Evolutionary history and functional implications of protein domains and their combinations in eukaryotes
© Itoh et al.; licensee BioMed Central Ltd. 2007
Received: 9 February 2007
Accepted: 25 June 2007
Published: 25 June 2007
In higher multicellular eukaryotes, complex protein domain combinations contribute to various cellular functions such as regulation of intercellular or intracellular signaling and interactions. To elucidate the characteristics and evolutionary mechanisms that underlie such domain combinations, it is essential to examine the different types of domains and their combinations among different groups of eukaryotes.
We observed a large number of group-specific domain combinations in animals, especially in vertebrates. Examples include animal-specific combinations in tyrosine phosphorylation systems and vertebrate-specific combinations in complement and coagulation cascades. These systems apparently underwent extensive evolution in the ancestors of these groups. In extant animals, especially in vertebrates, animal-specific domains have greater connectivity than do other domains on average, and contribute to the varying number of combinations in each animal subgroup. In other groups, the connectivities of older domains were greater on average. To observe the global behavior of domain combinations during evolution, we traced the changes in domain combinations among animals and fungi in a network analysis. Our results indicate that there is a correlation between the differences in domain combinations among different phylogenetic groups and different global behaviors.
Rapid emergence of animal-specific domains was observed in animals, contributing to specific domain combinations and functional diversification, but no such trends were observed in other clades of eukaryotes. We therefore suggest that the strategy for achieving complex multicellular systems in animals differs from that of other eukaryotes.
Protein domains are the basic building blocks that determine the structure and function of proteins, and they may be considered the units of protein evolution. Furthermore, combinations of protein domains provide a broad spectrum for potential protein function [1–4]. Eukaryotic genome sequencing projects have revealed complicated and varied domain architectures . In particular, the number of domains in a protein sequence is greater in higher eukaryotes, which have elaborate multicellular bodies. Sophisticated domain combinations are thought to have contributed to complicated multicellular functional systems, such as cell adhesion, cell communication, and cell differentiation. Here we perform a systematic survey of the eukaryotic genome sequence data currently available to elucidate how domain combinations evolved and how they are related to specific cellular functions in eukaryotes.
It is already known that the number of combinations involving a particular domain is quite varied, and that the distribution of the number of combination partners follows a power law distribution [6–10]. Preference for partner domains in combination varies depending on the domain. Functionally related genes frequently fuse and result in multidomain proteins that have multiple functions [11, 12]. In addition, for the three superkingdoms, namely eukaryotes, eubacteria, and archaea, kingdom-specific domains tend to combine within each other [6, 7, 9], and the domains that emerged later in eukaryotes tend to have a large number of combination partners . These observations are based on comparative analysis of extant eukaryotes or prokaryotes whose genomes have been sequenced. With recent rapid progress in various eukaryotic genome sequencing projects, comparative analysis of the evolutionary relationships among phylogenetic groups of eukaryotes, as opposed to among individual species, has become possible. This allows more detailed examination of the differences among specific domains and their combinations among phylogenetic groups of eukaryotes.
In this work, we focus on the relationship of domain combinations and functional diversification in eukaryotes, with consideration of hierarchical classification based on their phylogenies. We also explore how domains and their combinations are distributed and conserved in each group of eukaryotes. In order to define specific domains and combinations for each phylogenetic group, we modified the method developed by Mirkin and coworkers , which estimates ortholog contents of ancestral species based on the most parsimonious method. The most parsimonious method is a commonly used approach to estimating ancestral ortholog content [14–18].
Our analysis uncovers differences in specific domains and their combinations among different phylogenetic groups of eukaryotes. We observe a large number of animal-specific and vertebrate-specific domain combinations. However, those domains having a large number of combination partners are different in animals and vertebrates, and their functions are strongly linked to their characteristic functions that evolved in the common ancestors of animals and vertebrates. Examples include animal-specific combinations in tyrosine phosphorylation systems and vertebrate-specific combinations in complement and coagulation cascades. In animals, especially in vertebrates, the average connectivity of animal-specific domains is markedly high. In contrast, the older domains tend to have greater average connectivity in other groups of eukaryotes. These observations suggest that the properties of domains are nonuniform in terms of generating domain combinations.
Our findings also made it possible to reconstruct an evolutionary history of the domain combinations in each clade of eukaryotes and to observe changes of combinations based on a global network analysis. The global features of the reconstructed evolution of the network are consistent with the observed differences in properties of group-specific domains. Therefore, our analysis enables us to link local differences among group-specific domains with the global features of domain combination changes during evolution. From these observations, it is suggested that the strategy for achieving complex multicellular systems might be different, even among eukaryotes, in terms of the preference for generation of domain combinations.
