The identification and characterization of interacting domain pairs is an important step towards understanding protein interactions. In the last few years, several methods to predict domain interactions have been proposed. Understanding the power and the limitations of these methods is key to the development of improved approaches and better understanding of the nature of these interactions.Building on the previously published Parsimonious Explanation method (PE) to predict domain-domain interactions, we introduced a new Generalized Parsimonious Explanation (GPE) method, which (i) adjusts the granularity of the domain definition to the granularity of the input data set and (ii) permits domain interactions to have different costs. This allowed for preferential selection of the so-called "co-occurring domains" as possible mediators of interactions between proteins. The performance of both variants of the parsimony method are competitive to the performance of the top algorithms for this problem even though parsimony methods use less information than some of the other methods. We also examined possible enrichment of co-occurring domains and homo-domains among domain interactions mediating the interaction of proteins in the network. The corresponding study was performed by surveying domain interactions predicted by the GPE method as well as by using a combinatorial counting approach independent of any prediction method. Our findings indicate that, while there is a considerable propensity towards these special domain pairs among predicted domain interactions, this overrepresentation is significantly lower than in the iPfam dataset.The Generalized Parsimonious Explanation approach provides a new means to predict and study domain-domain interactions. We showed that, under the assumption that all protein interactions in the network are mediated by domain interactions, there exists a significant deviation of the properties of domain interactions mediating interactions in the network from that of iPfam data. Understanding of protein and domain interactions is necessary to comprehend the functioning of a cell. In the past few years, this area has been the subject of intensive study (surveyed in ) As the Protein interaction data is collected from studies of individual systems, and more recently through high-throughput experiments, such as yeast two-hybrid (Y2H) and tandem affinity purification followed by mass spectrometry (TAP/MS) -11. ThosAssociation method [EM) which computes domain interaction probabilities that maximize the expectation of observing a given protein-protein interaction network. Other approaches to this problem use linear programming [It has been estimated that more than half of eukaryotic proteins and a significant fraction  of prokaryotic proteins are multi-domain proteins -19. It in method , scores n method  proposedgramming , supportgramming , probabigramming , and lowgramming .Parsimonious Explanation (PE) method [Integrated Bayesian (IB) method. This method estimates the likelihood of domain interactions based on a protein interaction network from four different organisms (prediction from each network is treated as independent evidence), and on the amount of biological evidence relating two domains, such as co-occurrence of domains in the same protein and existence of common GO terms at the functional level.More recently, Riley and colleagues  introduc) method , which a) method  improved) method  by creatsupra-domain. The term supra-domain is borrowed from the work by Chothia and colleagues [While there is no agreement on the definition of a domain, it is often assumed that domains are independent evolutionary units, in the sense that a domain is either observed in isolation in nature, or in the context of different multidomain protein architectures . This delleagues  where itlleagues .Generalized Parsimonious Explanation (GPE), which allows for a differential treatment of different types of domain pairs. It also adjusts the granularity of the domain definition by incorporating the supra-domains and the propensity towards predicting interactions between co-occurring domains.The parsimony principle  states that the explanation of any phenomenon should make as few assumptions as possible. In evolution, a parsimony approach seeks an explanation that requires the smallest number of evolutionary changes . In the It has been observed that domains which can be found in distinct protein chains in one organism whereas in a different organism they are fused together in one protein chain often interact ,33. ThisDue to limited availability of domain-domain interaction data, developing and benchmarking of domain interaction prediction methods is particularly challenging. A standard solution in the field is to use as a benchmark set interacting domain pairs obtained from crystal structures of protein complexes and collected in the iPfam database . HoweverThe parsimonious explanation model seeks the smallest set of domain interactions that can explain all protein interactions in the network. The original parsimonious explanation method  treated The second difference between PE and GPE is the "granularity" of domain definition. Some domains have been fused together into one supra-domain according to the rules described above. For example, UreE urease accessory protein, C-terminal domain UreE_C is always observed together with UreE urease accessory protein, N-terminal domain UreE_N, and therefore these two domains are combined into one supra-domain. Additional file Similarly to the original PE method, GPE models the reliability of the edges in the protein interaction network using a randomized approach. This is done by constructing of a set of linear programming instances in a probabilistic fashion and averaging the results (for details see Methods). Two types of scores are reported: the LP-score and pw-score.The LP score is a value between zero and one and is computed by averaging the outputs from the set of randomized linear programs. Note that if we additionally enforce that the solution to our linear program is integer then, for each domain pair, only two values would be possible: 0 – indicating that domain pair is not a part of an optimal solution and 1 – indicating otherwise. The real valued solution measures, for each domain pair, the contribution of a given domain pair to the optimal solution where high scores correspond to high contribution. The pw-score combines the traditional p-value  and the so called witness-score. The need for this additional witness score is a consequence of the following observation. Pairs of frequently occurring domains usually have high p-values as they are often found by chance in our simulation. However, it is known that some of such frequently occurring domain pairs do interact. Therefore, rather than immediately rejecting a pair with high p-value we consider additional evidence in terms of the so-called witness. A witness to a domain-domain interaction is a pair of interacting single domain proteins where one protein contains the first domain in the pair and the second protein contains the other. Given the reliability of each protein interaction in the network, one can estimate the conditional probability that the domain interaction is correct subject to observing a given set of witnesses. .Additional file To evaluate the role of co-occurring domains in mediating protein interactions represented by the network, we first computed the percentage of co-occurring domains in the iPfam benchmark set and in the sets of predicted domain interactions. Table The survey of the predicted domain interactions revealed that the percentage of co-occurring domains among predicted domain pairs was 15.3% for the original PE method and 22.9% for the generalized method .difficult in [The seventy top-scoring pairs predicted with GPE are listed in Table icult in ), and alFirst, we analyzed the results of GPE by assessing the retrieval of benchmark iPfam domain pairs among the top-ranked pairs in the list. Counting the predicted iPfam domains provides an assessment of quality of domain interaction prediction that is far from ideal, since only a small percentage of interacting domains are captured by crystallization experiments. Furthermore, according to our estimations, what is captured is a biased sample of interactions in the network. Nevertheless, we expect the top-scoring interactions to be enriched in iPFAM domain pairs. An assessment of the retrieval of benchmark pairs among the top-scoring pairs predicted by GPE and PE is given in Figure To compare GPE to the top methods, we use each method to predict the mediating domain pair(s) of a given protein interaction. The domain pair(s) with the highest score among the potentially interacting domain pairs is returned as the result of such prediction. Each protein interaction considered in this experiment contains one or more potential contacts in the iPfam benchmark set, which are assumed to be the true mediating pairs . The results of the accuracy represented by the positive predictive value (PPV= TP/(TP+FP)) of the above experiment are shown in Figure To evaluate the statistical differences between the methods, we computed for each method the number of times it wins and loses relative to Random and compared the corresponding fractions. By this measure the performance of PE and GPE was non-distinguishable . The difference between the parsimony based methods and the next best method, the Integrative Bayesian method (IB) was, however, statistically significant . This is interesting, since the IB scores were defined based on a wider range of information . Due to Because interactions involving Pfam-B domains are not documented by crystal structures, any method that is benchmarked using crystal structure data achieves better accuracy when Pfam-B domains are excluded. To make all methods comparable, we excluded pairs involving Pfam-B domains from the analysis. However, for all methods for which we had the corresponding data, we examined how these methods are affected by including Pfam-B domains. The results are presented in Figure Since the difficulty of the problem decreases with exclusion of Pfam-B, one should assess the performance of a method in a given setting relative to a random selection under the same setting. We found that the performances of the Association and the EM methods are drastically reduced upon inclusion of Pfam-B (they become worse than Random), while the performances of the parsimony method and DPEA remain well above random. We note that both the DPEA method and the parsimonious explanation method attempt (in different ways) to find domain pairs that are most prominent in explaining the interaction of proteins in the network. It is