Drosophila, C. elegans, yeast, Plasmodium, E. coli and Arabidopsis. Our results show that only 23% of these DDIs are conserved in at least two species and only 3.8% in at least 4 species, indicating a rather low conservation across species. Pair-wise analysis of DDI conservation revealed a ‘sliding conservation’ pattern between the evolutionarily neighboring species. Our methodology and the high-confidence DDI predictions generated in this study can help to better understand the functional significance of PPIs at the modular level, thus can significantly impact further experimental investigations in systems biology research.Knowledge of specific domain-domain interactions (DDIs) is essential to understand the functional significance of protein interaction networks. Despite the availability of an enormous amount of data on protein-protein interactions (PPIs), very little is known about specific DDIs occurring in them. Here, we present a top-down approach to accurately infer functionally relevant DDIs from PPI data. We created a comprehensive, non-redundant dataset of 209,165 experimentally-derived PPIs by combining datasets from five major interaction databases. We introduced an integrated scoring system that uses a novel combination of a set of five orthogonal scoring features covering the probabilistic, evolutionary, evidence-based, spatial and functional properties of interacting domains, which can map the interacting propensity of two domains in many dimensions. This method outperforms similar existing methods both in the accuracy of prediction and in the coverage of domain interaction space. We predicted a set of 52,492 high-confidence DDIs to carry out cross-species comparison of DDI conservation in eight model species including human, mouse,  Proteins rarely function alone; a vast majority of proteins must interact with other proteins to perform their intended functions. In recent years, determination of protein-protein interactions (referred to henceforth as PPIs) has been at the forefront of systems biology research resulting in high-throughput generation of such interactions for many proteomes of model organisms i.e. interaction between a given pair of proteins is either ‘found’ or ‘not found’. In the case of multi-domain proteins, which constitute about 65–70% of the eukaryotic proteomes et al.et al.et al.Despite the availability of abundant PPI data, little is known about interactions at the domain level because the PPI data is available as ‘binary’ data, http://dip.doe-mbi.ucla.edu), IntAct (http://www.ebi.ac.uk/intact), BioGrid (http://www.thebiogrid.org), BIND (http://www.bind.ca), MINT (http://mint.bio.uniroma2.it/mint) and HPRD (http://www.hprd.org). Though each database uses a different set of criteria for collection and curation of interaction data and each covers a variety of species, there is a significant overlap among them Several PPI databases from high-throughput experimental studies are available online, including the Database of Interacting Proteins , IntAct (http://www.ebi.ac.uk/intact), BIND , HPRD  and MINT . These source databases use a combination of ‘spoke’ and ‘matrix’ models for expanding binary PPI interactions from complexes. These datasets were fairly overlapping both within and across databases, and protein sequences in these databases were originally indexed with different source identifiers from UniProt, DIP, GenBank, etc. To remove redundancy, we first created datasets of unique sequences (based on full-length protein sequence string comparison) within each database and then merged them to create a non-redundant dataset of interacting protein sequences, each indexed with our internal identifier. Note that this internal identifier can be used to map all the original identifiers for a given sequence in their source database(s). Finally, we obtained 70,769 unique protein sequences (denoted as P) representing 209,165 unique PPIs (denoted as intP). Note that proteins in P still exhibit some level of redundancy because splice variants with minimal sequence differences are included as unique proteins due to the fact that protein-protein interactions are isoform-specific.We created a comprehensive, non-redundant dataset of experimentally-derived interacting proteins by combining multiple datasets (downloaded in the PSI MI 2.5 format) from five major protein interaction databases that include DIP (Database of Interacting Proteins) . Some of the InterPro entries are as small as 3 residues that represent active sites, binding sites and prints. We filtered out these entries with a length cutoff of at least 10 residues to eliminate potential noise in the datasets. We mapped the remaining domains onto the set of proteins in P.Domain definitions were used from the InterPro domain database  were identified from the entire UniProt protein database . Proteins containing at least two domains from sdpD were considered as Rosetta stone proteins, and pairs of such domains that co-occur on Rosetta stone proteins were considered as Rosetta domain pairs (RDPs). Out of 4,015,827 protein sequences from UniProt containing InterPro mapping, we found 1,095,580 single domain proteins (27%) corresponding to 9,403 unique InterPro domains. Of these, 7,551 domains were found to co-occur in Rosetta stone proteins in 129,236 binary combinations (RDPs). Out of these, only 34,006 RDPs were found to match with the DDIs used in the current study.Rosetta stone proteins are those that have two or more fused domains encoded by a single gene in one species, but whose constituent domains are encoded by separate genes as single domain proteins in the same or another species http://www.sanger.ac.uk/Software/Pfam/iPfam) et al's method We downloaded a dataset of positive DDIs (based on Pfam database version 21.0) from the iPfam database  of the set.We adopt definitions from set theory, where the term intP = set of non-redundant PPIs, |intP| = 209,165.P = set of all proteins from intP, |P| = 70,769.∼intP = set of non-interacting protein pairs derived from P, |∼intP| = 16,738,143D = set of domains occurring in P, |D| = 10,389.intD = set of candidate interacting domain pairs (DDIs) constructed by considering all possible domain pairs that could occur in intP, |intD| = 614,579.∼intD = a set of putative non-interacting domain pairs extracted from ∼intP that were also matching with intD, |∼intD| = 360,739. Note that ∼intD represents only those DDIs from intD that exist in ∼intP.ijf – Frequency of domain pair ij in intD.∼ijf – Frequency of domain pair ij in ∼intD.totalf – Total number of domain pairs in intD.∼totalf – Total number of domain pairs in ∼intD.ijp – Average (expected) probability that domain pair ijd is an interacting pair, from all protein pairs in intP where ijd occurs.∼ijp – Average (expected) probability that domain pair ijd is a non-interacting pair, from all protein pairs in ∼intP where ijd occurs.D ; where in theory, all-to-all domain interactions among D can generate ∼54 million unique domain pairs. Nevertheless, such random interactions are not possible in biological systems due to evolutionary, spatial, temporal and functional constraints. The goal of this algorithm is to develop a scoring system that uses a top-down approach for inferring functionally meaningful DDIs from all possible interactions. First, all possible DDIs were extracted from all PPIs in intP. As illustrated in AB, AC, AD and BB are interacting protein pairs where the domains in respective proteins are denoted as A(m-p), B(n), C(m-p) and D(n-m-r), then possible set of DDIs from {AB, AC, AD, BB} are {mn, pn, mm, mp pp, mr, pr, nn}. It is possible that only mn, pp and nn are true interacting domain pairs in {AB, AC, AD, BB}, but this is not obvious from the ‘binary’ interaction data, except for the interaction BB. To discriminate functionally relevant DDI(s) in a given PPI from all possible ones, we developed an integrated scoring system that employs five discriminatory features. Each one of these features, denoted Si, have some measure of correlation with an aspect of the occurrence of interaction at the domain level. A score is derived for each feature independently. Scores are either based on log ratios between probabilities of features calculated from intP and ∼intP, or based on probabilities estimated from intP combined with prior expert knowledge (annotations). We assume that each individual score is an independent descriptor related to the domain pair being scored. Hence, each score reflects the individual probability of one descriptor and the final probability is a product of all five individual probabilities. We use log probabilities to aid in creating a more uniform score, thereby yielding a final score as a sum of the individual log measures.S1–S5) for each observed domain pair (ijd) are derived.The domain set considered in this study is ijS1 represents the event that domain pair ijd is interacting based on the expected frequency of occurrence estimated from a given set of protein pairs. Previous methods that performed similar analysis showed that such a measure can be used to infer DDIs ijd based on a ratio of the expected frequency of occurrence in Feature ijd is calculated as the average probability of occurrence of ijd multiplied by the observed number of times that ijd occurs in the dataset. If a protein contains repeats of the same domain, each unique domain is counted only once for calculation of ijd probability in a PPI. The probability is estimated empirically from the data. For example, based on the example described in the above paragraph  are possible. So, domain-domain interaction mn is possible in multiple PPIs, but with different probabilities. Hence, the probability of occurrence is estimated by calculating the average probability of interaction ijd over all protein pairs in intP (or ∼intP) where ijd occur. We denote this value as ijp (or ∼ijp when estimated from ∼intP). Before the calculation is complete, all observed frequencies of occurrence ijf (or ∼ijf) are normalized with respect to the size of each dataset due the disproportionality in the sizes of intP and ∼intP, resulting in a value between zero and one. The score for ijS1 is then calculated as:ijd will be found in the set of interacting proteins, against the number of times that the same pair is expected to be found in the set of non-interacting proteins. It was developed with the idea of balancing the average probability of a single occurrence of a domain pair against the actual frequency of occurrence over interacting and non-interacting protein datasets. We normalize each ijS1 score to a maximum of 10 by dividing with the maximum ijS1 value observed in the set and by multiplying the result with 10.The expected frequency of occurrence for aragraph , in PPI ijd that does not occur in ∼intP. Normally, this would cause the average probability ∼ijp to be zero. In this case, the first term in Equation (1) is replaced by:ijp has the desired property of simulating a probability estimate for ∼ijp that is inversely proportional to ijp, thus allowing a ratiometric score to be determined even if the domain pair did not occur in ∼intP. In this event only, we limit the maximum observed value for ijp to be 0.99 in order to prevent the score from