The entire evolutionary history of life can be studied using myriad sequences generated by genomic research. This includes the appearance of the first cells and of superkingdoms Archaea, Bacteria, and Eukarya. However, the use of molecular sequence information for deep phylogenetic analyses is limited by mutational saturation, differential evolutionary rates, lack of sequence site independence, and other biological and technical constraints. In contrast, protein structures are evolutionary modules that are highly conserved and diverse enough to enable deep historical exploration.Here we build phylogenies that describe the evolution of proteins and proteomes. These phylogenetic trees are derived from a genomic census of protein domains defined at the fold family (FF) level of structural classification. Phylogenomic trees of FF structures were reconstructed from genomic abundance levels of 2,397 FFs in 420 proteomes of free-living organisms. These trees defined timelines of domain appearance, with time spanning from the origin of proteins to the present. Timelines are divided into five different evolutionary phases according to patterns of sharing of FFs among superkingdoms: (1) a primordial protein world, (2) reductive evolution and the rise of Archaea, (3) the rise of Bacteria from the common ancestor of Bacteria and Eukarya and early development of the three superkingdoms, (4) the rise of Eukarya and widespread organismal diversification, and (5) eukaryal diversification. The relative ancestry of the FFs shows that reductive evolution by domain loss is dominant in the first three phases and is responsible for both the diversification of life from a universal cellular ancestor and the appearance of superkingdoms. On the other hand, domain gains are predominant in the last two phases and are responsible for organismal diversification, especially in Bacteria and Eukarya.The evolution of functions that are associated with corresponding FFs along the timeline reveals that primordial metabolic domains evolved earlier than informational domains involved in translation and transcription, supporting the metabolism-first hypothesis rather than the RNA world scenario. In addition, phylogenomic trees of proteomes reconstructed from FFs appearing in each of the five phases of the protein world show that trees reconstructed from ancient domain structures were consistently rooted in archaeal lineages, supporting the proposal that the archaeal ancestor is more ancient than the ancestors of other superkingdoms. Since Darwin established the general principles of natural selection in 1859 , most ev7 proteins [Several reliable classification systems of protein domains are available based on structural similarity and common evolutionary origin. For example, the Structural Classification of Proteins (SCOP) is a high-quality taxonomical resource that groups protein domains that have known three-dimensional (3D) structures into fold families (FFs), fold superfamilies and folds . FFs groproteins . While pproteins  that canproteins .7 proteins have been assigned to folds, fold superfamilies, and FFs in over 1,400 proteomes and trees of domain structures have been reconstructed at all levels of structural abstraction [As genes duplicate and diversify, ancient domain structures accumulate to larger extent in proteomes than younger structures. Although convergent evolution, horizontal gene transfer, and recruitment can occur over time, the magnitude of these processes has been shown to have little influence on the vertical inheritance of domain structures ,15. Theitraction ,19-21. Wtraction .The rooted trees of domain structures display in their branches the relative ancestries of domains, and these ancestries can be directly associated with chronologies of proteins, proteomes, molecular functions, biological networks, and evolutionary events of significance, such as the division of three superkingdoms and the emergence of aerobic metabolism and photosynthesis. Evolutionary studies of the protein world have been conducted primarily at the fold and fold superfamily levels ,19,21. HWe have searched for controlled vocabularies that have multiple genomic occurrences and that are appropriate for surveying ancient evolutionary history. We already found that domain structures at the fold and fold superfamily levels and their domain combinations harbor phylogenetic signatures that are congruent ,20,24-27E-value cutoff of 10-4 to extract reliable HMM hits of the FFs in individual proteomes. As a result, the structural census revealed that 2,493 FFs out of 3,114 FFs were present in the 645 proteomes. Data matrices of genomic abundance  and genomic occurrence (presence/absence) of FFs for all possible pairs of FFs and proteomes were generated from the census. These matrices were then used to build intrinsically rooted phylogenomic trees of FF domain structures (with FFs as taxa and proteomes as characters) and trees of proteomes (with proteomes as taxa and FFs as characters) by transposing columns and rows in the matrix.Figure nd) in a 0-1 scale (see Methods). The age of FFs derived from abundance-based trees a) (ndwas strongly correlated with the age derived from occurrence-based trees (ndo)  . In fact, the total set of 225 parasitic organisms were dispersed throughout the tree but their presence was particularly evident at the bases of their respective superkingdoms , confirming that FF data is appropriate for deep phylogenetic studies.Reconstruction of a tree of organisms describing the evolution of 645 proteomes resulted in one most parsimonious rooted tree  of individual FF characters and plotted them against the age of the corresponding FFs (nd) derived from the tree of FF structures we describe below. The index portrays the relative amount of homoplasy of individual phylogenetic characters (conflict in how data matches the reconstructed tree) and processes other than vertical inheritance, such as convergent evolution, horizontal gene transfer and recruitment [i rdistributions for informational and non-informational FFs were highly consistent with each other and consistency was still maintained in the FFs of Bacteria  despite the large number of taxa  and calculated the fraction of proteomes containing FFs  to examine the relationship between the age and genomic distribution of domains , indicating that the most ancient FFs are both widely distributed and are highly conserved. However, domain loss and their distribution in emerging lineages are expected to reduce the wide distribution of domains and decrease f values. Indeed, the f values of FFs decreased with the increase of nd until f reaches 0 at about nd = 0.550. After this point, the pattern of change reverses and both f and nd values become positively correlated. This probably results from horizontal gene transfer, domain duplication and recruitment, and rearrangement, among other factors.Intrinsically rooted trees of FFs were reconstructed from the structural census of FFs in the 420 proteomes of free-living organisms we analyzed. The most parsimonious tree describing the evolution of 2,397 FFs had significant phylogenetic signal , reached zero at about nd = 0.151, rose suddenly within 0.551 ≤ nd ≤ 0.661, an interval in which all Archaea-specific FFs  At nd ≥ 0.151, the f distribution of FFs shared by Bacteria and Eukarya  FFs that were unique to Bacteria or were shared by Archaea and Bacteria  After nd = 0.661, many FFs were lost . Finally, in the case of Eukarya, the f values in the early part of the timeline (nd ≤ 0.256) decreased more than those of Bacteria but less than those of Archaea. The extent of f-value dispersal in the 0.256 ≤ nd ≤ 0.550 interval was highly reduced in comparison to that of Bacteria. Starting at about nd = 0.550, f values increased dramatically along the timeline. In this period, the majority of FFs are Eukarya-specific. Consequently, while loss of the domain structures occurred in all superkingdoms before the inflection point at nd = 0.550, a new trend in architectural innovation by gain of domains became predominant after that time.The evolutionary patterns in these plots are remarkably similar to those observed in trees of folds and fold superfamilies  or theirThe 2,397 FFs are not equally distributed between superkingdoms. A Venn diagram shows FFs that are uniquely present in one , two  or three (ABE) superkingdoms, with A, B and E group labels representing Archaea, Bacteria and Eukarya, respectively Figure . Only 20nd ≤ 1) can be divided into five different phases by studying the emergence, distribution and diversity of FFs  protein world : In this ancient phase, domain structures diversified but were rapidly shared by the emergent cells. Proteomes of the three superkingdoms share all 76 FFs (ABE FFs). However, some FFs were lost in few proteomes  Reductive evolution of primordial proteins (phase II: 0.151 ≤ nd ≤ 0.256): This phase consists of 232 FFs, many of which (181 ABE FFs) experienced reductive evolution (f < 1) or were completely lost (f = 0) in archaeal lineages (51 BE FFs that are shared by Bacteria and Eukarya)   Figures  and 2C. s Figure . This phDevelopment of the three superkingdoms (phase III: 0.257 ≤ nd ≤ 0.550): Here, the ancestral lineage that is sister to Archaea gives rise to superkingdoms Bacteria and Eukarya. The primordial trend of domain loss responsible for superkingdom Archaea is still maintained . This suggests that the most recent eukaryal ancestor was derived from the common ancestor of Bacteria and Eukarya. Results also exclude the possibility that Eukarya originated from Archaea, a conclusion that is also supported globally by the archaeal rooting and the sister relationship between Bacteria and Eukarya in the trees of proteomes of free-living organisms  d Figure . FFs uniOrganismal diversification (phase IV: 0.551 ≤ nd ≤ 0.661): This period embodies the 'big bang' of domain organization in proteins [nd ≥ 0.551, f values for all superkingdoms are positively (instead of negatively) correlated with nd values. The looser trend was therefore replaced by massive domain gains and structural innovations. A total of 1,008 FFs appear as part of all seven taxonomic groups . Widespread appearance of domain structures in organismal lineages across the three superkingdoms signals massive diversification of proteins and proteomes. In addition, Archaea and Bacteria (but not Eukarya) showed abnormal peaks in the f distribution plots  proteins . Despites Figure  and ri vEukaryal diversification (phase V: 0.662 ≤ nd ≤ 1): The majority of new FFs appearing in this final period were unique to the emerging eukaryotic lineages  The accumulation of FFs along the timeline shows that the numbers of different FFs always increase in the proteomes of superkingdoms despite the early and massive episodes of domain loss and the lack of appearance of new FFs specific to microbial superkingdoms in the late phases of protein evolution Figure . This obGeneral, Information, Metabolism, Intra-cellular processes, Extra-cellular processes, Regulation, and Other) and into one of 49 minor categories of molecular functions. For simplicity, the names of the categories were displayed in italics and the initial letters of the major categories were capitalized. The emergence time points of the major and minor categories of molecular functions in the timeline of FFs revealed remarkable patterns of origin in the five evolutionary phases  of Intra-cellular processes, and nucleotide m/tr, other enzymes, coenzyme m/tr, transferases, and redox of Metabolism. Since small molecule binding and ion m/tr involve popular multifunctional enzymes and membrane transporters , the vast majorities of molecules emerging at the beginning of modern cellular life were involved in making up modern metabolic enzymes and enabling