also important to stress the fact that although EM and Association performed worse than Random on this test, it does not mean that they would not outperform Random on other measures. For example, for all these methods, it has been demonstrated that high scoring predictions are enriched in iPfam pairs.In this paper, we studied the utility of the parsimony approach in detecting interacting domain partners. Furthermore, we introduced several improvements to our earlier Parsimonious Explanation (PE) method . In its versus excluding Pfam-B domains from predictions. In general, there are no crystal structures documenting interactions between Pfam-B domains. Thus, any method benchmarked using crystal structures can only benefit from excluding Pfam-B domains from predictions. This is unfortunate; as the prediction of interactions involving those not so well-studied domains are also of great interest. Therefore, we considered the impact of including Pfam-B on parsimony, DPEA, EM, and the Association methods. We found that among those, only the parsimony and the DPEA retained performance better than random.We also studied the impact of including The new objective function employed in GPE allows for assigning different costs to different types of interactions. We used this feature of GPE to study the effect of assigning a lower cost to domain pairs involving co-occurring domains. Despite this low cost, only about 23% of predicted domain interactions were between co-occurring domains – much less than in the benchmark crystal structure data which included 62% of this type of interactions. To see if the difference between these propensities is not an artifact of our prediction method, we computed, under the assumption that protein interactions are mediated by domain interactions, a lower bound on the number of domain interactions needed to explain protein interactions in the network. This in turn allowed us to estimate that the fraction of co-occurring domains among all interacting domains is at most approximately 11%. This estimation is made under the assumption that protein interactions are mediated by domain interactions but it is independent of any prediction method.We also investigated another interesting observation that was made about domain-domain interactions, based on crystal structure data: enrichment in homodomain interactions . KeepingThese findings parallel the previously established fact that PDB data is not representative of genome wide protein data ,34,35. WOur implementation of the parsimony principle uses linear programming optimization (LP) to find the smallest weighted set of domain-domain interactions that explains all protein-protein interactions.CO is the set of domain pairs that co-occur in the architecture of some protein in the network, NCO is the set of pairs that do not co-occur, and PPI represents the set of the protein interactions in the network, we have:The implementation is similar to the original LP formulation for the PE method. Intuitively, the linear program formalizes the task of finding a smallest weighted set of domain pairs subject to the constraints that all protein interactions are "explained". Formally, there is a variable for each unique potentially interacting domain pair  such that A ∈ P1, B ∈ P2, and proteins P1 and P2 interact in the network), which can take any real value between zero and one. Additionally, each domain pair has assigned a cost (a number between 0 and 1) so that the interaction types that are known to be biologically more likely obtain a lower cost. Each protein interaction in the network is represented by a constraint requiring that the sum of the values assigned to the potentially interacting domain pairs must be at least 1.0. The goal of the LP is to minimize the weighted sum of the values assigned to all variables. Formally, if α, as long as α < 1; we have arbitrarily set α = 0.95.Since, as discussed in section 2.2, the results were  indistinguishable for a wide range of values of As in the original PE formulation, GPE also takes into account the reliability of the edges in the protein interaction network. This is done by creating 1000 random variants of LP instances where each constraint is included with probability equal to the reliability of the corresponding interaction. The actual LP-score of a variable is taken as the average of the values over all runs. Throughout this work, that reliability is assumed to be 50% -15.i, j) has w witnesses and the reliability of each witness is r, then the witness-score is (1 - r)w. That is, the witness support is the probability that all the witnesses of a given pair are false, as a function of the network reliability. The p-value is estimated in an independent randomization experiment where 1000 networks are created with the same proteins (with the same domain compositions) and the same number of protein interactions, but the protein pairs in the networks are chosen at random. The two indicators are then combined together to generate the pw-score as follows.Additionally, we provide the pw-score, which combines the traditional p-value (probability of obtaining a score at least this high by chance) and the so called witness-score. A witness to a domain-domain interaction is a pair of interacting single domain proteins where one protein contains the first domain in the pair and the second protein contains the other. If a domain pair . The list of the 162 supra-domains with the original domains that they encompass is in Additional file To formulate the LP we used only potential domain interactions involving Pfam-A domains or supra-domains. That led to a drastic reduction in the number of variables in the LP to 26,113 . Accordingly, in the protein interaction network we considered only the 10,025 proteins that had at least one Pfam-A domain in their architecture, which also affected the size of the LP, since the number of constraints went down to 20,625 .The benchmark set was a subset of the domain pairs in the iPFAM database , versionTo compare pairs of methods that use different domain definitions, we compared the performance of each method to the performance of the Random method for the same domain definition. For each method we computed the number of times it performs better or worse than the Random method. This in turn was quantified by computing the percentage of iPfam domains (if any) in the set of highest scoring domain pairs. The proportion of "wins" to "losses" was then compared using the (2-sided) Fisher test.To be able to include the IB method in the comparison of our method with others, we needed to restrict ourselves to the 25,352 domain pairs with likelihood greater than 0.0 listed by Lee and colleagues [In the comparison of the methods, we used the scores reported by Riley and colleagues  for DPEAKG participated in the design of the study, developed the experiments, and drafted the manuscript. TP conceived the study, participated in the design of the study, and helped to draft the manuscript. Both authors read and approved the final manuscript.Mapping of Supra-domains to domains. This file contains 162 rows, each containing one supra-domain name followed by the names of the original domains included in it, separated by a blank character.Click here for fileSet of gold standard pairs. This file contains the 691 generalized domain pairs in the gold standard set. Each line contains the names of two domains that comprise a pair, separated by a TAB character.Click here for fileGPE predictions. This file contains a heading line and 1399 rows with the GPE predicted domain pairs ordered by LP-score. All pairs listed have LP-score ≥ 0.60 and pw-score ≤ 0.01. Each row contains five fields: Generalized Dom A, Generalized Dom B, GPE LP-score, GPE pw-score, and Gold Std .Click here for fileGPE LP-scores ≥ 0.50. This file contains the 7,554 generalized domain pairs that had GPE LP-score ≥ 0.50, regardless of their pw-scores. Each row contains five fields: Generalized Dom A, Generalized Dom B, GPE LP-score, GPE pw-score, and Gold Std .Click here for fileImpact of pw-score cut-off on the quality of predictions. This file contains the numbers of domains pairs predicted and the fraction of those that are in the benchmark set, for different values of pw-score cut-off. As the cut-off becomes more stringent, the accuracy of the predictions improves.Click here for fileImpact of reliability cut-off on the quality of predictions. This file contains five tables for five different reliability cut-offs, showing the number of predictions and the number of predicted pairs in the benchmark set as a function of LP-score cut-off.Click here for fileSet of protein-protein interactions used to compare methods. This file contains the 192 interacting protein pairs used in the experiment of predicting the mediating domain pairs, used to compare GPE with other state-of-the-art methods. Each line contains two integer numbers, separated by a TAB character, that represent the DIP identifiers of the interacting proteins.Click here for filePairs Predicted By GPE and not by IB. This file contains the 50 top-scored original domain pairs that were predicted by GPE but had IB-score < 4.0. Each row contains six fields: ORIGdom A, ORIGdom B, GPE LP-score, GPE pw-score, IB-score, and number of Witnesses of that pair in GPE dataset in Generalized format.Click here for file


