Domains are modules within proteins that can fold and function independently and are evolutionarily conserved. Here we compared the usage and distribution of protein domain families in the free-living proteomes of Archaea, Bacteria and Eukarya and reconstructed species phylogenies while tracing the history of domain emergence and loss in proteomes. We show that both gains and losses of domains occurred frequently during proteome evolution. The rate of domain discovery increased approximately linearly in evolutionary time. Remarkably, gains generally outnumbered losses and the gain-to-loss ratios were much higher in akaryotes compared to eukaryotes. Functional annotations of domain families revealed that both Archaea and Bacteria gained and lost metabolic capabilities during the course of evolution while Eukarya acquired a number of diverse molecular functions including those involved in extracellular processes, immunological mechanisms, and cell regulation.  Proteins are made up of well-packed structural units referred to as domains. Domain structure in proteins is responsible for protein function and is evolutionarily conserved. Here we report global patterns of protein domain gain and loss in the three superkingdoms of life. We reconstructed phylogenetic trees using domain fold families as phylogenetic characters and retraced the history of character changes along the many branches of the tree of life.  Proteins are biologically active molecules that perform a wide variety of functions in cells. They are involved in catalytic activities (e.g. enzymes), cell-to-cell signaling (hormones), immune response initiation against invading pathogens (antibodies), decoding genetic information (transcription and translation machinery), and many other vital cellular processes . Proteins carry out these functions with the help of well-packed structural units referred to as domains. Domains are modules within proteins that can fold and function independently and are evolutionarily conserved de novo domain generation Different mechanisms have been described to explain the evolution of domain repertoires in cells DrosophilaProchlorococcus (a genus of cyanobacteria) Burkholderia (a genus of proteobacteria) RickettsiaIn addition to the frequent reuse of domains, the dynamics between gains and losses also impacts the evolution of proteome repertoires abundance  occurrence (presence or absence) Here, we describe the evolutionary dynamics of protein domains grouped into fold families (FFs) and model the effects of domain gain and loss in the proteomes of 420 free-living organisms that have been fully sequenced and were carefully sampled from Archaea, Bacteria, and Eukarya . The 420The analysis of retracing the history of changes in the occurrence and abundance of FF domains on each branch of the reconstructed ToLs revealed that FFs were subject to high rates of gains and losses. Domain gains generally outnumbered losses but both occurred with high frequencies throughout the evolutionary timeline and in all superkingdoms. Remarkably, the gains-to-loss ratios increased with evolutionary time and were relatively higher in the late evolutionary periods. Finally, functional annotations of FFs illustrated significant differences between superkingdoms and described modern tendencies in proteomes.E-value cutoff of 10−4concise classification strings (css) .The 420-proteome dataset used in this study included proteomes from 48 Archaea, 239 Bacteria, and 133 Eukarya. The dataset did not include any parasitic organisms as they harbor reduced proteomes and bias the global phylogenomic analyses e.g. . FFs werabundanceoccurrenceabg) were log-transformed and divided by the logarithm of maximum value in the matrix (maxg) to account for unequal proteome sizes and variances (see formula below) abundance trees. For occurrence trees, we simply used 0 and 1 (indicating absence and presence) as the valid character state symbols. We polarized both abundance and occurrence trees using the ANCSTATES command in PAUP* and designated character state 0 as the ancestral state, since the most ancient proteome is closer to a simple progenote organism that harbors only a handful of domains We considered the genomic abundance and occurrence trees, we used the nodal module implemented in the TOPD/FMTS package ver. 3.3 To determine congruence between f-value), defined by the total number of proteomes encoding a particular FF divided by the total number of proteomes. The f-value ranges from 0  to 1 (complete presence).The spread of each FF was given by its distribution index  is given on a relative scale and portrays the origin of FFs from 0 (most ancient) to 1 (most recent). The geological ages of FFs were derived from a molecular clock of protein folds nd correlates with geological time, following a molecular clock that can be used as a reliable approximation to date the appearance of protein domains To determine the relative age of FF domains in our dataset, we reconstructed trees of domains (ToDs) from the gin e.g. . The agemetabolism, ii) information, iii) intracellular processes, iv) extracellular processes, v) general, vi) regulation and vii) other . We assumed that FFs grouped into an FSF performed the same function that was assigned to their parent FSF. While this simplistic representation does not demonstrate the complete functional capabilities of a cell, it is sufficient to illustrate the major functional preferences in proteomes  to study the functional roles of FF domains in our dataset http://supfam.org/SUPERFAMILY/dcGO) resource and the most specific and significant associations to GO terms corresponding to different biological processes were retrieved. The statistical significance was evaluated by P-value computed under the hypergeometric distribution We conducted a GO enrichment analysis We first describe the patterns of FF use and reuse in superkingdoms and then build on this knowledge to infer the meanings of domain gain and loss in proteomes.A Venn diagram describes the sharing patterns of 2,397 FFs in seven Venn distribution groups . For simnd) for FFs in each taxonomic group to determine the order of their evolutionary appearance (nd = 0 in the ABE taxonomic group (nd of 0.24 (nd = 0.15), AB (0.26), B (0.26), E (0.551), A (0.555), and AE (0.57) taxonomic groups, in that order (We plotted the distribution of domain ages (pearance  (see Metpearance . The firic group . ABC tra of 0.24 . This suat order .nd = 0.15 (roughly >3.2 billion [Gyrs] years ago) with the complete loss of the ‘Heat shock protein 90, HSP90, N-terminal domain’ (d.122.1.1) FF in Archaea (nd = 0.26 (∼2.8 Gyrs ago), while both Archaea and Eukarya acquired unique FF domains concurrently at around nd = 0.55 (∼1.6 Gyrs ago) (early (0≤nd<0.15), a period before the start of reductive evolution in the archaeal superkingdom, (ii) intermediate (0.15≤nd<0.55), a period marked by early domain discovery in Bacteria, and (iii) late (0.55≤nd≤1), a period during which simultaneous diversification of Archaea and Eukarya occurred  . Emergenoccurred .f-value representing the fraction of proteomes encoding an FF. The median f-value decreased in the order, ABE>AE>E>BE>AB>A>B (f = 0.58). The FFs in AE and E were also distributed with higher f-values (median f = 0.54 and 0.27). In contrast, most of the bacterial taxonomic groups  had lower median f-values . The Venn diagram indicated that ∼22% of the total FFs were bacteria-specific   . This imA recent study proposed concepts of economy (i.e. organism budget in terms of number of unique genes and domain structures), flexibility  and robustness (ability to resist damage and change) to help explain the persistence strategies utilized by organisms in the three superkingdoms nd)  outnumbered FFs in other functional categories  . Metaboltegories . These rtegories . However Eukarya . The disnd vs. f plots for the seven taxonomic groups , informational (red circles), or with intracellular roles (light blue circles)   and that the sister relationship between Archaea and Eukarya inferred from the 16S rRNA trees f-values (We explored the order of evolutionary appearance of molecular functions by generating c groups . The ABE nd-axis . In factunctions . This suunctions . Howevercircles) . BE folld = 0.15 . This ald = 0.15 . This obd = 0.15 , just lither FFs . Most off-values  indicatial (red)  and regual (red) . This vaf-values .late epoch at nd≥0.55 and were generally distributed with lower f-values  that were involved in regulation (green circles) and extracellular processes (blue circles) and were distributed with relatively high f-values in the eukaryal proteomes (FFs unique to Archaea (A) appeared in the f-values . The dis domains . A largey epochs . We noteal roles . Lastly,te epoch , just lite epoch . Eukaryaroteomes .nd≥0.55, during and after the rise of the E taxonomic group, including the ‘Mitochondrial resolvase ydc2 catalytic domain’  and the ‘Mitochondrial cytochrome c oxidase subunit VIIb’  FFs . A formal test of this hypothesis is warranted and will be explored in a future study. The exercise also revealed that the lower median f-values observed earlier  also appeared during this period and thus affected the overall medians.Superkingdom-specific FFs appeared in both Archaea and Eukarya at around the same time, and both showed a tendency to become widespread in species . In cont.59) FFs . Thus, o.59) FFs  that acq earlier  were dueabundance (occurrence (Thermofilum pendens and Cand. Korarchaeum) (g1 = −0.33 and −0.28). Overall, phylogenomic patterns resembled traditional groupings and supported previous analyses of similar kind We generated rooted ToLs from bundance  and occucurrence  counts orchaeum) . The arcrchaeum)  and is irchaeum) . Bacterirchaeum)  and 78% rchaeum)  BS suppooccurrence trees.To quantify the relative contributions of domain gains and losses impacting the evolution of superkingdoms, we retraced the history of character state changes (i.e. changes in the abundance or occurrence of FFs) on each branch of the reconstructed ToLs. For each FF domain, we counted the number of times it was gained and lost in different branches of the phylogenetic tree. Gains were recorded when the abundance/occurrence of a particular FF at a node was higher than the corresponding value at the immediate ancestral node. In contrast, losses were incremented when the abundance/occurrence of a particular FF at a node was lower. Because we allowed character changes in both forward and backward directions (Wagner parsimony), each FF character could be both gained and lost a number of times across the many branches of the ToL. This assumption is reasonable as different lineages of organisms utilize domain repertoires differently. Because abundance counts are expected to be higher in the eukaryotic species  due to increased gene duplication events and a persistence strategy that favors flexibility and robustness  [64], weabundance model, 1,955 (86%) were gained, while only 236 (10%) were lost  and 30.5%  as lost  losses were common to both models. This suggested that abundance included nearly all the occurrence gains and likely overestimated the number of gains (due to gene duplications and domain reuse). In contrast, occurrence led to more balanced distributions and likely overestimated losses (read below).To evaluate the performance of both models, we first compared the number of FFs that were gained (i.e. net sum above zero) and lost (net sum below zero) in both reconstructions. Out of the total 2,397  FF domains in the ere lost . In cont as lost . Nearly Total , Archaea , Bacteria  and Eukarya . For each of the four trees, we calculated the sum of gain and loss events for all parsimony informative FF characters and represented the values in boxplots (abundance (abundance) and occurrence (occurrence) models. The exception was the eukaryal tree pruned from the occurrence model, for which the median