transport processes across primordial membranes. This suggests primitive cells acted as containers of the emerging protein domains already during this first evolutionary phase. The notable absence of molecular functions involved in Information indicates that ancient catalytic proteins with primordial metabolic functions initiated life in the absence of a translational apparatus. This conclusion is supported by the mapping of functions in a timeline of fold superfamilies [translation (Information), amino acids m/tr, carbohydrate m/tr, and energy (Metabolism), and proteases (Intra-cellular processes) appeared for the first time very late in phase I. The first FFs of translation were the catalytic domains of aminoacyl-tRNA synthetases [Phase I: Only three of the seven major categories were present very early in the FFs of phase I. They included minor categories families ,19 and bfamilies ,22. The thetases . Thus, tthetases ,22. Resuthetases  for an ethetases  for a reDNA replication/repair and transcription (Information), suggesting that early during this time nucleic acids started to be used as genetic repository. In addition, the appearance of protein modification and cell motility (Intra-cellular processes) suggests the start of cellular diversification. Late in phase II, functions related to signal transduction (Regulation), secondary metabolism and e-transfer (electron transfer) (Metabolism), and transport (Intra-cellular processes) suggest more advanced cellular systems capable of regulatory control of cellular processes and more efficient energy management.Phase II: This period starts with the emergence of FFs belonging to Extra-cellular processes and Other, all major categories are represented in this period and include ligand binding and general , DNA binding, kinases/phosphatases, RNA binding m/tr and other regulatory functions (Regulation), nitrogen m/tr, polysaccharide m/tr, lipid m/tr and cell envelope m/tr (Metabolism), RNA processing (Information) and cell cycle (Intra-cellular processes)   Figure . Functio) Figure . For exa Eukarya . Remarkachromatin structure (Information), cell adhesion (Extra-cellular processes), and viral proteins (Other), and are followed by ion binding and structural protein , receptor activity (Regulation), photosynthesis (Metabolism), phospholipid m/tr (Intra-cellular processes), and toxins/defense, blood clotting and immune response (Extra-cellular processes). These functions are quite advanced and involve complex variants of Bacteria and Eukarya that engage in multicellularity, cell communication, and interaction with the environment at various biological levels .Phase IV: The molecular functions added in this relatively short phase of protein and proteomic diversification start with lipid/membrane binding , storage (Metabolism), nuclear structure (Information), and intracellular trafficking/secretion (Intra-cellular processes). All of these processes are involved in establishing a much more complex cellular structure, such as the formation of compartments , lipid and polysaccharide storage, and targeting of proteins to proper compartments, sorting and translocation, and protein secretion mechanisms. All of these innovations are quite elaborated in Eukarya and involve many of Eukarya-specific FFs that appear abundantly in this phase.Phase V: This final phase has the longest time span but introduced only four functional innovations: In order to examine how the deep phyletic patterns of the three superkingdoms changed along the timeline, we reconstructed trees of proteomes for each of the five evolutionary phases Figure . Again, nd ≤ 0.150 time interval  of phase III Figure . Bacteri. Figure . Most liThe canonical rooting of the tree of organisms derived from phylogenetic analyses of rRNA and other sequences  generally shows hyperthermophilic bacteria  at the base of the tree . Our resPlots describing the evolutionary accumulation of FFs in proteomes that were directly derived from the intrinsically rooted trees of FFs Figure  show thaProtein structures are unevenly distributed in the world of proteins and proteomes . GenomicProtein functions are tightly coupled with protein structures and structures are much more conserved than sequences . This maE-value of 10-4 were extracted from the individual proteomes. This E-value cutoff is optimal to maximize the rate of true positives in the HMM searches [concise classification strings (ccs) . The lifestyles of the 645 organisms were manually determined based on various resources including public databases and literature review. Organisms were classified into free-living, facultative parasite, and obligate parasite categories.We downloaded the local MYSQL database from SUPERFAMILY ver. 1.73  that asssearches . FFs werg) value. A two-dimensional data matrix was constructed by calculating g values for all pair-wise combinations of proteomes and FFs. Empirically, g values ranged from 0 to hundreds and were normalized using the following formula [According to SCOP, protein sequences that have sequence identity of over 30% or that share a common ancestor in terms of structures and functions are grouped into FFs ,18. Indi formula ,ab gdescribes the g value of FF a in proteome b and gmax indicates the maximum g value in the matrix. The round function normalizes a g value for a particular FF in a proteome relative to the maxg, and standardizes values to a 0-23 scale. The 24 transformed values in the matrix were linearly ordered to discrete character states using an alphanumeric format of numbers (0-9) and letters (A-N) that are compatible with the phylogenetic package PAUP* ver. 4.0b10 [, where . 4.0b10 .Phylogenomic trees of domain structures at FF level of structural abstraction were reconstructed from the data matrix of genomic abundances (multiple occurrences of FFs) using maximum parsimony (MP) with 1,000 replicates of random taxon addition, tree bisection reconnection branch swapping, and maxtrees unrestricted. In addition, we generated trees of FFs from presence/absence of FFs (FF content) to compare phyletic patterns between the two approaches: abundance and content. The character states in the matrix were polarized from 'N' to '0' using the ANCSTATES command of PAUP*, where 'N' and '0' indicate the most ancient and recent character states, respectively. High genomic abundance is considered the ancestral character state because domains that are ancient had more time to accumulate in proteomes and to spread in the world of proteins than domains that have a more recent origin. Note that this is not a proteome-specific statement but a global statement, especially because trees of FFs describe the evolution of the protein world. Moreover, there is no cap in the growth of domains (imposed for example by the energetic costs of their replication) since they can be unpopular in one lineage but popular in another. In other words, their numbers can increase without constraints as long as they can be accommodated with major costs in a lineage or can be apportioned in different lineages. In summary, polarization refers to character state change, a property of characters that affect evolution of taxa. Characters are proteomes, which technically are infinite in number and for the most part evolve independently from each other . Taxa are FFs, proteomic parts that are finite in number and can grow unabated in proteomes. We thus claim no constraints in our model of FF evolution.Phylogenomic trees of proteomes were reconstructed after transposing the data matrix and polarizing character states from '0' to 'N', with '0' being ancestral. Low genomic abundance is thus considered the ancestral character state because we expect that proteome size will increase by the repeated accumulation of domains . Under this model, the primordial proteome contained a handful of domain structures that were rarely reused. With time, increases in domain diversity and reuse cause protein repertoires to enlarge, with the addition of each additional FF taking precious space in the limited proteomic make-up. Here, the energetic constraints of maintaining and replicating domains in proteomes limit proteome expansion. Consequently, there is a cap as proteomes have a finite space to accommodate domains (and their variants) and this space is "canalized" in evolution. In summary, polarization refers to characters that are parts of proteomes (FFs) that for the most part do not evolve independently from each other . Taxa are proteomes, technically infinite in number, but constrained by the numbers of FFs they can hold. Consequently, proteomes cannot grow unabated, especially when the number of parts increases and parts appear gradually in evolution. This limitation sets the pace of proteome growth, which in the cumulative plots of Figure We note that when polarizing trees of FFs or trees of proteomes, our model of evolution allows for both increases and decreases in genomic abundance, enabling processes of reductive evolution and of expansion to unfold in the phylogenies. Our model does not force tree reconstructions to fit patterns of distribution in the organismal world . Instead, these arise naturally from the phylogenetic reconstructions. The rationale for character coding and polarization as well as the discussion of the robustness of phylogenetic assumptions can be found elsewhere ,25-27.1g) test [ir) were calculated for individual FF characters with the "DIAG" (character diagnostics) command of PAUP*.Phylogenetic confidence was evaluated by BS values  and the g1) test . The cong1) test . Our phyg1) test  to generg1) test . When rend) was calculated using the following formula: α nd= /, where α represents a target terminal node, r is a hypothetical root node, and β is a terminal node that has the largest number of internal nodes from the root node. Consequently, the nd value of the most ancestral taxon is 0, whereas that of the most recent one is 1. Node distance can be a good measure of age given a rooted tree of FFs because the semipunctuated emergence of protein domains  is displayed by their ability to diverge (cladogenesis or molecular speciation) rather than by the amount of character state change that exists in branches of the tree (branch lengths) and is supported by the existence of a molecular clock of protein structures [f) that describes the fraction of proteomes that harbor a certain FF in a 0-1 scale. An f value of 0 implies the absence of that FF and a value of 1 its presence in all proteomes considered.Since trees of domain structures are intrinsically rooted and highly unbalanced, we calculated the relative evolutionary age of FF taxa by counting internal nodes in the tree between the hypothetical root and a terminal node on a 0-1 scale. The node distance  = 0.083; RI (retention index) = 0.771; g1 (tree skewness) = -0.288) describes the evolution of 645 proteomes and was generated from genomic abundances of 2,493 FFs . Terminal nodes of Archaea (A: 49 proteomes), Bacteria (B: 421), and Eukarya (E: 175) were labeled in red, blue, and gray, respectively. The dotted lines explicitly display the borders between two superkingdoms. The life-styles of proteomes were displayed using a vertical bar beside their terminal leaves. Proteomes from free-living (420 proteomes), facultative parasitic (94), and obligate parasitic (131) organisms were labeled in blue, red, and cyan, respectively. Figure S3. A phylogenomic tree of proteomes describing the evolution of free-living organisms. A most parsimonious rooted tree  was reconstructed from the genomic abundances of 2,397 FFs in 420 proteomes . Terminal nodes of Archaea (A: 48 proteomes), Bacteria (B: 239 proteomes), and Eukarya (E: 133 proteomes) were labeled in red, blue, and gray, respectively. BS values > 50% are shown above or below branches that cluster superkingdoms much higher groups. A Venn diagram shows the diversity of FFs in the three superkingdoms. Figure S4. The extent of homoplasy exhibited by phylogenomic characters (FFs) in trees of proteomes. The retention index (ir) of the FFs was determined from the tree of FL proteomes described in Figure i rvalues for the 2,262 parsimony-informative FFs (A) and 522 bacteria-specific FFs (B) were plotted against nd values obtained from a tree of FFs that is described in Figure Click here for file