The identification of protein domains plays an important role in protein structure comparison. Domain query size and composition are critical to structure similarity search algorithms such as the Vector Alignment Search Tool (VAST), the method employed for computing related protein structures in NCBI Entrez system. Currently, domains identified on the basis of structural compactness are used for VAST computations. In this study, we have investigated how alternative definitions of domains derived from conserved sequence alignments in the Conserved Domain Database (CDD) would affect the domain comparisons and structure similarity search performance of VAST.Alternative domains, which have significantly different secondary structure composition from those based on structurally compact units, were identified based on the alignment footprints of curated protein sequence domain families. Our analysis indicates that domain boundaries disagree on roughly 8% of protein chains in the medium redundancy subset of the Molecular Modeling Database (MMDB). These conflicting sequence based domain boundaries perform slightly better than structure domains in structure similarity searches, and there are interesting cases when structure similarity search performance is markedly improved.Structure similarity searches using domain boundaries based on conserved sequence information can provide an additional method for investigators to identify interesting similarities between proteins with known structures. Because of the improvement in performance of structure similarity searches using sequence domain boundaries, we are in the process of implementing their inclusion into the VAST search and MMDB resources in the NCBI Entrez system. As the amount of diverse biological data continues to grow, it is important for new methods of analysis to be devised and current methods to be improved. The ability to detect that two proteins have diverged from a common ancestor allows one to infer functional similarity between the two. A common method for identifying similarity between proteins is the use of sequence alignment tools such as FASTA  and BLASIt has long been understood that there is an intermediate organization in proteins, typically called a domain, that is greater than secondary structure and less than the full-length chain of amino acids -22. ThisThus, it becomes critical to identify the domains appropriately before performing structure similarity searches. Structurally compact domains are currently being used for computing related structures in MMDB. Recent studies investigating the performance of several structurally based domain parsers in comparison to expert curated structure domain boundaries have indicated the limitations of different methods and potential improvements ,24. Here2FO0, chain: A)  Figure  and is oIn this work, we first systematically compared the domain boundaries of the sequence-based domains in the Conserved Domain Database to the structure-based domains in the medium redundancy subset of MMDB. We have identified a noticeable fraction of sequence based domains that differ significantly from those derived based on structural compactness. The new domains were then used as queries in identifying related structures using VAST and changes in structure similarity search results were analyzed. Using SCOP as a standard of truth, interesting cases were observed where the new domain boundaries perform better than the original domains in terms of homologous structure recognition. We have also found that the overall performance of sequence domains is comparable to that of whole chain and structure domain based queries.Having tested a series of thresholds for identification of differences between sequence and structure-based domains as described in the methods section, we focused on the structure similarity search results using sequence based domains for which at least 90% of the domain consensus sequence was aligned to a structure. A sequence based domain was determined to be different from existing structure based domains when its secondary structure composition was at least four secondary structure elements (SSEs) different from both the most similar structure domain and the entire protein chain. These results are derived from applying the methods to the medium redundancy subset of MMDB, in which the structure database is reduced by clustering similar structures based on sequence similarity and then selecting a single representative from each cluster based on structure quality . We repoSince this study is looking at the performance of different queries using the same search method and database, search performance can be assessed simply by comparing the ratio of homologs identified to all similarities identified for the two query sets, the original structure based domains and chains and the new sequence derived domains. The sequence-based domains do perform slightly better as queries than the original domains, having a search result set consisting of 21% homologous structures as opposed to 15% for the original domains   covers residues 231 to 466 which stretches across structure domains 2, 3, and 4 of human DNA topoisomerase-I . This region covers residues 110 to 158 and consists of all but one of the 7 C-terminal strands  eliminates the entire N-terminal region and reduces the C-terminal region of the discontinuous structure domain 1 of the gelatin binding region of human Fibronectin  and medium redundancy Molecular Modeling Database (MMDB) were used for the sequence to structure domain comparisons, the current versions of which are available at us Groups,32 databThe SSE compositions of the sequence domains were then compared to the composition of the entire chain on which the sequence domain was identified, as well as the domains of the chain identified based on compactness. A SSE composition metric was used, rather than an amino acid sequence difference requirement, because the VAST algorithm applied later in the study uses SSE alignment to detect protein similarity. By requiring different SSE composition, we avoided the potential identification of domain differences resulting from the inclusion or removal of unstructured protein regions which would have no effect on structural similarity searches employed later in the study. The footprint of a sequence domain was considered different from the structure domains based on the following criteria: 1) The sequence domain must contain at least 4 secondary structure elements (SSEs), 2) the sequence domain must be at least 4 SSEs shorter than the whole chain, and 3) the sequence domain must be at least 4 SSEs different from the closest structure based domain. SSEs and structure domains for a given structure were those identified by the MMDB structure domain parser. Item 1) simply means that we are not considering very small domains with 3 or fewer SSEs, which corresponds fairly well to having 50 or fewer residues. Items 2) and 3) define when we consider a sequence domain to be "different" from a structure-based domain. Additional SSE difference requirements were tested, and as expected, reducing the number of SSEs required resulted in many domain differences identified, while more SSEs required for being classified as different quickly reduced the number of differences found. This testing led us to select the 4 SSE difference requirement as a "middle ground", allowing us to identify a large number of domain differences without selecting all sequence based domains in the structure database. The choice of 4 SSEs is natural, because this is about the size of a small domain, and it should allow us to clearly see the effect of using different domain boundaries in the structure similarity searches. The closest structure domain to a sequence domains was determined as follows: a) the structure domain completely covered by the sequence domain, b) the longest structure domain completely covered by the sequence domain, or c) the structure domain with the longest length covered by the sequence domain. To test the possibility that domain differences were due to unaligned ends of the sequence domains not being included in the regions to be used as queries, we repeated the method using the July 2005 databases in which the sequence domain boundaries were extended to the ends of the complete sequence domains and tested using the same SSE difference requirements described above. Differences in the size and composition of the footprint and extended footprint sets were then compared, revealing minimal differences in the domains identified as different. A flowchart of the order of operations can be seen in Figure . VAST is essentially a two-phase process, the first being the alignment of vectors of secondary structure and preliminary scoring. Those initial alignments whose scores exceed an empirically derived threshold are then refined in the second phase of structural alignment using the Ca coordinates. Only those refined alignments with a statistical significance of P < 10-5 are reported as structurally similar. Although available to the public on the web, our study used an in-house version of the VAST executable to allow the submission of multiple queries and more efficient use of computational resources. To evaluate the change in structure similarity search results when using the new domains based on sequence, we considered structurally similar domains classified within the same superfamily division as the query domain of SCOP 1.69, available at , to be homologs. Since the study explicitly looked for differences in domain boundaries, it was not possible to directly map both structure and sequence domains to corresponding entries in the SCOP database. For example, if a structure domain from MMDB has very similar domain boundaries as a SCOP domain, then a sequence domain found to be different from the MMDB domain would also be different from a SCOP domain definition. Thus, in order to measure the ability to identify similar domains, a homolog set for a query domain was identified as the SCOP superfamily members for all SCOP domains identified on the query chain. Although this 'collapsing' of superfamilies on a chain could introduce the possibility of some false homolog mapping or unrealistically large homolog sets, it allowed for sensitivity and specificity analysis of individual domains in the test set as well as overall assessment of the domain based structure similarity search result sets. In addition, to avoid missing data issues due to the smaller size of the SCOP database, all domains used as VAST queries and resulting similar structures were reduced to only those structures included in the 1.69 release of SCOP. Individual search results were also evaluated using SCOP fold classification members to test the possibly that previously identified non-homologs were potentially distant homologous structures that were not included in the superfamily classification. The structure similarity search results for each domain query and domain type sets were then compared based on the homologous and non-homologous structures found, as well as search result overlap, e.g. hits common to both sequence and structure domain similarity search results, regardless of the significance scores of the alignment other than the statistical significance of P < 10-5 required for being reported as similar by the VAST algorithm. Individual search results of the new domains were then compared to results of the original structure domains and visualized using PyMOL [The domain entries from MMDB and sequence domains identified as different were used as queries for structure similarity searches against the medium redundancy set of MMDB using Vector Alignment Search Tool (VAST), available on the web at ng PyMOL  and Cn3Dng PyMOL .KET designed the experiments, performed the analysis, and wrote the manuscript. YW helped design the experiments, provided feedback throughout the study, and edited the manuscript. TM provided feedback throughout the study, assisted with VAST search setup, and edited the manuscript. SB conceived of the study and directed the work. All authors read and approved of the final manuscript.