Assignment of domains and their combinations
Estimation of group-specific domains and combinations
We next identified group-specific domains for each group of eukaryotes, where 47 eukaryotes were divided into 14 groups. We classified the groups hierarchically, based on their phylogenetic relationships (for further details, see Additional data file 1). We considered two additional groups, namely deuterostomes (vertebrates plus ascidian) and opisthokonta (animals plus fungi), in the hierarchical classification. Because horizontal gene transfer among eukaryotes can be disregarded [14, 15, 20], we assigned the domain to the ancestral group when derived groups and species possess the domain. Among 3,104 domains in eukaryotes, 1,439 domains were shared in all eukaryotes, but the rest were group specific (Figure 3). We observed greater numbers of group-specific domains in higher multicellular eukaryotes: animals, deuterostomes, and land plants.
We then examined group-specific domain combinations. In contrast to the case of group-specific domains, a group-specific combination cannot be defined by simply tracing the last common ancestor because identical combinations can arise independently in different groups. We again used the method proposed by Mirkin and coworkers  to reconstruct the most parsimonious scenario and estimated that only 128 combinations were generated in multiple groups. In Figure 3, we show the number of group-specific combinations in the major eukaryote groups (also see Additional data file 7 [Supplementary Table 2]). In animals and deuterostomes, the numbers of group-specific domain combinations were large, at 875 and 610, respectively, in addition to the large numbers of group-specific domains themselves. On the other hand, the number of combinations specific to land plants was small compared with the number of specific domains.
Characterization of animal- and deuterostome-specific domain combinations
The Pfam domains having many combination partners in animal-specific combinations
Number of partners
Protein kinase domain
Laminin EGF-like (domains III and V)
Phorbol esters/diacylglycerol binding domain (C1 domain)
Ras association (RalGDS/AF-6) domain
Phosphotyrosine interaction domain (PTB/PID)
B-box zinc finger
Leucine rich repeat amino-terminal domain
DEAD/DEAH box helicase
Cyclic nucleotide-binding domain
WAP-type (whey acidic protein) 'four-disulfide core'
Type III restriction enzyme, res subunit
Leucine rich repeat carboxyl-terminal domain
Repeat of unknown function (DUF1136)
FERM domain (Band 4.1 family)
The Pfam domains having many combination partners in deuterostome-specific combinations
Number of partners
von Willebrand factor type A domain
WD domain, G-beta repeat
SAM domain (sterile alpha motif)
Lectin C-type domain
Kunitz/Bovine pancreatic trypsin inhibitor domain
Collagen triple helix repeat (20 copies)
Trypsin Inhibitor like cysteine rich domain
IQ calmodulin-binding motif
Latrophilin/CL-1-like GPS domain
GCC2 and GCC3
Calponin homology (CH) domain
Zn-finger in Ran binding protein and others
Fibronectin type II domain
Extracellular link domain
F5/8 type C domain
Zinc finger C-x8-C-x5-C-x3-H type (and similar)
Kazal-type serine protease inhibitor domain
Kazal-type serine protease inhibitor domain
These hub domains in group-specific combinations are presumably involved in different functions that have evolved in the common ancestors of respective groups. In animal-specific combinations, the protein kinase domain (Pkinase) was found to have the greatest number of partners. Other hub domains in animal-specific combinations include the SH2 domain, the protein-tyrosine phosphatase domain (Y_phosphatase), and the phosphotyrosine interaction domain (PID), which are all related to tyrosine phosphorylation signaling (Table 1) [21–24].
On the other hand, domains involved in the complement and blood coagulation cascade were frequently found in deuterostome-specific combinations (Table 2). In the complement and blood coagulation cascade, the trypsin-like serine protease domain plays an important role, and the cascade is distributed among species in deuterostomes. We observed the trypsin-like serine protease domain (Trypsin) and its inhibitors (TIL, Kazal_1, Kazal_2, and Kunitz_BPTI) as hub domains in deuterostome-specific combinations. Furthermore, other domains involved in the cascade, such as von Willebrand factor type A domain (VWA), Lectin (lectin_C), F5/8 type C domain (F5_F8_type_C), and kringle domain, were also hub domains in deuterostome-specific combinations.
Group-specificity and connectivity of domains
Global features of domain combination networks
Using this procedure we traced the changes of the γ value along the phylogenetic hierarchy for animals and fungi (Figure 5c; also see Additional data file 7 [Supplementary Table 2]). In the lineage of H. sapiens the γ value rapidly decreased after the divergence of animal and fungi, whereas in the lineage of S. cerevisiae the γ value gradually increased. In order to examine this difference, we defined the union domain combination network in each lineage of H. sapiens and S. cerevisiae. All nodes and all edges were accumulated in the union network along the phylogenetic hierarchy without considering the loss of domains or combinations. The γ values for the union networks are shown in dashed lines in Figure 5c, indicating a much greater decrease for the lineage of S. cerevisiae. Similar analyses were performed for all other lineages and the result is indicated by the dashed line in Figure 5b. Fungi and protists apparently exhibit a large decrease in γ value in the union network, probably reflecting a large number of gene losses.
