A study of yeast protein complexes, complexes of known three-dimensional structure in the Protein Data Bank and clusters of pair-wise protein interactions in the networks of several organisms revealed that duplication of homomeric interactions often results in the formation of complexes of paralogous proteins. Cellular functions are accomplished by the concerted actions of functional modules. The mechanisms driving the emergence and evolution of these modules are still unclear. Here we investigate the evolutionary origins of protein complexes, modules in physical protein-protein interaction networks.Saccharomyces cerevisiae, complexes of known three-dimensional structure in the Protein Data Bank and clusters of pairwise protein interactions in the networks of several organisms. We found that duplication of homomeric interactions, a large class of protein interactions, frequently results in the formation of complexes of paralogous proteins. This route is a common mechanism for the evolution of complexes and clusters of protein interactions. Our conclusions are further confirmed by theoretical modelling of network evolution. We propose reasons for why this is favourable in terms of structure and function of protein complexes.We studied protein complexes in Our study provides the first insight into the evolution of functional modularity in protein-protein interaction networks, and the origins of a large class of protein complexes. These networks, like many other naturally occurring networks, such as the transcriptional ,2 and meexample, ).The mechanisms that drive the emergence and subsequent evolution of modularity in cellular networks are unclear. This is in part due to the fuzzy nature of the concept and the difficulty in identifying functional modules in cellular networks. Here, we focus on the evolution of one specific type of functional module, protein complexes, and propose an evolutionary route that accounts for the origin and evolution of a proportion of these modules. We hypothesize that duplication of self-interacting proteins (homomers) is critical for the establishment and evolution of a proportion of protein complexes, and hence of functional modularity in protein interaction networks Figure . Our hypC) is a measure that quantifies how interconnected the proteins are [First, gene duplication and divergence is the most important force driving the expansion of eukaryotic proteomes . Consereins are , partly a priori, the most likely type of interface to occur [A second piece of evidence is that the oligomerization of multiple, identical subunits is a simple way of forming large, functional structures in a genetically economical manner. Smaller component subunits will fold more readily than a single large protein and are less prone to translational errors ,16. Multto occur . In protto occur . They arThe third consideration is that when genes coding for proteins that form homodimers duplicate, conservation of interactions will generate dimers of paralogous proteins. In these, the stability associated with the homodimer is maintained, while at the same time asymmetry is introduced into the interaction. This asymmetry provides more degrees of evolutionary freedom and represents a source of functional novelty (discussed in ). This iThese considerations suggest the following evolutionary scenario  of a network as a descriptor of the extent of modularity of that network. Clusters frequently correspond to known protein complexes, as shown by [We next investigated whether the duplication of homodimers is associated with protein complexes in PINs. We consider the average clustering coefficient  onto the protein complexes and asked whether the paralogous subunits of complexes physically interact. Of the complexes for which protein interactions can be superimposed, 27% to 30% have interacting homologous proteins , whereas the eukaryotic complex contains a dimer of paralogues (PsaA:PsaB). These two paralogous polypeptide chains are each decorated by different peripheral subunits, suggesting that in this class of photosystem (Type-I RC), the core was established prior to the accretion of further subunits [Our hypothesis is that many protein complexes start with homomeric interactions that duplicate and diversify, and serve as a seed for the coalescence of further subunits. The photosystem I shown in Figure subunits ,22. Anotsubunits ,32.To investigate whether this is a frequent mechanism of evolution of complexes, we tested the fifth corollary and asked whether homomeric interactions and interactions between homologous proteins precede interactions between unrelated proteins in evolution. Then we tested whether paralogues within complexes of known three-dimensional structure have asymmetric interactions.S. cerevisiae by phylogenetic profiling and analyzed both the Yeast and Yeast-large PINs as described in Materials and methods. Homomeric proteins and those that interact with paralogues are significantly older than other proteins, tending to be present in all Eukaryotes and either in Bacteria or Archaea (Yeast) or all Eukaryota (Yeast-large). Other proteins tend to be present only in Fungi and Metazoa, but not in other Eukaryota. The difference in evolutionary conservation is statistically significant in both data sets . These two results support the corollary that the establishment of homomeric interactions and the duplication to dimers of paralogous chains are early events in the emergence of a protein complex.To answer the first question, we compared PINs in different organisms and asked whether homomeric interactions and interactions between paralogues in one organism are likely to be conserved in the PINs in other organisms, that is, whether the protein(s) have orthologues that interact. Such pairs of interactions have been termed interologs in . In Tablp < 0.001). Thus, the hypothesis that duplication of homomers results in new asymmetrical complexes is supported by the data. This may represent part of the selective advantage for conservation of such duplications.To test whether paralogous proteins break the symmetry of a complex and allow accretion of different types of subunits, we considered the protein complexes of known three-dimensional structure again. We compared the set of complexes that contain paralogues to the complexes that contain homomeric interactions and no paralogues. As shown in Figure We present here a genome-wide, cross-species analysis of the origins