Vibrio parahaemolyticus type III secreted effector VopS contains a fic domain that covalently modifies Rho GTPase threonine with AMP to inhibit downstream signaling events in host cells. The VopS fic domain includes a conserved sequence motif (HPFx[D/E]GN[G/K]R) that contributes to AMPylation. Fic domains are found in a variety of species, including bacteria, a few archaea, and metazoan eukaryotes.The Drosophila melanogaster CG9523, and use sequence and structure based computational methods to identify related domains in doc toxins and the type III effector AvrB. The conserved sequence motif that contributes to AMPylation unites fic with doc. Although AvrB lacks this motif, its structure reveals a similar topology to the fic and doc folds. AvrB binds to a peptide fragment of its host virulence target in a similar manner as fic binds peptide substrate. AvrB also orients a phosphate group from a bound ADP ligand near the peptide-binding site and in a similar position as a bound fic phosphate.We show that the AMPylation activity extends to a eukaryotic fic domain in The demonstrated eukaryotic fic domain AMPylation activity suggests that the VopS effector has exploited a novel host posttranslational modification. Fic domain-related structures give insight to the AMPylation active site and to the VopS fic domain interaction with its host GTPase target. These results suggest that fic, doc, and AvrB stem from a common ancestor that has evolved to AMPylate protein substrates. The original description of fic derives from the E. coli fic-1 gene required for cAMP-induced filamentation L. pneumophila has an N-terminal fic domain required for disrupting host secretory vesicle transport V. parahaemolyticus that causes eukaryotic cell cytotoxicity The molecular function of fic , in the protein families database PFAM E.coli phage P1 that acts as a plasmid addiction system. In the absence of phd antitoxin, doc induces growth arrest of E.coli by targeting protein translation elongation The fic domain is classified together with a second family of sequences, doc , revealing a common α-helical fold topology Pseudomonas syringae type III effector protein avirulence protein B (AvrB), which is delivered into plant cells to illicit accelerated defense responses and hypersensitive cell death by targeting the host immune resistance protein RIN4. We propose to unite domains from these three families  into a single superfamily (fido) that likely perform similar signaling functions. We show that like the VopS fic domain, a Drosophila melanogaster fic homolog self-AMPylates.To help resolve fido classification, we explored evolutionary relationships using a combination of sequence-based and structure-based methods. Sequence evidence supports an evolutionary link between fic and doc, while structural evidence supports a homologous relationship between these two families and a domain from the Shewanella oneidensis (Helicobacter pylori (Neisseria meningitidis (not shown), which contribute the a' helix from the C-terminus. Both the Shewanella oneidensis fic and the Bacteroides thetaiotaomicron fic  for each fic domain-containing query correspond to representative fic structures . DespiteWhile the apo AvrB structure represented a novel fold with no recognizable homologs upon its release B. thetaiotaomicron structure identified a number of doc sequences  and converged without identifying sequences from other families. Similarly, the representative doc domain structure sequence identified a number of fic sequences . The PSI-BLAST hits encompass a significant portion of the fic core and identified the conserved fido sequence motif (HPFx[D/E]GN[G/K]R). Identified fic and doc domain-containing sequences are present in genomes from bacteria, archaea, and eukaryota, with the latter distributed among metazoa species (HypE in human) and a few fungi. The phylogenetic distribution suggests extensive horizontal gene transfer  identifies fic domain structures as top hits with 3D-Jury scores Despite the structure similarity between AvrB and fido, most sequence methods fail to identify connecting links. However, using AvrB as a sequence query with the sensitive profile-based homology detection method COMPASS The multiple sequence alignment illustrated in Haemophilus somnus virulence factor p76 (ibpA) includes an N-terminal haemagglutination activity domain (Haem), two fic domains, and a papain-like protease (peptidase C58) also found in YopT and AvrPphB avirulence proteins. Like VopS, YopT disrupts the actin cytoskeleton of the host cell by inhibiting Rho GTPase signaling. YopT achieves this inhibition by cleaving Rho GTPases after a post-translationally modified cysteine, which releases the GTPases from the membrane where they function H. somnus virulence factor includes multiple domains that modify the same signaling pathway. This functional domain organization suggests that other fusions might give similar clues to activity or help identify additional signaling components of AMPylation. The ibpA Haem domain is also found at the N-terminus of a doc domain containing protein from Comamonas testosteroni, providing further support for the homology and functional similarity between fic and doc.Similar to other signaling proteins, fic and doc are fused to a number of different domains that might function in AMPlyation pathways . The HaeRhizobium Y4CF, belongs to a replicon that allows bacteria to interact symbiotically with leguminous plants S. oneidensis fic structure (A number of fusions exist between fic domains and helix-turn-helix (HTH) DNA-binding domains. These HTH domains may regulate expression of fic or other components of AMPylation signaling. The DNA-binding component of these HTH domains is reminiscent of the phd antitoxin N-terminal DNA-binding domain that serves to autorepresses the doc toxin-antitoxin operon Branchiostoma floridae includes several additional domains that might hint at components involved in the eukaryotic fic domain signaling process. In addition to two fic and two Tpr, the B. floridae protein domain organization includes two low density lipoprotein receptor class a (LDL), two immunoglobulin (Ig), a trypsin-like serine protease (Trypsin), and five repeated calcium-binding EGF-like domains (EGF). The presence of these domains indicates an extracellular or intra vesicular localization of the pathway in eukaryotes, and the LDL might suggest involvment of AMPylation in cholesterol metabolism or trafficking.Eukaryotic fic sequences such as HypE contain a predicted N-terminal transmembrane helix followed by Tpr repeats. One interesting eukaryotic sequence from H. pylori fic) form a potential active site (Conserved fido motif residues (HPFx[D/E]GN[G/K]R) and additional conserved residues external to the motif  that corresponds to fic R50 maintains a similar local structure in the AvrB loop  to Rho GTPases in a process known as AMPylation 32P-α-ATP in an in vitro AMPylation assay  and the doc domain of the structure 3dd7 . Found homologs were grouped using linkage clustering (-lscut = 0.9), and representative sequences from each group were used as transitive queries for subsequent rounds of PSI-BLAST. To detect sequence homologs of AvrB, we searched the non-redundant database with PSI-BLAST To detect sequence homologs of fic and doc domains, we searched the non-redundant database  with PSI-BLAST To visualize the relationships between identified fido sequences, all against all pair-wise sequence similarities for collected sequences  were calculated using BLAST implementation  with the CLANS application DaliLite CG9523 was amplified off of a plasmid containing the coding region of CG9523 (kindly provided by Helmut Kramer) using VENT polymerase. The PCR product was restriction digested, ligated into pET28a, and transformed into E. coli . Positive clones were confirmed by DNA sequence analysis. Histidine to alanine mutants of VopS and CG9523 were generated using the Stratagene QuikChangeTM site-directed mutagenesis kit, according to the manufacturer's instructions. Mutants were confirmed by DNA sequence analysis.600 of 0.6–0.8 and induced