Bacillus subtilis, and compared to the classical analyses. This new method of analysis of the functional domain network reveals that a specific inhibition on the second domain of the lipoamide dehydrogenase (pdhD) component of pyruvate dehydrogenase complex leads to the loss of all fluxes. Such conclusion was not predictable in the classical approach.Metabolic network analysis is an important step for the functional understanding of biological systems. In these networks, enzymes are made of one or more functional domains often involved in different catalytic activities. Elementary flux mode (EFM) analysis is a method of choice for the topological studies of these enzymatic networks. In this article, we propose to use an EFM approach on networks that encompass available knowledge on structure-function. We introduce a new method that allows to represent the metabolic networks as functional domain networks and provides an application of the algorithm for computing elementary flux modes to analyse them. Any EFM that can be represented using the classical representation can be represented using our functional domain network representation but the fine-grained feature of functional domain networks allows to highlight new connections in EFMs. This methodology is applied to the tricarboxylic acid cycle (TCA cycle) of  Metabolic pathway analysis is important for assessing network properties in (reconstructed) biochemical reaction networks Thermotoga maritima are dominated by a small number of structural domains performing diverse but mostly related functions. This new type of description provides insight into the evolution of metabolic networks The notion of elementary flux mode (EFM) is a key concept derived from the analysis of metabolic networks from a pathway-oriented perspective. An EFM is defined as the smallest sub-network that enables the metabolic system to operate at steady state with all irreversible reactions proceeding in the appropriate direction Bacillus subtilis. We observed that enzymes-domains EFM analysis, in the case of the TCA cycle, has revealed the key role of the domain involved in NAD/FAD binding. This kind of observation has several implications in the domain of systems and synthetic biology and could help biologists to test, not only the role of the enzymes, but the domains in biological systems and to create new pathways and biological functions.An essential aspect of our work is to represent the metabolic networks using a new paradigm by looking at the enzymes functional units based on the protein domains instead of enzymes represented as a whole entity. Protein domains are part of the protein showing specific sub-functions as functional units (and often sub 3D structure). Most of the proteins are made of different domains. For a given metabolism, such a modification in enzymatic activity representation leads to a metabolic network made of functional units (domain with a new set of connectivities) but representing the same metabolism. We based our approach on this newly represented functional networks and we calculated the elementary flux modes to check whether the EFMs of the classical representation can be represented using the functional domain network and to identify new ones. We illustrated our approach with the tricarboxylic acid cycle (TCA cycle) of the bacteria To build a metabolic network of functional units represented by enzyme domains, we have to identify for each enzyme, its structural domains and the elementary actions they provide. To identify the structural domains we perform a systematic molecular modelling of all enzymes of the network, thanks to a pipeline dedicated to protein structure modelling using Python and Perl routines, and use the homology modelling software Modeller version 9v4 Briefly said, this routine recursively takes the list of target sequences as input file (profile.py) and does a multiple  or single alignment , then generates the model (model.py). The best model of each enzyme is selected based on the lowest modeller objective function score (MOF). After this step, the structural domains of the proteins are assigned to each of the protein models using fastSCOP Bacillus subtilis pyruvate dehydrogenase multienzyme complex (PDH). This complex, encoded by the pdhABCD operon, is composed of three different enzymes: enzyme 1 or pyruvate decarboxylase, composed of alpha and beta subunits, coded by pdhA and pdhB respectively; enzyme 2 or dihydrolipoyl transacetylase coded by pdhC, and enzyme 3 or lipoamide dehydrogenase coded by pdhD. After the molecular modelling and the structural and functional domain assignment, we can observe that these enzymes are composed of 1 to 3 domains, each of them responsible for different activities (Geobacillus stearothermophilusWe illustrate our approach on the tivities  composini.e the reactions) and the domains having an enzymatic activity are represented by nodes which are linked to the reaction with the symbol Another objective of this work was to represent the metabolic networks in a different way, by looking at the enzymes' functionalities supported by the enzyme domains instead of the enzyme as a whole entity. As soon as the activities of each domain are obtained, the functional domain networks can be built. A functional domain network is now defined as a set of biological activities which can be involved in enzymatic reactions or molecular interactions. It is represented by an oriented hypergraph where the nodes represent the molecular objects  and the arrows represent the reactions or the molecular interactions. The symbol Our representation of metabolic networks as functional domain networks is just a step forward to implement details on structure/function of molecules involved in a given metabolic networks. The change of representation method does not modify the role of the network to metabolize biochemical compounds . By implementing structural (3D domain organization) and functional details, our representation has to deal with elementary actions either binding-related (interaction) or chemical transformation-related . We decided to analyze such a new network representation with EFMs since it has already been proved that EFMs can deal with both interaction and chemical transformations ++CoA→Acetyl-CoA+CO2+NADH. This short example shows that the functional domain network representation is functionally consistent with the classical representation. Interestingly, it contains much more functional details which can be later used for further in depth interpretations in complex systems.Based on this formalism, we have built the new pyruvate dehydrogenase functional domain network see . The funBacillus species and most other cells (see The citric or tricarboxylic acid cycle (TCA cycle) is the metabolic pathway that oxidizes acetyl-CoA to carbon dioxide ells see . Recentlells see , only twWe can note that if the PDH is inhibited then the EFM 2 is not functional. It is however still possible to produce ATP through the EFM 1. The functional domains representation presented in the previous section, shows that in fact, the PDH is the result of several enzymatic activities involving reaction intermediates. The fluxes extracted from the classical TCA network description do not explicitly take into account these intermediate reactions where enzymatic domains and intermediate metabolites are involved.subtilist database α–ketoglutarate dehydrogenase (ODH) complex have similar mechanisms and similar domains structures. Moreover, Hoch and Coukoulis α–ketoglutarate dehydrogenase contains the same subunit (pdhD) as the PDH complex of Bacillus subtilis. Based on the activities of each domain, see To enrich the metabolic network description of the TCA cycle with functional enzyme domains, we applied the same methodology as the one used to describe pyruvate dehydrogenase complex in the previous section. To perform domain identification and functional assignment, we used all the sequences of all TCA enzymes from In this system, there are again six elementary flux modes performing the same overall reactions but they are described in a finer way compared to the classical TCA network representation . Some ofOn the other hand, if an intermediate metabolite described in our new representation is consumed by an enzymatic activity in the local environment, then our functional domain representation allows to show it. This was not predictable in the classical system. Because our functional domain network description is more detailed, it allows to address and analyse in a finer way the metabolic reactions in a network and predict the crucial role of the specific NAD/FAD domain (c.4.1) of lipoamide dehydrogenase (pdhD) in ATP production.Bacillus subtilis we showed that the analysis of EFMs of domain networks can lead to additional conclusions compared to the analysis of EFMs from classical metabolic networks. In particular, it is now possible to take into account interactions with the local environment of newly described intermediate reactions and metabolites. In our analysis of the TCA cycle, we observed that the enzymes analysed are made of 1 to 5 domains that perform different roles in the system. This detailed knowledge about enzyme domains functions was used in the EFM analysis. Our results showed that we gain detailed informations about the key role of a given domain in a functional domain network. It is the case of the second domain of enzyme PdhD, also found in two different enzymes, and responsible for NAD/FAD binding. Lipoamide dehydrogenase (pdhD) or Lpd, the third enzyme in Mycobacterium tuberculosis's pyruvate dehydrogenase complex (PDH), helps Mycobacterium tuberculosis to resist host reactive nitrogen intermediates. Recent works on this specific enzyme complex show that Lpd is a potential target for anti-infectives against Mycobacterium tuberculosis. The authors clearly identify the need of a finer functional network description, to drive their drug-design strategy as complement to gene knockout studies The understanding and engineering of metabolic networks requires powerful theoretical methods such as pathway analysis, in which the topology of metabolic networks is considered. Previous works already showed the interest in looking at the detailed sub-molecular organization in metabolic networks One limitation of this kind of analysis is the availability of detailed informations about enzyme activities and the annotation of their domain-supported functions. This requires strong interaction between structural and functional enzymatic activities. Our method, combining the representation of metabolic networks at the functional molecular domain level and the EFM analysis, allows more relevant functional studies. In the context of synthetic biology where biological networks are engineered, our method can provide a robust way to perform relevant functional analysis. This should strongly impact our capacity to better anticipate the potential behaviour of a newly designed synthetic network File S1Metatool input and the sbml files of the PDH domain model.(ZIP)Click here for additional data file.File S2Metatool input and the sbml files of the classical TCA model.(ZIP)Click here for additional data file.File S3Metatool input and the sbml files of the TCA domain model.(ZIP)Click here for additional data file.