dominating the other scores and to prevent a divide by zero error. This problem also causes the observed frequency ∼ijf to be zero. To overcome this problem, we apply the Laplace correction, a well known method used to handle situations where zero frequencies would cause an error in probability estimates intD occur at least once in ∼intP, effectively initializing all domain pairs in intD to have an initial frequency count of one occurrence in ∼intD. This method requires the addition of a corresponding constant in the denominator of the normalization calculation to compensate for this bias and ensure that all normalized values are still between zero and one. This has a desired effect of preventing zero frequency and probability calculation, which would cause errors in the calculation of the log score.A problem with this calculation arises when determining a score for a domain pair This feature uses evolutionary reasoning to infer the likelihood of two domains to interact. Fusion of multiple domains on a single polypeptide (known as Rosetta stone proteins) has been considered as the evolutionary solution to facilitate interactions among them. It has been shown that domains that co-occur on Rosetta stone proteins are more likely to interact than those that do not rdpP be the set of all known Rosetta stone proteins. We say that domain pair ijd is a Rosetta domain pair if domains of ijd co-occur in at least one Rosetta stone protein. Let RDP be the set of all Rosetta domain pairs determined from rdpP, and let ijRDP be defined as the number of proteins in rdpP where domain pair ijd is found. We let ijS2 represent the event that domain pair ijd is interacting based on the occurrence of ijd in rdpP. Each ijd has an observed number of occurrences in rdpP, with the possible range of 0–52996. A simple function of ijRDP is used to simulate a probability density function for the probability of ijd interacting based on information about its occurrence in Rosetta stone proteins. This probability, denoted P(ijS2), is calculated as follows:S2 was set to a value of 5.0 based on a simple empirical analysis of observing the behavior of the score over a wide range of values. This value approximately corresponds to the median frequency of occurrence of ijd in rdpP. This has the effect of returning probability estimates that are directly proportional to its frequency of occurrence in Rosetta stone proteins. The resulting probability values are multiplied by 10 to allow a maximum score of 10 for S2.Let ijd occurs in interacting proteins from multiple species, then there is an increase in the likelihood that the pair is potentially interacting. This measure also helps to contain the detrimental effects of false positives and false negatives that exist in the PPI datasets, as evidence is being drawn from the interaction data of multiple species. Thus, the effect of a false negative protein interaction in one species can be nullified by accurate data if the same interaction is found in many other species, and similarly for false positive data.This measure is based on the assumption that if domain pair ijS3 represent the event that domain pair ijd is interacting based on the number of occurrences of ijd in multiple species in intP and ∼intP , denoted as ij#species and ij#∼species, respectively. The score for ijS3 is estimated as a ratio of the occurrence of species in intP and ∼intP. For consistency, each frequency of occurrence over observed species is converted to a probability distribution. The value of ij#∼species can be zero for some domain pairs in intD that do not occur in ∼ntP, which causes an error in the score calculation. To handle this, we apply the Laplace correction in the same manner as in score S1. For each domain pair ijd, the score for S3 is based on a log ratio, estimated as:intP were observed from only 6 model species that include human, mouse, Drosophila, C. elegans, yeast and E. coli, while a small portion of PPIs were observed from a much larger range of species. The resulting log ratio was multiplied by a fixed constant to allow a maximum score of 10 for S3.Let Co-localized domains in a subcellular organelle are likely to interact more often than those localized across different organelles, because they are generally associated with a common pathway or a sub-pathway that is specifically carried out by that organelle. For instance, more than a dozen domains associated with ATP synthesis (ATPases) are found only in mitochondria of eukaryotic animal cells  because they are needed only in mitochondria for energy production. The domain datasets used in our previous study n-gram occurrence in the primary structure of a protein. The ngLOC method generates a probabilistic measure that a protein sequence is localized into a specific location in a cell. We extended the ngLOC method to predict the localization of proteins over 10 subcellular locations in animal cells, 12 locations in plant cells, five locations in Gram negative and four in Gram positive bacterial cells. These four evolutionary groups are loosely referred to as kingdoms in this work. We let K represent the set of kingdoms. For any given kingdom in K, we assume a distinct set of subcellular localization classes that are appropriate for that kingdom.We developed a method for the prediction of protein subcellular localization, called ngLOC kS represent the set of all subcellular localization classes for kingdom k, we use the ngLOC method to generate a separate probability distribution over kS, where P(mkscl|id) denotes the probability that domain id is localized into location mkscl∈kS . The problem with prediction of subcellular localization at the domain level is that the same domain can exist on multiple proteins which can be localized to different subcellular locations. We solved this problem by taking an average probability of the predictions from all sequences or subsequences representing a given domain, and renormalizing to ensure the new predictions still represent a probability space, i.e., Letting id and jd. First, we sum the absolute value of the difference between the probabilities of each localization. If id and jd have an identical probability distribution, then the difference between them is 0. If they are predicted to localize into different locations with probability of 1, then the measure would be 2. Then we use simple arithmetic (multiply by 0.5) to scale the measure to be between 0 and 1, where 1 implies 100% similarity. We calculate a similarity measure between two domains id and jd in kingdom k as:ijkSCLsim is between 0–1, where a value of 1 implies that domains id and jd have 100% identical subcellular profiles, and a value of 0 implies that domain id and jd are both predicted to localize into entirely different compartments with a probability of 1.Our goal is to calculate a single measure that can capture the similarity between subcellular profiles for any arbitrary domain id, ngLOC outputs two confidences scores: CS(id) – a confidence measure for the most probable prediction, and MLCS(id) – a confidence measure for the likelihood that id is localized into multiple organelles. The first score is equivalent to the probability of the predicted localization. The second score was developed to aid in predicting when a protein is multi-localized as well as which compartments the sequence is localized into. This is important, as we showed that about 24% of domains localize into more than one organelle  and CS(jd) in kingdom k. Similarly, we also determined ijkminMLCS – the minimum MLCS value observed for both predictions. The weight is then simply computed as the maximum of ijkminCS and ijkminMLCS:Using the subcellular profile, we recalculate the CS and MLCS according to the same calculation employed in the ngLOC method. Using these confidence scores, we calculate a weight for each domain pair id, we know the frequency of occurrence that the domain was found to occur in each of the four kingdoms separately. We incorporate this information in score S4 by first calculating a probability density modeling the event that any arbitrary domain id in D occurs in kingdom k. This is computed as the fraction of instances of domain id that are found in species belonging to kingdom k.k|ijd) as the probability that domain pair ijd is found in kingdom k. We can make a simplifying assumption that the probability of any two arbitrary domains being found in a particular kingdom are independent. Then, P(k|ijd) is simply:id and jd. The P term can counteract the similarity measure if both domains are found in entirely different kingdoms. The design criteria is that if the subcellular localization for each domain is predicted to be two distinct locations with high confidence, then they are not likely to interact, and thus the integrated score should be reduced accordingly. The multiplier 10.0 is used for scaling purposes, thereby allowing a maximum score of 10.Finally, we can factor in the number of occurrences in each kingdom because, for each domain ijd. The idea is that if two domains are associated with similar cellular processes and/or involved in similar function, they are more likely to interact. This concept has been successfully used for determining the functional similarity of PPIs This scoring feature was implemented based on the semantic similarity of GO annotation between the two domains in domain pair http://www.ebi.ac.uk/interpro). The probability of minimum subsumer, msP was determined separately for biological process (P) and molecular function (F) GO concepts using the following derivation. Let 1g and 2g represent the set of GO terms from domains i and j, respectively; let 1, g2)S, and vice versa. The total similarity score is the sum of similarity scores from concepts P and F. Hence, S5 can range from 0–10, where 0 indicates no match in GO terms and 10 indicate perfect match of GO terms in both concepts.A and B represent the set of DDIs that were predicted to interact in species A and B, respectively. The union of these two sets represents our sample space of all domain pairs that are predicted to interact in either species A or B, denoted as the set A ∪ B. We assume a uniform probability distribution across the sample space. The number of domain pairs that are shared between two species is the intersection of A and B, denoted as A ∩ B. The probability of each species is then the fraction of domain pairs in that particular set over all domain pairs in the union, where the size of the union is determined as: We derive a similarity measure between all pair-wise combinations of species based on the predicted proportion of domains or DDIs that are shared between species, by using the basic probability set theory. Let Sim, as:Sim will always be between 0 and 1, where 0 implies no similarity between A and B, and 1 implies that the two species have 100% identical domain interaction patterns over all possible interactions observed between the two species.Using the sample space of predicted interacting domain pairs between species A and B, we assume a uniform probability over the sample space, and assume that P) from 2,725 species, representing 209,165 unique binary PPIs (denoted as intP). Domain definitions were used from the InterPro