with 0.4 mM IPTG for 8–12 hours at room temperature. Cells were harvested and lysed using an Emulsiflex C-5 cell homogenizer (Avastin). His-tagged proteins (CG9523 and its mutant) were purified using Ni2+ affinity purification (Qiagen) and were aliquoted at stored at −80°C in 20 mM Tris, pH 7.5, 5 mM NaCl, 1 mM DTT, and 10% glycerol.VopSΔ30 and VopSΔ30-H348A were expressed and purified as described previously 2, and 32P-α-ATP (2 µCi) (Perkin Elmer). The AMPylation reaction was stopped by the addition of SDS sample buffer. Samples were boiled for 5 minutes, separated by SDS-PAGE, and visualized by autoradiography.For the AMPylation assays using purified recombinant protein, 5 µM of VopSΔ30 or VopSΔ30-H348A or 2.5 µM of His-tagged CG9523 or His-tagged CG9523-H375A was incubated for 20 minutes at 30°C in 20 mM HEPES buffer containing 20 µM ATP, 2 mM MgClFigure S1Tree illustrates extensive horizontal gene transfer in bacteria/archaea. The multiple sequence alignment of one clans group was limited to sequence ranges corresponding to the fic topology of 3cuc and purged for redundancy less than 95% identity. The resulting alignment was used to construct phylogenetic trees using the MOLPHY package [39]. JTT distances [40] were calculated using protml (-jfD options), initial neighbor-joining [41] tree topologies were built using Njdist, and maximum likelihood trees were built using local rearrangement search of initial tree topologies protlm (−R option). The tree topology reliability was assessed with estimated log-likelihood resampling (RELL) of MOLPHY.(0.93 MB TIF)Click here for additional data file.


Although extensive HIV drug resistance information is available for the first 400 amino acids of its reverse transcriptase, the impact of antiretroviral treatment in C-terminal domains of Pol  is poorly understood.We wanted to characterize conserved regions in RT C-terminal domains among HIV-1 group M subtypes and CRF. Additionally, we wished to identify NRTI-related mutations in HIV-1 RT C-terminal domains. We sequenced 118 RNase H domains from clinical viral isolates in Brazil, and analyzed 510 thumb and connection domain and 450 RNase H domain sequences collected from public HIV sequence databases, together with their treatment status and histories. Drug-naïve and NRTI-treated datasets were compared for intra- and inter-group conservation, and differences were determined using Fisher's exact tests. One third of RT C-terminal residues were found to be conserved among group M variants. Three mutations were found exclusively in NRTI-treated isolates. Nine mutations in the connection and 6 mutations in the RNase H were associated with NRTI treatment in subtype B. Some of them lay in or close to amino acid residues which contact nucleic acid or near the RNase H active site. Several of the residues pointed out herein have been recently associated to NRTI exposure or increase drug resistance to NRTI.in vivo. The findings into the conservation of RT C-terminal domains may pave the way to more rational drug design initiatives targeting those regions.This is the first comprehensive genotypic analysis of a large sequence dataset that describes NRTI-related mutations in HIV-1 RT C-terminal domains  HIV-1 RT is a heterodimer composed of two subunits, p66 and p51; p51 is derived from the proteolytic processing and removal of C-terminus (codons 441 to 560) from p66 et al. pointed out mutations in the RNase H domain that reduce its activity and concomitantly increase the resistance to thymidine analogues in vitroThe polymerase RT portion is currently targeted by two classes of ARV drugs, the nucleoside reverse transcriptase inhibitors (NRTI) and the non-nucleoside RTI (NNRTI). HIV drug resistance mutations in this domain are extensively characterized for these classes of ARV drugs Plasma samples from HIV-positive patients, both treatment-naïve and HAART-experienced, were collected from three Brazilian HIV/AIDS reference centers: two in Rio Grande do Sul (RS) state, Hospital de Clínicas de Porto Alegre and University Hospital of Rio Grande, and one located in Rio de Janeiro (RJ), Hospital Gaffrée e Guinle. Patients were recruited from 2002 to 2003 in RS and from 2004 to 2006 in RJ. All patients signed a written consent form for participating in the study, and the Internal Review Boards from all three Institutions have approved the study.5′tatacgtaagccacctggattc3′ (3762-3783 nucleotide positions relative to HXB2) and 5′cagtctacttgtccatgcatggcttc3′ (nt relative positions 4371-4396) with an annealing temperature of 55°C. The second round was done using primers 5′ggtaccagttagagaaagaaccca3′ (nucleotide positions 3826-3849) and 5′cattgcctctccaattactgtgatatttctcatg3′ (nt positions 4263-4295) with an annealing temperature of 56°C. A 469-bp fragment corresponding to RNase H codons was amplified, purified and sequenced in an automated ABI Prism 3100 Genetic Analyzer . Sequences obtained were assembled and manually edited using SeqMan .HIV viral RNA was isolated from 50 µl of patient's plasma using Trizol, followed by cDNA synthesis with Moloney murine leukemia virus RT and random primers as previously described http://hiv-web.lanl.gov) To eliminate sample contaminations or mix-ups and to determine the isolates' subtype, a FASTA file with all edited sequences and representative sequences of all HIV-1 subtypes obtained from Los Alamos HIV database (http://hiv-web.lanl.gov) http://hivdb.stanford.edu/) For the conservation analysis of thumb, connection and RNase H domains and potential drug resistance mutations, we searched all available HIV-1 group M sequences spanning all subtypes  and CRF01_AE and CRF02_AG from the Los Alamos HIV database . Those sequences were compared to the subtype B drug-naïve isolates described above for significance of amino acid residue frequencies.To facilitate the visualization of conserved positions and the inferred drug-related mutations, the atomic coordinates of the crystal structure of HIV-1 BH10 (subtype B) RT in contact with the DNA:RNA hybrid (PDB entry 1HYS) were used Five hundred and ten sequences of thumb and connection domains (RT codons 298-440) were retrieved from the databases, of which 280 were treatment-naïve  and 230 were NRTI-treated . For the RNase H domain (RT codons 441-560), we obtained 450 sequences, of which 276 were treatment-naïve  and 174 were NRTI-treated .We successfully amplified and sequenced the RNase H domain (codons 441-560) of 118 isolates from Brazilian HIV-positive patients. The phylogenetic inference showed that half of them belonged to subtype C (n = 59) and the other half was assigned to subtype B (n = 59) (data not shown). Regarding ARV treatment status, 58 patients were naive (B = 27 and C = 31) and 60 were on treatment (B = 32 and C = 28). Together with the sequences from public databases, we gathered 334 RNase H sequences of drug-naïve and 234 RNase H sequences of NRTI-treated patients. For the RT connection domain, we have obtained 510 isolates from the public databases for further analysis.Sequences of RT C-terminal domains were aligned, subtyped by phylogenetic analysis (data not shown), and evaluated by their extent of codon polymorphisms . In drugin vivo when comparing NRTI-naïve and treated subjects at each one of 263 codons included in the study. Since a number of amino acid residues are polymorphic across all HIV-1 group M subtypes, we decided to search for NRTI-related mutations only among subtype B sequences available. Subtype B represented alone 51% (260/510) of thumb and connection domain strains and 51% (288/568) of RNase H domain strains in our analysis. The results can be seen in p-values, can also be seen in We analyzed amino acid variations found The role of specific ARV drugs in selecting the mutations depicted here is not easily determined, since the vast majority of treated patients whose viral samples were analyzed have been subjected to multiple, complex drug combinations prior to sample collection. In our dataset, none of the connection domain sequences analyzed was from patients in use of NNRTI, but a small fraction of the RNase H viral sequence  had