Specific domain combinations in animals and deuterostomes
Using the 47 eukaryotic genomes now available, we were able to analyze protein domains and their combinations that are specific to different phylogenetic groups of eukaryotes. The number of domains per protein increased in higher multicellular species, especially in animals (Figure 1). We also observed large numbers of animal-specific or deuterostome-specific domain combinations (Figure 3). These observations indicate a rapid increase in complexity in domain architecture, which is termed 'domain accretion' .
Analyzing the hub domains in these group-specific combinations, we found that domain architectures became more complex within the systems that rapidly evolved in the common ancestors of animals and of deuterostomes (Tables 1 and 2). In animals, protein tyrosine phosphorylation mediated by protein tyrosine kinase plays a crucial role in the processing of signals from the environment and in the regulation of various cellular functions that were developed in early animals. In contrast, in the deuterostome-specific combinations, we found many hub domains involved in the complement and blood coagulation cascade, which is commonly known as a deuterostome-specific innate immune system involving serine protease [28, 29]. Note that invertebrates, such as arthropods, also have an independently evolved innate immune system that involves serine protease, but its molecular mechanism is different from that of deuterostomes [30, 31].
As shown in Figure 4, animal-specific domains largely contributed to the increase in these animal-specific or deuterostome-specific combinations. In previous reports it was suggested that rearrangement of existing domains in new combinations facilitated evolution of complex systems in multicellular organisms . However, our results indicate that the emergence of highly connected animal-specific domains was essential for the evolution of animals. In contrast, there are no highly connected domains in other multicellular species such as land plants and multicellular fungi, although they actually have a large number of domain combinations. Therefore, in nonanimal multicellular eukaryotes, an increase in complexity of domain architecture did not depend on new group-specific domains. However, the number of sequenced plant and multicellular fungi genomes is still very small, and further analysis taking phylogenetic relationships into consideration will refine our observations.
Alternative definitions of domains and combinations
Pfam domains are defined based on biologic knowledge. Thus, the criteria for defining sequence families differ from one domain to another depending on the granularity of knowledge regarding the domain. For example, some domains that were grouped together in the past have been categorized separately in newer versions of Pfam because of increased knowledge regarding that domain. Because group specificity of the Pfam domains is affected by these subfamily classifications, this granularity may have affected our results. Therefore, we examined the consistency of our results by using different definitions of domains in which we hierarchically classified eukaryote-specific Pfam domains into more granular subfamilies (see Materials and methods, below).
The number of subfamily divergences of eukaryote-specific domains
Mammals + bird
For completeness, we further analyzed the affect of the definition of the domain combination networks on our results. In related work, domain combination networks were simply defined as the co-occurrence of two domains in a protein sequence without considering domain order. Using this definition, all trends in our results were conserved (data not shown).
Comparison with previous findings on the connectivity of domains
Wuchty  indicated that the connectivity of domains did not correlate with their age and that domains with high connectivity emerged late in eukaryote evolution. These observations were based only on results from a comparison of prokaryotes, S. cerevisiae, Caenorhabditis elegans, and Drosophila melanogaster. Therefore, the results indicating high connectivity in late eukaryotes could not be generally claimed; high connectivity was actually found mostly in animals, and not necessarily in fungi and plants. In animals, we also found that the animal-specific domains have very high connectivity, which correlated well with their work. However, when considering group-specific domains in nonanimal groups, we observed a correlation between connectivity and age, in which the oldest domains inherited from the commonote had the greatest connectivity among nonanimal eukaryotes (Figure 4). Note that we computed connectivity based on the average domain connectivity for each age. That is, although in principle older domains had more combination partners, domain combinations differed depending on domain or clade identity, and as a result we could obtain these correlations between connectivity and age.
Linking molecular analysis and network analysis
By tracing and comparing the changes of domain combination networks together with the phylogenetic relationships between eukaryotes, we observed differences in the evolution of the combination networks in H. sapiens and S. cerevisiae (Figure 5c). In the H. sapiens lineage, the γ value decreased after the divergence of animals from fungi. Evolutionary analysis using molecular clock and fossil data suggests that the period between animal-fungi divergence and deuterostome-invertebrate (insects plus nematoda) divergence was about 300 million years, and that the lengths of the periods differed little from each other [33–36] (see the legend to Figure 5c). It is therefore suggested that the decrease of the γ value occurred rapidly. Such growth concurrent with the decrease of γ is called accelerated growth, which is a general and widespread feature of growing networks [37, 38]. Accelerated network growth during animal evolution is due to the high connectivity of animal-specific domains.
In the S. cerevisiae lineage, the γ value of the domain combination network increased, whereas that of the union network decreased. These observations suggest that there were more complicated domain networks in the ancestral species of fungi, and gene loss strongly affected network evolution in the S. cerevisiae lineage. In our dataset, most fungi are unicellular yeasts, and it is suggested that the size of the yeast genomes diminished by gene loss events during evolution . Similarly, the difference between the γ value of domain networks and that of union networks in protists was large, which can also be explained by gene loss events. Many of the protists are parasitic, and it is suggested that they have come to depend on their hosts, in the process losing a number of genes [40–43].
