologically relevant, full-length intersectin is further modulated by its other domains. Similar to the intersectins, Vav proteins are multi-domain GEFs. The GEF activity of the Vav1 DH domain is controlled by two coupled processes. Firstly, the DH active site is directly, but weakly, inhibited through an interaction from the N-terminally adjacent Acidic domain. Secondly, this core interaction is strengthened 10-fold by the contacts of the calponin homology (CH) domain with the Acidic, PH, and DH domains 4, 0.1 M Hepes pH 7.8). Hexagonal-shaped crystals routinely appeared after 72 hours of equilibration. Crystals were cryoprotected in precipitating reservoir solution enriched in 10% glycerol before flash-freezing to 100 K. A dataset was collected at the Advanced Light Source (ALS) at beamline 8.3.1, using an ADSC Q315r CCD detector. The data was processed with the HKL 2000 program suite 221  with 2 molecules per asymmetric unit. The structure was solved by molecular replacement with Phaser Crystals were grown at room temperature in hanging drops by mixing 2 µl of 10 mg/ml protein with 2 µl of reservoir buffer RIL cells. Transformants were grown at 37°C in Luria-Bertaini media (containing 100 mg/ml ampicillin) to an A600 of 0.6. IPTG was then added to the culture to a final concentration of 0.2 mM to induce protein expression. Growth was continued for an additional 4 hours, after which the cells were harvested by centrifugation. Cell pellets were lysed by sonication, and hexahistadine fusion proteins were purified by Ni-NTA affinity chromatography (Qiagen). Hexahistidine tags were removed by incubation with hexahistidine-tagged TEV protease at room temperature. Uncleaved protein, free hexahistidine, and hexahistidine-tagged TEV protease were removed by subsequent incubation with Ni-NTA resin. The GEFS were further purified by size-exclusion chromatography, with columns Superdex 200 or Superdex 75 (GE Biosciences) equilibrated in 150 mM NaCl, 20 mM Tris pH 8.3, and 1 mM TCEP.The pET-19(b) based constructs were used to transform 2. Excess nucleotide was removed by dialysis into GEF Assay Buffer .A fragment of human Cdc42 (residues 1–179) was expressed as a hexahistidine fusion and purified as described above for GEFs, with the exception of the replacement of size-exclusion chromatography with anion-exchange chromatography on a Source Q column (Amersham). Residual bound nucleotide was removed by dialysis in 20 mM Tris, 50 mM NaCl, 5 mM EDTA, 2 mM DTT, pH 7.5. Cdc42 was preloaded with GDP by incubation with excess nucleotide. Nucleotide exchange was quenched by addition of 50-fold molar excess of MgClRelative activities of Intersectin GEF fragments were quantified with an assay that measures an increase in fluorescence observed following the incorporation of mant-GDP into Cdc42 100 µl of SH3(E)-DH protein at 10 mg/mL was loaded onto a Superdex-75 HR 10/30 column at 0.5 ml/minute. Elution volume of protein was monitored by absorbance at 280 nm. The column was equilibrated in 150 mM NaCl, 20 mM Tris pH 8.3, 1 mM TCEP, and had been calibrated with proteins from the GE Life Sciences LMW Gel Filtration Calibration Kit.SH3(E)-DH (Intersectin 1151–1431) was monitored at 280 nm at sufficient concentration to give an absorbance reading of 0.3–0.6 (8 µM-17 µM). Samples were centrifuged at 14,000, 20,000, and 28,000 rpm (in succession) for 22 hours at each speed (1 scan/hour). These experiments were done at 20°C on a Beckman Optima XL-A ultracentrifuge with an An-60 Ti rotor. Data were processed using the program Reedit9  and then fitted to effective reduced molecular weight (σ) values with WinNonlin


Predicting protein function from structure remains an active area of interest, particularly for the structural genomics initiatives where a substantial number of structures are initially solved with little or no functional characterisation. Although global structure comparison methods can be used to transfer functional annotations, the relationship between fold and function is complex, particularly in functionally diverse superfamilies that have evolved through different secondary structure embellishments to a common structural core. The majority of prediction algorithms employ local templates built on known or predicted functional residues. Here, we present a novel method (FLORA) that automatically generates structural motifs associated with different functional sub-families (FSGs) within functionally diverse domain superfamilies. Templates are created purely on the basis of their specificity for a given FSG, and the method makes no prior prediction of functional sites, nor assumes specific physico-chemical properties of residues. FLORA is able to accurately discriminate between homologous domains with different functions and substantially outperforms (a 2–3 fold increase in coverage at low error rates) popular structure comparison methods and a leading function prediction method. We benchmark FLORA on a large data set of enzyme superfamilies from all three major protein classes  and demonstrate the functional relevance of the motifs it identifies. We also provide novel predictions of enzymatic activity for a large number of structures solved by the Protein Structure Initiative. Overall, we show that FLORA is able to effectively detect functionally similar protein domain structures by purely using patterns of structural conservation of all residues. Understanding how the three-dimensional (3D) molecular structure of proteins influences