It is accepted that vertebrate annexins derive from a double genome duplication event. It has been postulated that a single domain annexin, if found, might represent a molecule related to the hypothetical ancestral annexin. The recent discovery of a single-domain annexin in a bacterium, Cytophaga hutchinsonii, apparently confirmed this hypothesis. Here, we present a more complex picture. Using remote sequence similarity detection tools, a survey of bacterial genomes was performed in search of annexin-like proteins. In total, we identified about thirty annexin homologues, including single-domain and multi-domain annexins, in seventeen bacterial species. The thorough search yielded, besides the known annexin homologue from C. hutchinsonii, homologues from the Bacteroidetes/Chlorobi phylum, from Gemmatimonadetes, from beta- and delta-Proteobacteria, and from Actinobacteria. The sequences of bacterial annexins exhibited remote but statistically significant similarity to sequence profiles built of the eukaryotic ones. Some bacterial annexins are equipped with additional, different domains, for example those characteristic for toxins. The variation in bacterial annexin sequences, much wider than that observed in eukaryotes, and different domain architectures suggest that annexins found in bacteria may actually descend from an ancestral bacterial annexin, from which eukaryotic annexins also originate. The hypothesis of an ancient origin of bacterial annexins has to be reconciled with the fact that remarkably few bacterial strains possess annexin genes compared to the thousands of known bacterial genomes and with the patchy, anomalous phylogenetic distribution of bacterial annexins. Thus, a massive annexin gene loss in several bacterial lineages or very divergent evolution would appear a likely explanation. Alternative evolutionary scenarios, involving horizontal gene transfer between bacteria and protozoan eukaryotes, in either direction, appear much less likely. Altogether, current evidence does not allow unequivocal judgement as to the origin of bacterial annexins.Annexins are Ca As of January 19th, 2013, 12795 PubMed articles mention annexins in the title or abstract, making them one of the well studied protein families. Annexins are a multi-functional protein family, widespread in eukaryotes. The archetypical annexin proteins (e.g. the vertebrate ones) are made of four repeated domains  of approximately 70 amino acid residues Giardia lambliaCytophaga hutchinsonii has been reported by Fernandez, Morgan and co-workers to be the most evolutionarily distant annexin discovered so far, however its phylogenetic relationship with other annexins is yet to be determined Stramenopiles-Alveolata-Rhizaria clade Molecular phylogenetic analysis suggested that plant and protist annexins evolved prior to their animal counterparts from a common ancestor Bacteroidetes/Chlorobi phylum, Cytophaga hutchinsoniiAlthough annexins have been described as eukaryote-specific proteins, a single domain annexin protein has been reported in a bacterium of the A survey of bacterial annexins, performed by the Jackhmmer tool using the definition of the annexin domain from the Pfam database (see Methods), brought twelve potential annexin proteins from eight bacterial species See . These dAlthough as many as 48 bacterial proteins in the curated RefSeq database at NCBI Archaea or viruses.Using the same approach, no annexins were found in Haliangium ochraceum, and a lipase domain in annexin from Microscilla marina. Also, different toxin domains were identified in annexins from Burkholderiales bacterium JOSHI_001, H. ochraceum and M. marina. Careful examination of the remaining regions of bacterial annexin sequences by sequence comparison tools, HHpred and FFAS, revealed additional annexin domains in some bacterial proteins , the ‘missing’ domains might have simply diverged beyond recognition. This is further corroborated by the genomic arrangement of annexin proteins in genomes of members of the The unique domain architectures of bacterial annexins, the presence of specific structural domains (e.g. enzymatic ones) and transmembrane regions suggest ancient origin of bacterial annexins. Of note, no such additional domains have been described in eukaryotic annexins.2+ -binding motif between helices A and B. This feature is very strongly conserved in bacterial annexins, present in more than half of the bacterial domains, and partly substituted in most of the remaining domains. Only in the Nakamurella multipartita annexin, the GxGTD motif is missing altogether. The Glu residue located in the D/E turn is less conserved , one to beta-Proteobacteria  and three to delta-Proteobacteria, Myxococcales . Annexin genes were also found in Actinobacteria, Rhodococcus imtechensis and Nakamurella multipartita DSM 44233. Of note, among the taxons that contain annexin-possessing species, there are several Gram-negative phyla  and the Gram-positive phylum Actinobacteria. Thus, these phyla are not part of a single “superphylum”.Bacterial annexins are found in just a few bacterial species spread throughout the bacterial tree of life See . Most anC. gingivalis). Most of the seventeen bacterial strains are aerobic and mesophilic. Although there is no apparent strict rule regarding the preferred habitat and lifestyle for the annexin-bearing bacteria, notably, majority of them live in an aquatic milieu where high frequency of horizontal gene transfer (HGT) has been reported Of the seventeen annexin-bearing species, twelve species are aquatic, mostly marine, while one species was isolated from waste-water treatment plant see . Three aFor elucidation of the evolutionary relationships between bacterial and eukaryotic annexin domains, building of a reliable phylogenetic tree is not straightforward. As a primary approach, we used an alternative, albeit less strict solution. The approximate topology of sequence similarity networks can be visualized by CLANS, a graph approach utilising sets of pairwise sequence similarities Giardia nor with the annexin domains from other unicellular eukaryotes. Some bacterial annexins apparently show no similarity to eukaryotic annexins or to each other in the CLANS graph. This is only because the graph utilises BLAST derived similarity relationships. The original discovery of bacterial annexin-like domains was done using more sensitive tools . Nevertheless, the clustering picture reflects the large diversity of bacterial annexins and suggests their ancestral origin. Yet, the clustering analysis does not suffice to clearly resolve the origins of bacterial and eukaryotic annexins.In the CLANS clustering, using various sequence similarity significance thresholds, one obtains a consistent picture in which bacterial annexin domains locate away from the known annexins See . NotablyHaliangium ochraceum and Flavobacteriae, e.g. Kordia algicida) group together which could suggest that these bacterial strains probably acquired single annexin domains, and then duplication occurred within some species. The Flavobacteriae branch is split into two sub-branches indicating a common origin of flavobacterial annexins and the presence of a pair of single-domain annexins in the flavobacterial ancestor.In a phylogenetic tree of bacterial annexins See , some doBurkholderiales bacterium, G. aurantiaca, C. coralloides and M. stipitatus. Such a pattern suggests that the individual multiple annexin domains within some bacterial proteins did not arise by gene duplication in the particular strains. Instead, this pattern suggests that a multi-domain annexin protein existed already at some earlier time point in bacteria and was later transferred to various phyla and individual strains by vertical evolution or, possibly, by horizontal gene transfer.Strikingly, in some cases, different annexin domains from a single bacterial protein occur in two separate main branches of the tree  present in Haliangium annexin proteins, regulation of proteolysis may be a function of at least some bacterial annexins. One specific hypothesis would be annexin-mediated regulation of protease excretion in K. algicida, where protease excretion has been shown to be required for algicidal action of the bacterium on diatoms Several hypotheses as to