and evolution of protein complexes. At the beginning, we hypothesized that duplication of self-interacting proteins (homomers) is an evolutionary path leading to the establishment and evolution of many complexes. To substantiate this hypothesis, we tested five corollaries that arise from such an evolutionary scenario.The first corollary is that duplication of genes coding for homodimers is frequently accompanied by conservation of protein interactions. Conservation of protein interactions after gene duplication has been shown to be frequent ,19. We sNext we tested the association between clustering of the network and interactions between paralogous proteins. Clusters in protein interaction networks frequently represent protein complexes. We have shown that removal of interactions between paralogues causes a small but highly significant decrease in the global clustering level of the network. This is consistent with our theoretical modeling results.We then observed that about 30% of protein complexes from proteomics experiments contain duplicated subunits. In protein complexes of known three-dimensional structure, a similar proportion of complexes have duplicated subunits, and more importantly, there is preferential binding of paralogous subunits. This supports the corollary that interactions between paralogues are frequent in complexes.We observed that proteins involved in homomeric interactions and interactions between paralogues were more conserved than other proteins: more than half of yeast proteins had orthologues in all eukaryotes and either archaea or bacteria, whereas more than half of the other yeast proteins had orthologues only in fungi and animals. Homomeric interactions and those between paralogous proteins were also three to seven times more likely to be conserved, when compared to other interactions. Thus, this supports the corollary that homomers and oligomers of paralogues represent the first steps in the evolution of new protein complexes, with other subunits added later.Finally, we showed that amongst three-dimensional structures of complexes, 32% of dimers of paralogues establish asymmetric interactions with other proteins whereas only 3% of homodimers show such asymmetry, further substantiating that the duplication of homomeric interactions helps to create asymmetry in protein interactions, and allows the coalescence of other subunits in the complex.Altogether, our data suggest an evolutionary route to the formation and specialization of many extant protein complexes. On this route, homomers and oligomers of paralogous subunits represent an ancestral core around which further subunits can coalesce in evolution. Sequence divergence of the paralogous subunits creates the asymmetry that permits the accretion and diversification of interactions. In addition, divergence of paralogues may be involved in functional specialization of complexes. The biases inherent in each data type make it difficult to determine the exact fraction of protein complexes that evolved via the proposed route. A higher bound is about one-third, estimated by the fraction of proteomics complexes that display duplicated subunits. A lower bound is less than one-tenth, estimated by the fraction of dimers of paralogues in one of the yeast two-hybrid data sets Table .Another issue that at this stage is difficult to ascertain is the nature of the complexes that emerged by the proposed route. If we assume that both the proteomics data and the crystallographic data represent an enrichment for stable protein complexes, then our proposed evolutionary route appears to be more prevalent in stable complexes. In fact, most examples discussed in the text are stable complexes. They also appear to be complexes that were established very early in evolution, which is illustrated by the ages of the proteins that establish homomeric interactions and interactions between duplicates.We have shown previously that duplication of protein interactions and of entire protein complexes is accompanied by specialization of function . InspectOur investigations of protein interactions and protein complexes, as well as theoretical modeling, reveal that many protein complexes evolved by the initial establishment of self-interactions followed by duplication of these self-interacting proteins. Our study provides the first insight into the evolution of functional modularity in protein-protein interaction networks, and the origins of a large class of protein complexes.S. cerevisiae [Drosophila melanogaster (high confidence interactions) [Caenorhabditis elegans. [S. cerevisiae [Binary physical protein-protein interactions for revisiae ,37, Drosactions)  and Caenelegans. , as wellrevisiae ,40,41 anrevisiae  are summA non-redundant set of protein complexes of known structure, based on the PQS database as of June 2005, was prepared by considering complexes as graphs where nodes are the protein subunits (labeled by the domain architecture and chain identity) and edges are a contact between these subunits: two complexes were considered identical when they had the same subunits  and the same contact topology between subunits. Details of this procedure can be found in .We used domain architecture as defined in the Superfamily database ,44 to idh and Pp, respectively) within a complex was calculated by counting the number of times the given chain made one or more homo- or paralogous contacts  in a set resulting from 500 randomizations of that protein complex. Randomizations consisted of considering the topology of each complex fixed, and shuffling the position of each chain within the complex. The expected frequencies were estimated by Ph = Nh/500 and Pp = Np/500.In the analysis of protein complexes from PQS we considered two chains to be identical when strict sequence identity was found, and accepted gaps at the amino and carboxyl termini of the sequences. Two chains were considered homologous when they displayed the same amino- to carboxy-terminal SCOP superfamily domain architecture, and to be different when they did not satisfy any of the above criteria. We used a cut-off of five amino acids with atoms within their van der Waal's radii plus 0.5 Å for two chains to be considered in contact. The expected frequency for a given chain to form a homo- or a paralogous contact . We considered two proteins to be orthologous if they were bidirectional best hits. The 'age' groups we can define based on available genomes are, starting from the most recent, 'S. cerevisiae specific'; 'Saccharomyceta'; 'Fungi'; 'Fungi/Metazoa'; 'Fungi/Metazoa/Amoebozoa'; 'Eukaryota'; 'Eukaryota+Archaea' or 'Eukaryota+Bacteria'; 'universal'. The eukaryotic tree used as reference is that in [We used Smith-Waterman alignments to identify orthologs of yeast genes in the genomes of 40 organisms, representing the three branches of the tree of life, and the major taxonomical groups within each of the branches. We used the Smith-Waterman implementation of the TimeLogic's DeCypher that in .The following additional data are available with the online version of this paper. Additional data file Additional figures and raw data for the plots in Figures 5Click here for file