domain database, which provides the most comprehensive, expert-curated set of protein domains. Out of 70,769 proteins in P, 58,999 (83.4%) have at least one InterPro domain mapped in our study. Thus, out of 209,165 PPIs in intP, only 166,259 are usable, i.e. where each partner protein has at least one InterPro domain mapping with a domain length of at least 10 amino acids. The InterPro database contains 15,064 entries, of which about 69%  were found at least once in our comprehensive dataset, covering over two-third of the entire domain space.We created a comprehensive, non-redundant dataset of experimentally-derived PPIs by combining data from five major interaction databases that include BIND, DIP, HPRD, IntAct and MINT. This dataset contains 70,769 unique interacting protein sequences (denoted as ∼intP) from the proteins in intP, using criteria that minimize the likelihood of interaction between a selected protein pair  and putative non-interacting domain pairs (denoted as ∼intD) were derived from intP and ∼intP, respectively. The size of intD is 614,579 while the size of the original ∼intD is about 8.4 million; however, there are only 360,739 DDIs at the intersection of intD and the original ∼intD, which are the useful ones for calculating ratiometric feature scores for intP against ∼intP. Hence, the size of usable ∼intD is only 360,739.Experimentally-determined negative PPIs are not available other than a few sporadic literature-based instances. Hence, we generated a set of putative non-interacting protein pairs (pair see . AdditioS1) ratio of expected frequency of occurrence, (S2) co-occurrence in Rosetta stone proteins, (S3) frequency of occurrence in multiple species, (S4) co-localized domain interactions in subcellular space, and (S5) semantic similarity of GO annotations. The maximum value for each feature score is set at 10 to ensure even contribution of each score to the final integrated score. Scores S1 and S3 are determined as ratios of feature values in intP versus ∼intP, while S2, S4 and S5 are calculated from the feature values of the domain pair in consideration. The sum of these five scores gives the integrated score (IS) for each DDI in a given PPI. Note that only S1 and S3 scores could be determined for the entire set of DDIs in intD, while the coverage of S2, S4 and S5 varies widely across intD. The coverage for S2 is rather low at 5.5%, while S4 and S5 have 79.6% and 39.2% coverage, respectively. The reason for the sparse nature of these scores is due to the fact that they are based on the evolutionary (S2), spatial (S4) and functional (S5) knowledge attributed to domains from experimental studies, and such information is not available for all protein domains. As a result, only 1.8%  of the intD has a score available for all the five features.We selected five orthogonal scoring features that include:  is capable of discriminating functionally relevant DDIs from random interactions.First, we compare the performance of individual feature scores and the integrated score using the iPfam DDIs  compared to 2,080  predicted by Lee et al; however, the percentages are calculated with respect to the size of the iPfam version used in each method. These results emphatically suggest that the current integrated method is robust with superior accuracy and coverage compared to the existing methods.We compared the performance of our method against two recently published methods that include a generalized parsimonious explanation (GPE) method Homo sapiens), mouse (Mus musculus), Drosophila melanogaster, Caenorhabditis elegans, yeast (Saccharomyces cerevisiae), Plasmodium falciparum, Escherichia coli, and Arabidopsis thaliana.We used a conservative score threshold of IS > = 10 to predict potential DDI(s) in a given PPI of each species. This score roughly corresponds to a true positive rate of 78.1% and a false positive rate of 10.3%. For comparative analysis of DDIs, we selected 8 model organisms including a bacterial and a plant species. These species include human (E. coli (74%) followed by Drosophila (41.6%), and human (32.4%) presumably due to their extensive use as model organisms. Consequently, these proteins are well annotated resulting in high number of usable PPIs (where each partner protein has at least one InterPro domain mapping) in these species. The domain space covered by interacting proteins is the highest in human (5099 unique domains) followed by Drosophila, yeast and E. coli. The number of unique DDIs in each species predicted with a score threshold of 10 or more vary widely and there appears to be no correlation between the number of covered domains and the number of predicted unique DDIs. This can be explained by the power law behavior where a few domains have many interacting partners and a majority of other domains interact with only a few partners. The scale-free behavior of domain interaction networks is well documented in the literature E. coli (80.3) followed by yeast (61.9%), human (60%) and Drosophila (35.5%). These results appear to be correlated with the PPI coverage in corresponding species.We calculated species-specific DDIs that are found exclusively in a particular species, which range from 19.8–80.3%, with the exception of mouse which has only 7.2%. This is presumably because the coverage of PPIs in mouse is very small relative to its proteome size , and mouse is very closely related to human than any other pair of species in this study resulting in a number of DDIs shared between these two species. The converse effect is not true in human because the PPI coverage is over ten-fold higher in human compared to mouse. The highest percentage of species-specific DDIs are preserved in Drosophila (4139) and human and mouse (3759). In general, human, yeast and Drosophila showed higher conservation of DDIs with other species, which is at least in part attributed to the higher coverage of PPIs in these species. Yet E. coli, despite having the highest coverage of PPIs relative to its genome size, appears to have poor conservation of DDIs with other species. Itzhaki et alE. coli, where they compared DDI conservation of E. coli against four eukaryotic species. However, it is difficult to draw conclusions based solely on these raw numbers because the experimental coverage space of PPIs is highly unbalanced across these species. Hence, we further computed a similarity measure to accurately determine the similarity of DDI conservation between a pair of species by considering the total DDI coverage space, and the number of species-specific and overlapping DDIs. This measure outputs a value between 0–1, where 1 implies absolute conservation of DDIs between the two species and 0 implies no conservation.The results shown in E. coli. Hence, the similarity measure values closer to the diagonal correspond to evolutionarily closer species, and values further away from the diagonal correspond to distant species. Despite the unbalanced coverage of PPI datasets for different species, this table shows that the computed similarity values mostly correlate with the expected evolutionary distance between a pair of species. The values closer to the diagonal are higher compared to those away from the diagonal in each column, indicating that the DDI conservation is more prominent between evolutionary neighboring species and the conservation gradually disappears as the species become more distant. Note that the similarity between E. coli and any other species is the smallest indicating that the bacterial domain-domain interactions are the least conserved in eukaryotic species. This ‘sliding conservation’ pattern of DDIs along the evolutionary path is expected because domain evolution is thought to be the primary evolutionary force for speciation The lower diagonal of We performed a global analysis of 52,492 DDIs that are observed in eight model organisms. We sorted all the conserved DDIs into eight conservation groups based on the number of species containing those DDIs Thus, the first group contains only those DDIs that are conserved in all the 8 species and similarly, the last group contains only those that are found in only one species. Analyzing the functional role of all conserved DDIs is beyond the scope of this study; hence, we looked at the functional role of domains that are associated with the top 5 DDIs in each conservation group. Arabidopsis. Similarly, domains such as Integrase and Integrase-like catalytic core, RNA polymerase sigma-70, etc. are predominantly bacterial domains and interactions between these domains are predicted only in E. coli. The Haemoglobin-beta domain is present only in chordates and the interactions between this domain and the Globin-subset domain is predicted only in human and mouse species. The complete list of conserved DDIs with prediction scores, names of partner domains, and the species conserved can be found in the supporting dataset . To our knowledge, such a large size dataset has not been used before for computational inference of DDIs. This robust dataset has enabled us to develop the current universal method without the biases associated with using smaller datasets that are skewed toward a few species.∼intP) from the same set of proteins (P) involved in known interactions (intP ) after filtering the PPIs in intP . By doing so, we ascertain that these proteins are expressed  and involved in at least one known PPI. We then imposed a constraint that the two interacting proteins in a PPI must not be localized to the same subcellular location, where the chances of interaction are higher. This constraint effectively filters out most of the unlikely negative interactions in our putative dataset of negative PPIs.Experimentally-determined datasets representing non-interacting protein pairs are virtually lacking; thus limiting the development of negative models by computational methods to compare against the positive datasets. As a result, numerous approaches have been used to establish potential negative PPIs including the use of random protein interactions intP and ∼intP from the same protein set (P), the set of PPIs differ between them. Yet, due to the modular architecture of proteins, the same DDI can be found in PPIs belonging to either dataset, but with a different probability. By using the same domain space for comparison, our scores ensure that the probability of interaction between a domain pair in intP is fairly validated against ∼intP, Hence we are able to effectively contain false positive DDIs from obtaining higher scores as shown in the ROC plot (Despite the origination of both ROC plot .