was exactly zero. The result revealed that while both gains and losses occurred quite frequently, the former was more prevalent in proteome evolution.To provide additional support to the gain/loss model, we pruned taxa from the original ToLs leaving only one superkingdom and recalculated character state changes on the pruned trees. This eliminated any biases resulting from the differences in the persistence strategies of the three superkingdoms and yielded four phylogenetic trees, boxplots . In all Total dataset. When plotted against evolutionary time (nd), results highlighted remarkable patterns in the evolution of domain repertoires. Domain gains outnumbered losses in both abundance  and occurrence  tree reconstructions , Bacteria , and Eukarya  (abundance). The overall gain to loss ratios decreased from 2.30 in Archaea to 1.81 in Bacteria and 1.62 in Eukarya (abundance). The increased gain-to-loss ratios in akaryotic microbial species are remarkable; it implies that the rate of gene discovery in akaryotic microbes  is higher than the rate in eukaryotes. This tendency in microbial species could be a novel ‘collective’ persistence strategy to compensate for their economical proteomes. For histograms representing occurrence models, global gain-to-loss ratios decreased in the order, Archaea>Bacteria>Eukarya (occurrence). Remarkably, the ratio in Eukarya dropped below 1 indicating prevalence of domain loss events relative to gains. This result supports recent studies that have proposed the evolution of newly emerging eukaryal phyla via genome reduction We note that different evolutionary processes may be responsible for shaping the proteomes in individual superkingdoms. For example, the origin of Archaea has been linked to genome reduction events early, intermediate, and late evolutionary epochs, the gain-to-loss ratios exhibited an approximately linear trend towards increasing gains  that were compensated by significantly higher amounts of gains (1.68) in the late epoch. In contrast, abundance failed to illustrate this effect and indicated overwhelming gains in all evolutionary epochs.When partitioned into the ng gains . For abuectively . In factectively . Remarkaabundance and occurrence models. For example, Archaea exhibited gain-to-loss ratios of 2.06 and 2.14, in comparison to 1.26 and 1.39 in Bacteria, and 1.55 and 1.67 in Eukarya for early and intermediate evolutionary epochs (abundance). In contrast, Bacteria exhibited an overwhelming gain-to-loss ratio of 2.88 in comparison to 2.67 in Archaea and 1.61 in Eukarya, in the late evolutionary epoch. Overall, the gain-to-loss ratios increased with evolutionary time in all superkingdoms with the sole exception of Eukarya that had a lower ratio in the late (1.61) compared to the intermediate (1.67) epoch (abundance).When looking at the individual epochs for pruned trees , we notioccurrence indicated similar trends but with relatively more balanced gain-to-loss ratios and still highlighted the abundance of domain gains in evolution. The individual ratios were 1.42, 1.66, and 2.44 in Archaea, 0.60, 0.91, and 2.61 in Bacteria, and 0.51, 0.95, and 0.95 in Eukarya (occurrence). Both Bacteria and Eukarya showed increased levels of ancient domain loss. However, Bacteria compensated this decrease by engaging in massive gain events during the late evolutionary epoch (ratio of 2.61). In contrast, Eukarya exhibited an even exchange between FF gain and loss events (ratio = 0.95) in both the intermediate and late epochs. Occurrence results also supported the evolution of Eukarya by gene loss, which is in line with recently published analyses Abundance also indicated this drop in gene discovery rate for recent domains in Eukarya. However, the drop appears to be compensated by increased duplications of other domains that lead to an increase in the overall number of domains that are gained (abundance). This apparent discrepancy can be explained by the power of both models in depicting true evolutionary relationships between organisms. Abundance accounts for a number of evolutionary processes such as HGT, gene duplication, and gene rearrangements while occurrence merely describes presence and absence of FFs and because of its more ‘global’ nature fails to illustrate a complete evolutionary picture  and lost (net sum below 0) directly from the pruned superkingdom trees. To eliminate any redundancy, we only kept FFs that were gained (or lost) in both abundance and occurrence of FF domains in proteomes. Standard character reconstruction techniques were then used to trace domain gain and loss events along the branches of the universal trees. Finally, molecular functions and biological processes of FFs were studied using traditional resources. The exercise revealed remarkable patterns:We report the evolutionary dynamics of gain and loss events of protein domain FFs in hundreds of free-living organisms belonging to the three cellular superkingdoms. Structural phylogenomic methods were used to reconstruct ToLs from genomic The tracing of character state changes along the branches of ToLs revealed that both domain gain and loss were frequent outcomes in proteome evolution. However, a global trend of gains was pervasive along the entire evolutionary timeline and in all superkingdoms  and 6. Rnd≥0.55) to the FF repertoires point to another interesting evolutionary adaptation of Archaea: the late discovery and sharing of FFs with other superkingdoms  to compensate for the initial evolutionary reductive trend. This secondary archaeal adaptation to offset ancient genome reduction events and the proteomic trends towards economy may also be occurring  in Bacteria, which also exhibited higher levels of gene discovery. In contrast, eukaryal species favored the reuse of already existing domains rather than engaging exclusively in novel domain discovery. Thus, akaryotic microbes persist by fostering trends towards economy while eukaryotic species favor patterns of more flexibility and robustness , highlighting ongoing secondary adaptations in the superkingdoms. In comparison, the BE taxonomic group was enriched in metabolic FFs  . Withoutnd = 0.15 represents a fundamental evolutionary event of complete loss of ancient FFs in the archaeal superkingdom  of 1,739 FSFs in 70 archaeal proteomes revealed that many of the ancient folds were completely absent in archaeal species. This hypothesis is strengthened by our data of minimal sharing of FFs in archaeal taxonomic groups , then by generating novelty in Bacteria (nd = 0.26), and finally by generating novelty and co-opting bacterial lineages as organelles in Eukarya (nd<0.55). The eukaryotic group was able to deploy massive structural and functional innovation despite concomitant streamlining, which we show spread through eukaryotic lineages at high frequency , as indicated by microfossil evidence and the molecular clock Our data is incompatible with fusion scenarios between akaryotic cells that are used to explain the origin of eukaryotes abundance and occurrence were congruent with respect to the overall tree topologies and general conclusions drawn from the analyses. Both supported the existence of overwhelming gains in evolution. However, discrepancies also existed especially in the numerical differences for the gain-to-loss ratios among superkingdoms. In general, abundance (apparently) overestimated gains while occurrence underestimated losses. The higher number of gain-to-loss ratios in abundance models is an expected outcome as we are accounting for evolutionary processes such as gene duplications, gene rearrangements, and HGT that are known to increase the representation of genes in genomes. Ancient genes have more time to multiply and increase their genomic abundance compared to newly emergent genes. In contrast, occurrence merely describes the presence or absence of genes and provides a simplified view of the overall landscape of change. Another explanation is the possible existence of methodological artifacts when dealing with genomic occurrence in parsimony analysis that excludes most of the ancient FFs as non-informative characters, when these are present in all proteomes. Moreover, occurrence fails to take into account the weighted contribution of ancient genes to the phylogeny and treats all characters equally. Thus trees built from abundance counts are better resolved at their base while trees built from occurrence behave poorly in this regard How reliable is our study? Both In our phylogenomic model, we rooted ToLs by character absence (i.e. 0) using the Lundberg method. We assumed that proteomes became progressively richer during the course of evolution. However, this implicit assumption did not lead to an increased number of domain gains as character state changes in both forward (e.g. 9 to 22) and reverse (12 to 5) directions were allowed and carried equal weights. Moreover, we evaluated the effects of ToL rooting on the calculations of domain gain and loss statistics by considering outgroup taxa instead of the Lundberg method. Superkingdom trees rooted with outgroup taxa led to similar tree topologies and supported the conclusion of overwhelming gains that we here report . HoweverWe used maximum parsimony to search for the best possible tree and described the evolution of 420 free-living proteomes using the entire repertoire of 2,397 FFs as phylogenetic characters. We note that parsimony is most appropriate (and gives superior performance) for this kind of analysis as it performs better when the characters are evolving under different evolutionary rates We propose that grouping of protein domains into FFs provides a reliable character for a global evolutionary analysis that involves large number of proteomes. FF domains are both sufficiently conserved and informative to explore the many branches on the ToLs. The age and distribution of FFs in organismal groups is biased and carries the power to unfold superkingdom history and explain important structural and functional differences among superkingdoms. Based on our data, we propose the primacy of domain gains over losses over the entire evolutionary period, ongoing evolutionary adaptations in akaryotic microbes, evolution of emerging eukaryotic species by domain loss, an early origin for Archaea, and endosymbiosis leading to mitochondria as a crucial event in eukaryote diversification. Each of these conclusions is important for reconstructing the evolutionary past and predicting evolutionary events in the future.Dataset S1Names and classification of organisms used in phylogenetic analyses.(XLS)Click here for additional data file.Dataset S2css, and the number of times each of the 2,262 parsimony informative FFs was gained/lost in abundance reconstruction. Eight FFs from the 420-proteome dataset were given new ids in SCOP 1.75 and were therefore renamed.Names, SCOP (XLS)Click here for additional data file.Dataset S3css, and the number of times each of the 2,249 parsimony informative FFs was gained/lost in occurrence reconstruction. Eight FFs from the 420-proteome dataset were given new ids in SCOP 1.75 and were therefore renamed.Names, SCOP (XLS)Click here for additional data file.Figure S1Thermus thermophilus (Deinococcus-Thermus) was used to root the archaeal tree while Methanocaldococcus jannaschii (Euryarchaeota) was used as outgroup for both Bacteria and Eukarya. The x-axes indicate evolutionary time (nd). Numbers in parenthesis represent the total number of taxa (proteomes) in each reconstruction, while n is the number of parsimony informative characters. Outgroup taxa were excluded from the calculations of gains and losses to eliminate any biases resulting from the artificial introductions of taxa into the dataset. Bars in red and blue indicate gains and losses respectively.Histograms displaying FF gain and loss dynamics for the phylogenetic trees rooted by the outgroup method. (TIF)Click here for additional data file.