previous exposure to that drug class. Therefore, we cannot completely rule out the possibility of a potential influence of NNRTI in the selection of mutations characterized here. However, a limited number of those samples (n = 28) are from patients older in the epidemic and exclusively subjected to AZT monotherapy. We have compared these sequences with those from the drug-naïve dataset in the positions pointed out above. It noteworthy that comparisons were possible only up to RT codon 500, as scarce sequence information is available after that codon for those viral sequences. The comparison can be seen in Mutations subjected to selective pressure were highlighted onto the structural C-terminal domains of HIV-1 RT . In the The present study determined the conserved amino acid positions of thumb, connection and RNase H domains of HIV-1 group M RT. The comparison of polymorphisms between drug-naïve and NRTI-experienced patients has evidenced a total of 86 (33%) invariable sites common to both groups. Other studies recently published on the polymorphism of PR (codons 1-99) and RT N-terminal domains (1-320) observed highly conserved sites in approximately half of PR Regions of C-terminal RT domains lying closer to the DNA:RNA hybrid were found conserved, including amino acid residues that interact with the primer DNA  and the template RNA strand  The motif ETFYVDGA spanning RT codons 438-445 was highly conserved (0–5% variation), which is consistent with the fact that it overlaps the protease cleavage site AETFYVD (codons 437-443) located between the connection and the RNase H domains. Mutagenesis experiments in this motif caused defects in virion RT and integrase processing Another conserved region comprises codons 492-511 and represents the RNase H β4 and αB secondary structures The function of the connection domain has not been fully characterized. The crystal structure of HIV-1 RT has shown five positions of the connection domain that interact with DNA  and a unique position (K390) that interacts with RNA The most conserved region of the connection domain encodes amino acids 387-430, where 38 codons (86%) presented high conservation (0–4% variation). This is an interesting finding because this region contains one of two RT sites which interact with the viral integrase (codons 387-422) et al. evidenced the importance of the RNase H domain to drug resistance. They showed that mutations which decreased in vitro RNase H activity greatly increased resistance to AZT and d4T in vivo. However, three of the mutations described here  were present only in NRTI-experienced isolates. Mutation A360V lies in a residue that contacts nucleic acid, while mutation I506L is close to two residues of the RNase H primer grip in vivo. Such hypothesis was corroborated by recent work of Delviks-Frankenberry and colleagues When we compared subtype B naïve and treatment-experienced sequence groups, differences between them were found. We discovered 15 mutations related to NRTI treatment. Until now, all studies published on HIV RTI drug resistance relied on the N-terminal half of RT, the target of both RTI drug classes. Such studies neglected the role of C-terminal RT domains in drug resistance. In 2005, Nikolenko et al. also reported that the reversion of mutation I326V increased AZT resistance et al. correlated some mutations in the RT connection domain with TAMs, G359S, A360V/T and A371V In view of potential biases created by differences in subtype distribution in drug-naïve and NRTI-experienced sequence groups, we conducted an exclusive analysis of subtype B, the one with the largest number of isolates sequenced, particularly those on NRTI treatment. A small number of NNRTI-experienced patients was present in the dataset, and as possible interactions between NRTI- and NNRTI-selected mutations is known to occur, we cannot completely discard a putative effect of NNRTI in the selection of mutations described here. Nine changes in the connection domain and 6 in the RNase H domain were found related to treatment  and 3. SAmong the NRTI-related mutations characterized herein, four of them have been associated with AZT in patients with exclusive exposure to that drug. Those included mutations A360V and A371T, found in patients with TAMs in vivo and in most HIV-1 subtypes. The findings corroborate data by other studies This work was the first to comprehensively evaluate genotypic variation in the C-terminal domains of RT in the context of drug resistance


Serine proteases are one of the most abundant groups of proteolytic enzymes found in all the kingdoms of life. While studies have established significant roles for many prokaryotic serine proteases in several physiological processes, such as those associated with metabolism, cell signalling, defense response and development, functional associations for a large number of prokaryotic serine proteases are relatively unknown. Current analysis is aimed at understanding the distribution and probable biological functions of the select serine proteases encoded in representative prokaryotic organisms.etc. were found tethered to the serine protease domains. Some domain combinations  were found to be widespread in the prokaryotic lineages suggesting a critical role in prokaryotes.A total of 966 putative serine proteases, belonging to five families, were identified in the 91 prokaryotic genomes using various sensitive sequence search techniques. Phylogenetic analysis reveals several species-specific clusters of serine proteases suggesting their possible involvement in organism-specific functions. Atypical phylogenetic associations suggest an important role for lateral gene transfer events in facilitating the widespread distribution of the serine proteases in the prokaryotes. Domain organisations of the gene products were analysed, employing sensitive sequence search methods, to infer their probable biological functions. Trypsin, subtilisin and Lon protease families account for a significant proportion of the multi-domain representatives, while the D-Ala-D-Ala carboxypeptidase and the Clp protease families are mostly single-domain polypeptides in prokaryotes. Regulatory domains for protein interaction, signalling, pathogenesis, cell adhesion Domain architectures of many serine proteases and their homologues identified in prokaryotes are very different from those observed in eukaryotes, suggesting distinct roles for serine proteases in prokaryotes. Many domain combinations were found unique to specific prokaryotic species, suggesting functional specialisation in various cellular and physiological processes. Proteolysis also contributes to the timely inactivation of proteins and is a major biological regulatory mechanism in living systems GDSA [S/T]) ) ,64. TheyA [S/T]) . In addiA [S/T]) .Rubrobacter xylanophilus) ; NP_623361.1 ; NP_953479.1  etc suggesting that some of the LonB-like proteins in bacteria were disseminated to different species through multiple lateral gene transfer events  and NP_578196.1 (Pyrococcus furiosus). Similarly, a Thermococcus kodakaraensis KOD1 Lon protease-like protein YP_184581.1 co-clusters with NP_126400.1 (Pyrococcus abyssi) and NP_579167.1 (Pyrococcus furiosus)  co-clusters with two archaeal Lon protease-like proteins NP_616787.1 (Methanosarcina acetovirans) and NP_635142.1 (Methanosarcina mazei), suggesting the possibility of the lateral transfer of Lon protease genes between bacteria and archaea  were identified in the present analysis. They display a higher representation in Euryarchaeota than trypsins, DD-peptidases and Clp proteases (Tables  SPHs wer) Figure . YP_6446ccus sp.  suggestiSerine protease-like domains often exist as a part of multi-domain polypeptides. In several enzyme families, changes in the domain alliances are known to modulate the enzyme function, usually by altering the substrate specificity or enzyme efficiency. The co-existing domains may also play a key role in the substrate specificity of these proteins, either by facilitating protein-protein interactions or their specific involvement in pathways ,68. SuchOf the 966 serine protease-like proteins