To describe the scale-free behavior and evolutionary mechanisms of various biologic networks, evolutionary models have often been studied [44–48]. The simplest of these models is the preferential attachment model , in which new nodes link to an existing node with a probability proportional to its degree. In this model, older nodes have greater connectivity, and the degree distribution is conserved during network growth. However, our results show that the degree distributions were not conserved during evolution because of the accelerated growth in animals and the diminished genome in fungi. Moreover, the connectivity of animal-specific domains was very high (although, in nonanimal groups, average connectivity could be correlated with the age of specific domains). This apparent disagreement is supported by findings reported by Przytycka and coworkers [50, 51]; they found the topologic structure of the observed co-occurrence network of real biological data was to be different from synthetically generated random scale-free networks constructed according to the preferential attachment model.
Our findings indicate that the changes in domain combinations differed between periods of evolution as well as among phylogenetic groups, implying that the evolutionary driving force for domain combination generation changed during eukaryotic evolution. Therefore we claim that simple comparison of extant species using a uniform model is insufficient in this case. Consequently, individual species lineages, periods of evolution, and differences in domain propensity for generating combinations must all be taken into consideration.
Comparison of group specificities of domains and their combinations in different phylogenetic groups of eukaryotes revealed nonuniform properties that could be strongly correlated with the characteristics and evolution of the respective groups. In plants, fungi, and protists, more ancestral domains tend to be reused as hub domains, but the domains that emerged early in animals tend to have large numbers of combination partners. These domain combinations apparently contributed to the functional diversification of animals, including the tyrosine phosphorylation signaling and the coagulation cascades. The distinction of animal and nonanimal groups also helps reconcile two previously reported conflicting views on preferential attachment in the evolution model for the domain combination network.
Materials and methods
Proteins, domains, and phylogenetic relationship
We used the proteomes of 47 eukaryotes and 223 prokaryotes obtained from the genome and draft genome sequences stored in the Kyoto Encyclopedia of Genes and Genomes (KEGG) GENES and DGENES databases  and the Ensembl database  (Figure 1 for eukaryotes). The domains of the protein sequences were assigned based on the Pfam database using the HMMER package [54, 55] with threshold E value below 10-3. When two or more domains overlapped (>50% of the shortest domain length) on a protein sequence, we selected the domain with the most significant E value. We used precomputed HMMER results stored in KEGG Sequence Similarity Database (SSDB) with Pfam ver. 14 for protein sequences in KEGG GENES, and we computed the HMMER assignments for proteins obtained from KEGG DGENES and Ensembl with the same Pfam version as stored in KEGG SSDB.
To define specific domains and combinations for each clade of eukaryotes that are hierarchically classified (Figure 1), we consider the most parsimonious scenario of gains and losses of domains and their combinations by considering phylogenetic trees for eukaryotes and prokaryotes. Because of the uncertainty of some phylogenetic relationships and the low coverage rate of the draft genomes, we used multifurcated trees. We inferred a multifurcated consensus tree among 47 eukaryotes based on the recent view of eukaryotic evolution [56, 57] as shown in Additional data file 1. On the other hand, there was no clear consensus regarding the relationships among prokaryotes. Therefore, the phylogenetic tree for prokaryotes was inferred from 16S ribosomal RNA sequences and was arranged as a multifurcated tree.
The most parsimonious scenario with multifurcated trees
Although it is commonly believed that a new gene emerges only once in a single lineage during evolution, genes can also be gained through horizontal gene transfer . Mirkin and coworkers  developed an algorithm to estimate the most parsimonious scenario by taking into consideration horizontal gene transfer and the differences in frequency between gene gains and gene losses. Their method computes the scenario with the smallest number of events, taking into consideration the difference in frequency between ortholog gains and losses.
In step 3, graft the child c in C i to τ i and graft the child c in C n to τ n . In step 4, consider two cases - τ i and τ n inherit the gene (Figure 6c) and τ i and τ n do not inherit the gene (Figure 6d) - and count the events for τ i and τ n . In step 5, apply the method of Mirkin and coworkers  to bifurcated branching at v with children τ i and τ n .
If the tentative nodes τ i and τ n inherit a gene from internal node v, then the smallest number of events is satisfied when the gene is lost in τ n ; this is because the numbers of events for children c i become smaller when the gene is inherited from their parent τ i , and those for children c n become smaller when the gene is lost in their parent τ n and not inherited from τ n (Figure 6c). If τ i and τ n do not inherit the gene from v, then the smallest number of events is satisfied when the gene is gained in τ i (Figure 6d). Any phylogenetic relationships within nodes in C i or within C n do not affect the smallest number of events because no event should occur among them.
Domains inherited from the commonote
Domains existing in eukaryotes include domains inherited from the commonote, which is the common ancestor of eukaryotes, eubacteria, and archaea. Horizontal gene transfer often occurred from eukaryotes to prokaryotes, and hence it may not necessarily be true that a domain emerged in the commonotes, even if the domain is contained in both eukaryotes and prokaryotes. So we estimated the most parsimonious scenario of domain gains and losses in prokaryotes with the method described above, to find domains inherited from the commonote. As a result, domains in eukaryotes that existed in the common ancestor of eubacteria or the common ancestor of archaea were estimated, and we assume that these domains were inherited from the commonote to eukaryotes.