their function can provide insights into the workings of biological systems. Structural Genomics Initiatives have been set up to investigate these structures on a large scale and make the data available to the wider biological research community. However, in a significant number of cases, there is little known about the functions of the structures that are solved. To address this, computational methods can be used as a predictive tool to guide future experimental investigations. One such approach is to exploit global structural comparison to assign the protein in question to an evolutionary family, which has already been functionally characterised. However, this is problematic in some large evolutionary families, which contain a number of different functional sub-families. We have developed a new method (FLORA) which is able to calculate 3D “motifs” which are specific to each of these sub-families. Any new protein structure can then be compared against these motifs to make a more accurate prediction of its function. Our paper shows that FLORA substantially outperforms other standard approaches for predicting function from structure. We use our method to make confident functional predictions for a set of proteins solved by the structural genomics projects, which could not have been assigned reliably by global structure comparison. The prediction of protein function from structure has become of increasing interest as a significant proportion To address the problem of generating templates for all protein structures, there are a number of methods that aim to do this automatically. For example, the reverse template method α atoms, to account for both local and global relationships between residues across the protein structure. In a recent ligand-binding site comparison analysis, SOIPPA was able to detect distant similarities between very different protein folds binding a range of adenine-containing ligands One inherent complexity of using PDB structures to transfer annotations between enzymes is the binding state in which the protein is crystallised — for example, structures crystallised with non-cognate ligands, substrate analogs, transition states or apo-enzymes Despite the many template methods present in the literature, very few are publicly available to the general user. Hence, the first step in assigning function by structure is often to use global structure comparison methods e.g. CE , which cet al.Analyses of CATH The FLORA algorithm presented here was designed to derive structural templates for functional sub-groups (FSGs) within diverse CATH superfamilies. FLORA first performs global structure alignment across the superfamily to recognise the distinctive structural patterns associated with each FSG and builds templates based on these patterns. New functional homologues are then detected by using the global structural alignments to relatives in each FSG again, but only scoring the similarity over positions identified by the FLORA motif. This approach performs very well in discriminating between different enzymatic functions, compared to global methods and another motif-based approach. Although we benchmark here on enzyme superfamilies, the method is applicable to superfamilies containing non-enzymatic relatives. To test FLORA, we have automatically generated a large data set of domains from 29 diverse superfamilies (containing multiple FSGs). Our data set allows us to look at the variation of FLORA results between superfamilies and to stress the importance of using a large test data set for benchmarking methods. We have benchmarked FLORA against CE In order to benchmark FLORA as a protein function prediction method, it was important to generate a relatively large and unbiased data set. We focussed on functionally diverse superfamilies  were removed. This exclusion criterion removed less than 8% of enzymatic chains in the PDB. In addition, any domains with an incomplete E.C. number (e.g. 2.7.-.-) were also excluded.All annotated domains in CATH were clustered at 60% sequence identity and a representative taken from each cluster (S60Rep). This threshold was applied as 60% has been found to be an appropriate sequence cut-off for functional similarity S60Reps were then grouped within the superfamily if they shared at least the first three E.C. numbers; to create what we will subsequently refer to as a functional sub-group (FSG). A CATH superfamily was then included in the data set if it contained at least 3 FSGs, where each enzyme family contained at least 4 S60Reps. These criteria were chosen to create a sufficiently diverse data set, which could be effectively assessed using leave-one-out benchmarking.The final domain data set  compriseAn outline of the FLORAMake algorithm is shown in Methods which attempt to create structural templates of residues associated with a given function rely on a range of methods all pairs of domains within each superfamily in the data set.The first step in our protocol was therefore to generate structural alignments using CATHEDRAL between All pairs of structure-structure alignments between domains in a given FSG were analysed to identify aligned residues. A set of residues for each domain was then generated from the pairwise alignments to include only those residues that were aligned to residues in at least 75% of other domains in the FSG . A cut-off of 75% was chosen after exploring a range of cut-offs (0–100%) and gave the fastest performance without affecting the precision/recall of FLORA. These were designated rescons positions.