specific roles of bacterial annexins may be proposed. Analysis of the genomic neighbourhoods of bacterial annexins gives no general clue to their function, however in Capnocytophaga gingivalisBurkholderiales bacterium JOSHI_001.Another hypothesis could be related to a role of bacterial annexins in the regulation of biofilm formation Myxococcales, Corallococcus coralloides DSM 2259, Haliangium ochraceum, Myxococcus stipitatusSome bacteria are known to form multicellular, spore-filled fruiting bodies in processes analogous to multicellular morphogenesis and cellular differentiation in eukaryotes. It may be hypothesized that such bacteria might be more likely than others to use eukaryotic-like proteins to assist in this kind of processes. Among the annexin-possessing bacteria, fruiting bodies are formed by some Burkholderiales bacterium JOSHI_001, the annexin molecule has a peculiar domain architecture. It is predicted to be cytosolic, membrane anchored, with at least three annexin domains proximal to the membrane anchor, and a Colicin D domain, with a sequence significantly similar to the Colicin D toxin which is acting as specific ribonuclease against tRNA(Arg) In Microscilla marina ATCC 23134, the annexin domain is fused to a lipase type 3 domain (Pfam PF01764). The lipase active site Ser, His and Asp residues are perfectly conserved. Thus, in M. marina, the annexin domain may be involved in regulating a lipase activity. This is not surprising given the known involvement of eukaryotic annexins in lipid signalling M. marina annexin possesses also a novel toxin domain, Ntox_3 (Toxin_58) predicted to act as RNAse In Haliangium ochraceum annexins  that lacks the Ntox_3 domain, a second metalloprotease domain  located at the C-terminus may act as toxin.Interestingly, some Haliangium ochraceum, has been reported to possess the first actin homologue identified in a bacterium Some bacterial annexin proteins have one or more predicted transmembrane regions see . This suBacteroidetes/Chlorobi, Gemmatimonadetes, Proteobacteria (beta- and delta-), and Actinobacteria.We have surveyed putative bacterial annexins and found only thirty four of them in seventeen bacterial species coming from as many as four phyla: In summary, three evolutionary scenarios could explain the observed occurrence of annexin domains in bacterial proteins:annexin domain(s) were present in the Last Universal Common Ancestor (LUCA) and were subsequently lost in many lineages, including most bacterial taxa. Alternatively, in many of the apparently ‘annexin-free’ lineages the annexin homologues might have diverged beyond recognition by the currently available sequence comparison methods.annexin domain(s) originate from some specific bacterial phylum and were transferred to early eukaryotes  by horizontal gene transfer.annexin domain(s) originate from an early eukaryotic ancestor, and were transferred to bacteria by one or more events of horizontal gene transfer.similarity between eukaryotic and bacterial annexins could be a result of convergent evolution and there could be no homology between them. However, the conservation of typical annexin sequence motifs and overall sequence similarity makes this possibility seem unlikely.Bacteroidetes/Chlorobi phylum, there are 10 species possessing annexins while 88 complete genomes for this phylum are listed at ncbi.nlm.nih.gov. For beta-Proteobacteria, there is one annexin-bearing species and 216 genomes known, for delta-Proteobacteria the numbers are 3 vs 48, for Actinobacteria 2 vs 144 and finally for Gemmatimonadetes, 1 vs 1. Thus, this scenario would implicate massive annexin gene losses in many lineages or very divergent evolution leading to the oversight of more divergent annexins in the current analysis. The gene losses could be related to lack of functional requirement for annexins in many bacterial lineages.The scenario number 1 has to account for the paucity and scarce phylogenetic distribution of annexins in bacteria. In the On the other hand, horizontal gene transfer (HGT) of annexins between eukaryotes and bacteria and within bacteria would be probably the most parsimonious explanation of the observed presence of rare bacterial homologues. The phenomenon of HGT between multicellular eukaryotes and bacteria is gaining in recognition since the landmark studies of gene exchange between intracellular bacterial parasites and their insect hosts Although the HGT scenario could be most parsimonious one, no strong evidence for it can be seen in the sequence data; in particular there are no strong similarities observed between annexins from bacteria and eukaryotes as would be expected in case of xenologues. Assuming the HGT scenario holds, we could not determine whether eukaryote-bacteria HGT occurred once or several times. Also, several events of bacteria-to-bacteria HGT would be required to account for the current annexin distribution.Nevertheless, notwithstanding the peculiar phylogenetic distribution of annexins in bacteria, the most likely evolutionary scenario that the data suggest is the following: bacterial annexins descend from ancestral molecules that were probably already multidomain proteins, and were present early in bacterial evolution, or, possibly, in the last universal common ancestor, LUCA. This view is supported by the large variation of bacterial annexin sequences and by the rich repertoire of structural domain combinations observed alongside annexin domains in bacterial proteins. However, until more distant homologues of annexins are found in prokaryotes and possibly viruses, until more genomes of unicellular eukaryotes from different taxonomic lineages are charted, the origin of the bacterial annexins cannot be proven beyond any doubt. Further, the precise delineation biological roles of annexins in bacteria awaits detailed experimental studies.For identification of remote annexin homologues in bacteria, the Jackhmmer tool from the HMMER suite was used The Jackhmmer and HHsenser hits were confirmed using HMMER3 Additionally, the curated RefSeq database at NCBI For additional domain assignments in the bacterial annexin proteins, the HMMER3 Multiple sequence alignment of bacterial annexin domains was built using the Promals3D and MUSCLE programs For sequence logos, the WebLogo tool was used http://phylemon.bioinfo.cipf.es by removing columns with more than 50% of gaps. Then, sequences containing 12 gaps or more were removed. The set thus built consisted of 774 annexin domain sequences. The CLANS algorithm For survey of sequence similarities among annexin domains, a set of representative annexin domains was built as follows. All the occurrences of the Pfam annexin domain from the Uniprot database  with the addition of bacterial annexin domains identified in this study were clustered at 60% sequence identity threshold using CD-HIT http://www.atgc-montpellier.fr server).For constructing a phylogenetic tree, the MAFFT alignment of representative annexin domain sequences was used, the Prottest server was used to select the most appropriate model of protein evolution, and the LG+G model was found to suit the data best. Thus, the phylogenetic tree was built using the PhyML algorithm  of bacterial annexin domains. Branches with approximate bootstrap values (aLRT) above 0.9. marked with dots. Branch colouring by taxonomy: yellow: Bacteroidetes/Chlorobi, green: Gemmatimonadetes, blue: beta-Proteobacteria, red: delta-Proteobacteria, grey: Actinobacteria.(PNG)Click here for additional data file.Figure S2Expanded version of  Phylogenetic tree of 774 representative annexin domain sequences, numbers correspond to identifiers shown in the list.(PDF)Click here for additional data file.Figure S3HHpred results of sequence similarity analysis of wider regions of bacterial annexin proteins, showing weak sequence similarities to annexin tetrads.(PDF)Click here for additional data file.Figure S4Multiple sequence alignment  of bacterial annexin domains. This is full, unedited version of the alignment shown in (PNG)Click here for additional data file.Table S1List of bacterial annexin domains. Protein identifiers, location of the domains within the protein sequence, and their sequence similarity to known eukaryotic annexins.(XLS)Click here for additional data file.