Specific domains for each clade of eukaryotes
Horizontal gene transfer between major clades of eukaryotes can be disregarded [14, 15, 20]. Thus, the most parsimonious scenario is that a domain emerged in the last common ancestor of the existing species having proteins with the domain and only gene loss followed. We defined that the domain be specific for the clade rooted at the common ancestor.
Generation of domain combinations
Identical domain combinations may have been independently generated in multiple clades. Thus, we estimate the parsimonious scenario with the method in the previous section by using the consensus tree of eukaryotes. Then, as in the case of specific domains, we defined a combination as being specific for the clade rooted at the common ancestor in which the combination was generated.
Frequencies of gene gains and losses are not the same, and we assume that gene losses occurred more frequently than gene gains. It is crucial for parsimonious estimation to assess the ratio of the frequency of losses to gains, and this ratio is referred to as 'gain penalty' in the method proposed by Mirkin and coworkers . We implemented the gain penalty in the same way. The ratio is not the same for individual genes and domains, and hence it is difficult to estimate these values, but we found that this was not essential for the present work because all tendencies were found to be conserved when we tested values between 1 and 3. Here, we show the results when the gain penalty was set to 3 for all domains and combinations.
Fitting to the power law distribution
To reduce the effect of noise in the data, we calculated the cumulative distribution of the degrees in each domain combination network. The cumulative distribution of the power law distribution also follows a power law, but with a different exponent. When the exponent of the original distribution is γ, the exponent for the cumulative distribution becomes γ - 1 . Thus, we obtained γ by least squares fitting of the cumulative distribution.
Estimation of specific subfamilies
Domain subfamily emergence was defined according to the species included in the subtree of the dendrogram obtained from hierarchical clustering of the domain sequences. To construct multiple alignments of each domain, we extracted sequences corresponding to the domain defined by a hidden Markov model profile in Pfam and aligned them to the profile by using HMMalign in the HMMER package. After eliminating insertions not aligned to the profiles, we carried out hierarchical clustering of the domain sequences with UPGMA using QuickTree , which computes a distance matrix with the method used in CLUSTAL W .
Then, a branch at an internal node v on the dendrogram can be one of the following two types, namely orthologous branching by the divergence of species
S(c1) ∩ S(c2) = ∅,
and paralogous branching by gene duplication
S(c1) ∩ S(c2) ≠ ∅,
where c1 and c2 are the children of v (Figure 7b,c). Here, we defined subfamilies as having diverged with gene duplication, and we only considered the first duplication if serial duplications occurred more than once in the same ancestral species. Therefore, we extracted the internal nodes v at paralogous branches satisfying the following condition:
lca(S(c2), T Species ) ∈ ancestors(c1, T Species )
Where lca(S, T Species ) denotes the last common ancestor of a set of species S, and ancestors(s, T Species ) denotes the set of all nodes in the path from the root to the parent of node s in the phylogenetic tree T Species (all ancestral species at each branch of the clade to species s in evolution). Then, the time when the subfamily diverged was estimated to be lca(S(c1), T Species ). Because the domain sequences were hierarchically classified, subfamilies were defined hierarchically.
Additional data files
The following additional data are available with the online version of this paper. Additional data file 1 contains a figure showing detailed phylogenetic relationship among 47 eukaryotes. Additional data file 2 contains a figure showing the number of combination partners of group-specific domains in deuterostomes. Additional data file 3 contains a figure showing the number of combination partners of group-specific domains in invertebrates. Additional data file 4 contains a figure showing the number of combination partners of group-specific domains in fungi. Additional data file 5 contains a figure showing the number of combination partners of group-specific domains in protists. Additional data file 6 contains a figure showing the number of combination partners of group-specific domains in plants. Additional data file 7 contains tables showing the statistics of domain assignments for eukaryotes (Supplementary Table 1) and all results of history reconstruction (Supplementary Table 2).
We should like to thank Dr Ichigaku Takigawa, Dr Kiyoko F Aoki-Kinoshita, and Dr Nelson Hayes for their helpful comments. This work was supported by grants from the Ministry of Education, Culture, Sports, Science and Technology of Japan and the Japan Science and Technology Agency. The computational resource was provided by the Bioinformatics Center, Institute for Chemical Research, Kyoto University.