β atoms of residues A and B and then multiplied by a co-ordinate frame calculated from the tetrahedral geometry of the bonds of the Cα of residue A as described in α geometry of residues A and B are not identical, vectors were calculated in both the A→B and B→A direction. However, we found that taking only one of these vectors forward to the next steps in the algorithm gave the same performance as using both, but increased the speed of FLORA by halving the number of vectors that needed to be analysed.For each domain, vectors were calculated between all rescons positions. To allow vectors to be appropriately compared between domains, a vector was calculated between the CA given vector from a domain in the FSG was compared to equivalent vectors in domains across the whole superfamily. Equivalent vectors were obtained from the CATHEDRAL structural alignment of the domains being compared. For example, residues 93 and 105 in CATH domain 1vl2A01 are equivalent to residues 92 and 108 in 1k92A01 according to the structural alignment. Hence, the vectors 93→105 (v1) and 92→108 (v2) were scored for similarity using the formula below . The aim was to eliminate vectors that are conserved mainly to preserve the common fold of the superfamily. Two distributions were calculated for each vector: a) scores to domains in the same FSG (DIST-F) and b) scores of domains in different FSGs (DIST-S). The means of DIST-F and DIST-S were then calculated and the vector was initially determined to be FSG-specific if it satisfied the following inequality:We experimented with various statistical tests , but found that the set of selected vectors could be best reduced by jack-knifing the data set and repeating the calculation above. That is, each domain in the training set was removed in turn and FLORA only selects a vector if the inequality is always satisfied.We also explored incorporating measures of sequence similarity when scoring vectors, but in our hands this degraded the performance of FLORA. This could be due to the fact that the benchmark data set contained very diverse relatives and hence exploring the sequence signal requires a more sophisticated approach.At this point, each domain in the FSG is associated with a set of FSG-specific vectors, which we termed the “FSG-domain template set”.To score a given query domain against the template for a given domain in a given FSG relies again on the global structural alignment by CATHEDRAL. Hence, the first step is to align the query domain against an FSG domain but then only score the similarity across the subset of template vectors. Essentially, we are calculating a local score over the FLORA template from the correspondence determined by a global structural alignment. Each vector in the template set associated with the FSG domain is scored against the equivalent vector in the query domain (using equation 1), based on the aligned residues from the global alignment. Any vectors that are not aligned (i.e. gapped positions) are given a score of zero. The total similarity of the query domain against enzyme domain (the florascore) is simply the sum of these similarities, normalised by the total number of vectors in the template (Equation 2).We hypothesised that the extent to which the structure of a domain can change before its enzymatic function changes might be specific to the homologous superfamily. For each FLORA domain-function template, a distribution of all scores is calculated against all domains in different FSGs. The florascore between a given pair of query and enzyme domains is then transformed into a Z-score.As FLORA is essentially a pattern discovery method, it was vital to assess its performance in an unbiased fashion. We took a standard leave-one-out (or jack-knifing) approach as is often used to test machine learning methods. For each superfamily, one test domain was removed, while training on the remaining domains. The test domain was then scored against all the resulting templates. The aim of this process to was accurately reproduce a situation where a novel domain is classified into a CATH superfamily and then needs to be assigned to a functional group.The performance of FLORA, CATHEDRAL In order to examine where residues identified by FLORA overlapped with known functional residues, we compared the location of FLORA positions to those in the Catalytic Site Atlas For each functional sub-group (FSG), we selected the domain that had the highest mean global structural similarity (measured by CATHEDRAL) to all other members of the FSG as a representative. All residues, from each relative within an FSG, identified by FLORA and CSA annotations were then mapped onto this representative using the CATHEDRAL structural alignment. Consequently, for each FSG we had a representative structure where all residues were annotated as FLORA positions, catalytic residues, or neither. The CSA provided annotations for 61 out of 82 FSGs (74%). We then calculated the average distance between the FLORA residues to the catalytic residues and the average distance between non-FLORA and the catalytic residues.FLORA produces a set of inter-residue vectors for each domain in a given FSG that are considered to be specific to its enzymatic function, in the context of its evolutionary superfamily. In order to visualise where these vectors lay, we took each set of domain templates for a given enzyme family and mapped them onto the most representative structure — i.e. the