Motivation: Burgeoning sequencing technologies have generated massive amounts of genomic and proteomic data. Annotating the functions of proteins identified in this data has become a big and crucial problem. Various computational methods have been developed to infer the protein functions based on either the sequences or domains of proteins. The existing methods, however, ignore the recurrence and the order of the protein domains in this function inference.Results: We developed two new methods to infer protein functions based on protein domain recurrence and domain order. Our first method, DRDO, calculates the posterior probability of the Gene Ontology terms based on domain recurrence and domain order information, whereas our second method, DRDO-NB, relies on the naïve Bayes methodology using the same domain architecture information. Our large-scale benchmark comparisons show strong improvements in the accuracy of the protein function inference achieved by our new methods, demonstrating that domain recurrence and order can provide important information for inference of protein functions.Availability: The new models are provided as open source programs at http://sfb.kaust.edu.sa/Pages/Software.aspx.Contact:dkihara@cs.purdue.edu, xin.gao@kaust.edu.saSupplementary information:Supplementary data are available at Bioinformatics Online. Proteins play vital roles in biological systems. Understanding their functions is one of the most important problems in biology today. Due to rapid advances in genomic sequencing techniques and computational gene identification, the number of explored protein sequences has increased dramatically. A number of experimental methods has been developed to predict the functions of proteins , and the other that relies on the naïve Bayes methodology (referred to as the DRDO-NB model).We tested our newly developed models on several large-scale benchmark datasets. The test results show that our models outperform to a great extent a number of state-of-the-art predictors of protein functions on the curated datasets. This supports our hypothesis of the importance of the domain recurrence and order information in inference of protein functions.Drosophila (An adaptor protein DRK (downstream of receptor kinase) is known to play an important role in sevenless receptor signaling in Q08012. A2AVZ2, a protein, contains one SH3 domain followed by one SH2 domain, whereas Q920I1, another protein, contains one SH2 domain followed by one SH3 domain. Neither A2AVZ2 nor Q920I1 has the GO term ‘GO:0045500: sevenless signaling pathway’, which belongs to DRK. The pairwise molecular function (MF) similarity when measured by simGIC  receptor  from the GO terms of proteins that are associated with subsets of these domains up to M −1 domains. Thus, in order to predict the function, i.e. GO terms, of a query protein that has M domains, we use the GO terms for all proteins that have up to M −1 of these domains and have GO annotation. Given a query protein, the domains are assigned to it based on information from the SwissPfam database  denotes probability and G. G given the domain set D. By using the conditional independence assumption, which says that the distinct sets in which P(D|G) and α can be estimated asiD's are the subsets of D that contain sequentially consecutive domains, and K represents the number of such subsets iD, such that K = M (M + 1)/2–1, where M is the number of domains in D. All the probabilities can be estimated by maximum likelihood estimation, i.e. counting the frequency in the training set. In contrast to the cross-validation training procedure for traditional machine learning methods, the training of our model follows that of M domains, proteins with up to M – 1 domains are used as the training set. For instance, given a query protein with three domains, GO terms for each single domain are extracted from the UniProtKB database. Then the GO terms for each subset of two consecutive domains are predicted by using GO terms for single domains according to Given a domain set, D. That is, the domain set in the FS model is actually the unordered set of distinct domains in D. Therefore, for a domain set D = A*B*B*A*C, the corresponding iD subsets are {A}, {B}, {C}, {AB}, {AC}, {BC} and {ABC}.Note that our model generalizes and extends a related model proposed in A*B is assumed to have the same functions as another protein with the domain architecture B*A. This assumption is not always true as shown by the first example presented in A*A*A is assumed to have the same functions as proteins with domain architectures A*A or A. This assumption, again, is not always true according to both examples presented in Section 2.The FS model has two drawbacks. First, the model does not consider the order of the domains. That is, a protein with a domain architecture D. According to the FS model, D consists only of distinct domains. We can therefore preserve the domain recurrence and order information relatively well by defining D as the original set of all the domains in the protein and by defining the remaining orders of the domains when enumerating the iD subsets. That is, when we enumerate the subsets, we require each subset to contain only the sequentially neighboring domains on the protein. Therefore, given a protein with domain architecture D = A*B*B*A*C, the corresponding subsets are {A}, {B}, {B}, {A}, {C}, {AB}, {BB}, {BA}, {AC}, {ABB}, {BBA}, {BAC}, {ABBA} and {BBAC} (see the difference in subset construction presented above).It is clear that the main source of both of these drawbacks is the construction of the domain set, iD's because a domain can appear several times in a protein. Therefore, the conditional independence assumption may not hold. In fact, this is also an issue in the FS model. Even if only the distinct domains are used, the subsets are still not conditionally independent because they share common pairwise domains. Forslund and Sonnhammer tried different methods to reduce the Bayesian naïvete in their model. The best way was to normalize the odds ratio α by the size of the domain set, K, as shown in α as shown in Our hypothesis raises two intuitive concerns. The first is that if the original domain set is used for enumeration, there will be many recurring N – 1, where N is the number of distinct domains in the protein. For our case, because we only consider the sequentially neighboring domains, the number of subsets is M (M +1)/2–1, where M is the total number of domains in the protein. As shown in N = 11 in the FS model. Further details about the practical runtime are discussed in Supplementary Materials.The second concern is that since we enumerate all the subsets that contain the sequentially neighboring domains, of the original domain set, the number of subsets might be too large. In fact, the number of subsets enumerated by k = 2) as shown in We start first by illustrating our naïve Bayes model with pairwise dependency . It is thus possible to combine the prediction results from DRDO and DRDO-NB to further enhance the accuracy.The main problem with sequence-based function predictors is that the number of false positives is high. Domain-based predictors, on the other hand, can achieve much higher precision. Therefore, an ideal protein function annotation predictor should be able to encode both the amino acid information and the domain information. We are currently exploring how to use this sequence information in combination with our proposed models. Another possible improvement to our models can be achieved by using the spatial positions of the domains with predicted or native tertiary structural information. In addition, we are also trying to consider the hierarchical nature of GO terms as a directed acyclic graph (As shown in The main focus of this article is to demonstrate that domain recurrence and order is important for predicting protein functions. Previous methods showed that considering domain architecture improves prediction over conventional sequence similarity-based methods. Here, we show that domain recurrence and order further enhance protein function inference.