- Murzin A, Brenner S, Hubbard T, Chothia C: SCOP: a structural classification of proteins database for the investigation of sequences and structures. J Mol Biol. 1995, 247: 536-540. 10.1006/jmbi.1995.0159.PubMedGoogle Scholar
- Riley M, Labedan B: Protein evolution viewed through Escherichia coli protein sequences: introducing the notion of a structural segment of homology, the module. J Mol Biol. 1997, 268: 857-868. 10.1006/jmbi.1997.1003.PubMedView ArticleGoogle Scholar
- Orengo C, Michie A, Jones S, Jones D, Swindells M, Thornton J: CATH: a hierarchic classification of protein domain structures. Structure. 1997, 5: 1093-1108. 10.1016/S0969-2126(97)00260-8.PubMedView ArticleGoogle Scholar
- Vogel C, Bashton M, Kerrison N, Chothia C, Teichmann S: Structure, function and evolution of multidomain proteins. Curr Opin Struct Biol. 2004, 14: 208-216. 10.1016/j.sbi.2004.03.011.PubMedView ArticleGoogle Scholar
- Koonin E, Aravind L, Kondrashov A: The impact of comparative genomics on our understanding of evolution. Cell. 2000, 101: 573-576. 10.1016/S0092-8674(00)80867-3.PubMedView ArticleGoogle Scholar
- Apic G, Gough J, Teichmann S: Domain combinations in archaeal, eubacterial and eukaryotic proteomes. J Mol Biol. 2001, 310: 311-325. 10.1006/jmbi.2001.4776.PubMedView ArticleGoogle Scholar
- Apic G, Gough J, Teichmann S: An insight into domain combinations. Bioinformatics. 2001, 17 (Suppl 1): S83-89.PubMedView ArticleGoogle Scholar
- Wuchty S: Scale-free behavior in protein domain networks. Mol Biol Evol. 2001, 18: 1694-1702.PubMedView ArticleGoogle Scholar
- Ye Y, Godzik A: Comparative analysis of protein domain organization. Genome Res. 2004, 14: 343-353. 10.1101/gr.1610504.PubMedPubMed CentralView ArticleGoogle Scholar
- Wuchty S, Almaas E: Evolutionary cores of domain co-occurrence networks. BMC Evol Biol. 2005, 5: 24-10.1186/1471-2148-5-24.PubMedPubMed CentralView ArticleGoogle Scholar
- Marcotte E, Pellegrini M, Ng H, Rice D, Yeates T, Eisenberg D: Detecting protein function and protein-protein interactions from genome sequences. Science. 1999, 285: 751-753. 10.1126/science.285.5428.751.PubMedView ArticleGoogle Scholar
- Enright A, Ouzounis C: Functional associations of proteins in entire genomes by means of exhaustive detection of gene fusions. Genome Biol. 2001, 2: R34-10.1186/gb-2001-2-9-research0034.View ArticleGoogle Scholar
- Mirkin B, Fenner T, Galperin M, Koonin E: Algorithms for computing parsimonious evolutionary scenarios for genome evolution, the last universal common ancestor and dominance of horizontal gene transfer in the evolution of prokaryotes. BMC Evol Biol. 2003, 3: 2-10.1186/1471-2148-3-2.PubMedPubMed CentralView ArticleGoogle Scholar
- Koonin E, Fedorova N, Jackson J, Jacobs A, Krylov D, Makarova K, Mazumder R, Mekhedov S, Nikolskaya A, Rao B, et al: A comprehensive evolutionary classification of proteins encoded in complete eukaryotic genomes. Genome Biol. 2004, 5: R7-10.1186/gb-2004-5-2-r7.PubMedPubMed CentralView ArticleGoogle Scholar
- Ogura A, Ikeo K, Gojobori T: Estimation of ancestral gene set of bilaterian animals and its implication to dynamic change of gene content in bilaterian evolution. Gene. 2005, 345: 65-71. 10.1016/j.gene.2004.11.036.PubMedView ArticleGoogle Scholar
- Makarova K, Wolf Y, Mekhedov S, Mirkin B, Koonin E: Ancestral paralogs and pseudoparalogs and their role in the emergence of the eukaryotic cell. Nucleic Acids Res. 2005, 33: 4626-4638. 10.1093/nar/gki775.PubMedPubMed CentralView ArticleGoogle Scholar
- Babenko V, Krylov D: Comparative analysis of complete genomes reveals gene loss, acquisition and acceleration of evolutionary rates in Metazoa, suggests a prevalence of evolution via gene acquisition and indicates that the evolutionary rates in animals tend to be conserved. Nucleic Acids Res. 2004, 32: 5029-5035. 10.1093/nar/gkh833.PubMedPubMed CentralView ArticleGoogle Scholar
- Snel B, Bork P, Huynen M: Genomes in flux: the evolution of archaeal and proteobacterial gene content. Genome Res. 2002, 12: 17-25. 10.1101/gr.176501.PubMedView ArticleGoogle Scholar