structure with the greatest cumulative global structural similarity to all other domains in the family. A given residue was then coloured if it was involved in the top 30% of FLORA template vectors. Residues that are conserved across the whole superfamily (in 75% of relatives) were also identified and those which overlapped with FLORA residues were coloured gold.Despite targeting proteins with no significant sequence similarity to existing structures in the PDB, Protein Structure Initiative (PSI) structures can often be classified into one of the large, diverse superfamilies in CATH by structure comparison methods once their structure has been solved. However, these superfamilies contain a significant number of relatives with different functions and therefore to be able to further assign these proteins to a specific functional sub-group is of great use for guiding future functional studies. We took all PSI structures solved up to January 2008 that had been newly classified in v3.2 of the CATH database and selected the 276 domains which fell into one the superfamilies in our data set. These 276 were further clustered at 60% sequence identity to produce a non-redundant test set of 104 domains, which was then scanned against the FLORA templates for each FSG in order to predict their function. To exclude hits that could have been fairly confidently assigned using global structure comparison, we removed any structures that matched a CATH domain in v3.1 library with a SIMAX score<1.5 FLORA was designed as a generic method to create structural motifs that can discriminate between different functional sub-groups (FSGs) within diverse domain superfamilies, purely using patterns of structural conservation — FLORA makes no assumptions as to the physico-chemical properties of functionally important residues and uses a purely structure-based conservation score , FLORA identifies two major motifs, one of which is located in the amino-acid and ATP binding site, whereas the other covers residues in loops that bind the tRNA . These enzymes are essential for protein translation as they catalyse the ligation of amino-acids to their cognate tRNAs in a two-step mechanism that involves ATP. The HUP domains of aminoacyl-tRNA synthetases are found in many different multi-domain contexts in CATH, which appear to partially depend on the amino-acid substrate (data not shown). In representatives from this group, (the tRNA .Th. Thermophilus pantetheine phosphate adenylyltransferase PDB: 1od6), is a relatively small protein and consists of a homo-hexamer of single HUP domain subunits. FLORA identifies two motifs in this domain, one of which locates in the main active site in the C-terminal half of the central β-sheet, whereas the other maps to the inter-subunit interface , which transfer nucleotidyl groups from nucleotide tri-phosphates to other compounds. The nucleotidyltransferase we have analysed further , another consists of residues at the interface with other subunits of the tetramer, whereas the third motif is comprised of residues from N-terminal α-helices that are not involved in any identified interactions to our knowledge , which catalyse the ATP-dependent synthesis of argininosuccinate from citrulline and aspartate. These enzymes are homo-tetramers in which each subunit is comprised of a nucleotide-binding HUP domain and an additional domain involved in multimerisation and catalysis. Three motifs are identified by FLORA in nowledge . The locAnalyses of residues identified by FLORA in these domains and others in this superfamily (data not shown) suggest that FLORA is generally able to target motifs known to be involved in different aspects of molecular function, like binding interfaces or catalytic sites. This behaviour is somewhat expected from FLORA, which was specifically designed to detect such function-related signatures in homologous domains. By mapping catalytic residues from the CSA onto each FSG representative see , we founExamining similar representatives from the Class I aldolase superfamily (3.20.20.70) reveals that FLORA template residues  tend to Our analysis thus far has shown that FLORA is able to substantially improve on the performance of global structure comparison for reliably assigning domains to functional sub-groups. We therefore sought to use it to make novel predictions for structural genomics targets from the PSI. As a data set, we took structures that had been assigned to superfamilies in the latest version of CATH (v3.2) and scanned these against the FLORA templates. Using the benchmark curve from the leave-one-out benchmark, we took a score cut-off corresponding to a precision of 95% (Z-score>3.4) to ensure high confidence in our assignments. All hits above this cut-off were collated, rather than simply taking the top hit so that we could account for bi-functional enzymes and observe any conflicting predictions (i.e. those structures which hit more than one FSG template). A complete table of results is shown in 104 domains from our v3.2 PSI set correspond to 94 PDB structures. Of these 94, we were able to make predictions for 66 (70.4%) with FLORA. To assess the added value of using FLORA over global structure comparison, we took out any PSI structures that matched a domain in CATH with a SIMAX score<1.5 see . This leFLORA predicted NESG structure 2bdt with the E.C. number 2.7.1.