identified in the present study, 311 32%) were found to carry co-existing domains. However, the distribution of the multi-domain proteins is not uniform across the five families, which display unequal preferences to enter into domain alliances. While, trypsins and subtilisins have a significant proportion of the multi-domain representatives, DD-peptidases and Clp proteases are overwhelmingly single-domain polypeptides. Most Lon protease representatives are multi-domain proteins  were multi-domain polypeptides, where 15 different domain architectures were observed. The impact of some unique domain combinations observed in prokaryotic trypsin-like proteins and their likely influence on the biological functions of these proteins are discussed.Hahella chejuensis  or two (49 of 112) PDZ domains tethered to the C-terminus of the trypsin-like domain  domains N-terminal to the protease domain, FHA domain which is found in diverse proteins associated with metabolic processes such as DNA repair, signalling, transport etc [Streptomyces avermitilis was associated with a CBM_5_12 module C-terminal to the protease domain; these are presumed to have a carbohydrate-binding function. YP_273108.1 in Pseudomonas syringae was associated with TerD module N-terminal to the protease domain; this domain, found in tellurite resistance proteins, is required for growth in toxic medium. This is not functionally characterised to our knowledge. NP_604177.1 in Fusobacterium nucleatum carries an Endonuclease_NS domain, which encodes an endonuclease that acts on double and single-stranded nucleic acids [Some trypsin homologues were found associated with regions most similar to modules likely to function in signalling and metabolism Table . Trypsinport etc . NP_8221Streptomyces avermitilis were associated with Alpha lytic protease prodomain (Pro_Al_prot), usually associated with Alpha-lytic endopeptidases – a subset of trypsins involved in lysing and degrading soil organisms (Table Trypsin homologues (NP_822175.1 and NP_827729.1) from Pelodictyon luteolum was found associated with six Sel1 repeats that were originally identified in a negative regulator of Notch signalling pathway in Caenorahbditis elegans [Modules of indeterminate function were also found associated with some trypsin-like proteins. For example YP_374752.1 from  elegans . HoweverSubtilisins were found to be the most versatile of the five serine protease families. 85 out of 227 37%) proteins with subtilisin-like domains were identified as multi-domain polypeptides and a total of 38 different domain combinations were discerned, many of which are specific to bacteria or unique to certain prokaryotic species (Table 7% proteiIdiomarina loihiensis) or two  PPC domains C-terminal to the protease domain were identified in different prokaryotic lineages. While the single PPC-domain containing subtilisin-like proteins were identified only in archaea and Gram-negative bacteria , subtilisin-like proteins with two PPC domains were identified in both Gram-positive and Gram-negative bacteria as well as archaea (Tables Bacillus subtilis) were identified that carry PA domain inserts in the subtilisin protease domain. PA domain is suggested to form a lid-like structure that covers the active site in the protease and is believed to be involved in protein interactions or mediate substrate recognition by proteases [Natromonas pharaonis) associated with PKD domain were also identified. PKD domains are predicted to be involved in protein-protein and protein-carbohydrate interactions [Several subtilisins identified in the present study reveal their association with domains that contain regions facilitating protein-protein interactions Table . Subtiliroteases . Subtiliractions  (Table 3Pseudomonas fluorescens) were identifiedthat carry an Autotransporter beta-domain, C-terminal to the subtilisin domain . NP_689039.1 in Sterptococcus agalacticae additionally carries a closely related motif called YSIRK type signal peptide [Halobacterium. Cleaved_Adhesin domain found in hemagglutinins and peptidases that in Porphyromonas form components of extracellular virulence complex RgpA-Kgp [Symbiobacterium thermophilum. Big_2 domain possibly associated with cell adhesion in bacteria was found encoded by subtilisin-like protein NP_969490.1 in the predatory bacterium Bdellovibrio bacteriovorus where it is likely to be associated with the hydrolytic machinery that facilitates the bacterium's predatory lifecycle [Thermoanaerobacter tengcongenesis, which also carries a pair of SLH domains believed to anchor the peptidoglycans [Pseudomonas putida; NP_927988.1 in Photorhabdus luminescens); CARDB-cell adhesion related bacterial domain (NP_954260.1 in Geobacter sulfurreducens) [Salinibacter ruber)  carries a NosD module C-terminal to the subtilisin domain; NosD is a periplasmic protein believed to insert copper into exported reductase apoenzyme [Lactobacillus johnsonii, encodes a multi-domain protein with five FIVAR modules, a putative sugar binding domain mostly found in cell-wall associated proteins [Bdellovibrio bacteriovorus was found to carry a CUB domain, an extracellular module associated with diverse functions in development and signalling in eukaryotes, however, its role in prokaryotes is not clear [Many subtilisin-like proteins were identified with the regions most similar to the modules likely to function in signalling and metabolism flanking the protease domain Table . NP_6169poenzyme . NP_9658ot clear  . NP_967370.1 gene product in Bdellovibrio bacteriovorus codes for a Proprotein convertase P-domain C-terminal to the subtilisin domain. It is associated with the kex2/subtilisin endopeptidases in eukaryotes, gammaproteobacteria and few others and is believed to be necessary for the folding and maintenance of the subtilisin domain and regulating its calcium pH specificity [Thermoplasma acidophilum (archaea) encodes for a thermopsin module, N-terminal to the subtilisin domain, similar to those found in the thermostable acid proteases in archaebacteria [Some subtilisin-like proteins were found associated with the domains that likely modulate the function of the adjacent subtlisin protease domain Table . A subticificity . NP_3942bacteria   in NP_951948.1 in Geobacter sulfurreducens; BNR repeats in NP_824495.1 in Streptomyces avermitilis; PilZ domain in NP_969350.1 in Bdellovibrio bacteriovorus; Tryptophan rich (W_rich_C) domain in YP_382882.1 in Synechococcus [Several modules of unknown or indeterminate function were also found associated with subtilisin-like proteins in prokaryotes Table . These itilisins  seen in Hahella chejuensis was found to encode a region most similar to ABC transporters that function in translocation of diverse compounds across biological membranes [Streptomyces avermitilis carries three each of Condensation (associated with enzymes that synthesise peptide antibiotics [Bacteroides thetaiotaomicron) and YP_444518.1  were found associated with the Glyco_hydro_3 module found in the O-Glycosyl hydrolases that hydrolyse the glycosidic bond between two or more carbohydrates. YP_444518.1 also carries a Glyco_hydro_3_C module, often found in association with Glyco_hydro_3 and is involved in catalysis and binding β-glucan [DD-peptidase-like proteins were found to be extremely rigid in terms of domain combinations. Only five of 254 proteins carrying DD-peptidase-like domains were identified as multi-domain polypeptides, in sharp contrast to other serine protease families analysed here, except Clp proteases Table . These eembranes . Anotheribiotics ), AMP-biβ-glucan  (Table 3Aeropyrum pernix), NP_126341.1 (Pyrococcus abyssi) and NP_579262.1 (Pyrococcus furiosus) were identified in hyperthermophilic archaea and are associated with NfeD-like module C-terminal to the protease domain (Table ATPases Associated with diverse cellular Activities) modules, which are one of the most diverse and promiscuous modules known to associate with diverse domains and function in a wide range of physiological processes [Clp proteases show an overwhelming preference for existence as single domain polypeptides. Only three of 121 