Envoplakin, periplakin and desmoplakin are cytoskeletal proteins that provide structural integrity within the skin and heart by resisting shear forces. Here we reveal the nature of unique hinges within their plakin domains that provides divergent degrees of flexibility between rigid long and short arms composed of spectrin repeats. The range of mobility of the two arms about the hinge is revealed by applying the ensemble optimization method to small-angle X-ray scattering data. Envoplakin and periplakin adopt ‘L’ shaped conformations exhibiting a ‘helicopter propeller’-like mobility about the hinge. By contrast desmoplakin exhibits essentially unrestricted mobility by ‘jack-knifing’ about the hinge. Thus the diversity of molecular jointing that can occur about plakin hinges includes ‘L’ shaped bends, ‘U’ turns and fully extended ‘I’ orientations between rigid blocks of spectrin repeats. This establishes specialised hinges in plakin domains as a key source of flexibility that may allow sweeping of cellular spaces during assembly of cellular structures and could impart adaptability, so preventing irreversible damage to desmosomes and the cell cytoskeleton upon exposure to mechanical stress. The plakin family of proteins connect the intermediate filaments that form the cell cytoskeleton to cadherin-mediated cell-cell junctions, and must be able to withstand mechanical stresses to provide integrity to tissues Dysfunctional plakin proteins contribute to diverse diseases, with autoantibodies and mutations perturbing their activities with profound consequences. Envoplakin and periplakin are target antigens in the autoimmune blistering disease paraneoplastic pemphigus, and offer epitopes in their plakin domains in vitroEnvoplakin, periplakin and desmoplakin share a common architecture with a N-terminal head region, a central coiled coil rod domain and a C-terminal tail domain . The heaSpectrin repeats have traditionally been viewed as rod-like building blocks that assemble into long extended structures. X-ray diffraction studies of crystallized sets of SRs have revealed exclusively linear structures, including a rigid rod for the first four SRs of the desmoplakin plakin domain E.coli strain BL21 (DE3) and purified as described for human desmoplakin 6 tagged plakin domains were then bound to Ni sepharose, washed and eluted with imidazole.DNA encoding the plakin domains of human envoplakin (residues 144–891) and periplakin (residues 131–871) was cloned in-frame with glutathione S-transferase (GST) in the expression vector pGEX-6P-1 . DNA encoding the plakin domain of human desmoplakin (residues 180–1022) was cloned as described A Beckman XL-1 analytical ultracentrifuge with an eight-cell 50Ti rotor was used for sedimentation velocity analysis of purified proteins. Proteins in 100 mM NaCl and 20 mM sodium phosphate (pH7.4) buffer were loaded into double-sector cells at three concentrations between 0.05 and 4 mg/ml (0.5–35 µM). Samples were centrifuged at 25,000 and 40,000 rpm for 20 h at 20°C and detected at 280 nm. Sedimentation coefficients and molecular masses were determined using the continuous c(s) analysis method and SEDFIT software Proteins were dialysed into 20 mM sodium phosphate (pH7.2) buffer and CD spectra were measured at room temperature with a Jasco J-810 spectropolarimeter using a 10 mm path length cuvette. The protein concentrations were 0.1 mg/ml (1–4 µM) and the scanned wavelength range 190–260 nm. Spectra were analysed by DICHROWEB Rg) and maximum particle sizes (Dmax) were determined using GNOM SAXS data were collected at the X33 beamline at the EMBL Hamburg outstation as described 2) was then calculated by GAJOE. GAJOE was used to calculate Rg and Dmax values for the random pools and selected ensembles, and distribution graphs comparing the distances between selected plakin domain amino acids in the random pools and selected ensembles were calculated.In order to gauge the flexibility of the envoplakin, periplakin and desmoplakin plakin domains from their SAXS scattering profiles, three types of models were calculated. Firstly, rigid ‘linear extended SR3-CT’ models were created using the I-TASSER server E. coli with N-terminal glutathione S-transferase (GST) and C-terminal 6xHis tags, purified by glutathione chromatography, cleaved to remove the GST tag and further purified by Ni affinity chromatography  spectroscopy . The expIn order to identify the potential regions of flexibility in plakin domains, the sequences of 45 homologues from across vertebrate evolutionary space were aligned based on conservation of structural elements . This yiDue to their potential importance in plakin protein function we investigated the degree of flexibility in the linkers of plakin domain structures. Initially Kratky plots were created to investigate potential flexibility in each plakin domain. Each plakin domain revealed a peak followed by a slight downward trend indicative of a predominantly structured protein with some flexible elements . To inve2 values reported for each fit  or lower (envoplakin and desmoplakin) than those obtained by analysis by GNOM Click here for additional data file.Figure S22) of the plakin domains of (a) envoplakin, (b) periplakin and (c) desmoplakin were used to investigate the potential flexibility. A subtle peak is observed at a value of 1 followed by a slight downward trend indicative of a predominantly folded protein with flexible elements for each plakin domain.The Kratky plots (I(s)/I/0*s(EPS)Click here for additional data file.Figure S3Distance distributions were calculated for the random pools (blue) and selected ensembles (red) for (a) envoplakin (P144-V746); (b) periplakin (E131-L725); (c) desmoplakin (W180-L881).(EPS)Click here for additional data file.


H. sapiens, S. cerevisiae, C. elegans, D. melanogaster, and 15 plant genomes. These DCNs have the hallmark features of scale-free networks. We investigated the possibility of using DCNs to predict protein and domain functions. Based on our experiment conducted on 66 randomly selected proteins, the best of top 3 predictions made by our DCN-based aggregated neighbor-counting method achieved a semantic similarity score of 0.81 to the actual Gene Ontology terms of the proteins. Moreover, the top 3 predictions using neighbor-counting, χ2, and a SVM-based method achieved an accuracy of 66%, 59%, and 61%, respectively, when used to predict specific Gene Ontology terms of human target domains. These predictions on average had a semantic similarity score of 0.82, 0.80, and 0.79 to the actual Gene Ontology terms, respectively. We also used DCNs to predict whether a domain is an enzyme domain, and our SVM-based and neighbor-inference method correctly classified 79% and 77% of the target domains, respectively. When using DCNs to classify a target domain into one of the six enzyme classes, we found that, as long as there is one EC number available in the neighboring domains, our SVM-based and neighboring-counting method correctly classified 92.4% and 91.9% of the target domains, respectively. Furthermore, we benchmarked the performance of using DCNs to infer species phylogenies on six different combinations of 398 single-chromosome prokaryotic genomes. The phylogenetic tree of 54 prokaryotic taxa generated by our DCNs-alignment-based method achieved a 93.45% similarity score compared to the Bergey's taxonomy. In summary, our studies show that genome-wide DCNs contain rich information that can be effectively used to decipher protein function and reveal the evolutionary relationship among species.Protein Domain Co-occurrence Network (DCN) is a biological network that has not been fully-studied. We analyzed the properties of the DCNs of  These molecules interact and form networks to carry out biological functions. Departing from the traditional reductionist approach of studying single targets, systems biology aims to identify the cellular molecular components and their interactions and analyze cellular responses at a large scale using high-throughput experimental techniques  and computational methods Currently, network biology primarily focuses on metabolic, gene regulatory, and/or protein-protein interaction networks Despite many successful applications, the study of protein interaction networks is hindered by two serious problems domain co-occurrence networks (DCN) de novoHere, we propose to use protein domain co-occurrence networks (DCN). A DCN is a graph consisting of all the protein domain types of a species as nodes. Two domain types (i.e. nodes) are connected by an edge if they co-exist in one protein Accurate and reliable. Domain co-existence (or combination) relationship constructed from sequential analysis is almost 100% accurate, which is much more reliable than protein interactions predicted from experimental approaches . (2) Higher coverage. A DCN constructed from homologous sequence analysis usually can recall about 70% of domain co-existence relationships Easy to construct. It is much easier to construct the DCN of a genome by comparing its protein sequences against known protein domain databases, such as Pfam Domain combination represents a strong, permanent and definite interaction between domains, which can be captured by In addition, DCNs also have two other distinct features. First, each node represents a domain type instead of an instance (i.e. sequence). Since DCNs of differing species share a large number of common domain types, DCNs can be more readily compared or aligned across species than sequence-based networks, such as protein-protein interaction networks. Second, an edge in a DCN represents a permanent combination relationship that is stronger than the relationship defined in protein-protein interaction networks, which is often intangible. Therefore, DCNs can provide richer information about the functional relationship between connected domains. Therefore, DCN is a very valuable target for biological network research and a useful tool for studying the evolution, function, and interaction of proteins.H. sapiens, S. cerevisiae, C. elegans, D. melanogaster, and 15 plant genomes and performed statistical analysis and attack simulations. A “domain” used in our study is a Pfam entry, corresponding to a Pfam domain “family”, such as “Pkinase_Tyr (PF07714)” and “Helicase_C (PF00271)” In this study, we constructed the DCNs for Arabidopsis thaliana (Arabidopsis), Chlamydomonas reinhardtii , Zea mays (maize), and Physcomitrella patens (moss). Supplementary Figure S1 shows the node-degree distributions of H. sapiens, S. cerevisiae, C. elegans, D. melanogaster, and 15 plant genomes. This log-log plot shows that the number of nodes with a specific degree value (degree is the number of edges linked to a node) mathematically follows a power law distribution, because the logarithmic relationship between two variables approximates a linear relationship. This property shows that the DCNs are scale-free networksP(k) is the probability of having a node with k edges linking to it, and γ is a species-specific constant.We analyzed the statistical properties of the DCNs of 15 plant species, yeast, and human, and found that they share several common features. k . Supplementary Figure S2 shows the shortest path length distributions of H. sapiens, S. cerevisiae, C. elegans, D. melanogaster, and 