-, which is a group including enzymes such as fructose 1-,6 bisphosphate. When this structure was published, it was assigned as a putative gluconate kinase but currently has no official E.C. annotation.PDB 1vm8 from the JESG consortium was functionally characterised when the structure was solved as UDP-n-acetylglucosamine pyrophosphatase and given the E.C. number E.C. 2.7.7.23. Again, FLORA correctly predicts the E.C. number as 2.7.7.-, despite low global structural similarity to any domains in the template data set.1ylo is a hypothetical protein solved by the MCSG consortium in 2005. FLORA predicted the E.C. number 3.4.11.-, which comprises a group of amino-acid specific peptidases, with significant hits (Z-score>4) to three domain templates in our data set. A BLAST search indeed reveals significant hits (>99% sequence identity) to annotated amino peptidases, as the protein has now been functionally characterised since its structure was solved. Again, these trivial hits were not in the data set we used, which demonstrates the power of FLORA to find functional homologues even after significant evolutionary divergence.FLORA is a novel algorithm which exploits patterns of structural conservation to derive templates for different functional sub-groups (FSGs) within diverse domain superfamilies. Unlike many other methods which focus on generating templates based on known or predicted functional residues By generating a superfamily-specific Z-score, we found that the performance of FLORA increases significantly. This suggests that the degree of structural variation that confers a change in function is specific to each superfamily and the absolute structural similarity must be compared to a background distribution. Therefore, as has also been identified at the sequence level Another important novelty in our approach was to create a large data set comprising 29 superfamilies . Although FLORA performed well across the majority of superfamilies, this was not universally true, which suggests that function prediction methods should be benchmarked across as diverse a data set as possible. We have also shown that CATHEDRAL outperforms CE, probably due to producing superior alignments outside of the conserved structural core. Although global structure comparison is not always able to reliably find distant functional relatives, we feel it is appropriate for benchmarking new methods to give a guide of the value they add to structure-based function prediction.β side chain atoms. However, a re-implementation using just Cα co-ordinates produces almost identical performance on the data set (data not shown). This is encouraging as it increases applicability of our method to theoretical and homology-based models.As detailed in the One of the major ways in which FLORA differs to other methods is by focussing on the domain, rather than at the whole chain or protein complex level. Simply because a domain is present in a given enzyme does not necessarily mean it contributes to or confers catalytic activity. Indeed it might be responsible for protein-protein interactions or other aspects of function, such as locating the protein in a given part of the cell. We have shown that except in the case where there has been a domain duplication (superfamily 3.30.830.10), deriving structural motifs at the domain level performs as well as aligning whole multi-domain chains. Our hypothesis is that where FLORA does not locate conserved positions around the active site, it is able to find parts of the domain that interact with other catalytic domains. We intend to undertake more detailed analysis of other CATH superfamilies to confirm this.FLORA makes no assumptions about the physico-chemical (e.g. solvent accessibility or polarity) or sequence conservation properties of residues in the templates it derives, only that they show high structural conservation within a given functional sub-group. As a consequence, we observed residues both around the enzymatic active sites and in other locations in the protein. In two of the example superfamilies presented here, we have shown that FLORA template vectors co-locate around the active site. This is possibly due to structural changes in the protein that allow for different relatives to bind different ligands. However, this trend is not observed across the whole data set, where only 59% of FLORA template vectors are on average closer to the active site than other residues in the protein. This suggests that it is not only the enzymatic site that is important for discriminating between different FSGs, but other locations in the structure related to domain-domain or protein-protein interfaces.The substantial improvement in performance of FLORA over global structure comparison has allowed us to assign 70% of structural genomics targets, assigned to superfamilies in our data set to functional sub-groups, in this case predicting the type of catalytic reaction they perform. Of our FLORA predictions, 78% could not have been reliably made by standard structure comparison techniques, as we were able to transfer annotation from far more distant relatives (RMSD>4 Å). Although some of the predictions we made are supported by experimental work that occurred after the structure was solved, the accuracy of the rest remains for future functional characterisation work.Taken in the context of our previous analysis of functional divergence across large domain superfamilies in the CATH database Dataset S1Benchmark data set for FLORA.(0.08 MB XLS)Click here for additional data file.Text S1Supporting Information.(0.31 MB DOC)Click here for additional data file.