Gag protein, and stimulate the budding of viral Gag proteins. The curved Bro1 domain structure has also been proposed to mediate membrane bending. To date, crystal structures have only been available for the related Bro1 domains from the Bro1p and ALIX proteins, and structures of additional family members should therefore aid in the identification of key structural and functional elements.Bro1 domains are elongated, banana-shaped domains that were first identified in the yeast ESCRT pathway protein, Bro1p. Humans express three Bro1 domain-containing proteins: ALIX, BROX, and HD-PTP, which function in association with the ESCRT pathway to help mediate intraluminal vesicle formation at multivesicular bodies, the abscission stage of cytokinesis, and/or enveloped virus budding. Human Bro1 domains share the ability to bind the CHMP4 subset of ESCRT-III proteins, associate with the HIV-1 NCWe report the crystal structure of the human BROX protein, which comprises a single Bro1 domain. The Bro1 domains from BROX, Bro1p and ALIX adopt similar overall structures and share two common exposed hydrophobic surfaces. Surface 1 is located on the concave face and forms the CHMP4 binding site, whereas Surface 2 is located at the narrow end of the domain. The structures differ in that only ALIX has an extended loop that projects away from the convex face to expose the hydrophobic Phe105 side chain at its tip. Functional studies demonstrated that mutations in Surface 1, Surface 2, or Phe105 all impair the ability of ALIX to stimulate HIV-1 budding.Our studies reveal similarities in the overall folds and hydrophobic protein interaction sites of different Bro1 domains, and show that a unique extended loop contributes to the ability of ALIX to function in HIV-1 budding. Membrane remodeling is an essential component of many important biological processes, including cell division, membrane protein trafficking, and enveloped virus budding. Membrane deformations are typically controlled by peripheral membrane binding proteins that help to shape membrane and catalyze fission/fusion events e.g., . One impThe human ESCRT pathway comprises more than 30 proteins that function in cargo sorting, membrane remodeling, and membrane fission. The recruitment of soluble ESCRT factors to different membranes begins when site-specific adaptor complexes bind directly to early-acting ESCRT factors such as ALIX (Bro1p in yeast) and/or the ESCRT-I complex. These factors help to concentrate protein cargoes, stabilize the necks of nascent vesicles, and recruit downstream factors. The late-acting ESCRT-III and VPS4 factors then act together to mediate membrane fission. The ESCRT-III subunits form helical  rings within the bud necks and recruit the VPS4 ATPase, which in turn uses the energy of ATP hydrolysis to remodel the ESCRT-III proteins, resulting in membrane fission and ESCRT factor release.nL sequences (where X represents any residue and n = 1–3) within structural viral Gag proteins ALIX plays particularly important roles in abscission In addition to ALIX, the human proteome contains four other known Bro1 domain-containing proteins: HD-PTP  In addition to connecting membrane-specific adaptors to the downstream ESCRT-III fission machinery, there are indications that Bro1 domains may make additional interactions that promote cargo sorting and membrane remodeling. For example, the Bro1 domains of ALIX, HD-PTP, BROX, and RHPN1 can bind the NC domains of HIV-1 Gag proteins and stimulate the release of virus-like particles To date, high-resolution structural information has only been available for the Bro1 domains from yeast Bro1p 6-BROX was expressed in E. coli, enriched by Ni2+ affinity chromatography, treated with TEV protease to remove the (His)6 tag, and then purified to homogeneity by anion exchange and gel filtration chromatography. The construct used in our studies spanned BROX residues 1–401, and therefore lacked 10 C-terminal residues (402–411), including the farnesylated Cys408 residue. The 47 kDa protein eluted from a gel filtration column as a single species with an apparent molecular weight of 57 kDa. This result indicated that recombinant BROX is a monomer because the protein has an elongated structure that will tend to increase its apparent molecular weight slightly (see below). In this regard, BROX apparently differs from ALIX, which can dimerize in an antiparallel orientation, with the V domains associating centrally and the Bro1 domains displayed at either end of the crescent-shaped complex Human (His)BROX crystallized in space group C2 with three molecules in the asymmetric unit. The structure of the selenomethionylated protein was solved at 2.7 Å resolution using the MAD method, and the final structure was refined against a 2.5 Å resolution data set collected from crystals of the native protein . The eleBROX forms a single curved domain of overall dimensions: 90×37×38 Å. As expected, the structure resembles the Bro1 domains of ALIX and Bro1p A structure-based sequence alignment of the Bro1 domains from Bro1p, ALIX and BROX is provided in 408CYIS411 farnesylation site. 3) The detailed conformations of the helical bundles at the wide end of the domain differ in all three structures  only the ALIX Bro1 domain has an extended loop between β strands 1 and 2 that projects away from the convex face . 2) The ructures .Hurley and colleagues have proposed that Bro1 domains bind membranes and generate negative curvature by wrapping the membrane along the basic convex surface 205–224). As shown in 205–224, but not to a control GST surface. The dissociation constant for the BROX-CHMP4B205–224 interaction was 98±22 µM, which is similar to that of the ALIX Bro1-CHMP4B205–224 interaction (48±6 µM) Previous biochemical and structural studies have revealed that C-terminal helices from the CHMP4 family of ESCRT-III proteins bind across Surface 1 of the ALIX Bro1 domain  [36]. Im11PTAP14 late domain sequence in the p6Gag protein cannot recruit the ESCRT-I complex normally, and are therefore only released efficiently when functional ALIX proteins are overexpressed NL4-3 (ΔPTAP) vector, as assayed by the increased levels of virion-associated HIV-1 CA proteins released into the culture supernatant (I212D) stimulated virion release and infectivity much less  stimulated HIV-1 virion release and infectivity less effectively than the wild type ALIX protein , even though the mutant and wild type proteins were expressed at similar levels , and 2) wild type HIV-1 vectors .As illustrated in Together with previous work, our studies demonstrate that at least three different structural elements within the Bro1 domain of ALIX contribute to the protein's ability to function in HIV-1 release. Specifically, we have confirmed the functional importance of Surfaces 1 activate (rather than inactivate) ALIX because Src binding and phosphorylation of Tyr319 reportedly antagonize ALIX activity The functions and/or binding partners of Surface 2 and the extended Phe105 loop are not yet known with certainty. Surface 2 has been implicated in binding Src kinases E. Coli in 2 L of ZYP-5052 auto-induction media 2+ matrix, washed with 20 column volumes of lysis buffer, and eluted with 50 mM Tris pH 8.0, 100 mM NaCl, 5% glycerol, and 250 mM imidazole. Fractions containing the protein were pooled and treated with 0.2 mg TEV protease while being dialyzed against 2 L 25 mM Tris pH 8.0, 100 mM NaCl, 1 mM DTT, and 5% glycerol . The cleaved protein was collected as flow-through from a second Ni2+ resin column and applied to a HiTrap Q Sepharase Fast Flow Column , and eluted by a NaCl gradient from 25 mM to 1 M . Finally, the protein was purified to homogeneity by size exclusion chromatography . This procedure typically yielded 20 mg of pure monomeric native BROX.BROX coding sequence was amplified from the human open reading frames (ORFs) from the Mammalian Genome Collection free values of 19.2/25.5  were grown at 13°C in sitting drops  and cryoprotected in a solution of reservoir components made up with 30% glycerol. Diffraction data were collected from SeMet protein crystals to 2.5 Å resolution at three wavelengths at beam line X-29 of the National Synchrotron Light Source and processed using HKL2000 9.2/25.5 .205–224E. coli expression lysates. Pure BROX proteins were injected in triplicate in a 1.5-fold dilution series, starting at 400 µM. All interactions reached equilibrium rapidly and dissociated within seconds during the dissociation phase. Equilibrium binding responses were fit to 1∶1 binding isotherms to obtain equilibrium constants.Binding experiments were performed using a Biacore 2000 instrument. CM5 sensor chips were derivatized with anti-GST antibody using amine coupling and were used to capture GST-CHMP4B5 cells/well in 6-well plates) following calcium-phosphate (Clontech) co-transfection of the following plasmids: 1.35 µg of an HIV-1 RΔ8.2 construct (either wild-type (WISP04-151) or ΔPTAP (WISP04-150)), 1.35 µg pLOX-GFP I212D (WISP11-401), ALIXF105Y (WISP11-402), ALIXI318A (WISP11-403), or ALIXY319A (WISP11-404)). The medium (2 ml/plate) was replaced 8 hr after transfection, and the supernatant was harvested 16 hr later and syringe-filtered through 0.45 µm membranes. Cells were harvested with cold PBS and lysed in 200 µL of RIPA ). The supernatant was collected and adjusted 1∶3 with 3X SDS loading buffer. Virions were collected by pelleting 1 mL of medium through a 250 µL 20% sucrose cushion. The supernatant was removed and the virion pellet was dissolved in 60 µL of 2X SDS. Western blots were performed on the cell and virion samples using anti-CA, 1∶3000  and anti-FLAG, 1∶3000  and analyzed using an Odyssey imaging system . Vector titers were determined using serial dilutions of vector preparations added to 293T target cells (2×105 cells/well in 6-well plates) for 12 h at 37°C. The medium was replaced with normal culture medium for 60 h at 37°C, and the transduction efficiency, determined as the percentage of green fluorescent protein (GFP)-positive cells, was measured using flow-cytometry.HIV-1 virions were produced using a vector system and analyzed using methods similar to those described previously


A central challenge in host-pathogen systems biology is the elucidation of general, systems-level principles that distinguish host-pathogen interactions from within-host interactions. Current analyses of host-pathogen and within-host protein-protein interaction networks are largely limited by their resolution, treating proteins as nodes and interactions as edges. Here, we construct a domain-resolved map of human-virus and within-human protein-protein interaction networks by annotating protein interactions with high-coverage, high-accuracy, domain-centric interaction mechanisms: (1) domain-domain interactions, in which a domain in one protein binds to a domain in a second protein, and (2) domain-motif interactions, in which a domain in one protein binds to a short, linear peptide motif in a second protein. Analysis of these domain-resolved networks reveals, for the first time, significant mechanistic differences between virus-human and within-human interactions at the resolution of single domains. While human proteins tend to compete with each other for domain binding sites by means of sequence similarity, viral proteins tend to compete with human proteins for domain binding sites in the absence of sequence similarity. Independent of their previously established preference for targeting human protein hubs, viral proteins also preferentially target human proteins containing linear motif-binding domains. Compared to human proteins, viral proteins participate in more domain-motif interactions, target more unique linear motif-binding domains per residue, and contain more unique linear motifs per residue. Together, these results suggest that viruses surmount genome size constraints by convergently evolving multiple short linear motifs in order to effectively mimic, hijack, and manipulate complex host processes for their survival. Our domain-resolved analyses reveal unique signatures of pleiotropy, economy, and convergent evolution in viral-host interactions that are otherwise hidden in the traditional binary network, highlighting the power and necessity of high-resolution approaches in host-pathogen systems biology. The goal of host-pathogen systems biology is to examine the complex interactions between species, such as those between a virus and its host. Analysis of protein-protein interaction (PPI) networks can identify general principles that distinguish between within-species and between-species interactions. However, PPI data are limited by their low resolution, and cannot provide detailed information about the physical mechanisms underlying interactions between proteins. Using protein domain-based annotation methods, we have constructed an integrated human-virus PPI network