- Finn R, Mistry J, Schuster-Böckler B, Griffiths-Jones S, Hollich V, Lassmann T, Moxon S, Marshall M, Khanna A, Durbin R, et al: Pfam: clans, web tools and services. Nucleic Acids Res. 2006, D247-D251. 10.1093/nar/gkj149. 34 Database
- Aguinaldo A, Turbeville J, Linford L, Rivera M, Garey J, Raff R, Lake J: Evidence for a clade of nematodes, arthropods and other moulting animals. Nature. 1997, 387: 489-493. 10.1038/387489a0.PubMedView ArticleGoogle Scholar
- Pawson T: Protein modules and signalling networks. Nature. 1995, 373: 573-80. 10.1038/373573a0.PubMedView ArticleGoogle Scholar
- Pawson T: Specificity in signal transduction: from phosphotyrosine-SH2 domain interactions to complex cellular systems. Cell. 2004, 116: 191-203. 10.1016/S0092-8674(03)01077-8.PubMedView ArticleGoogle Scholar
- Yaffe M: Phosphotyrosine-binding domains in signal transduction. Nat Rev Mol Cell Biol. 2002, 3: 177-186. 10.1038/nrm759.PubMedView ArticleGoogle Scholar
- Machida K, Mayer B: The SH2 domain: versatile signaling module and pharmaceutical target. Biochim Biophys Acta. 2005, 1747: 1-25.PubMedView ArticleGoogle Scholar
- Jeong H, Tombor B, Albert R, Oltvai Z, Barabási A: The large-scale organization of metabolic networks. Nature. 2000, 407: 651-654. 10.1038/35036627.PubMedView ArticleGoogle Scholar
- Jeong H, Mason S, Barabási A, Oltvai Z: Lethality and centrality in protein networks. Nature. 2001, 411: 41-42. 10.1038/35075138.PubMedView ArticleGoogle Scholar
- Wuchty S: Small worlds in RNA structures. Nucleic Acids Res. 2003, 31: 1108-1117. 10.1093/nar/gkg162.PubMedPubMed CentralView ArticleGoogle Scholar
- Nonaka M, Yoshizaki F: Evolution of the complement system. Mol Immunol. 2004, 40: 897-902. 10.1016/j.molimm.2003.10.009.PubMedView ArticleGoogle Scholar
- Nonaka M: Evolution of the complement system. Curr Opin Immunol. 2001, 13: 69-73. 10.1016/S0952-7915(00)00184-9.PubMedView ArticleGoogle Scholar
- Iwanaga S, Lee B: Recent advances in the innate immunity of invertebrate animals. J Biochem Mol Biol. 2005, 38: 128-150.PubMedView ArticleGoogle Scholar
- Iwanaga S: The molecular basis of innate immunity in the horseshoe crab. Curr Opin Immunol. 2002, 14: 87-95. 10.1016/S0952-7915(01)00302-8.PubMedView ArticleGoogle Scholar
- Scott J, Pawson T: Cell communication: the inside story. Sci Am. 2000, 282: 72-79.PubMedView ArticleGoogle Scholar
- Doolittle R, Feng D, Tsang S, Cho G, Little E: Determining divergence times of the major kingdoms of living organisms with a protein clock. Science. 1996, 271: 470-477. 10.1126/science.271.5248.470.PubMedView ArticleGoogle Scholar
- Kumar S, Hedges S: A molecular timescale for vertebrate evolution. Nature. 1998, 392: 917-920. 10.1038/31927.PubMedView ArticleGoogle Scholar
- Hasegawa M, Thorne J, Kishino H: Time scale of eutherian evolution estimated without assuming a constant rate of molecular evolution. Genes Genet Syst. 2003, 78: 267-283. 10.1266/ggs.78.267.PubMedView ArticleGoogle Scholar
- Douzery E, Snell E, Bapteste E, Delsuc F, Philippe H: The timing of eukaryotic evolution: does a relaxed molecular clock reconcile proteins and fossils?. Proc Natl Acad Sci USA. 2004, 101: 15386-15391. 10.1073/pnas.0403984101.PubMedPubMed CentralView ArticleGoogle Scholar
- Dorogovtsev S, Mendes J: Effect of the accelerating growth of communications networks on their structure. Phys Rev E Stat Nonlin Soft Matter Phys. 2001, 63: 025101-PubMedView ArticleGoogle Scholar
- Dorogovtsev S, Mendes J: Accelerated growth of networks. Handbook of Graphs and Networks: From the Genome to the Internet. Edited by: Bornholdt S, Schuster H. 2002, Berlin, Germany: Wiley-VCH, 320-343.Google Scholar
- Dujon B, Sherman D, Fischer G, Durrens P, Casaregola S, Lafontaine I, De Montigny J, Marck C, Neuvéglise C, Talla E, et al: Genome evolution in yeasts. Nature. 2004, 430: 35-44. 10.1038/nature02579.PubMedView ArticleGoogle Scholar
- Abrahamsen M, Templeton T, Enomoto S, Abrahante J, Zhu G, Lancto C, Deng M, Liu C, Widmer G, Tzipori S, et al: Complete genome sequence of the apicomplexan, Cryptosporidium parvum. Science. 2004, 304: 441-445. 10.1126/science.1094786.PubMedView ArticleGoogle Scholar