∼intP to improve our score (i.e. S1 and S3), we can directly compute a log odds ratio, further improving on the ability of the score to distinguish between interacting and non-interacting domains. Despite the fact that the remaining three scores do not use negative data, they are still found to be quite effective at distinguishing between interacting and non-interacting domain pairs , which is a novel scoring feature used in this study based on the localization predictions from our ngLOC method th percentile that corresponds to 48% of all possible homotypic DDIs in this study . These results corroborate previous observations that homotypic DDIs are very abundant in PPIs We predicted 5,035 homotypic interactions in the 90et al. was the only other similar study, which compared the conservation of about 3000 DDIs across five model organisms intD| = 614,579) in the known PPIs, and systematically filtered out random interactions using five scoring features to infer high-confidence DDIs. et al. We report 80% (E. coli), 62% (yeast), 20% (C. elegans), 36% (Drosophila) and 60% (human) of species-specific DDIs, while Itzhaki et al. report 62% (E. coli), 18% (yeast), 10% (C. elegans), 3% (Drosophila) and 41% (human) of such DDIs. The disagreement of results between these two studies may be as a result of a 15-fold difference in the number of DDIs used for analysis in our study.Comparison of large datasets of DDIs across eight model organisms has revealed interesting insights into the conservation patterns within and across species. About 80% of the high confidence DDIs  were also predicted in these eight species giving us abundant DDI space to compare across species. A previous work by Itzhaki th percentile and about 86% in the 80th percentile; thus helped to identify a large number of high-confidence DDIs that lack structural coverage in the protein data bank.Due to the incomplete and unbalanced nature of PPI datasets used from different species, it is a challenging task to make a fair comparison of cross-species conservation. The similarity measure we developed in this study to compare the DDI conservation similarity has provided a normalized value to facilitate pair-wise comparisons among all eight species. Cross-species comparison  reveals As expected, DDIs conserved across multiple species appear to perform the essential core of functions to sustain genome integrity, including binding, unwinding, replication, and repair of nucleic acids and other vital enzymatic processes by kinases, proteinases, ATPases, dehydrogenases, and oxido-reductases, etc. . We beliIn conclusion, the top-down approach developed in this study is able to predict high-confidence DDIs that will have a multitude of applications in biomedical research. Comparison of the current method with two other methods showed that the prediction accuracy and coverage of our method is strikingly higher. Despite the incomplete and unbalanced nature of available datasets, this method performed very well because (i) the PPI dataset used for training this method is very comprehensive, (ii) the negative PPI dataset contains the same domain space as the positive dataset providing the basis for an effective comparison, and (iii) the scoring algorithm includes a novel combination of orthogonal scoring features that could map the interacting propensity of two domains in many dimensions. Since S2, S4 and S5 scores are based on additional annotation from protein sequences, this method has a high potential to perform better as more experimental annotations become available in the future. This method has generated large-scale datasets of high-confidence DDIs which are effective for cross-species comparison of DDI conservation. This has opened up new opportunities to investigate DDI conservation patterns at various sub-classes of species in the evolutionary spectrum. The methodology and data generated in this project, complemented by experimental validation, can have a significant impact on systems biology research.Table S1(0.04 MB DOC)Click here for additional data file.Table S2(0.04 MB DOC)Click here for additional data file.Figure S1(0.14 MB DOC)Click here for additional data file.Data Sets S1List of 65,515 high-confidence DDIs predicted by the current method.(9.95 MB XLS)Click here for additional data file.


Correction for:TRAF1/C5 Region as a Risk Factor for Rheumatoid Arthritis. PLoS Med 4(9): e278. doi:10.1371/journal.pmed.0040278Kurreeman FAS, Padyukov L, Marques RB, Schrodi SJ, Seddighzadeh M, et al. (2007) A Candidate Gene Approach Identifies the b: Cases, Controls) refers to allele A: allele B and not allele1:allele2 as described in footnote b, with Allele A being the Susceptibility Allele as denoted in column seven.In Table 1, the allele ratio in column eight (Allele RatiosThe footnote should read:bNumber of alleles were compared in cases versus controls: allele A: allele B cases, allele A: allele B controls. Allele A refers to the susceptibility alleles as given in column seven.


Protein structural domains are evolutionary units whose relationships can be detected over long evolutionary distances. The evolutionary history of protein domains, including the origin of protein domains, the identification of domain loss, transfer, duplication and combination with other domains to form new proteins, and the formation of the entire protein domain repertoire, are of great interest.A methodology is presented for providing a parsimonious domain history based on gain, loss, vertical and horizontal transfer derived from the complete genomic domain assignments of 1015 organisms across the tree of life. When mapped to species trees the evolutionary history of domains and domain combinations is revealed, and the general evolutionary trend of domain and combination is analyzed.We show that this approach provides a powerful tool to study how new proteins and functions emerged and to study such processes as horizontal gene transfer among more distant species. Originally discovered as spatially distinct regions of proteins, protein domains are now considered discrete evolutionary units. One basic physical property–the ability to fold independently–defines the features of protein domains including their evolutionary significance. As stable 3D structures, each covering significant sequence space, with relationships between those sequences perhaps undetectable by sequence methods, domains have much to offer in the study of evolution. Add to that features of domain arrangements Given these properties, protein domains have been used recently in the study of evolution on genome-wide and species-wide scales Since protein domains are major evolutionary units, their evolutionary histories are of great interest Protein domain evolution is already a well studied area. Having started with identifying the distribution of single-domain and multi-domain proteins in the three superkingdoms We consider the evolution of protein domains as two distinct but related events: changes to the characteristics of a protein domain and changes to the occurrence of the protein domain in the genomes of different organisms. The former includes the innovation of new domains, the gradual change in domain sequence and structure, and the formation of new domain combinations. Although protein domains have stable 3D structures and are more conserved than sequences, progressive fold changes do occur during evolution, resulting in variations in sequence and structure within a superfamily Given the ability to detect these domains, evolutionary domain events, such as duplication, combination, loss and transfer of a domain between species, change the genomic content of domains or domain combinations, but not their identities. The emergence of a new domain in a species depicts the origin of the domain, unless there is evidence of horizontal transfer from a species believed to have evolved earlier. The duplication of a domain induces divergence of the duplicate domain through mutations, insertions or deletions, producing modified structures and functions that distinguish it from its ancestor, but in our methodology it is only identified if it retains detectable structural similarity.Here we propose an approach that takes full advantage of existing phylogenetic information to derive the entire evolutionary history of each domain throughout the tree of life. First, the evolutionary processes that change the existence of protein domains and domain combinations in each species, such as loss and transfer, are directly obtained from domain trees or combination trees. Then, the changes to domain identity, such as the divergence of a domain superfamily into different families and formation of new combinations of domains, can be inferred.In previous studies, a Venn diagram analysis has often been used to show the distribution of protein domains in the three superkingdoms, archaea, bacteria and eukaryotes, thus depicting the number and types of protein domains in the last universal common ancestor of life (LUCA) and their early evolution A domain tree is simply constructed by labeling and characterizing each leaf organism of the phylogenetic tree by the type and numbers of protein domains in its genome. Even though no general agreement has been reached about the universal tree of life, the NCBI taxonomy, which is based on extensive genetic and morphological evidence and built by standard molecular phylogenetic methods, is used as the standard species phylogeny in this study. The hierarchical structure of the NCBI-derived phylogenetic tree is identical for every domain; each domain, however, has its own corresponding domain tree, depicting its unique distribution on the species tree and its distinct evolutionary history.Amniota except Danio rerio. With regard to the principle of maximum parsimony, the evolutionary history of a.109.1.1 can be explicitly derived according to this distribution: a.109.1.1 originated from the root of Amniota, and was inherited by all sibling organisms but lost from Danio rerio. Note we cannot discount the possibility that the domain exists in Danio rerio, since our approach to domain homology detection might not be sensitive enough to detect remote domains. The abundance of domains in the genome of each species allows us to infer possible duplication events as discussed subsequently. In principle, the inference of evolutionary events can be applied to any protein domain and combination thereof.For instance, Using species phylogeny to study the evolutionary origin and history of proteins or protein domains is straightforward and has been widely used Amniota, and isoform II (b in Euteleostomi and thus was most likely created by losing a Thyr-1 domain (g.28.1.1) from the C-terminus after duplication of isoform I.As discussed, domain combination is a major way of creating new proteins and new functions. Similar to the single domain trees, when and how each domain combination was formed can be observed and mapped to domain combination trees, where each combination type is considered a distinct evolutionary unit. For example, using domain a.109.1.1, there are two combination types that differ by a g.28.1.1 domain, the thyroglobulin type-1 domain (Thyr-1), at the C-terminus of corresponding proteins. The Thyr-1 domain has about 65 amino acids and exists in proteins with various functions and origins; its activity and function is not fully understood  II b in , N/A∼a.1Not only can the evolution of new combination types be inferred from domain trees, so can the divergence of two evolutionarily related domains. According to SCOP, different domain families within a superfamily  originated from a common ancestor, but their sequences have diverged so much that their evolutionary relationship can only be recognized by structural and/or functional relatedness. The distribution of different families within the same superfamily indicates where the