which better highlights the mechanistic differences between human-human and human-virus PPIs. Our findings suggest that viral proteins use unique strategies to interact with human proteins, a finding with significant implications for pathogen research. The most well-studied host-pathogen interaction systems are host-virus interactions, and the combined results of decades of detailed studies on specific host-virus interactions suggest that such global principles may exist. Endogenous interactions among host proteins are expected to be cooperative: proteins encoded within the same genome interact with one another to carry out biological function in a coordinated and synergistic fashion. On the contrary, exogenous interactions between viral proteins and host proteins are expected to be largely antagonistic: viruses physically manipulate host cell machinery to perpetuate their genomes at the host's expense. In addition to hijacking host macromolecular complexes to make new viral products, viruses are known to modulate the host response to infection in order to escape detection and prevent the host from interfering with viral replication e.g. marking them for degradation by the host's own machinery Protein-protein interactions (PPIs) can be broadly classified into two fundamentally different classes: those within the same species, such as within-host PPIs, and those between different species, such as host-pathogen PPIs. Are there general, systems-level principles that distinguish host-pathogen PPIs  A systems biology approach is therefore essential in order to obtain a global perspective on host-pathogen interactions. Recent advances in high-throughput experimental and computational biology have enabled the reconstruction and analysis of large-scale host-pathogen PPI networks One approach to increase resolution in PPI networks has been to construct three-dimensional (3D) structural models to protein interactions In this work, we probe high-resolution principles governing exogenous and endogenous PPI networks using a domain-resolved approach that annotates proteins with known domains, and PPIs with known domain-centric interaction mechanisms  and within-human (endogenous) protein-protein interaction (PPI) networks by annotating proteins and PPIs with known domain information. We considered two major categories of domain-centric interaction mechanisms: domain-domain interactions (DDIs), in which a globular domain from one protein binds to a globular domain from a second protein  P<10−10; In the absence of 3D structural information, it is not possible to determine if two proteins bind to the same interface on a third protein. However, in our domain-resolved human-virus PPI network, it is possible to determine if two proteins bind to the same domain in the third protein , which iP<10−15; P = 0.012; Viruses have been known to use linear peptide motifs to target endogenous LMB domains Previous work has revealed a tendency for viral proteins to target host protein hubs We examined the effects of endogenous degree on the relationship between a human protein containing an LMB domain and the likelihood of that protein being a viral target. We stratified human proteins according to endogenous degree and then compared the probability of being a viral target among proteins with and without LMB domains . Consistconcordant if the LMB domain-containing protein was a viral target whereas the LMB domain-free protein was not, and discordant if the LMB domain-containing protein was not a viral target whereas the LMB domain-free protein was. We observed a strong preference for concordant protein pairs over discordant protein pairs (58% concordant versus 42% discordant), favoring a degree-independent association between LMB domain-containing proteins and viral targets. The degree-independent association between a human protein containing an LMB domain and being a viral target is statistically significant  having an LMB domain and (ii) being a viral target, among pairs of human proteins with the same degree. We picked a pair of proteins with the same degree in which one had an LMB domain while the other did not, and considered the pair The results of the previous section establish that viruses tend to preferentially target human proteins containing LMB domains by comparing properties of human proteins targeted by viruses against all other human proteins. Next, we determined if the viral preference for targeting LMB domain-containing proteins also holds at the level of PPIs, when comparing the fraction of domain-motif interactions (DMIs) between viral proteins and human proteins.P = 0.047; P = 0.018; We observed that viral proteins have higher fraction of DMIs out of total number of PPIs per protein than human proteins  is 34% smaller than the median human protein length (464 residues).P = 0.012; P = 0.049; We first calculated the density of unique LMB domains targeted per residue for viral proteins and human proteins. We found that viral proteins target a greater variety of unique LMB domains per residue than human proteins  networks to probe general, systems-level principles that distinguish host-pathogen, exogenous PPIs from within-host, endogenous PPIs. Annotation of proteins and interactions with known domain information yields a domain-resolved network with higher resolution and quality than the binary PPI network, and higher coverage than the 3D structural interaction network. Classification of endogenous and exogenous PPIs into domain-domain interactions (DDIs) and domain-motif interactions (DMIs) reveals global differences in domain interaction patterns between host-pathogen and within-host networks that are otherwise hidden in traditional binary PPI networks. While our domain-centric annotations reduce the rate of false positives in PPI networks, additional potential limitations include false negatives, incomplete annotation, and methodological biases. In this work, we have minimized the effects of such incompleteness and biases by carefully controlling for them when performing systematic comparisons between exogenous and endogenous networks. A potentially more significant limitation is investigator bias: most host-pathogen studies are conducted on clinically significant human pathogens, such as HIV. Despite this investigator bias, our exogenous network represents a wide variety of viral families . We empht-test, two-tailed P<0.0001). Additionally, considering only motifs in “disordered regions” .Our analyses reveal systematic, mechanistic differences between exogenous and endogenous interactions. The most pronounced of these differences is the tendency for viruses to mimic human interactions by means of convergent evolution. We find that viral proteins and human proteins tend to target the same domain of another human protein without any shared sequence similarity, extending the results of our previous work using 3D structural interaction networks Our results demonstrate that viral proteins and virus-host PPIs are in general very different from host proteins and within-host PPIs: viral proteins are small, complex, multifunctional polypeptides which can mediate multiple host-virus interactions, typically using the highly economical and highly pleiotropic method of domain-motif interactions largely through convergent evolution. These signatures of pleiotropy, economy, and convergent evolution in the virus-host PPI network are a direct consequence of the intense selection pressure on viruses to establish and maintain, with very limited genomic resources at their disposal, extensive and effective physical interactions with the host necessary for their survival. These global trends are applicable in general to viral proteins and exogenous interactions, and do not reflect a bias in a specific viral type, nor in a specific methodology for determining PPIs. Our results suggest that annotating viral proteins with domain-centric interaction mechanisms, especially by scanning viral protein sequences for linear motifs, can provide a novel approach to identifying host protein interaction partners for study. It may also be possible to use this domain-centric annotation approach to identify therapeutic treatments based on competition for motif binding sites. Thus, our study highlights the importance of a high-resolution, domain-resolved approach to host-pathogen network biology for revealing general mechanistic principles governing host-pathogen interactions.We collected reports of endogenous (human-human) protein-protein interactions (PPIs) from the IntAct database, and reports of exogenous (human-virus) PPIs from the IntAct and VirusMINT databases E-value cutoff of 10−2et al.We assigned Pfam domains to the human and viral proteins in our networks using the Pfam batch search utility, subject to an To assess the quality of the endogenous and exogenous networks, we compared them individually against a gold standard set of endogenous and exogenous PPIs; these subsets of interactions were constructed by querying for interactions that were reported by at least two independent publications.concordant if the LMB domain-containing protein was a viral target and the LMB domain-free protein was not a viral target. Conversely, the protein pair was discordant if the LMB domain-containing protein was not a viral target and the LMB domain-free protein was a viral target. All other protein pairs were considered to be non-informative. To evaluate the statistical significance of this test, we completed 1,000 repetitions of random permutation of the LMB domain and viral target annotations among sets of human proteins with the same endogenous degree and repeated our procedure.We measured concordance between having an LMB domain and being a viral target by picking pairs of human proteins with the same degree in which one had an LMB domain while the other did not. The protein pair was e.g. 1000), the fraction of permutations in which the random difference is at least as large as the observed difference approximates the probability of observing such a difference at random , and serves as a measure of the statistical significance of the observed measurement.For permutation-based comparisons of virus and human proteins, we first compute the mean of each group and then evaluate the difference between these means. To evaluate if such a difference is likely to arise at random, we repeatedly permute the “virus” and “human” protein labels and then calculate the difference in the means of the newly randomized groups. Over a large number of trials Click here for additional data file.Table S2This table lists the 17 unique human domains that are observed in viral proteins to mediate 30 domain-domain interactions with human proteins.(XLSX)Click here for additional data file.Table S3This table lists the endogenous domain-resolved PPIs. Included in the table are UniProt IDs, protein descriptions, and species annotation for the interacting protein pair. We also provide the classification of domain-centric interaction mechanism: (1) a human protein with a human domain binding to a second human protein with a human domain (“Human Domain : Human Domain”); and, (2) a human protein containing a human linear motif-binding domain, binding to a human protein containing the corresponding motif (“(Human Domain : Motif) OR (Motif : Human Domain)”). Interacting domains or domain-motif pairs are separated by a colon (:). If a protein has more than one pair of interacting domains or domain-motifs, they are separated by a vertical bar (|). Domains are listed by their Pfam annotation ID, and motifs are listed as a searchable regular expression.(XLSX)Click here for additional data file.Table S4This table lists the exogenous domain-resolved PPIs. Proteins are listed with human proteins followed by viral proteins, with UniProt ID, protein description, and species annotation for each protein. We have included the classification of domain-centric interaction mechanism: (1) a human protein with a human domain binding to a viral protein with a human domain (“Human Domain : Human Domain”); (2) a human protein with a human domain binding to a viral protein with a viral domain (“Human Domain : Virus Domain”); (3) a human protein containing a human linear motif-binding domain, binding to a viral protein containing the corresponding motif (“Human Linear Motif-Binding Domain : Virus Motif”); and, (4) a viral protein containing a human linear motif binding domain, binding to a human protein with the corresponding motif (“Human Motif : Human Linear Motif-Binding Domain”). Interacting domains or domain-motif pairs are separated by a colon (:). If a protein has more than one pair of interacting domains or domain-motifs, they are separated by a vertical bar (|). Domains are listed by their Pfam annotation ID, and motifs are listed as a searchable regular expression.(XLSX)Click here for additional data file.