15 plant genomes. The majority of the node pairs have a path shorter than 5, indicating that most of node pairs can be reached within five steps.n is calculated by:ne is the number of connected node pairs among immediate (one edge away) neighboring nodes of the central node n, and nk is the number of immediate neighboring nodes of the central node nFigure S3 shows the average clustering coefficient distributions of H. sapiens, S. cerevisiae, C. elegans, D. melanogaster, and 15 plant genomes. Average clustering coefficient is another important property of scale-free networks. For un-directed networks, the clustering coefficient of a node Experiments conducted on yeast and human DCNs show that they follow the same properties (data not shown). Therefore, they share the same characteristics as other scale-free networks, such as social networks, the World-Wide Web, and some biological networks, including protein-protein interaction networks and metabolic networks.Scale-free networks are usually very recalcitrant  to random removal of nodes, named as “failure”, but very vulnerable to “attack”, in which the nodes with highest degree are removed first The Average Shortest Path (ASP) of a network is the average of all the shortest paths between all pairs of nodes. A smaller ASP means the network has a better interconnectivity. If a network is fragmented, i.e., containing several independent sub-graph(s) whose nodes have no connections to the other sub-graphs, the path between these un-connected nodes become infinity, so does the ASP of the entire networks. As mentioned in the “Helicase_C, the ASP of the network went to infinity, indicating that this attack behavior caused at least one node to be disconnected with the remaining network (i.e. network fragmented). Compared with “attack”, scale-free networks are usually less vulnerable to “failure”, in which nodes are removed randomly, because the probability of randomly selecting a node with high degree value is very low, according to the power law distribution. This trend is also found true in DCNs, but to a lesser extent. We performed 10 rounds of failure simulations on the DCNs of human, yeast, and Arabidopsis, and the average number of nodes deleted before the DCN had at least one nodes fragmented were 5.5, 4.0, and 7.3, with standard deviations 4.2, 3.5, and 5.9, respectively. These are the 0.4%, 1.6%, and 1.6% of all the vertices in their main networks, respectively.We performed simulations on the DCNs of yeast, Arabidopsis, maize, soybean, and human. Taking the main network of Arabidopsis as an example, after removing the node with the highest degree, domain The DCN was considered fragmented even if one node became fragmented. In order to study the interconnectivity of the networks after some of the nodes became fragmented and to make comparison with the study performed on yeast protein-protein interaction networks In summary, the results show that DCNs follow the general robustness characteristics of scale-free networks, but are more vulnerable to perturbations than protein interaction networks and other high-density scale-free networks. One possible reason is that the “domain” used in our DCNs sometime may be a coarse unit, which sometime may be further divided into smaller units. The other reason is probably because DCNs are less densely connected than protein interaction networks and the internet, or because of the much smaller size of DCNs. This may indicate that the interconnectivity of DCNs is not indispensably important, mainly because of the nature of DCNs. For the networks showing great robustness, their functions can only be carried out by interconnectivity, such as the Internet. On the other hand, for the domains that do not have a direct connection in the DCN, they may also be able to physically interact with the others and carry out functions. For example, two domains from two different proteins may be interactive and bind with each other, but they do not co-exist in one protein.2, and a SVM-based method, as shown in 2 method not only considers the occurrence frequencies of GO terms in the neighboring nodes, but also the overall distributions of the GO terms in the entire DCN. The SVM-base method uses the known examples of the target domains to train a SVM model, and then uses this SVM model to make predictions. Details can be found in the “2 value methods, we calculated the percentage of correctly predicted domains. For the SVM-based method, we performed a leave-one-domain-out cross-validation, and report the average percentage of correctly predicted domains. These three methods were tested on the target domains whose “radius one” neighboring domains have at least one GO term available. The number of target domains for Arabidopsis, yeast, and human are 736, 518, and 953, respectively.To assess the capability of DCN to predict the GO terms of a target domain, we developed and evaluated three methods: neighbor-counting, χThe evaluation criteria mentioned above is a yes-or-no binary criteria. Considering that two different GO terms may share functional similarity, we also calculated the average similarity score between the best prediction and the real GO terms Table 2, and  and For the SVM-based method, we tested each of the five set features built on GO terms, EC numbers, amino acid sequences, secondary structures, and solvent accessibilities. We first used only GO term frequency and kept adding one of the other features. After comparing the performances of several leave-one-domain-out cross-validations, we found that the “GO terms frequency” feature, generated from the neighboring domains, had the largest positive influence on final prediction accuracy. Adding any one of the remaining features slightly decreased the accuracy by 1–3% on Arabidopsis. We also tested four kernel functions for the SVM model: linear, polynomial, radial basis function (RBF), and sigmoid tanh, and found that the linear kernel function yielded the best accuracy. Therefore, the SVM method used in our evaluations  and 2 only –2-based method when considering only top 1 predictions; and the simple neighbor-counting method, which relies solely on DCN topology, achieves the best performance, in terms of both the “yes-or-no” evaluation and the average similarity between the best predictions and real GO terms. The accuracy of top 3 predictions using neighbor counting on three species is more than 65% and top 1 predictions more than 47%. The average GO similarity of top 3 predictions is more than 0.81 and top 1 prediction more than 0.66. The good performance suggests that the connectivity of DCNs contains rather rich information for protein function prediction. To further investigate the neighbor counting method, we plotted Our results –4 show 2-based method, the accuracy drops by 10% on average (data not shown). This indicates that directly expanding the neighboring radius may not help improving accuracy, as more noise may be included. In the future, we plan to incorporate more advanced algorithms such as graph kernels We also conducted experiments on integrating both radius one and two neighboring domains. For neighbor-counting and SVM-based method, our results show that the accuracy has a small decrease, by 2–3% on average, after including radius two neighbors (data not shown). For the χ2, and SVM-based method, to predict their functions , the sensitivity of negatives (Qn), and the Matthews correlation coefficient (MCC) pQ  and  nQ of SVM-based method are 0.66 and 0.87 on average, which indicates the predictions are more biased towards the negative side. However, the neighbor-reference method shows more balanced results, with the pQ  and nQ  of 0.79 and 0.78. The overall classification accuracy and the Matthews correlation coefficients of these two methods are very similar .For SVM-based enzyme “yes or no” predictions, we found that using “GO term frequency” feature with linear kernel function generated the best accuracy. Adding other features such as EC number, secondary structure, and solvent accessibilities did not significantly influence performance (data not shown). To classify an enzyme domain into one of six enzyme classes, we developed and evaluated two methods, neighbor-counting and a SVM-based method Table 10We also tested other kernel functions in the SVM model and tried to integrate radius two neighboring domains. Linear kernel function worked the best, and integrating more neighboring domains slightly decreased the accuracy (data not shown).http://archive.geneontology.org/lite/2010-12-11/go_20101211-seqdb-data.gz). The functions of these 100 proteins had been annotated, and their GO terms stored in the Gene Ontology database. Therefore, we treated these GO terms as their real GO terms. We ran HHsearch H. sapiens, S. cerevisiae, C. elegans, D. melanogaster, 15 plants species, and 398 single-chromosome prokaryotic species. In total, 96 proteins had at least one PSI-BLAST hit with an e-value< = 0.01, and the other four had an e-value: 1.8, 1.9, 2.7, and 3.7, respectively. The DCN of the species of the most significant hit of a protein is used to predict is function.In order to test the performance of applying the aggregated neighbor-counting method to predict protein functions in a real life scenario, we randomly selected 100 proteins from the Gene Ontology FASTA sequence database  A method based on Structural Protein Domain Universe Graph (PDUG) We evaluated our DCNs-alignment-based phylogeny inference method on six different combinations of 398 single-chromosome prokaryotic species (strains). We benchmarked our DCNs-based method and compared its performances with that of five other state-of-the-art phylogeny inference methods Table 13n is the number of unique domains in the two DCNs), the average time of aligning two DCNs of the 398 species is ∼1.6 seconds using a single 2.4 GHz Intel(R) Xeon CPU at a 64-bit Linux machine.We present a set of new DCN-based methods to predict protein function and to infer species phylogeny. We tested our methods on the genomes of several representative species and a large phylogeny benchmark. The results showed that DCN-based methods can predict protein function rather accurately, probably because DCNs constructed from all the proteins of a genome are rather complete and reliable in comparison with other protein networks. Our unique approach of constructing phylogeny by aligning species' DCNs also yields good performance that are comparable to other established methods, making it a complementary and valuable addition to the repository of phylogenetic analysis tools.Despite initial promising results, there is still room to improve DCN-based function prediction and phylogeny inference. The “domains” used in our current version of DCNs are “domain families” defined by the Pfam database, which sometime could be further divided into sub-units. Using a more specific definition of domains might show different characteristics of DCNs. For example, we plan to use the structural domain definitions in the SCOP database In our current work, when predicting the GO terms of a target domain, we treated different GO terms in the neighboring domains independently. However, the semantic similarities between GO terms may be incorporated into the SVM and neighbor counting methods to improve