divergence event happened in the tree of life.Vibrio cholera. As shown in the domain trees of the two families , including the origin of the pilin protein family (d.24.1.1) at the root of bacteria, domain duplication in some genomes, domain loss in some bacterial clades, and importantly the divergence of the superfamily and the formation of a new family (d.24.1.2) in the Vibrio species.For example, pilin refers to a class of fibrous proteins that oligomerize and form the pilus structure in many bacterial species families , the pilNot every domain is orderly distributed in the tree of life; some exist sporadically across different clades. For example, the phycocyanin-like phycobilisome proteins (a.1.1.3) are light harvesting antennae of photosystem II In summary, protein domains mapped to species trees illustrate evolutionary processes such as the origin of domains, domain loss and transfer, domain combination and divergence. In principle, the entire evolutionary history of every domain can be visualized and derived based on the phylogenetic distribution of that domain. Subsequently domain combinations can be mapped with a complexity that is related to the number of combinations of a given domain; some domains are promiscuous and appear in many families and superfamilies, others do not.Mapping of domains and domain combinations to species trees is too time-consuming to do manually. Our approach see , similarhgt = 12), accounting for more than half of the total domains (3464 families in SCOP 1.73). This prediction is significantly higher than what is generally believed Notwithstanding, these data suggest that a large proportion of protein domains were invented in the root or after the separation of the three major superkingdoms but before the further differentiation of each lineage. When tracing outward along the tree from the root, the number of novel domains invented at each node decreases . Many brde novo, in accordance with the general trend of genome evolution by means of duplication and recombination Conversely, the evolution of domain combinations shows the opposite trend. There are 4631 combinations at the root of tree, which accounts for only 4% of total combinations. Relatively more combinations were invented in the descendant nodes of the tree, as indicated by the increase in the ratio of number of new combinations and domains at each node along the tree of life , and theGiven the origin of every domain combination, we can determine the average number of domains in proteins originating at each node in the tree of life . As showThe major problem with the representation of a taxonomy-based phylogeny is that it is not a well-resolved tree that reflects every bifurcation and speciation event. The six or seven major hierarchical levels of the taxonomy result in multiple clades at the same level whose evolutionary relationships are not determined. As a result, the evolutionary origin of a domain or domain combination determined by the taxonomy-based phylogeny is biased towards the higher levels of the phylogeny. This bias in evolutionary origin also results in an over-estimation of the extent of gene loss. For instance, a domain exists in archaea and eukaryotes but not in bacteria. Because the NCBI taxonomy does not have the branching order for the three superkingdoms, according to our method, the origin of this domain is in LUCA and it was lost in the bacterial branch. If, we suppose, bacteria diverged first from the root, followed by the branching of archaea and eukaryotes, then the derived origin of the domain is located in the common ancestor of archaea and eukaryotes, and the bacteria never contained this domain.The problem brought about by taxonomy can be corrected by using bifurcating phylogenetic trees that contain detailed evolutionary relationships for every taxon. Currently, many branches of the tree of life are still unsolved and in debate, such as the separation of the three superkingdoms and the divergence of bacteria and eukaryote taxa. In those cases, the taxonomy-based phylogeny that allows multiple leaves under a node must be used. As the phylogenetic tree of life becomes more accurate and reliable, our understanding of the evolutionary history of protein domains will also improve.The average domain coverage of each genome is between 40% and 60%; genes in the rest of the genomes are either unannotated or lack a 3D structure, and are in many cases species-specific genes. New folds and superfamilies are assigned to protein structure classification schemes as more protein 3D structures are solved; this increased the average domain coverage of genomes from 53% in SCOP 1.63  to 60% in SCOP 1.73  over a period of four years , implying that changing the value of the score will have significant impact on the history of domains; whereas the slope becomes flatter with larger ratios.As shown in the tio Rhgt . When thhgt ratio. Domain combinations show more genesis or HGT than single protein domains in the flat region (Rhgt>9) of the graph. One explanation is that domain combinations are relatively easy to reinvent from existing domains multiple times in different lineages during evolution. It is also possible that, as explained in the later section, because protein domains are less sensitive to HGT than proteins or domain combinations, some HGTs among closely related species are enclosed in the same evolutionary profile and not revealed.As indicated in th 2005, Apr 6th 2008 and Mar 8th 2009, with a total number of completed sequenced species of 315, 772 and 1015, respectively. With the increase in the total number of species, the average genesis/HGT also increases under the same penalty score. To normalize this effect, an empirical factor, the square root of the total number of species (N), is used such that Rn = Rhgt/sqrt(N). As shown in The penalty score for genesis/HGT is an empirical score based on statistical analysis, its true value must be evaluated from the accuracy of its predictions hgt. hgt values  is a major force in the evolution of prokaryotes. Genome comparison suggests that up to 20–30% of genome variation is due to this process The methodology used here, namely the use of multiple sequence alignments to construct hidden Markov models (HMMs), means that the HMM does not distinguish orthologs and paralogs. In other words, a protein domain represents an ensemble of evolutionarily-related sequences that include both orthologs and paralogs. Moreover, after domain assignment only the presence and absence of domains are evaluated in domain trees and domain combination trees. Thus HGT of homologous proteins within closely related species (within-phylum HGT), which is estimated to happen extensively and more vigorously than HGT between phyla Typically the first step in understanding the properties and function of a protein is to analyze its domain architecture. Many domains have different functions in different proteins, especially when in combination with other dissimilar domains. The specific function of each domain and their domain combinations are better understood when considered in conjunction with their evolutionary relationships. Previous studies of protein evolution emphasized finding homologs to the full-length protein, thus neglecting evolutionarily related proteins that differ by one or more domains. Our domain-level approach investigates the evolution of each individual domain and domain-based evolutionary processes, such as domain fusion and fission, which give rise to various domain combinations. The methodology is relatively straightforward and domains and domain combinations can be updated as new genomes are sequenced. Moreover, as more protein structures are determined and more domains assigned the repertoire of domains that can be mapped to a given genome will also increase.The same procedure as discussed in our previous work The NCBI taxonomy used here was retrieved on March 8th, 2009 from the NCBI Entrez Database The domain assignment provided by the SUPERFAMILY database gives the position and length of each domain within a given protein. Thus for every protein, its domain composition and domain order relative to the protein sequence is readily available. However, unassigned parts of the protein sequence introduce complications in designating domain combinations. Using the SCOP definition, domains are identified which cover between 40–60% of complete genomes. Among the 6.2 million open reading frames (ORFs) from all 1015 complete genomes used in this study, 38.9% of the ORFs are fully assigned, a further 22.4% of the ORFs have partial domain coverage and 38.7% of the ORFs have no coverage at all. Unassigned regions could be linkers between domains, one domain, or even multiple domains. Unassigned regions make the identification of each combination type difficult; one solution is to only consider fully annotated proteins, but this excludes most combinations.In this work, we consider each unassigned region as a potential domain and include it as part of our combination nomenclature. An unassigned region is considered as one unknown domain if it is longer than 50 amino acids. , indicating the relative likelihood of domain genesis or HGT with respect to loss. As the only parameter in this model, the genesis or HGT to loss ratio Rhgt largely influences the outcome of the prediction, so it is evaluated at different values.The origin of a domain can be found by tracing back the existence of domains on the tree of life based on the principle of parsimony. Four evolutionary processes, vertical inheritance, domain loss, horizontal gene transfer (HGT) and domain genesis can change the status of domain content. We assign each process a penalty score according to their relative likelihood of occurring during evolution. Vertical inheritance is the default evolutionary process and its penalty score is 0. The relative penalty score for gene loss is assigned as 1. Domain genesis indicates the origins of protein domains. Although convergent evolution exists, a recent study indicated that domain convergence and multiple domain genesis are rare and most domains emerged only once during evolution hgt.To find the ancestor domain content that best fits the current domain distribution is equivalent to finding the most parsimonious present/absent dataset for each node on the tree so as to minimize the total score for the whole tree. The details of the iterative algorithm we developed can be found in the supplementary Text S1Algorithm developed to derive the history of protein domains(0.07 MB DOC)Click here for additional data file.Text S2Discusses some issues related to genome coverage and unassigned regions.(0.09 MB DOC)Click here for additional data file.Figure S1Complete proteobacteria domain tree of the pilin family and TcpA-like family(1.19 MB TIF)Click here for additional data file.Table S1Species with complete genomes involved in this study(0.81 MB DOC)Click here for additional data file.Table S2hgt values.Predicted number of protein domains originated from each ancestor node in the tree of life with respect to different R(0.49 MB DOC)Click here for additional data file.Dataset S1NCBI phylogenetic tree involved in this study(0.02 MB TXT)Click here for additional data file.