Tobacco Joka2 protein is a hybrid homolog of two mammalian selective autophagy cargo receptors, p62 and NBR1. These proteins can directly interact with the members of ATG8 family and the polyubiquitinated cargoes designed for degradation. Function of the selective autophagy cargo receptors relies on their ability to form protein aggregates. It has been shown that the N-terminal PB1 domain of p62 is involved in formation of aggregates, while the UBA domains of p62 and NBR1 have been associated mainly with cargo binding. Here we focus on roles of PB1 and UBA domains in localization and aggregation of Joka2 in plant cells. We show that Joka2 can homodimerize not only through its N-terminal PB1-PB1 interactions but also via interaction between N-terminal PB1 and C-terminal UBA domains. We also demonstrate that Joka2 co-localizes with recombinant ubiquitin and sequestrates it into aggregates and that C-terminal part (containing UBA domains) is sufficient for this effect. Our results indicate that Joka2 accumulates in cytoplasmic aggregates and suggest that in addition to these multimeric forms it also exists in the nucleus and cytoplasm in a monomeric form. Autophagy is a highly evolutionary conserved process among all eukaryotic organisms. It is responsible for degradation of cellular components in ubiquitin-proteasome system (UPS) independent manner  domains and the C-terminal UBA (ubiquitin associated) domains of p62 and NBR1 as well as of their homologs from animals, fungi, and plants are recognized as modules mediating protein-protein interaction  motif composed of about 20-amino acid with highly conserved acidic and hydrophobic residues and/or lysine residue conserved on the first β-strand  domain and  linked to YFP were designed based on previous results . For bimolecular fluorescent complementation (BiFC) experiments the cell suspensions were adjusted to 4 × 108 cfu/ml and mixed 1:1 before infiltration. Young N. benthamiana plants with fully expanded leaves of about 5 cm in diameter were infiltrated by bacterial suspension using a needless syringe. Leaves were harvested and analyzed under confocal microscope 3 days after agroinfiltration.For transient co-expression of proteins in For staining of nuclei, prior the microscope analysis, agroinfiltrated leaves were incubated with a fluorescent dye DAPI (1 μg/ml) for 15 min in the darkness at room temperature. After the treatment, plant material was washed in water  and immediately observed in a confocal microscope. For LMB treatment plant material was incubated with leptomycin B (20 ng/ml) up to 24 h before observation. All images were obtained in the Laboratory of Confocal and Fluorescence Microscopy at IBB PAS using a Nicon confocal microscope, Eclipse TE2000-E and processed using EZ-C1 3.60 FreeViewer software. For GFP/YFP the 488-nm line from an Argon-Ion Laser (40 mW) was used for excitation, and a 500–530 nm band pass filter for detection of emission. For RFP the 543 nm line of a Green He-Ne Laser (1.0 mW) was used for excitation and the 565–640 nm filter was used for detection. The same 543 nm line of a Green He-Ne Laser (1.0 mW) but with a 650 nm long pass filter was used for chlorophyll emission and detection, respectively. The blue fluorescence of DAPI or CFP was imaged using 404 nm Violet-Diode Laser MOD (44.8 mW) for excitation and 430–465 nm or 435–485 nm bands pass filter for emission.Nicotiana tabacum plants  indicated that Joka2 is present in ubiquitin-containing protein aggregates Figure . It coulin planta using the BiFC method in which fusion proteins linking Joka2 with either N- or C-terminal part of YFP  domain and ubiquitin-like (UbL) domain, selective autophagy cargo receptors  might be included into a family of ubiquitin receptor proteins containing both UbL and ubiquitin-associated (UBA) domains Figures . Moreove2 Figure . Additio2 Figure . The BiFIt is obvious that aggregation of selective autophagy cargo receptors is possible due to their ability to polymerize. Molecular modeling of PB1 domain of Joka2 revealed that it has a basic/acidic surface structure, which is similar to PB1 domain of p62 which, in turn, has the ability to polymerize , while for PB1 all four combinations were used. The fluorescence was observed in all analyzed combinations except PB1-YC+YN-PB1  than in the case of full Joka2 containing all three domains, PB1, ZZ, and double UBA  contribution of UBA domains to the process of aggregates formation (or stabilization).During subsequent analysis, various Joka2 fragments linked to YFP or CFP were tested for their subcellular localization and ability to form cytoplasmic aggregates. Cassettes encoding the respective fusion proteins were transiently expressed in y Figure . Interesy Figure . The truA Figure . In summThis problem was investigated further by transient co-production of the full length Joka2 (Joka2-YFP) with truncated versions  linked to CFP, what enabled monitoring of both types of the proteins in one cell Figure . Interesin vivo  was observed only in aggregates (Figure N. benthamiana cells (see Figures It is known that PB1 domains are also able to interact with other domains. For example, PB1 domain from p62 can directly interact with PB1 domain from NBR1 protein or with the Rpt1 subunit of 26S proteasome (Seibenhener et al., o Figure . The strs Figure  despite The main focus of this study was on characterization of the role of PB1 and UBA domains in multimerization and aggregation of Joka2 in plant cells. The results are shown in a form of a model summarizing and explaining detected interactions Figure . It has in planta and that the C-terminal region containing UBA1 and UBA2 domains additionally promotes Joka2 aggregation. Moreover, the aggregates formed by the truncated proteins lacking the fragment with UBA domains are mostly not co-localized with ubiquitin aggregates (Figure Joka2 is a strongly aggregating protein. PB1 domain of Joka2 has similar basic/acidic surface to PB1 of p62 protein (Svenning et al., s Figure , what iss Figure . We notiin vivo and that it is much weaker than PB1-PB1 interaction. Interestingly, such PB1-UBA interaction was only observed in aggregates despite the fact that both truncated proteins were spread in the whole cytoplasm. Also for PB1-PB1 interaction the fluorescent signal was observed mostly in aggregates. This conclusion is supported by the observation that detection of the PB1-PB1 interaction in the cytoplasmic non-aggregated fractions of PB1 proteins was possible only in one combination of the vectors. Therefore, we conclude that PB1-PB1 and PB1-UBA interactions take place mainly in aggregates. Aggregates formation is a consequence of self oligomerization of Joka2 and both types of interactions are necessary for multimerization of Joka2 in poly-ubiquitin-containing aggregates (Figure We have demonstrated that the specific protein-protein interaction between PB1 and at least one of UBA domain of Joka2 is possible. Such interaction was only hypothesized for other selective autophagy cargo receptors due to the high similarity in domain architecture between, for example, p62 and Dsk2 or Rad23 (Su and Lau, s Figure .Interestingly, since the aggregates of p62 have been reported to contain proteasomal components (Seibenhener et al., The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The precise prediction of protein domains, which are the structural, functional and evolutionary units of proteins, has been a research focus in recent years. Although many methods have been presented for predicting protein domains and boundaries, the accuracy of predictions could be improved.In this study we present a novel approach, DomHR, which is an accurate predictor of protein domain boundaries based on a creative hinge region strategy. A hinge region was defined as a segment of amino acids that covers part of a domain region and a boundary region. We developed a strategy to construct profiles of domain-hinge-boundary (DHB) features generated by sequence-domain/hinge/boundary alignment against a database of known domain structures. The DHB features had three elements: normalized domain, hinge, and boundary probabilities. The DHB features were used as input to identify domain boundaries in a sequence. DomHR used a nonredundant dataset as the training set, the DHB and predicted shape string as features, and a conditional random field as the classification algorithm. In predicted hinge regions, a residue was determined to be a domain or a boundary according to a decision threshold. After decision thresholds were optimized, DomHR was evaluated by cross-validation, large-scale prediction, independent test and CASP  tests. All results confirmed that DomHR outperformed other well-established, publicly available domain boundary predictors for prediction accuracy.http://cal.tongji.edu.cn/domain/.The DomHR is available at  Protein domains are the structural, functional and evolutionary units of proteins. A domain is a segment of a polypeptide chain that can fold into a compact and stable three-dimensional structure independently of other segments in the chain Identifying domains and boundaries requires a clear definition of each; however, domain and boundaries are ambiguously defined in the literatures Currently, studies about protein domain boundary prediction are divided into two categories: those that use template-based methods Among these algorithms, Liu and Rost In recent years, SVM has been used by DomainDiscovery Here, we present a novel approach, DomHR, which is an accurate predictor of protein domain boundaries based on a creative hinge region strategy. DomHR uses a nonredundant dataset as the training set, domain-hinge-boundary (DHB) and predicted shape strings as features, and a conditional random field as the classification algorithm. In predicted hinge regions, a residue is determined as domain or boundary according to the decision threshold. DomHR is a hybrid method such as DOMAC Three datasets  and one database (B25936) were used in this study. S628 was extracted from Cheng’s package The key database with sequence and domain/boundary information was derived from the annotated domains in the CATH and PDB (Protein Data Bank) Similar with the different definitions of domain, as mentioned above, domain boundary definitions also vary. Zou In this study, the domain boundary was defined as the nondomain residue defined by CATH and the N- and C-terminal residues of a domain . In addiRecently, binary classification has tended to be framed as a three-class problem. Cheng In this study, we defined a hinge region as containing 2*R  residues that are centrally located at the