protein function prediction. More advanced graph-kernels and random-walk graph kernels Homo sapiens , Saccharomyces cerevisiae , Drosophila melanogaster , 15 plants species, and 398 single-chromosome prokaryotic species . The 15 plant species include: Chlamydomonas reinhardtii , Ostreococcus lucimarinus, Ostreococcus tauri, Ostreococcus RCC809, Chlorella vulgaris, Volvox carteri, Physcomitrella patens (moss), Selaginella moellendorffii (gemmiferous spikemoss), Oryza sativa (rice), Zea mays (maize), Sorghum bicolor (sorghum), Vitis vinifera (grape), Arabidopsis thaliana, Populus trichocarpa (black cottonwood), and Glycine max (soybean). The protein sequences of Arabidopsis thaliana were downloaded from TAIR8 Oryza sativa (rice) from the “TIGR rice genome annotation resource” Zea mays (maize) from the “MAGI website” (http://magi.plantgenomics.iastate.edu/), Vitis vinifera (grape) from “Genoscope” (http://www.genoscope.cns.fr/externe/GenomeBrowser/Vitis/), Glycine max (soybean) from “phytozome” website (http://www.phytozome.net/soybean), and the others from the Joint Genome Institute (JGI) website (http://www.jgi.doe.gov/). These 15 plants species represent virtually all major evolutionary stages of plants, including alga, primitive land plants, and higher plants.The only input data needed for constructing a domain co-occurrence network (DCN) is the whole genome protein sequences of the target organism. In order to have a broad coverage of various species with known genome sequences and cover several model species for our experiments, we construct DCNs for ftp://ftp.sanger.ac.uk/pub/databases/Pfam/Tools/), together with Pfam databases and hidden Markov model (HMM) libraries. This domain searching tool is locally installed, which incorporates HMMER (http://hmmer.janelia.org/) and BLAST to search against Pfam domain libraries. Each protein sequence of the target species was searched against Pfam using PfamScan. The domain hits with an e-value< = 0.01 were kept. When constructing a DCN, each domain is considered as a node, or vertices, in the undirected graph; and every two domains (i.e. nodes) are connected by an edge if they co-exist in one protein Arabidopsis thaliana proteome visualized by Cytoscape The program PfamScan is downloaded from the Pfam spb1_C+FtsJ) can be inferred from another protein (e.g. KOW+Supt5) that is connected through a path in the sub-graph and the central node, e.g. domain S4.Because domains involved in the same biological process are more likely to co-occur in one protein, domains with similar functions tend to cluster in DCNs. S4, we can use the GO terms of its radius one (immediate) neighboring domains: FtsJ, tRNA-synt_1b, Ribosomal_S4, PseudoU_synth_2, KOW, and Ribosomal_S4e to infer the GO terms of the central domain. We can also do that by incorporating radius two neighboring domains, including Spb1_C, Supt5, and RS4NT. In our experiments of predicting domain functions, we used Arabidopsis thaliana, Saccharomyces cerevisiae, and Homo sapiens as benchmarks. We included Arabidopsis thaliana because it has been well-studied as a model plant species and has high-quality genome annotation. We incorporated Saccharomyces cerevisiae and Homo sapiens because they are often used as model species for protein function prediction.In order to demonstrate the potential of DCNs to infer protein function, we first focused on predicting the specific GO terms Three methods were used in our experiments to predict domain functions. The first is the most straightforward method, majority vote or neighbor-counting: count the appearance number of every GO term occurred in the neighboring nodes of the target domain; and then rank the GO terms based on this occurrence frequency; and the top ranked GO term(s) is (are) considered as the predicted GO term(s). This method was used by 2 value:i denotes a GO term, for example, GO:0090295, negative regulation of transcription by nitrogen catabolites; in denotes the observed number of GO term i in m-neighboring domains; and ie denotes the expected number of GO term i appeared in m nodes.The second method in our experiment considers the distribution of every GO term in the entire DCN. This method was used by Besides the above two methods, we also designed a Support Vector Machine (SVM) -based method. For each domain, we generated a feature-vector which included: (1) The occurrence frequency of each of the 31,398 GO terms defined by Gene Ontology a in b, c, and d in b and c, so the training dataset of “GO term 2” contains two positive examples with feature vector b and c. The negative examples are randomly selected from all of the domains that do not have the GO term. If a feature vector of a domain has been included as a positive example, it will not be selected as a negative example.According to TAIR8, Arabidopsis has 1,454 domains in total. 736 of these domains have at least one GO term available in Pfam (i.e. domains with known function) and have at least one GO term available in its radius one neighboring nodes. From these 736 nodes, we performed a leave-one-domain-out cross-validation. lighta in A binary SVM model was trained for each of the GO terms occurring in the remaining domains using SVMMany nodes in the DCN have a self-loop, i.e. an edge starting from and ending to the same node. If a domain with self-loop is selected as a target domain, it also exists in its own radius one neighboring domains, which makes the GO terms of the neighboring domains contain the real GO term(s) of the target domain. Therefore, the self-loop of every target domain, if exists, is removed.Oxidoreductases, Transferases, Hydrolases, Lyases, Isomerases, and Ligases, which have an EC number starting from 1 to 6, respectively. A mapping file between GO terms and Enzyme Commission (EC) numbers was downloaded from Gene Ontology website . If a domain contains at least one GO term that maps to an EC number, this domain is considered as an enzyme domain.We also used DCN to predict whether a domain is an enzyme domain; and if so, which of the six Enzyme Commission (EC) classes it belongs to. These six enzyme classes are: lightTo predict whether a domain is an enzyme domain, we applied SVM along with all the features mentioned in the previous section. In Arabidopsis, there are a total of 307 enzyme domains that have at least one GO term or EC number available/known in the radius one neighboring domains, i.e. the GO term and EC classes occurrence frequency are not all 0. Each of these domains was considered as a positive example. The negative examples consisted of the domains with known functions that are not enzyme-related, i.e. none of its GO terms maps to an EC number. In this way, we eliminated the “Domain with Unknown Function” (DUF) domains, because these domains may not be non-enzyme domains. We gathered a total of 429 negative domains/examples in Arabidopsis. A binary SVM model was built by SVMmulti-classEach of the 307 Arabidopsis enzyme domains having an enzyme class number starting from 1 to 6 based on the first digit of their EC numbers. SVMftp://toolkit.lmb.uni-muenchen.de/HHsearch/databases/) to detect Pfam domains. In order to determine the most relevant species of the query proteins, a PSI-BLAST search was performed against the whole genome protein sequences of H. sapiens, S. cerevisiae, C. elegans, D. melanogaster, 15 plants species, and 398 single-chromosome prokaryotic species, and the species having the most significant PSI-BLAST hit  was considered the relevant species. The DCN of the relevant species was used to make functional prediction for the query protein. The aggregated neighbor-counting was then used to predict functions. From the HHsearch result report of each query protein, the detected Pfam domain(s) with an e-value< = 0.01 was (were) extracted. For each of these extracted domain(s), we retrieved its (their) radius one neighboring domains, and the neighboring domains of all extracted domains were put together. The GO terms for these neighbor domains were retrieved from Pfam database and ranked by their occurrence frequencies. This list of ranked GO terms was our final prediction. To predict the functions of a query protein, we used a DCN-based aggregated neighbor-counting method. Given the amino acid sequence of a query protein, we run a profile-profile alignment tool HHsearch To construct a phylogenetic tree of a group of species, we aligned their DCNs to identify conserved sub-networks, or common network topology, which can reveal the evolutionary significant patterns between species. This novel method is different from existent sequence-based methods, as when comparing the network topologies, since it uses the entire proteome of each species to infer the phylogenetic relationship.a and b, we define the DCN of species a as a graph b as a graph a and b) are defined as set A) and (B), which are b, c, d, and e as shown in A) and graph (B): c–d, c–e, and e–d are picked as shown in A) and (B) are shown in c and d, which is due to the fact that two proteins contain both domain c and d. These two redundant edges are considered as one edge. Thus the number of unique edges on the common vertices is 6. Therefore, the similarity score between graph To align the DCNs of two species n is the total number of unique domains in the two DCNs, the computational complexity will be at most This alignment algorithm is straightforward, easy to implement, and has low computational complexity compared to complicated global alignment algorithms. If Given a group of species, we used our graph alignment algorithm for pair-wise comparisons and generated a distance matrix (distance score = 1 - similarity score). We generated a phylogenetic tree using the program “NEIGHBOR” in the phylogeny inference package PHYLIP Bergey's Manual of Determinative BacteriologyLactobacillus casei has a Bergy's code B13.3.2.1.1 indicating it belongs to kingdom Bacteria, Division 13 (Firmicutes), Class 3 (Bacilli), Order 2 , Family 1 (Lactobacillaceae), and Genus 1 (Lactobacillus). In our work, we use Bergey's classification as the reference, and compared our phylogenetic trees to this reference. The similarity between our phylogenetic tree and Bergey's classification was calculated by ComPhy Unlike the phylogenetic study of more advanced organisms, which has plenty of morphology and archeology evidence available, there are still uncertainties in bacteria taxonomy. However, the scientific community usually considers the classification presented in the book In order to comprehensively evaluate the potential of DCNs to infer phylogeny, we conducted experiments on six datasets with different combinations of 398 single-chromosome prokaryotic genomes (strains). These six datasets were previously used to compare several different phylogeny inference methods Figure S1The node degree distributions of H. sapiens, S. cerevisiae, C. elegans, D. melanogaster, and 15 plant genomes.(TIF)Click here for additional data file.Figure S2The shortest path length distributions of H. sapiens, S. cerevisiae, C. elegans, D. melanogaster, and 15 plant genomes.(TIF)Click here for additional data file.Figure S3The average clustering coefficient distributions of H. sapiens, S. cerevisiae, C. elegans, D. melanogaster, and 15 plant genomes.(TIF)Click here for additional data file.