Staphylococcus aureus to fibronectin, fibrinogen and elastin. We previously reported that S. aureus strain P1 encodes an FnBPA protein where the fibrinogen/elastin-binding domain (A domain) is substantially divergent in amino acid sequence from the archetypal FnBPA of S. aureus NCTC8325, and that these variations created differences in antigenicity. In this study strains from multilocus sequence types (MLST) that spanned the genetic diversity of S.aureus were examined to determine the extent of FnBPA A domain variation within the S. aureus population and its effect on ligand binding and immuno-crossreactivity.Fibronectin-binding protein A (FnBPA) mediates adhesion of fnbA allelic variants in strains of different multilocus sequence type were identified by DNA hybridization using probes specific for sequences encoding the highly divergent N3 sub-domain of different isotypes. Several isotypes were not restricted to specific clones or clonal complexes but were more widely distributed. It is highly likely that certain fnbA genes have been transferred horizontally. Residues lining the putative ligand-binding trench were conserved, which is consistent with the ability of each A domain isotype to bind immobilized fibrinogen and elastin by the dock-latch-lock mechanism. Variant amino acid residues were mapped on a three-dimensional model of the FnBPA A domain and were predicted to be surface-exposed. Polyclonal antibodies raised against the recombinant isotype I A domain bound that protein with a 4 – 7 fold higher apparent affinity compared to the A domains of isotypes II – VII, while some monoclonal antibodies generated against the isotype I A domain showed reduced or no binding to the other isotypes.Seven different isotype forms (I – VII) of the FnBPA A domain were identified which were between 66 to 76% identical in amino acid sequence in any pair-wise alignment. The S. aureus to evade the host's immune responses. These findings have implications for the development of vaccines or immunotherapeutics that target FnBPA.The FnBPA A domain occurs in at least 7 different isotypes which differ antigenically and exhibit limited immuno-crossreactivity, yet retain their ligand-binding functions. Antigenic variation of the FnBPA A domain may aid  Staphylococcus aureus is a commensal of the moist squamous epithelium of the human anterior nares [or nares . It is aor nares .S. aureus can express an array of surface proteins that mediate bacterial binding to plasma proteins and constituents of the extracellular matrix [fnbA and fnbB [S. aureus into epithelial and endothelial cells [r matrix , which pand fnbB ,5. Howevand fnbB . FnBPA cand fnbB -9. FnBPAal cells ,10, promal cells ,12, and al cells ,14.S. aureus ClfA and Staphylococcus epidermidis SdrG [In silico docking studies and mutagenesis revealed that the trench separating N2 and N3 in ClfA is the binding site for the C-terminal fibrinogen γ-chain peptide [Previous work in our group has focused on the binding of the FnBPA N-terminal A domain to fibrinogen and elastin ,15. The dis SdrG ,17. The dis SdrG ,17. Sub- peptide . A strucS.aureus strains of different MLST genotypes indicating horizontal transfer. Each isotype bound to immobilized fibrinogen and elastin with similar apparent affinities. Polyclonal and monoclonal antibodies raised against the isotype I FnBPA A domain showed reduced binding to other isotypes demonstrating differences in surface exposed epitopes between isotypes.We previously demonstrated that the FnBPA A domain from strain P1 differs substantially from 8325-4 FnBPA, sharing only 73.5% identical amino acids. This was sufficient to create differences in surface-exposed epitopes which affected immuno-crossreactivity, while ligand binding activity was conserved . This stfnbA gene encoding the fibrinogen and elastin-binding A domain  were analysed to determine if any further variation within the A domain was apparent. Amino acid sequences were aligned in pairwise combinations and the identities calculated. Strains COL, USA300 and 8325-4 contained highly similar FnBPA A domains (one amino acid substitution in COL). This FnBPA A domain is classified as isotype I. These three strains belong to MLST clonal complex 8 (CC8) [fnbA genes specifying identical A domains, but shared only 76.7% and 80.5% residue identity with isotypes I and II respectively, and are here called isotype III. CC-5 strains  specified identical A domains that were very similar (94.5% amino acid identity) to the isotype III A domains of ST-1 strains. Variation between the A domains of ST-1 and ST-5 strains was confined to the N1 sub-domains, while the N23 sub-domains were 100% identical. The classification of FnBPA A domains used in this study (for reasons discussed below) is based on the minimal ligand-binding N23 sub-domains, and therefore the ST-1 and ST-5 FnBPA A domains are classed as isotype III.FnBPA A domain sequences from published  8 (CC8) , indicat 8 (CC8) , encodedS. aureus strain P1 (ST-973), which we analyzed previously [The FnBPA A domain from eviously , was div P1 ST-97, which wS. aureus strains isolated from carriers and invasive disease patients has previously been analyzed by MLST [S. aureus strain 3110 (ST-12) which was between 67.0% and 73.9% identical to A domain isoypes I – IV (Table A collection of  by MLST . Eleven S. aureus strains of different MLST genotypes we used DNA hybridization with isotype-specific probes homologous to a portion of the highly divergent N3 sub-domain. DNA encoding the entire A domain was amplified from strains to be typed (Table fnbA fragment (top row of blots). This allowed for rapid typing of multiple strains without the requirement for sequencing. In the example shown, fnbA gene fragments of any given strain only hybridized with one probe  and 3153 (ST-207) did not hybridize with any probe  was novel and is called isotype VI , while sub-domains N2 and N3 are more divergent . Neighbour joining (NJ) trees based on DNA sequences of the e Figure  and the e Figure  sequence Figures  and 3c. The N1 sub-domain sequences appear to have evolved in distinct manner from the N23 sub-domain sequences Figure , and theS. aureus strains studied here  contained FnBPA isotype II  directed against isotype I were tested by ELISA for binding to each isotype. Antibodies 7C5 and 1E6 bound efficiently to isotype I but showed little binding to isotypes II – VII Figure  and 6e iWe previously reported that monclonal antibodies 7C5, 1E6, 1F9 and 1G8 failed to bind to isotype IV by Western blotting and that they bound weakly to isotype I . 1E6 andfnbA genes amongst S. aureus isolates giving rise to 7 isoforms of the FnBPA A domain, which are between 24.5 and 34.1% divergent in amino acid sequence. There was strong conservation of residues known to be involved in ligand binding by isotype I, which were predicted to line the putative ligand-binding trench in a 3D structural model [S. aureus in vivo.We have reported on the substantial sequence diversity in al model . This coThe variant residues between isotypes were mapped on the 3D-structural model of the A domain, and the vast majority were predicted to be surface exposed. Antibody binding experiments were performed to demonstrate that variation between isotypes affected their antigenicity. Polyclonal antibodies against isotype I had reduced affinity for isotypes II – VII. Some monoclonal antibodies raised against isotype I had little or no affinity for all other isotypes, while others reacted well with some isotypes and not others. Sequence variation has therefore created differences in epitopes between different FnBPA A domain isotypes.S. aureus, contributing to experimental endocarditis and septic arthritis infections [fnbA gene by S. aureus was found to be significantly more common in invasive isolates compared to carriage isolates [S. aureus infections have shown that serum antibody titres against FnBPA rise as a result of infection [S. aureus, compared to sera of healthy controls [FnBPA has been documented as an important virulence factor of fections ,14. Carrisolates , suggestnfection ,26. Anticontrols . This infnbA genes from sequenced genomes reveals that the FnBPA C-terminal fibronectin-binding motifs 2 – 11 are highly conserved in amino acid sequence. This is an unfolded domain lacking secondary structure, and undergoes a conformational change to an ordered β-sheet structure upon binding to fibronectin [It is interesting to note that variation amongst FnBPA appears to be confined to the A domain. Analysis of entire ronectin . Anti-Fnronectin ,28. The ronectin . In contronectin .S. aureus have an increased chance of developing invasive disease that is usually caused by the carriage strain, but have a lower mortality rate than non-carriers that develop S. aureus bacteremia [Individuals who carry cteremia ,31. Carrcteremia . Generatcteremia . A similfnbA allelic variants encoding many of the 7 A domain isotypes were widely distributed. Several isotypes were not restricted to specific clones