N- and C-terminus of a domain and bidirectionally extended into the domain and the boundary R residues . An addie-values below a given threshold . All selected sequences were subsequently ranked by e-value in ascending order, reserving the top S (default S = 10) of these sorted sequences, which contained rich evolution information. . The DHB elements in the matched unions were scored in three boxes that contained the profiles of three-state (DHB) information. The boxes constituted DHB profiles of the original sequence. The profile was clearly the frequency of the DHB elements, with three elements for each amino acid in the sequences. For the position p of an amino acid in the target, the normalized s profile  was counted asp was the position of the amino acid in the target sequence and s was one of the three DHB states. A equaled 1 when the state of the p amino acid in the matched sequences was s. L was the number of matched sequences. In the denominator, the summation was carried out for three states.The novel technology in this study was DHB profiles. The DHB feature was generated by sequence-domain/hinge/boundary alignment. For a query, sequence alignment was carried out first against B25936 using PSI-BLAST The DHB of each amino acid had three elements: domain, hinge and boundary profiles. The DHB was a distinctive PSSM-like profile composed after alignment, with rich DHB information. Finally, the DHB was used as a feature to identify boundary regions in sequences./Ψ torsion angle pairs of protein backbones in a Ramachandran space into eight distinct regions, and assigning these clusters as eight symbols to describe the protein backbone. The real-value of protein backbone torsion angle has been predicted by Real-SPINE The architecture of DomHR is shown in http://crfpp.sourceforge.net/). The environment for training and testing was a Windows 7 64-bit operating system with Intel Core 2 Quad CPU and RAM 6 GB.The CRFs  was used for modeling and prediction. CRFs offer several advantages over other machine learning methods, including the ability to relax strong independence assumptions made in those models and avoid the fundamental limitation of maximum entropy Markov models and other discriminative Markov models based on directed graphical models; these can be biased towards states with few successor states To evaluate predictor performance, we used multiple measurements that are seldom used in the literature for predicting protein domain boundaries, for a comprehensive assessment. We used sensitivity [Sn = TP/(TP+FN)], specificity [Sp = TN/(TN+FP)], accuracy [Ac = (TP+TN)/(TP+FN+TN+FP)], Matthews’s correlation coefficient (MCC), weighted score (Sw), and area under the receiver operating characteristic (ROC) curve (AUC)In this study, S628 was used to demonstrate the novel DHB feature that was generated using a BLAST tool modified from PSI-BLAST by aligning a query against B25936. Because B25936 was derived from a large and diverse database, when the BLAST function was performed, any sequence in B25936 with an exact match in S628 was discarded for fairness. To determine applicable size of the hinge region we assessed prediction performances dependent on hinge region size  See Tab, S2. ResA good feature greatly improves the accuracy of a predictor. To validate and select an optimal combination of features, 10 fold cross-validation tests were performed on S628. We randomly divided the S628 dataset into 10 subsets on average. One subset was used as a testing set, while the other nine subsets were merged into a training set. S628 was used to demonstrate the hinge region strategy. The definition of the new hinge region improved the imbalance problem, and the ratio of the number of boundary residues to the number of domain residues changed from 1∶15 to 0.14∶1:4.80 for boundary:hinge:domain. Thus, a two-class problem was transformed to a three-class problem.Second, because the hinge region was defined to overlap parts of the domain and boundary regions, we determined the residues in the predicted hinge regions to be either domain or boundary according to a decision threshold that assigned a residue to the boundary or to the domain. The distributions of the probabilities at each residue position in the hinge region are provided in Third, another feasible adjustment of decision threshold focused on the predicted domain regions. The decision threshold of the predicted domain regions could be adjusted to optimize sensitivity and specificity . If a prFinally, we combined the adjustments in the predicted hinge and domain regions. The results showed no significant improvement, so the adjustments of the decision thresholds in the hinge and domain regions were both effective and could be used flexibly.According to our results, adjusting the hinge probability did not substantially affect Sw greatly, however this worked well in blind testing. The adjustment of the domain probability greatly improved Sn and Sp. When the test was similar to the training, Sw achieved or approximated the maximum. However, when the blind testing was very different from the training, the optimized decision threshold based on the training did not guarantee the maximum Sw.To summarize, the hinge region strategy proposed in this study had three central features: (i) definition; (ii) DHB feature; and (iii) adjusting the decision threshold. The hinge region strategy is an innovative technology that was confirmed to improve protein domain boundary identification.To understand the performance of the DomHR on large-scale prediction, we carried out a 10 fold cross-validation on S3845, an ensemble nonredundant set of CATH 3.4.0. The features used were DHB and predicted shape string, with a decision threshold of 0.4 in the predicted hinge region optimized based on the S628. S3845 was used as training set and S1508 as the independent testing set. Any sequence in B25936 with an exact match in S1508 was discarded for fairness. The results are in Two difficulties hindered comparison of our approach with previous studies. One is the small number of comprehensive assessments of published approaches. The other is that the datasets used were different, and lacked large-scale results. We compared DomHR with DoBo In addition, we compared DomHR with DomSSEA For large-scale prediction, we compared DomHR with Cai Improving the accuracy in predicting multidomain boundaries is a challenging task because the accuracy usually is considerably less than 40% CASP 9 is a challenging subset for domain boundary prediction.Zhou Because most single domains in CASP 9 were discontinuous, as multidomain sequences, predicting single domains was as difficult as predicting multiple domains. The difference in prediction accuracy between single-domain and multidomain proteins was not large. For the multidomain CASP 9, the Sn from DoBo was 0.68, while our Sn was 0.70.In this study, CASP 9 was considered to be a blind test, so we did not adjust the decision threshold according the performance of the CASP 9 prediction to achieve a reasonable Sn and Sp. Because the decision threshold optimization was based on S628, the specificities in http://cal.tongji.edu.cn/domain/. The DomHR server predicts protein domain boundary for query sequence(s), and results are provided in the form of a web page and/or an e-mail.We have created a DomHR server for scientific users on our local infrastructure. This is available at In this work, we proposed a hybrid method, DomHR, to accurately predict domain boundaries in proteins based on a creative hinge region strategy. DomHR was tested in cross-validation, large-scale prediction, independent tests, and CASP tests. All results confirmed that DomHR outperformed well-established publicly available domain boundary predictors for accuracy of prediction. The kernel technology is a hinge region strategy that generates an effective feature: a DHB feature by alignment. For the idea, it came from the fact that the most predicted errors existed in the connecting regions of domains and boundaries. We considered firstly the new defined hinge region would reduce the imbalance of dataset. Then, the proposed DHB feature can accurately predict the hinge regions. At the last, the adjustments of the decision thresholds of the predicted hinge regions were local adjustments, which could help to optimize the performance of the predictor. However, similar to other alignment-based methods, the performance of DomHR requires homology information obtained when the alignment is carried out. The most distinct characteristic of DomHR was that only four features were used during modeling and prediction. This number is much lower than previous methods. A few features and high accuracy make our approach competitive with state-of-the-art domain boundary predictors. We propose that as more structures of proteins are determined, obtaining homology information will become easier. We also believe this strategy benefits other predictions of protein structure and function.Figure S1The distributions of the probabilities at each residue position in the hinge region. The negative values on the abscissa refer to residue positions in the hinge region close to domain region, and the positive values on the abscissa refer to residue positions in the hinge region close to boundary region. The values on the ordinate refer to average probability at each residue position in the hinge region. The probabilities at residue positions close to boundary region are generally higher than those close to domain region.(TIF)Click here for additional data file.Table S1Effects of size of hinge region on performance .(DOCX)Click here for additional data file.Table S2Effects of size of hinge region on performance (including SE).(DOCX)Click here for additional data file.Table S3Performance comparison with different features on S628 .(DOCX)Click here for additional data file.Table S4Performance comparison with different features on S628 (including SE).(DOCX)Click here for additional data file.Table S5Performance on large-scale prediction .(DOCX)Click here for additional data file.Table S6Performance on large-scale prediction (including SE).(DOCX)Click here for additional data file.Table S7All predicted results of S1508.(XLSX)Click here for additional data file.Table S8Performance on CASP9 trained on CASP8 .(DOCX)Click here for additional data file.Table S9Performance on CASP9 trained on CASP8 (including SE).(DOCX)Click here for additional data file.Table S10All predicted results of CASP 9.(XLSX)Click here for additional data file.Text S1Detailed descriptions of CRF  methodology.(DOCX)Click here for additional data file.