Binding of SCF to its receptor triggers transactivation, leading to the recruitment of kinases and phosphatases to the docking platforms of c-Kit catalytic domain. Tyrosine phosphatase-1 (Shp-1) deactivates/attenuates 'Kit' kinase activity. Whereas, Asp816Val mutation in the Kit activation loop transforms kinase domain to a constitutively activated state (switch off-to-on state), in a ligand-independent manner. This phenomenon completely abrogates negative regulation of Shp-1. To predict the possible molecular basis of interaction between c-Kit and Shp-1, we have performed an Study revealed a stretch of conserved amino acids (Lys818 to Ser821) in the Kit activation domain, which makes decisive H-bonds with N-sh2 and phosphotyrosine binding pocket residues of the phosphatase. These H-bonds may impose an inhibitory steric hindrance to the catalytic domain of c-Kit, there by blocking further interaction of the activation loop molecules with incoming kinases. We have also predicted a phosphotyrosine binding pocket in SH2 domains of Shp-1, which is found to be predominantly closer to a catalytic groove like structure in c-Kit kinase domain.This study predicts that crucial hydrogen bonding between N-sh2 domain of Shp-1 and Kit activation loop can modulate the negative regulation of c-Kit kinase by Shp-1. Thus, this finding is expected to play a significant role in designing suitable gain-of-function c-Kit mutants for inducing conditional proliferation of hematopoietic stem cells. The c-Kit controls major signaling cascades in hematopoietic stem cells. Earlier study indicated that the catalytic domain of c-Kit consists of many important loop regions, such as catalytic loop (786-796), activation loop 810-839) and substrate-binding loop (829-837), which play crucial role in the activation of Kit kinase domain  associated protein kinase 70 kDa; JMD: Juxtamembrane domain; APS: adapter protein with Pleckstrin homology and Src homology two domain.The authors declare that they have no competing interests.in silico protein interaction study, multiple alignments, analysis and interpretation of data and initial drafting of the manuscript. GUG: strategic planning to conduct in silico experiments in the initial phase of the study and revising the manuscript. OPK: revising the manuscript and final approval. AM: analysis and interpretation of data and revising the manuscript critically for important intellectual content.SP: conceptualization and designing the objectives of the paper, All authors have read the final version of the manuscript and approved for the submission.Figure S1. Motif Scan analysis of c-Kit juxtamembrane with other kinase relatives. Tyr568 and Tyr570 of YVY motif in c-Kit juxtamembrane domain are found to be involved in interaction with different molecules such as EGFR kinase and CRKL binding domain, as detected by motif scan.Click here for fileFigure S2. Analysis of Shp2 bound and unbound Kit kinase structures. (A) The unbound form of Shp-1 displayed two sh2 domains and PY pocket. (B&C) The conserved domains like N/C-sh2, PY pocket, Y568V569Y570 and residue Tyr100 of N-sh2 domain, found to be closer within an interactive zone, are displayed both in unbound and bound structures of c-Kit in different colors.Click here for fileFigure S3. Detection of a catalytic groove like structure in Kit kinase and validation of hydrogen bonding. (A) A distinct groove like structure was found in the Shp2-Kit bound complex (shown within a square in the center). (B) The cleft also show one H-bond between N-sh2 domain and residue Tyr100 and Asn819 of Kit activation loop. Change in torsion angle from -156.66 to -116.66 show the same hydrogen bonding between the cleft and Tyr100 in a closer distance of 3.67A°.Click here for fileFigure S4. Interaction of c-Kit with various down-stream signaling molecules. Lys818 and Asn819 make intramolecular hydrogen bonding with Asp816 in inactive c-Kit, making a closed conformation of c-Kit kinase. Upon binding of SCF, a chain of molecular events take place, comprising disruption of above intramolecular H-bonds and recruitment of tyrosine kinases and tyrosine phosphatases at the respective phosphorylated tyrosine containing docking sites at c-Kit catalytic domain.Click here for fileMathematical relationship. The method for calculating ligand-receptor interaction energyClick here for file