In summary, a new method is presented that relates critical residues for protein function with the most traversed residues in networks derived from protein structures. A unique feature of the method is the inclusion of the conformational diversity of proteins in the prediction, thus reproducing a basic feature of the structure/function relationship of proteins.Despite the increasing number of published protein structures, and the fact that each protein's function relies on its three-dimensional structure, there is limited access to automatic programs used for the identification of critical residues from the protein structure, compared with those based on protein sequence. Here we present a new algorithm based on network analysis applied exclusively on protein structures to identify critical residues. Our results show that this method identifies critical residues for protein function with high reliability and improves automatic sequence-based approaches and previous network-based approaches. The reliability of the method depends on the conformational diversity screened for the protein of interest. We have designed a web site to give access to this software at  Deciphering protein function is one of the most active areas of research in biology involving both experimental and theoretical approaches With the increased capacity to determine the three-dimensional structures of proteins there has come an exponential growth in the public database of protein structures We have recently described a method that uses only the protein structure to identify critical residues for protein function, based on the centrality measurement closeness centrality First, we describe the method and its overall reliability to identifying known critical residues for protein function. Then, we show that including multiple structures of a protein of interest may be used to improve the reliability of our method and makes it more reliable than other automatic methods based on either protein sequence or protein structure. Our results indicate that JAMMING may be used to identify critical residues for protein function that are either critical for keeping the protein structure and/or for its biological function .The underlying idea of JAMMING is that residues central for residue-residue contacts should be critical for protein function. Hence, our method is divided into three steps:Building networks from protein structures.Tracing the shortest path connecting every pair of residues in the network derived in step 1.Find the residues with the largest dynamic connectivity (dk).The results of each step (1 through 3) constitute the input for the next step. Additionally, in order to allow for JAMMING calculations to be executed at the same time either in multiple machines (JavaParty implementation) or through a web interface (Servlet implementation), the three steps were embedded in independent remote objects or Java™'s thread. Briefly, JavaParty Here, we report the training of our method with the dynamic connectivity (dk) as a centrality measurement see . BrieflyWe plotted the frequency of the dk values in order to identify the most traversed residues from the networks derived from the protein three-dimensional structure. Such distribution showed a tendency to separate highly traversed residues see . In thisWe are interested in identifying critical residues with the highest specificity and lowest error values, even if the sensitivity is low. As we will show below, the sensitivity of our method relays on the number of structures analyzed, so a method with high specificity in a single structure is desirable. We show for the T4L-TEM1-HIV1P set, that building networks pairing every residue (disregarding charge or any other criterion) rendered the best values for error and specificity see . This trTo extend these studies to a larger dataset, we used the FSSP128 set, where some of the critical residues are known. A similar set of proteins has been previously used to evaluate the predictive value of functionally important residue predictions As with other centrality measurements Considering that proteins are not static molecules, it is accepted that an ensemble of protein structures accomplishes protein function In order to provide public access to JAMMING, we developed a web site using the Java™'s servlet technology. The servlet (master program) may render requests to a group of registered machines (workers). Yet, the current servlet implementation only deploys the requests to a single computer. Alternatively, we have made available a command-line version of our program through our web site that is ready to be used in a cluster environment as well as a GUI to be run on a single computer. As noted above, sometimes it is important to include multiple structures for a given protein to have a good reliability in the predictions. So, it is important to keep in mind that running multiple analyses in a single CPU may not be feasible, depending on the specifications of the hardware, so it is usually convenient to have the chance to use multiple processors.The accumulated information on protein structures and community efforts such as the structural genomics initiative We trained JAMMING with different types of protein structure-derived networks and found that pairing every residue at 5 Å apart or closer rendered the most reliable predictions on average. Our results show that pairing residues that display complementary physicochemical properties does not improve the identification of critical residues, indicating that the optimization of our network model does not require residue-type discrimination. It is important to note that our previous work using closeness centrality to identify critical residues from protein structures