Clp protease homologues identified in the current study were found to carry additional domains  and the Sigma54_activat-AAA-S16 in gene products such as YP_183677.1 (Thermococcus kodakaraensis) (Table Methanococcus jannaschii). An archaeal Lon protease-like protein NP_578196.1 (Pyrococcus furiosus) carries a Magnesium chelatase and a Sigma54_activat domain N-terminal to the predicted Lon protease domain  N-terminal to Lon protease domain, which may facilitate interactions with specific substrates  gene products were found to retain co-existing domains and display a conserved domain architecture  suggests their probable functions as virulence factors and in antibiotic resistance. Interestingly, while trypsins are also well represented in the eukaryotes, subtilisins (with the exception of plants) and DD-peptidases are less abundant in higher organisms suggesting that such enzymes were likely lost during the evolution as an adaptation to the cellular (and the extracellular) environment in the eukaryotes. Phylogenetic analysis suggests putative lateral transfer of serine protease genes between different bacterial and archaeal species and also between some bacteria and archaea. It is likely that some serine protease-like proteins may have been disseminated in the different prokaryotic species through probable horizontal gene transfer events. The lateral transfer of the serine protease genes in bacteria may possibly confer an evolutionary advantage on the recipient [Genome-wide studies reveal a large number of serine proteases belonging to the trypsin, subtilisin, DD-peptidase, Clp protease and Lon protease families in prokaryotes. However, there is only a limited knowledge available about their probable biological functions. Trypsins, subtilisins and the DD-peptidases have a higher number of representatives than the Clp protease and the Lon protease families in the genomes considered for the present analysis. The differences in the representations of the five serine protease families probably arose due to the selection of specific classes of serine proteases during evolution as an adaptation to different cellular and extracellular environments. For instance, the high abundance of the trypsins and the subtilisins in ecipient .etc were found tethered to the serine protease domain. Addition of new domains would permit these enzymes to acquire new functions and specificities contributing to the functional diversities of these gene families. However, a lack of significant repertoire of accessory domains does not necessarily indicate lack of functional diversity. Enzyme families may adopt alternative mechanisms to expand their functional repertoire, such as associating with limited but functionally diverse modules and other proteins or effecting changes in key amino acid residues. For instance, the Clp proteases form extensive complexes with the functionally diverse AAA+ modules that would enable them to modulate various physiological processes. The presence of multiple copies of the same accessory domain  in many serine protease-like proteins is another likely approach to expand their functional repertoire. Some domain combinations  were found to be widespread and conserved in prokaryotes suggesting a critical roles. Unique domain combinations of some prokaryotic serine protease-like proteins suggest their involvement in species-specific functions. Several domain architectures identified in prokaryotic serine proteases in the present analysis are very different from those reported in eukaryotic serine proteases. This highlights the distinct biological roles for the prokaryotic serine proteases compared to those in the eukaryotes. Some of these prokaryotic serine protease-like proteins with atypical domain combinations are attractive targets for experimental characterisation. Some pathogen peptidases identified in the present analysis with no identifiable homologues (unique domain architectures) in their hosts may be promising drug targets [Streptococcus pneumoniae, a human pathogen, are postulated to function in pathogenesis based on the domains associated with the serine protease domain. Some serine protease-like proteins such as NP_967057.1 (CUB domain) in Bdellovibrio bacteriovorus and YP_374752.1 in Pelodictyon luteolum (Sel1 repeats) are associated with domains similar to eukaryotic signalling modules with no known functions in prokaryotes. A systematic deletion of the one or more co-existing domains in the gene products with atypical domain combinations and the resulting phenotypes may help understand their roles in pathogenesis and other prokaryotic physiological processes and the role of the co-existing domains in modulating the functions of these serine proteases. Similarly, a phylogenetic cluster of the trypsin-like proteins (such as NP_302493.1 in Mycobacterium leprae) that contain a Colicin_V domain known to function in pathogenesis, tethered C-terminal to the protease domain, suggests an acquisition of the unique patterns in the interface region of the trypsin domain in these gene products. Identification of the conserved domain-domain interface regions and mutagenesis may help understand the function of these gene products and the role of the interactions between the adjacent domains.In the absence of the experimental characterisation for the most of the proteins sequences, an approximation of their biological functions is often inferred based on their sequence similarities to the proteins of known function. Studies have shown that the overall biological functions and the interactions of the multi-domain proteins are conserved by the retention of the domain composition and sequential arrangement . Therefo targets . For exaThe systematic analysis of the five serine protease families in the representative prokaryotic genomes is expected to enable a better understanding of the previously uncharacterised serine proteases encoded in the various genomes. The numbers of the serine protease-like proteins is likely to increase with the increasing amounts of the prokaryotic genomic data and the present analysis should help provide paradigms that would be useful in extending such analyses to a broader repertoire of the prokaryotes. The diversity of the functional domains co-existing with the protease domain in the serine protease-like proteins has provided clues to their biological functions, much of which are yet to be characterised experimentally. Experimental characterisation of some of these gene products as proposed here may help uncover the specific functional roles for the serine proteases in various cellular and physiological processes and help understand their influence on growth and development in the prokaryotic species.ATP: Adenosine triphosphate; NCBI: National Center for Biotechnology InformationLT and RS conceived of the study and participated in its design and coordination. LT carried out the computational sequence analysis. LT authored the first draft of this manuscript and RS provided comments and revisions to the final version of this text. Both authors read and approved the final manuscript.An inventory of the serine protease-like proteins belonging to the five chosen families identified in the various prokaryotic genomes and their inferred domain architectures. An inventory of serine proteases belonging to five chosen families identified in various prokaryotic genomes using the multi-fold approach (see Methods for details) and the details of co-existing domains (adjacent to the serine protease domain) identified for each putative serine protease.Click here for fileA list of the various prokaryotic genomes considered for the present study. The species abbreviations employed in the illustrations of the phylogenetic trees accompany the species name in parentheses. A list of the genomes in which serine protease-like proteins were identified in the current study. The genomes have been categorized accoding to their taxonomic lineages and the abbreviated species IDs (in parenthesis) that have been suffixed to the protein identifiers in the phylogenetic trees accompany the species name.Click here for file