or clonal complexes. Indeed, the same A domain isotype was often found in strains that are distantly related based upon the phylogenetic analysis. This suggests that horizontal transfer of fnbA loci has occurred. Similarly, there is evidence of horizontal transfer of staphylocoagulase serotypes [fnbA allelic variants found in strains representative of CC1, CC5, CC22, CC25 and CC45 encoded isotype III A domains that were extremely similar (> 99% amino acid identity), yet these strains belong to distantly related clonal complexes. Indeed, the fnbA allele we sequenced from a ST-22 strain was identical to the fnbA gene of ST-1 strains MSSA476 and MW2, which suggests a recent recombination event. Further evidence for horizontal transfer is the presence of two different FnBPA A domains isotypes in strains of the same MLST genotye (ST-45). We found allelic variants encoding isotype III in ST-45. The study of Kuhn et al [fnbA alleles 11 and 12, which encode isotype II A domains, while other ST-45 strains were found carrying alleles 14 and 16, which encode type III A domains. This shows that fnbA genes encoding isotype II or III A domains are present in ST-45 strains. It has been suggested that CC45 and CC30/39 share a common ancestor [The erotypes . For exahn et al  describeancestor , so perhThe FnBPB A domain appears to have evolved in the same way as FnBPA described here. Analysis of the FnBPB A domains obtained from genome sequences has identified three FnBPB isotypes and a further four FnBPB A domain isotypes were identified by hybridization and DNA sequencing . Evidence for interstrain recombination came from the occurrence of different FnBPA and FnBPB isotype combinations in different strains.fnbA sub-domain sequences, where a particular N2 sub-domain is always found in association with a particular N3 sub-domain. The phylogeny of the N1 sub-domain appeared to differ from that of the N23 sub-domain. For example, the amino acid sequence of the FnBPA N23 sub-domain from N315 and Mu50  is identical to the N23 sub-domain from MSSA476 and MW2 (both ST-1). However, their N1 sub-domains are only 86.2% identical. Perhaps recombination may have occurred within fnbA genes to switch segments encoding domains N23 and N1.Amongst the FnBPA A domain, it appears that the minimum ligand-binding truncate (sub-domains N2 and N3) have evolved in parallel into at least 7 distinct isotype groupings. This is clearly illustrated in the neighbour-joining phylogenetic trees based upon S. aureus strains representing the genetic diversity of the species. Isotypes are on average 29.3% divergent in amino acid sequnce. Each isotype is functional with respect to ligand-binding activity, but they differ in immuno-crossreactivity, which may play a role in immune evasion. Isotypes are distributed randomly throughout the S. aureus population, and homologous recombination may be involved in dissemination of fnbA loci amongst S. aureus. These data have implications for the FnBPA A domain as a target for a vaccine or immunotherapeutic.We have identified seven isotypes of the N-terminal A domain of FnBPA in a collection of Escherichia coli strains were cultivated on L-agar and L-broth with shaking at 37°C. E. coli strain XL1-Blue (Stratagene) was routinely used for cloning. E. coli Topp 3 (Qiagen) was used for the expression of recombinant FnBPA A domain proteins. Ampicillin (100 μg ml-1) was incorporated into growth media where appropriate. S. aureus strains .Cultures were grown in 5 ml BHI broth at 37°C with shaking for 24 h. Genomic DNA was prepared using the Edge Biosystems Bacterial Genomic DNA purification kit with the addition of 100 μg mlfnbA genes from publicly available S. aureus genomes. DNA encoding the entire FnBPA A domain along with flanking sequences was amplified, cleaved with BamHI and HindIII (Boehringer Mannheim) at restriction sites incorporated into the primers, ligated using T4 DNA ligase (Roche) to pBluescript KS+ DNA cleaved with the same enzymes, and finally transformed into E. coli XL1-Blue cells using standard procedures [fnbA gene fragments were selected on L-agar with ampicillin and 80 μg ml-1 of the chromogenic substrate 5-bromo-4-chloro-3-indolyl-β-D-galactoside . Plasmid DNA was purified from transformants using the Favorgen Plasmid DNA Extraction Mini Kit (Favorgen Biotech Corp). The cloned fnbA gene fragments were sequenced using T3 and T7 primers by GATC Biotech AG (Germany). The EMBL accession numbers for the sequences reported in this paper are AM749002 – AM749015.Primers pfnbA Adom F1 and pfnbA Adom R1, corresponding to DNA encoding the signal sequence and fibronectin binding domain 2 respectively (Table ocedures . TransfofnbA DNA was spotted onto positively-charged nylon membranes (Roche) and allowed to air-dry. Membranes were incubated for 5 min on blotting paper soaked in denaturation solution , 5 min in neutralization solution , and finally for 15 min on blotting paper soaked with 2× SSC solution . DNA was fixed on the membranes by incubation at 120°C for 30 min. Membranes were incubated for 2 h at 68°C in pre-hybridization solution ). DIG-labelled probes complementary to DNA encoding isotype I – V N3 sub-domains were synthesised by PCR using type-specific probe primers  were described previously [E. coli Topp 3 for protein expression and purification. Proteins were purified by Ni2+ chelate chromatography [pQE30 expressing the minimal ligand-binding FnBPA A domain truncate (N23: residues 194 – 511) from eviously . pQE30 etography . Concent-1) for 18 h under UV light and at 4°C respectively. Wells were washed three times with phosphate-buffered saline (PBS) containing 0.05% v/v Tween 20 (PBST) and blocked with 2% w/v bovine serum albumin  in PBST buffer (BSA-PBST) for 2 h at 37°C. Following three washes with PBST, various concentrations of purified FnBPA isotype proteins in 2% w/v BSA-PBST were added to the wells and incubated at room temperature for 1 h with shaking. Wells were washed three times with PBST buffer and bound proteins were detected with monoclonal antibody 7E8 (90 ng well-1) that recognises the N-terminal hexahistidine fusion tag. Following incubation for 1 h with shaking at room temperature, the wells were washed three times with PBST followed by 100 μl per well of goat-anti-mouse antibodies conjugated to horseradish-peroxidase  diluted 1:2000 in 2% w/v BSA-PBST. After incubation for 1 h at room temperature, wells were washed three times with PBST, and bound HRP-conjugated antibodies were detected with 10 μg per well of 3,3',5,5'-tetramethylbenzidine  in 0.05 M phosphate-citrate buffer containing 0.006% (v/v) hydrogen peroxide. After incubation at room temperature for 5 min the reaction was stopped by adding 50 μl of 2 M H2SO4. The absorbance at 450 nm was measured with an ELISA plate reader .Human aortic elastin (Elastin Products Company) and human fibrinogen  were coated onto Nunc 96-well microtitre dishes , were added to each well and incubated for 1 h at room temperature with shaking. Polyclonal and monoclonal antibodies 7C5, 1E6, 1F9 and 1G8 raised against the isotype I A domain of FnBPA from S. aureus 8325-4 were described previously [Various concentrations of recombinant FnBPA A domain proteins in PBS were coated onto Nunc 96-well microtitre dishes for 18 h at 4°C. Wells were washed and blocked with BSA for 2 h as described above. Following three washes with PBST, 100 μl of anti-FnBPA A domain antibodies diluted in BSA-PBST (1.8 μg polyclonal IgG mleviously ,15. Monoeviously . After 1S. aureus strains were downloaded from the MLST database [fnbA gene sequences and concatenated MLST allele sequences were conducted using MEGA version 3.1 [S. aureus was visualized using eBURST [Protein sequences were aligned in pairwise combinations to calculate amino acid identity using the ExPASY SIM alignment tool . Alignmedatabase . Phylogesion 3.1 . The oveg eBURST ,42.fnbA genes for sequencing and protein expression, DNA and amino acid sequence analysis, purification of recombinant A domain proteins, ELISA experiments, and drafted the manuscript. TS carried out DNA hybridization experiments. FMK carried out cloning of fnbA genes for recombinant expression and provided polyclonal antisera to the isotype I A domain. GP and PS were responsible for production of monoclonal antibodies against the isotype I A domain. TJF conceived and coordinated the study, and helped to draft the manuscript. All authors read and approved the final manuscript.AL carried out cloning of