Previous studies have indicated that critical residues tend to be buried in the protein's core Other approaches to identify critical residues with different basis than ours On the other hand, JAMMING is shown to improve the reliability of automatic sequence-based approaches by considering multiple normal mode perturbed models of a given protein. In that sense, JAMMING may be especially useful in cases where there are a limited number of sequences to perform a sequence analysis.When using our approach to analyze other protein structures, we noticed that some structures only hold as central residues those in the protein's core (data not shown), but including multiple structures of a protein always render critical residues on the protein's surface as well. So, as a general approach, the researcher may choose first to identify critical residues based exclusively on a protein structure provided that the specificity of the method is quite good in that condition, and depending on the goal of the researcher it may be useful to run JAMMING using multiple protein structures.So far, we have described the reliability of JAMMING in average terms. Now, we will describe some specific predictions obtained with it see . Our goaThe Arg145 residue in the bacteriophage T4 lysozyme was identified by our method to be critical for the protein function, yet this residue is not conserved Now we will analyze a couple of residues involved in binding sites. As we have shown One of these residues is Thr71 in the TEM-1 beta-lactamase. This residue is not conserved among the class A beta-lactamases (enzymes capable of hydrolyzing beta-lactam antibiotics mostly encoded in plasmid in Gram(+) and Gram(−) bacteria), but it is conserved among the TEM1 beta-lactamases (the most common plasmid-mediated beta-lactamase). This conservation pattern suggests a possible role in specificity. Random mutagenesis at this position supports this notion Another residue involved in binding is Leu24 from the HIV-1 protease. This is a conserved residue and it is located in the interface of the protease homodimer. Interestingly, our method predicted this residue as critical for the protein function considering only the monomer of the protease. In agreement, mutagenesis of Leu24 showed small tolerance to substitutions at this position e.g., proteins with known structure but limited sequence information).The ability to identify critical residues for protein structure/function represents a basic tool for many important areas in bioinformatics, including protein structure prediction, protein function design or functional classification, among others. In many of these areas, a program capable to be run in a systematic fashion is desirable. JAMMING is the first available software of its class that may be used to complement other approaches or be used where others are limited . Additionally, 1 Linux and 1 MacOS×computers were used to test our implementation in a heterogeneous computer system (see Implementation below). This was distributed using the JavaParty framework (http://www.ipd.uka.de/JavaParty/).Three Linux boxes (RedHat 7.2) with 2 Pentium III processors each were used to run the described calculations. These boxes were configured as a cluster with the Rocks framework (http://java.sun.com). Two types of implementations were developed in this work. The first one was developed to test JAMMING when multiple protein structures are being analyzed. This program may be executed on a cluster of computers using the JavaParty framework. Also, this program may be executed in a single computer without the JavaParty framework. In both cases we used Java's Threads allowing to switch from one to another without major modification on the code (see below) The second implementation was a Java™'s servlet that provides a web interface to access JAMMING for single protein structure analysis. The WebMol Applet  was adapted to graphically display the results of the predictions by the servlet. Only protein atoms are considered in this version of the program.All of the procedures described in this work were coded in the Java™ programming language , HIV-1 protease (PDB code 1HIV) and T4 lysozyme (PDB code 2LZM)). The FSSP128 set includes 128 other proteins for which there is a partial annotation regarding the critical residues (single chained FSSP protein entries with SITE annotations). The 128 PDB names used in this set are: 1a3c, 1a6q, 1a7j, 1aac, 1ac5, 1ah7, 1ak1, 1ako, 1amj, 1an8, 1apq, 1arv, 1atg, 1auz, 1ayl, 1ayx, 1az9, 1b64, 1bag, 1bdb, 1bea, 1bfd, 1bia, 1bif, 1bix, 1bk0, 1bli, 1bn5, 1bor, 1boy, 1bp1, 1bqk, 1brt, 1btl, 1c25, 1ca1, 1cby, 1cex, 1cfb, 1chc, 1chd, 1csh, 1ctn, 1ctt, 1cvl, 1dmr, 1drw, 1dxy, 1ecl, 1eh2, 1emn, 1esl, 1eut, 1far, 1fnc, 1gca, 1htn, 1hyt, 1iba, 1ido, 1iow, 1iyu, 1kcw, 1kpf, 1lam, 1lay, 1lbu, 1lgr, 1lml, 1lox, 1mfs, 1mla, 1mrp, 1mup, 1nif, 1opc, 1pda, 1pdc, 1pfo, 1phd, 1phm, 1pii, 1pkp, 1poa, 1poc, 1rfs, 1rie, 1rkd, 1rlw, 1skf, 1snc, 1sra, 1thx, 1uch, 1uox, 1ush, 1whi, 1wod, 1xbd, 1xpa, 1ytw, 2abk, 2adr, 2af8, 2cba, 2cmd, 2dkb, 2dri, 2fha, 2fua, 2liv, 2mcm, 2mnr, 2rn2, 2sas, 2vil, 3dfr, 3dni, 3ebx, 3gcb, 3pte, 3ssi, 3tgl, 4enl, 4icb, 4pah, 5eat and 7rsa.Networks were derived from protein structures by a distance criterion. That is, two residues were considered neighbors and consequently paired in the network if they were within a given distance from each other. Two different distance criteria were used: a) two residues were considered neighbors if any of their atoms were within the specified distance, and b) two residues were considered neighbors if their centers of mass were within the specified distance. Additionally, four filters were used to define neighbors, that is, given that the distance criteria were satisfied, the residues being paired were considered neighbors if: i) the two residues were polar or charged and had complementary charges , ii) the two residues had complementary charges or both were hydrophobic , iii) the two residues were hydrophobic , or iv) any two residues were paired. Therefore, different types of networks were built specifying 7 separation distances, 2 distance criteria and four pairing criteria Click here for additional data file.Figure S1Relationship between the surface area and dk centrality measurement for the FSSP128 set of proteins(0.25 MB TIF)Click here for additional data file.Figure S2Structural location of the most traversed residues(0.31 MB TIF)Click here for additional data file.