Mycobacterium tuberculosis ClpC1 is a member of the Hsp100/Clp AAA+ family of ATPases. The primary sequence of ClpC1 contains two N-terminal domains and two nucleotide binding domains (NBD). The second NBD has a long C-terminal sub-domain containing several motifs important for substrate interaction. Generally, ClpC proteins are highly conserved, however presence of C-terminal domains of variable lengths is a remarkable difference in ClpC from different species. In this study, we constructed deletion mutants at the C-terminus of M. tuberculosis ClpC1 to determine its role in the structure and function of the protein. In addition, a deletion mutant having the two conserved N-terminal domains deleted was also constructed to investigate the role of these domains in M. tuberculosis ClpC1 function. The N-terminal domains were found to be dispensable for the formation of oligomeric structure, and ATPase and chaperone activities. However, deletions beyond a specific region in the C-terminus of the ClpC1 resulted in oligomerization defects and loss of chaperonic activity of the protein without affecting its ATPase activity. The truncated mutants, defective in oligomerization were also found to have lost the chaperonic activity, showing the formation of oligomer to be required for the chaperonic activity of M. tuberculosis ClpC1. The current study has identified a region in the C-terminus of M. tuberculosis ClpC1 which is essential for its oligomerization and in turn its function. Molecular chaperones mediate correct folding, assembly, transport as well as disaggregation of proteins inside the cell during growth in normal as well as stressful conditions. Many molecular chaperones are expressed at higher level during biological stress and have been classified under heat shock proteins (Hsps) Listeria monocytogenes, ClpC has been shown to be important for its virulence and survival in macrophages, and in Bacillus subtilis it controls the competence gene expression and survival under stressful conditions Staphylococcus aureusMycobacterium tuberculosis, ClpC1 was shown to be up regulated upon re-exposure to favorable conditions after hypoxia along with ClgR, a Clp protease gene regulator Streptococcus pneumoniae in a murine intraperitoneal infection model Staphylococcus aureus, ClpP deletion caused complete down-regulation of heat shock regulon and partial depression of genes involved in oxidative stress indicating a strong impact of ClpP proteolytic activity on virulence and stess response M. tuberculosis impaired the ability of the bacteria to establish a chronic infection as compared to the wild type Clp proteins have been shown to play a role in the virulence of many pathogenic bacteria. In M. tuberculosis H37Rv ClpC1 is an ATP-dependent molecular chaperone which belongs to the class I of Hsp100 family of AAA+ proteins M. tuberculosis ClpC1 to manifest chaperonic activity in vitro in the absence of any adaptor protein M. tuberculosis ClpC1 has been shown to interact with RseA, an anti sigma factor, and ClpC1P2 complex has been shown to proteolytically cleave RseA in vitroM. smegmatis and M. tuberculosis resulted in an inhibition of RseA degradation M. tuberculosis has been shown to associate with ClpP1P2 complex to degrade the model substrate casein in the presence of some activators in vitroThe M. tuberculosis consists of two N-terminal domains (NTD) and two AAA+ domains . The mixture was incubated on ice for 5 min with intermittent shaking, and centrifuged at 4°C at 16,000 g for 15 min. Radioactivity in the supernatant was measured using a liquid scintillation counter, and the concentration of released Pi was calculated using the specific activity of the radioactive substrate. The standard errors (SE) of means were calculated using the Sigma plot software.For a standard ATPase assay, 4 µg Clp protein was incubated in a 50 µl reaction mixture containing buffer A, 10 mM ATP containing , [18l domain . Sequencbacteria . The seqbacteria . In thisWe prepared seven ClpC1 mutants having deletions at C-terminus to study the role of conserved as well as unconserved regions . ClpC1–8E. coli BL21 cells as reported earlier for the wild type ClpC1 In addition to the C-terminal deletion mutants, another mutant, ClpC1-Δ3 having the two N-terminal domains deleted was also constructed. The ClpC1-Δ3 mutant starts from amino acid 114 of ClpC1. All the mutants were expressed in The proteins were found to be localised in the cytosol from where they were purified to near homogeneity by a combination of ammonium sulphate fractionation, anion exchange and gel filtration chromatography, as described earlier The effect of deletions on overall ClpC1 structure was investigated by CD spectral analysis of the purified proteins in the far-UV region. ClpC1-WT showed a typical α+β conformation with minima at 208 nm and 224 nm . The CD M. tuberculosis ClpC1 has been earlier shown to have an inherent ATPase activity M. tuberculosis ClpC1 remains predominantly in a monomeric form, however in the presence of ATP a large fraction is shifted towards the hexameric form Earlier, we have shown that in the absence of ATP ClpC1 is known to prevent aggregation of luciferase upon heating in an ATP-dependent manner Luciferase lost more than 90% of its catalytic activity upon heating at 43°C. Earlier, it was shown that when heat inactivated luciferase is incubated with ClpC1 it is partially reactivated Enterococcus faecalis, ClpB is linked to virulence Bacillus subtilis and Listeria monocytogenesM. tuberculosis contains two protease components ClpP1 and ClpP2, and two ATPase components ClpC1 and ClpX of the caseinolytic protease family. In M. tuberculosis, ClpP1-ClpP2 knockdown resulted in reduced growth and reduced virulence inside macrophages M. smegmatis, ClpP1 and ClpP2 were shown to be essential for growth as depletion of either one of them resulted in rapid bacterial death M. tuberculosis and many Gram positive bacteria ClpC is present as an ortholog of ClpA. It has been seen that interference with the functioning of ClpC1 of M. tuberculosis is bactericidal in culture as well as inside the macrophage M. tuberculosis appear to be essential for survival of the pathogen, a better understanding of their function would help in exploring them as drug targets.Caseinolytic proteases are ubiquitously present in bacteria, though their distribution varies. Clp proteins have been shown to have a role in the pathogenesis and survival in many pathogenic bacteria. In M. tuberculosis in its function. With the help of seven C-terminal deletion mutants of M. tuberculosis ClpC1, we demonstrate that extension beyond DVDN motif after amino acid 802 is not required for the functional activity of ClpC1. However, deletion beyond amino acid 746 is detrimental for the chaperonic activity and the oligomerization of the protein. The ATPase activity was not reduced by any of the deletions, on the contrary two mutants, ClpC1–786 and ClpC1–746 had about 2-fold increased ATPase activity. It appears that the deletions in these mutants result in a change in the quaternary structure of the protein, facilitating access of nucleotide to its binding sites. In addition, the study also shows that the deletion of the two N-terminal domains does not affect the in vitro chaperonic activity of M. tuberculosis ClpC1.We have earlier shown that ClpC1 self associates to form oligomers in the presence of ATP, and contains inherent ATPase and chaperonic activity M. tuberculosis ClpC1 (ClpC1 has five conserved motifs, Sensor1, Box VII, Box VII′, Box VII″ and Sensor 2/Box VIII at its C-terminus . Deletiois ClpC1 .E. coli, the small D2 domain present at the C-terminus of second ATPase domain was found to be required for the oligomerization of the protein M. tuberculosis ClpC1 and E. coli ClpB, ClpX, and ClpY suggests that the C-terminal domains of ClpC1 may assume conformations similar to analogous regions in ClpY (M. tuberculosis ClpC1 hexamer. The current study identifies a region within the C-terminal region of M. tuberculosis ClpC1 which primarily supports protein self-association (In a hexameric ring in AAA+ proteins, the C-terminal domain known as SSD domain contacts its own as well as adjacent subunit's ATPase domain, and is involved in substrate interaction  in ClpY . Howeverociation .M. tuberculosis ClpC1. Also, a region at the C-terminus, between amino acids 720–747 in the small D2 domain, of M. tuberculosis ClpC1 is involved in the oligomerization of the protein and in turn in its function. However, the two N-terminal domains are not crucial for the ATPase and chaperonic activities of M. tuberculosis ClpC1.In conclusion, our study demonstrates that the ATP-induced oligomerization is necessary for the chaperonic function of


Regulated transcription termination provides an efficient and responsive means to control gene expression Pseudomonas aeruginosa (amiECBRS operon) Klebsiella oxytoca (nasFEDCBA operon) Studies conducted in the 1990s characterized two related antitermination proteins: AmiR, which regulates aliphatic amide catabolism in amiE and nasF operons, respectively nasF operon leader identified three subelements essential for NasR binding and antitermination: the P1 stem; residues A1 and G4 in the P1 loop; and part of the linker region that connects the P1 and terminator stem-loop structures AmiR and NasR target the transcribed leader RNA upstream of the eut operons) in Enterococcus faecalisAlthough different ANTAR proteins use a variety of signal input domains, at least half contain a two-component signal transduction receiver domain eut gene cluster contains intrinsic terminators upstream of four genes , facilitating the identification of shared sequence features. Each of these leaders contains a terminator along with two upstream stem-loop structures, P1 and P2 eutG leader P2 structure was noted independently by Baker and Perego amiE and nasF leaders. Strikingly, both the P1 and P2 loops contain the critical residues A1 and G4, identified as essential for NasR-mediated antitermination nasF operon leader Ramesh et al. eutP leader, and confirmed the subelements important for EutV binding and antitermination: the P1 and P2 structures; their relative spacing; and the P1 and P2 loop residues A1 and G4 To test this model, Ramesh et al. first constructed a variety of site-specific alterations in the Finally, Ramesh et al. conducted bioinformatic analyses to identify ANTAR target leaders in bacterial genomes. Examples were found in a broad range of species from the Actinobacteria, Bacteroidetes, Firmicutes, and Proteobacteria. Some of the leaders are adjacent to genes of unknown or uncertain function, whereas many others are adjacent to genes involved in inorganic nitrogen acquisition  or assimilation (glutamine synthetase and associated regulators). Many ethanolamine utilization clusters also were identified.Together, these results provide concrete hypotheses for the structure and function of both ANTAR domain proteins and their leader RNA targets. Moreover, the association of many ANTAR regulators with aspects of nitrogen metabolism provokes intriguing questions about their physiology and evolution.Proteins of the BglG-LicT-SacY family, present in both gram-negative and gram-positive lineages, control genes for sugar catabolism. These homodimeric proteins contain PRD domains (PTS regulation domain) that place them under control of the phosphotransferase system for sugar uptake The CAT domain is a homodimer of four-stranded β-sheets Mycobacterium tuberculosis Rv1626, receiver X-ray structures are known for three ANTAR domain proteins, each with a different amino-terminal input domain: AmiR, pseudo-receiver 70 region 4.2, which binds the −35 region of promoter DNA as well as 6S RNA The ANTAR domain adopts a helix-turn-helix conformation, but how it binds RNA is unknown. RNA-binding helix-turn-helix proteins include σFuture progress in understanding ANTAR-mediated antitermination requires knowledge of ANTAR domain conformation and interaction with its RNA target; conformational shifts mediated through the coiled-coil; and kinetics of RNA binding in relation to transcription-driven formation of the P1, P2, and terminator structures.