C) plays a critical role in controlling the catalytic rate.Cellular RNA polymerases (RNAPs) are complex molecular machines that combine catalysis with concerted conformational changes in the active center. Previous work showed that kinking of a hinge region near the C-terminus of the Bridge Helix (BH-HN) is presented. The nanomechanical properties of BH-HN emerge as a direct consequence of the highly conserved primary amino acid sequence. Mutations that are predicted to influence its flexibility cause corresponding changes in the rate of the nucleotide addition cycle (NAC). BH-HN displays functional properties that are distinct from BH-HC, suggesting that conformational changes in the Bridge Helix control the NAC via two independent mechanisms.Here, new evidence for the existence of an additional hinge region in the amino-terminal portion of the Bridge Helix domain (BH-HThe properties of two distinct molecular hinges in the Bridge Helix of RNAP determine the functional contribution of this domain to key stages of the NAC by coordinating conformational changes in surrounding domains. RNA polymerases (RNAPs) play a central role in the regulation of gene expression. Like the majority of the enzymes involved in fundamental biological information-processing functions , RNAPs are probably best viewed as intricate molecular machines. The movement of nucleic acid substrates, coupled with various types of active site chemistries, requires a precisely orchestrated sequence of conformational changes of protein domains during the transcription cycle (for recent reviews see -4).The nanomechanical mechanisms guiding the structural rearrangements of domains within the active site are still very poorly understood. Thus far, models of the fundamental reaction catalyzed by RNAPs, the nucleotide addition cycle (NAC), have predominantly been derived from a series of crystal structures that contain RNAPs as apoenzymes (for example -9), or cin vitro, it is possible to modify any residue within an intact RNAP to introduce a variety of targeted mutations into any functional domain participating in the NAC ; EdgeBio, Gaithersburg, Maryland, USA). After growth for 16 to 18 hours at 37°C in 24-well plates in autoinduction medium  the bacteria were harvested and used for a centrifugation-based robotic inclusion body purification protocol. The mjA' inclusion bodies were automatically solubilised in the presence of 8 M urea and quantitated at 562 nm with the bicinchoninic acid assay. Recombinant RNAPs containing the mjA' variants were assembled in a 96-well format dialysis cell using an urea-gradient from 6 M to urea-free spanning 16 hours at room temperature [in vitro at least in quadruplicate to assure consistency and reproducibility. The assembled RNAPs were harvested and used immediately for robotic transcription assays (see below). The assembly efficiencies of key mutants  were compared to assembly rates achieved with the wildtype mjA' subunit by assaying the reconstituted polymerases at limiting and saturating template DNA concentrations  have been described previously ,20,58. Bperature . Each mu32P) rUTP into TCA-insoluble products, were carried out exactly as previously described [32P) rUTP quantified with a microplate counter .The robotically implemented high-throughput trichloroacetic acid (TCA) precipitation assays, measuring the incorporation of  using the SwissModel server in automated mode [http://ambermd.org/) on a CPU cluster of the National Grid Service (NGS). During pre-processing the system was warmed to 200K under the control of a Berendsen thermostat with a coupling constant of 1.0 ps. All structures were energy-minimized in pre-equilibrated simulation boxes filled with TP3 water, and sodium and chloride ions were added to a final concentration of approximately 150 mM. For production runs the temperature was increased to 300 K (27°C). The equations of motion were integrated using a step-size of two femtoseconds. The trajectories generated by 27 independent 200 picoseconds simulation runs, were analyzed using STRIDE [Molecular dynamics (MD) simulations were performed using GROMACS . In prepted mode . The simg STRIDE , as implg STRIDE . The frec: molecular hinge located within the carboxy-terminal portion of the Bridge Helix; BH-HN : molecular hinge located within the amino-terminal portion of the Bridge Helix; NAC: nucleotide addition cycle; rNTP: ribonucleotide triphosphate; ps: picosecond; RNAP: RNA polymerase; TCA: trichloroacetic acid.BH-HEvolutionary conservation and structure of the β-D II domain. (A) Alignment of β-D II domain sequences from bacteria  and eukaryotes (S. cerevisiae and H. sapiens) against the archaeon M. jannaschii. Residues identical to the archaeal sequence are shown in red. The numbers flanking the sequences represent the location of the sequences within the open reading frame of the complete subunit. One of the residues in close contact with the nascent transcripts is boxed and identified with an arrow. (B) Arrangement of the β-D II domain relative to the RNAP active site. Most structures are shown in space-filling mode to emphasize spatial connections. The Bridge Helix is shown in green (with the BH-HN region (corresponding to mjA' M808-E812) highlighted in yellow), the β-D II domain in turquoise, the template DNA is pale blue, the RNA is red, the NTP in the insertion site shown as a pink stick model and catalytic metal ions as magenta spheres. The rNTP binding pocket is indicated with a white-dashed oval. (C) Close-up view of the β-D II domain. Note the extensive contacts between the β-D II domain with the rNTP and the i-1 position of the nascent transcript. Two potential β-D II/Bridge Helix contacts are mediated via residues orthologous to mjA' R811 and L814. ((PDB #2E2H); visualized with PyMOL).Click here for fileEvolutionary conservation and structure of the Link domain. (A) Alignment of Link domain sequences from bacteria  and eukaryotes (S. cerevisiae and H. sapiens) against the archaeon M. jannaschii. Residues identical to the archaeal sequence are shown in red. The numbers flanking the sequences represent the location of the sequences within the open reading frame of the complete subunit. Residues in close contact with the rNTP or nascent transcripts are indicated by boxes and arrows. (B) Arrangement of the Link domain relative to the RNAP active site. Most structures are shown in space-filling mode to emphasize spatial connections. The Bridge Helix is shown in green (with the BH-HN region (corresponding to mjA' M808-E812) highlighted in yellow), the Link domain in light purple, the template DNA is pale blue, the RNA is red, the NTP in the insertion site shown as a pink stick model and catalytic metal ions as magenta spheres. The rNTP binding pocket is indicated with a white-dashed oval.Click here for fileEvolutionary conservation and structure of the Link domain. (A) Alignment of F-Loop domain sequences from bacteria  and eukaryotes (S. cerevisiae and H. sapiens) against the archaeon M. jannaschii. Residues identical to the archaeal sequence are shown in red. The numbers flanking the sequences represent the location of the sequences within the open reading frame of the complete subunit. (B) Arrangement of the F-Loop domain relative to the RNAP active site. Most structures are shown in space-filling mode to emphasize spatial connections. The Bridge Helix is shown in green (with the BH-HN region (corresponding to mjA' M808-E812) highlighted in yellow), the F-Loop domain in lime, the template DNA is pale blue, the RNA is red, the NTP in the insertion site shown as a pink stick model and catalytic metal ions as magenta spheres.Click here for fileAnalysis of proline conformational space in proteins. (A) Conformation of peptide backbones containing a single , two , or three subsequent proline residues (X-P-P-P-X) in protein structures displayed as Ramachandran plots (data generated using the web-based server described in [(B) Model of the extended poly-proline stretch in the mjA' A822-P/Q823-P/S824-P triple proline substitution mutant. The triple substitution mutant displays approximately 150% of activity in comparison to the wildtype enzyme . The three proline substitutions are shown as yellow stick models and T821 is shown in red as a reference point pointing towards the catalytic site. The structure shown here was constructed using the M. jannaschii Bridge Helix sequence and conforms to the typical φ/ω angle combinations observed in α-helices and in polyproline structures. The structure is not necessarily an accurate model, but serves to demonstrate the increased local flexibility due to the presence of three subsequent proline residues. The model was created with Abalone http://www.biomolecular-modeling.com/Abalone/index.html.ribed in ). The reClick here for fileGaussian Network Model Analysis. The yeast RNAP elongation complex (PDB #2E2H) was subjected to Gaussian Network Model simulation  to assess the distribution of forces within intact RNAPs. Slow-mode motions  affect particularly the center of the Bridge Helix (as indicated by green/gold color-coding), whereas the N- and C-terminal region remain immobile. The slow modes identify predominantly the response of individual domains to mechanical forces exerted on them from other structures during the simulation. It should be noted that such simulations do not take the chemical nature of residues into account and would therefore not be able to detect the intrinsic kinking properties of the BH-HN and the BH-HC regions.Click here for file