Our aim is to find evolutionary The Molecular Ancestry Network (MANET) project traces evolution of protein architecture in biomolecular networks. We describe metabolic MANET, a database that links information in the Structural Classification of Proteins (SCOP), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and phylogenetic reconstructions depicting the evolution of protein fold architecture. Metabolic MANET literally 'paints' the ancestries of enzymes derived from rooted phylogenomic trees directly onto over one hundred metabolic subnetworks, enabling the study of evolutionary patterns at global and local levels. An initial analysis of painted subnetworks reveals widespread enzymatic recruitment and an early origin of amino acid metabolism.MANET maps evolutionary relationships directly and globally onto biological networks, and can generate and test hypotheses related to evolution of metabolism. We anticipate its use in the study of other networks, such as signaling and other protein-protein interaction networks. Cellular metabolism represents a collection of enzymatic reactions and transport processes that convert metabolites into molecules capable of supporting cellular life. It is the best-studied biological network, with highly branched pathways describing the enzymatic processing of metabolites. Though underappreciated, it also represents one of the greatest achievements of science, resulting from almost two centuries of biochemical research.There is considerable interest in the processes underlying the evolution of cellular metabolism. The existence of a core ensemble of metabolic reactions common to most organisms suggests that the global metabolic structure has been the subject of strong evolutionary constraint. Similarly, network connectivity properties suggest modular components typical of evolved systems -3 and emin vitro experiments that try to simulate pre-biotic chemistry. It is likely that polypeptides became metabolic catalysts through takeover of pre-biotic reactions [Metabolism is largely driven by enzymatic specificities. Consequently, the origin and evolution of metabolic networks can be explored advantageously by focusing on protein molecules. However, metabolism is very ancient and parts of the metabolic network probably evolved prior to the origin of cellular life from reactions that could have proceeded without catalysis or with inorganic catalysts . This vieactions . The eareactions .en masse from entire pathways. In this regard, several possible scenarios for the evolution of enzymes in metabolic pathways have been proposed [As enzymatic pathways became more complicated, new enzymatic functions and metabolic pathways could have been generated by recruitment of individual enzymes from the same or different pathways, or by enzymatic recruitments proposed . One popproposed . Here, bproposed . An alteproposed . This hyproposed .Escherichia coli [There is considerable evidence supporting the patchwork recruitment scenario . For exahia coli ,14. Recrhia coli . It is nhia coli . HoweverProteins consist of domains, compact sections of the protein molecule that have distinct structure, function and evolutionary history  and are We recently used an approach based on a census of folds to study protein diversification and reconstruct universal phylogenomic trees describing the evolution of protein fold architecture ,30,31. OThe Molecular Ancestry Network (MANET) project uses information embedded in our phylogenomic trees to trace evolution of protein fold architecture in biomolecular networks Fig. . In thisMANET links three flat files describing the metabolic pathways database of the Kyoto Encyclopedia of Genes and Genomes (KEGG), the Structural Classification of Proteins (SCOP) database, and phylogenomic trees reconstructed from a genome census of protein folds Fig. . KEGG prgenomic abundance (G). At global levels, it describes how popular is a fold architecture in nature. In order to calculate G, we assigned structural domains to proteins. We did this using the HMM-based SUPERFAMILY database [E of 0.02. Differences in topologies of trees reconstructed with more stringent cut-off values were found negligible [G was normalized using gap-recoding techniques to compensate for differences in genome size and proteome representation, and was then subjected to logarithmic transformation to account for unequal variances. The data was range standardized to a 0–20 scale compatible with most phylogenetic analysis programs, treated as linearly ordered multistate characters using an alphanumeric format with numbers 0–9 and letters A-K, and encoded in the NEXUS format. The ANCSTATES command was used to polarize characters assuming that the number of protein representatives in a genome exhibiting a particular fold increases in the course of evolution. Character argumentation is supported by model and assumptions described and discussed previously [g1) of the length distribution of > 104 random trees and permutation tail probability (PTP) tests of cladistic covariation using >103 replicates. Homoplasy and synapomorphy were measured with ensemble consistency (CI) and retention (RI) indices.A set of phylogenetic features (characters) was used to describe the frequencies with which individual protein folds occur in an individual genome. This frequency was termed database . SUPERFAdatabase . The HMMgligible , so we dgligible . G was neviously ,30,31. Peviously , and phyeviously . Optimalnode distance (nd) depicts the number of cladogenic events along a lineage and was used as an indicator of the ancestry of each metabolic enzyme for which a protein structure is known or could be inferred. A Perl script was written to extract ancestry values from phylogenetic trees.Because reconstructed trees were intrinsically rooted, we established the relative age of individual protein folds by measuring a distance in nodes from the hypothetical ancestral fold on a relative 0–1 scale. Concretely, we counted the number of nodes in every lineage from the root to the terminals of the tree and divided this number by the maximum number of nodes in a lineage. This Note that the central assumption that folds are more prevalent and widely shared the more ancient is their origin follows a parsimony rationale supported by patterns of distribution and sharing of protein folds across life, statistical analyses, and phylogenetic considerations . Our modWe retrieved flat files directly from KEGG  and SCOPJoin operations necessary to combine information from entities in MANET and coloring of enzymes are described in the supplementary data see . The joiIn order to increase protein fold assignments to enzymes in metabolic pathways, we used a library of HMMs for remote homology detection in SUPERFAMILY. We used the model library, Perl wrapper scripts for sequence alignment, and the Sequence Alignment and Modeling System (SAM), and ran the SUPERFAMILY software package locally in a 15-node dual-processor Xserve cluster using the genes catalog file obtained from KEGG. This file includes amino acid and nucleotide sequence information from