- Berriman M, Ghedin E, Hertz-Fowler C, Blandin G, Renauld H, Bartholomeu D, Lennard N, Caler E, Hamlin N, Haas B, et al: The genome of the African trypanosome Trypanosoma brucei. Science. 2005, 309: 416-422. 10.1126/science.1112642.PubMedView ArticleGoogle Scholar
- Loftus B, Anderson I, Davies R, Alsmark U, Samuelson J, Amedeo P, Roncaglia P, Berriman M, Hirt R, Mann B, et al: The genome of the protist parasite Entamoeba histolytica. Nature. 2005, 433: 865-868. 10.1038/nature03291.PubMedView ArticleGoogle Scholar
- Xu P, Widmer G, Wang Y, Ozaki L, Alves J, Serrano M, Puiu D, Manque P, Akiyoshi D, Mackey A, et al: The genome of Cryptosporidium hominis. Nature. 2004, 431: 1107-1112. 10.1038/nature02977.PubMedView ArticleGoogle Scholar
- Rzhetsky A, Gomez S: Birth of scale-free molecular networks and the number of distinct DNA and protein domains per genome. Bioinformatics. 2001, 17: 988-996. 10.1093/bioinformatics/17.10.988.PubMedView ArticleGoogle Scholar
- Dokholyan N, Shakhnovich B, Shakhnovich E: Expanding protein universe and its origin from the biological Big Bang. Proc Natl Acad Sci USA. 2002, 99: 14132-14136. 10.1073/pnas.202497999.PubMedPubMed CentralView ArticleGoogle Scholar
- Karev W, Rzhetsky B, Koonin : Birth and death of protein domains: a simple model of evolution explains power law behavior. BMC Evol Biol. 2002, 2: 18-10.1186/1471-2148-2-18.PubMedPubMed CentralView ArticleGoogle Scholar
- Deeds E, Shakhnovich B, Shakhnovich E: Proteomic traces of speciation. J Mol Biol. 2004, 336: 695-706. 10.1016/j.jmb.2003.12.066.PubMedView ArticleGoogle Scholar
- Qian J, Luscombe N, Gerstein M: Protein family and fold occurrence in genomes: power-law behaviour and evolutionary model. J Mol Biol. 2001, 313: 673-681. 10.1006/jmbi.2001.5079.PubMedView ArticleGoogle Scholar
- Barabási A, Albert R: Emergence of scaling in random networks. Science. 1999, 286: 509-512. 10.1126/science.286.5439.509.PubMedView ArticleGoogle Scholar
- Przytycka T, Yu Y: Scale-free networks versus evolutionary drift. Comput Biol Chem. 2004, 28: 257-264. 10.1016/j.compbiolchem.2004.07.001.PubMedPubMed CentralView ArticleGoogle Scholar
- Przytycka T, Davis G, Song N, Durand D: Graph theoretical insights into evolution of multidomain proteins. J Comput Biol. 2006, 13: 351-363. 10.1089/cmb.2006.13.351.PubMedPubMed CentralView ArticleGoogle Scholar
- Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita K, Itoh M, Kawashima S, Katayama T, Araki M, Hirakawa M: From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res. 2006, D354-D357. 10.1093/nar/gkj102. 34 Database
- Birney E, Andrews D, Caccamo M, Chen Y, Clarke L, Coates G, Cox T, Cunningham F, Curwen V, Cutts T, et al: Ensembl 2006. Nucleic Acids Res. 2006, D556-D561. 10.1093/nar/gkj133. 34 Database
- Eddy S: Hidden Markov models. Curr Opin Struct Biol. 1996, 6: 361-365. 10.1016/S0959-440X(96)80056-X.PubMedView ArticleGoogle Scholar
- Eddy S: Profile hidden Markov models. Bioinformatics. 1998, 14: 755-763. 10.1093/bioinformatics/14.9.755.PubMedView ArticleGoogle Scholar
- Stechmann A, Cavalier-Smith T: Rooting the eukaryote tree by using a derived gene fusion. Science. 2002, 297: 89-91. 10.1126/science.1071196.PubMedView ArticleGoogle Scholar
- Baldauf S: The deep roots of eukaryotes. Science. 2003, 300: 1703-1706. 10.1126/science.1085544.PubMedView ArticleGoogle Scholar
- Watanabe H, Mori H, Itoh T, Gojobori T: Genome plasticity as a paradigm of eubacteria evolution. J Mol Evol. 1997, 44 (Suppl 1): S57-S64. 10.1007/PL00000052.PubMedView ArticleGoogle Scholar
- Newman M: Power laws, Pareto distribution and Zipf's law. Contemporary Physics. 2005, 46: 323-351. 10.1080/00107510500052444.View ArticleGoogle Scholar
- Howe K, Bateman A, Durbin R: QuickTree: building huge neighbour-joining trees of protein sequences. Bioinformatics. 2002, 18: 1546-1547. 10.1093/bioinformatics/18.11.1546.PubMedView ArticleGoogle Scholar
- Thompson J, Higgins D, Gibson T: CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 1994, 22: 4673-4680. 10.1093/nar/22.22.4673.PubMedPubMed CentralView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.