complete or partial genome sequences. Details can be found in the supplementary data file.HMM-based structural prediction increased assignments of protein fold to enzymes in metabolic pathways. However, biases in fold superfamily prediction could affect evolutionary tracings in networks. To test the effect of prediction bias, we selected amino acid sequences associated with enzymes that had structural PDB entries. Statistical tests were performed using the SAS software package . The freWe also performed a global statistical analysis of distribution of ancestries in mesonetworks. All average ancestry values of enzymes in the 11 mesonetworks described in KEGG were analyzed by ANOVA and by multiple pair-wise comparisons with the SAS package. There is a high probability of declaring at least one pair of means significantly different when running multiple comparisons unless the per-comparison error rate α is small among sample means . We therEscherichia coli has shown extended distribution of structural homologues across metabolism [A substantial body of literature has shown that linking protein structure to proteins in biomolecular networks can be advantageous. Some of these studies involved comprehensive structure-enzyme mapping exercises and explored mechanistic aspects related to enzymatic function. For example, global analysis of small-molecule metabolic pathways in tabolism ,14,44,45tabolism . It appetabolism . KnowledHowever, proteins with similar structures may or may not be evolutionary related, and other approaches that introduce phylogenetic views are therefore needed. For example, Copley and Bork  used seqOur metabolic MANET database adds an evolutionary component to global assignment of protein structure to enzymes in metabolic subnetworks. Phylogenomic trees that describe the evolutionary relationship between protein fold architectures were used to define ancestries of individual folds, and these were traced onto metabolic subnetwork diagrams representing modern metabolism. Because our phylogenies were reconstructed from fold occurrence in hundreds of organisms with fully sequenced genomes spanning all three domains of life, they represent global phylogenetic statements about the protein world . These sTracing evolution of protein architecture in metabolic networks is useful. The exercise can uncover evolutionary patterns of architectural diversification within individual pathways of a subnetwork or between subnetworks and mesonetworks. Because metabolism is highly conserved, with about half of enzymes present in at least one species from the three domains of life , it is tThe web interface of MANET implements server-side scripts with a system of database management that provides visualization, query, and statistical analysis dynamically. All components of MANET are Perl-based and easily updated. Enzymes associated with protein folds are painted directly on subnetwork diagrams. Each enzyme is also hyper-linked to the KEGG database so that the user can retrieve additional information by clicking in the diagram. MANET also provides numerous functionalities, which enable searching for specific protein folds with defined ancestry values, displaying the distribution of enzymes that are painted, and exploring folds in individual subnetworks. Finally, the frequency distribution of ancestry values for each subnetwork can also be visualized.The MANET database project traces the evolution of protein structure in biomolecular networks with bioinformatic, phylogenetic, and statistical methods. Metabolic MANET links the SCOP and KEGG databases to universal phylogenies of protein fold architecture. The database was assembled in multiple steps. We first reconstructed phylogenomic trees describing the evolution of protein folds in 174 proteomes belonging to Eucarya, Archaea and Bacteria. These trees are large and can be visualized using hyperbolic tree visualization tools . Figure Reconstructed trees were based on a genomic census of protein architecture. Consequently, they depend on the accuracy of genomic databases, a balanced genomic sampling of the living world, efficient and accurate assignment of structures to proteins, a structural classification scheme that depicts evolutionary patterns, and methods of phylogenetic tree and character state reconstruction. The influence of these factors has been discussed previously ,30,31. W8 barrels [Our study also rests on the accuracy of SCOP, a robust protein classification scheme ,49, and  barrels ,51.2 fixation), and novobiocin biosynthesis. Large subnetworks such as those belonging to nucleotide, carbohydrate and amino acid mesonetworks were painted similarly to others. Interestingly, some subnetworks contained more evolutionary information. Subnetworks such as purine metabolism and pyrimidine metabolism that contain many more enzymes than others had about 83% and 79% of enzymes painted, respectively. Only 10 subnetworks (7.6%) in metabolic MANET did not have entries associated with ancestry values. These were beta-lactam resistance and clavulanic acid biosynthesis in mesonetwork "biosynthesis of secondary metabolites", 1,1,1-Trichloro-2,2-bis(4-chlorophenyl) ethane (DDT) degradation and bisphenol A degradation in "biodegradation of xenobiotics", glycosylphosphatidylinositol(GPI)-anchor biosynthesis in "glycan biosynthesis and metabolism", and biosynthesis of ansamycins, biosynthesis of siderophore group nonribosomal peptides, biosynthesis of vancomycin group antibiotics, and biosynthesis of type II polyketide products in mesonetwork "biosynthesis of polyketides and nonribosomal peptides". The efficiency of painting was not biased by subnetwork size  for a one-tailed test was greater than the expected value for α = 0.05 , failing to reject the null hypothesis that ancestry values distributions for groups A and B were identical. We therefore conclude that ancestry value distributions derived from structural models or HMM predictions were not significantly different at the 95% confidence level.Evolutionary tracing in MANET reflects information derived from structural models present in the PDB or represents HMM-based inferences of structural classification. In order to test if biases in fold superfamily predictions could affect evolutionary tracings in networks, we designed a statistical test that compared frequency distributions of ancestries derived from the join operation defined by structural models (population group A) or derived from HMM-based predictions (population group B). We selected amino acid sequences associated with enzymes that had structural PDB entries and participated in the join operation. A total of 72,354 amino acid sequences within this category were selected, and resulting ancestry values were calculated and analyzed Fig. . The meaWe also tested the accuracy of the HMM prediction. We selected PDB entries corresponding to enzymes in KEGG that participated in the join operation and were classified structurally, assigned PDB sequence records downloaded from ASTRAL  to the Pe.g. [en masse enzymatic recruitment processes occurring in subnetwork pathways. We envision that uncovering take-over patterns in MANET at global levels will require extensive information about possible pre-biotic chemistries and novel phylogenetic approaches.The assignment of numerical ancestry values to enzymes in cellular metabolism uncovers evolutionary patterns of architectural diversification within the metabolic network. A quick examination of ancestry distributions depicted in each subnetwork and mesonetwork diagram of the MANET database reveals that enzymes of old origin generally coexist with those of recent origin , pla12. This scenario suggests that compounds in shell D evolved after enzymes (derived from shell B and C) and were not a part of prebiotic chemistry. The energy amphiphile core is consistent with Wächtershäuser's proposal that life evolve on pyrite . Furthermore, the analysis revealed that amino acid mesonetworks were the oldest and lipid (including steroid) and glycan mesonetworks were relatively recent evolutionary additions . The early evolutionary appearance of mesonetworks related to amino acid metabolism suggests that metabolic routes leading to the synthesis of polypeptides (shells B and C of Morowitz) 'internalized' early into the protein-based enzymatic machinery.When ancestry patterns embedded in the subnetworks of MANET were analyzed, sequential evolution of metabolic "shells" was not obvious. However, pervasive enzyme recruitment could have masked the original pre-biotic evolutionary patterns. In fact, we performed a global statistical analysis of the distribution of ancestries of enzymes in metabolism, testing if global evolutionary patterns in metabolism matched possible "shell" scenarios Fig. . We calcWhile mesonetworks may pool subnetworks of different average ancestry complicating interpretation, our results are nevertheless consistent with the shell hypothesis of Morowitz . In thisWe constructed a database that links biomolecular networks, protein structure, and phylogenomics. Metabolic MANET traces the evolution of protein structure directly onto metabolic networks defined by KEGG, enabling the study of evolutionary patterns in metabolism at global and local levels. MANET can be a valuable resource and constitutes a discovery tool. Individual pathways within subnetworks and mesonetworks can be examined and evolutionary patterns can be detected by visual inspection or statistical analysis of ancestry distributions. The database has many possible applications. For example, it can be used to search for patterns of fold superfamily sharing between subnetworks, with the aim of displaying the coordinated evolution of the subnetworks. Evolutionary information deposited in metabolic MANET will be enhanced by the exponential increase in the number of genomes that have been sequenced, the number of fold architectures uncovered, and the number of metabolic enzymes with gene assignments. The principles used in the construction of metabolic MANET are general and can be extended to other biomolecular networks. In the near future, we plan to analyze other networks of importance, such as cell signaling and other protein interaction networks.. Data materials are formatted as EXCEL or column-delimited flat files for parsing with programming tools such as Perl and are openly available at our web site. The use of information obtained from the KEGG and SCOP databases is restricted by licensing conditions specified elsewhere. Contact information: THE MANET PROJECT, Atelier of Plant Bioinformatics, E-mail: evolutionary-manet@uiuc.edu.The MANET database can be accessed via the Internet at HSK acquired data, developed methods, wrote the codes, created the database, and performed statistical analyses. GCA and JEM initiated and conceived the project. GCA guided HSK in research and interpretation of results. GCA and HSK created figures and did most of the writing with input from JEM. All authors read and approved the final manuscript.Description of join operations, superfamily prediction using HMMs, coloring, and analysis and sorting of data. Table 1 describes painting efficiency in metabolic MANET.Click here for file


Molecular recognition is all pervasive in biology. Protein molecules are involved in enzyme regulation, immune response, signal transduction, oligomer assembly, etc. Delineation of physical and chemical features of the interface formed by protein-protein association would allow us to better understand protein interaction networks on one hand, and to design molecules that can engage a given interface and thereby control protein function on the other hand.ProFace is a suite of programs that uses a file, containing atomic coordinates of a multi-chain molecule, as input and analyzes the interface between any two or more subunits. The interface residues are shown segregated into spatial patches (if such a clustering is possible based on an input threshold distance) and/or core and rim regions. A number of physicochemical parameters defining the interface is tabulated. Among the different output files, one contains the list of interacting residues across the interface. Results can be used to infer if a particular interface belongs to a homodimeric molecule.) has been developed for dissecting protein-protein interfaces and deriving various physicochemical parameters.A web-server, ProFace (available at  Most proteins function by interacting with other molecules; the binding sites have evolved for achieving specific interactions and avoiding undesirable associations that would be deleterious to the normal functioning of the cell. Thus the interfaces between two protein subunits provide context for understanding the principles of molecular recognition. A large volume of structural data on protein interactions, either complexes between independent polypeptide chains, or oligomeric assembly of subunits, is available in the Protein Data Bank (PDB) , which hprotein-protein interface and obtains various parameters to characterize it.Recently, protein-protein interfaces have been dissected from new perspectives ,14. It hAll the protein chains should be contained in the input file in the PDB format and the user must indicate which chains  constitute each of the two components forming the interface between them. Also, one has to specify the way to display the dissected interface, i.e., to show the residues belonging to core and rim and/or in spatial patches. For clustering into patches the threshold distance has to be supplied. This distance should typically be half the maximum distance between any two interface atoms on a given protein chain – the latter distance is listed along with the other parameters in the output. Ideally, the number of patches should be the same on both the components and if this is not the case the threshold value may have to be slightly changed (increase to reduce the number of patches and vice-versa) to achieve this. The suggested values are 15 Å for protein-protein complexes  and 22 ÅThere are five types of output: a) plot of interface residues with secondary structural information; b) statistics of interface parameters; c) coordinates of interface atoms and the PDB files in which the interface residues are tagged; d) list of residue contacts across interface; and e) the view of the interface atoms.The secondary structural elements (α-helix and β-strand) are computed using the program DSSP  and show2 to the interface area are counted as interface atoms or residues, whose numbers are tabulated. Non-polar interface area is the area contributed by non-polar interface atoms . Interface area/surface area is the ratio of the interface area to the rest area of the protein surface in the two components. Fraction of non-polar atoms is based not on the area contributed, but on the number of atoms. Fraction of fully buried atoms is the ratio of interface atoms that are completely buried in the complex (with ASA = 0) to the total number of interface atoms . Residue propensity score and local density are defined in Bahadur et al. [A typical example of output parameters is shown in Table r et al. . ResidueThe 4-digit code used to name the output files are randomly generated and does not have any correspondence to the input file name. The coordinates are stored in two types of files (with extensions .pdb and .int) and there are two files  of each type. In the .pdb file the interface residues are distinguished from the remaining atoms in the structure on the basis of the content in the two columns – occupancy factor and B-factor. The non-interface residues have a value of 0.00 in these columns. For the interface residues, a) the occupancy is replaced by -5.00 (if it is a core residue) or 5.00 (if it is a rim residue); b) the B-factor column is replaced by a value 1.00 through 9.00, depending on the patch to which the residue belongs. In the .int file, only the interface atoms are kept, with the occupancy and the B-factor column modified as above . Moreover, there are two additional columns, in which the ASAs of the constituent atoms in the individual component and in the complex are provided. One can use this information to calculate the interface area contributed by individual residues and, for example, correlate with the thermodynamic data on the free energy of binding .Another output file (with extension .cont) provides the list of residue contacts across the interface. For an interface residue in the first component the list shows the interface residues from the other component which are within a distance of 4.5 Å. If a pair of residues in contact have the same residue name and number, this is indicated by the symbol '<< ---' at the end of the line. This interaction has been designated as self-contact and indicates that the interface may have been formed by components/chains related by a 2-fold symmetry . An examThis can be done using either RasMol  or CHIMEProFace can be used to dissect a protein-protein interface, deriving physicochemical parameters. The output can be used to display the interface with standard softwares and understand the biological significance of the interaction.• Project name: ProFace• Project home page: • Operating system(s): Platform independent• Programming language: Java, C++• Other requirements: JRE 1.4.2.04 or higher, Chime plug-in 2.6 or higher; all of them are available for download at the above web address• License: Free• Any restrictions to use by non-academics: NoneRPS and RPB wrote the source codes, participated in developing the server. AP and SM participated in developing the server. PC conceived the study, and participated in its design, analysis, and coordination. RPS, RPB, AP, SM and PC all contributed to writing the final manuscript and interpretation of data.


Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, Arabidopsis thaliana, Rattus norvegicus, Mus musculus, and Homo sapiens.Protein-protein interaction has been used to complement traditional sequence homology to elucidate protein function. Most existing approaches only make use of direct interactions to infer function, and some have studied the application of indirect interactions for functional inference but are unable to improve prediction performance. We have previously proposed an approach, FS-Weighted Averaging, which uses topological weighting and level-2 indirect interactions (protein pairs connected via two interactions) for predicting protein function from protein interactions and have found that it yields predictions with superior precision on yeast proteins over existing approaches. Here we study the use of this technique to predict functional annotations from the Gene Ontology for seven genomes: Our analysis shows that protein-protein interactions provide supplementary coverage over sequence homology in the inference of protein function and is definitely a complement to sequence homology. We also find that FS-Weighted Averaging consistently outperforms two classical approaches, Neighbor Counting and Chi-Square, across the seven genomes for all three categories of the Gene Ontology. By randomly adding and removing interactions from the interactions, we find that Weighted Averaging is also rather robust against noisy interaction data.We have conducted a comprehensive study over seven genomes. We conclude that FS-Weighted Averaging can effectively make use of indirect interactions to make the inference of protein functions from protein interactions more effective. Furthermore, the technique is general enough to work over a variety of genomes. Saccharomyces cerevisiae (bakers' yeast) genome from the General Repository of Interaction Datasets (GRID) 18]. For ER – Endoplasmic Recticulum; FS-Weight – Functional Similarity Weight; GTP – Guanosine triphosphate; GTPases – GTP binding proteins; GDP – Guanosine diphosphate; NC – Neighbour Counting; ROC – Receiver Operating Characteristics; WA – FS-Weighted AveragingThe authors declare that they have no competing interests.HNC was responsible for conceptualization and implementation and drafted the manuscript. WKS and LW participated in conceptualization and discussion as well as revision of the draft.Supplementary Text and Figures. Precision vs. recall and ROC analyses of the predictions of informative GO terms from the Gene Ontology molecular function and cellular component categories using Neighbor Counting (NC), Chi-Square, and FS-Weighted Averaging (WA) for seven genomes.Click here for file


A new method for inferring domain interactions from databases of interacting proteins was used to deduce 3,005 high-confidence domain interactions from over 177,000 potential interactions. Eij, reflecting confidence that domains i and j interact. We analyzed 177,233 potential domain interactions underlying 26,032 protein interactions. In total, 3,005 high-confidence domain interactions were inferred, and were evaluated using known domain interactions in the Protein Data Bank. DPEA may prove useful in guiding experiment-based discovery of previously unrecognized domain interactions.We describe domain pair exclusion analysis (DPEA), a method for inferring domain interactions from databases of interacting proteins. DPEA features a log odds score,  Post-genomic biological discoveries have confirmed that proteins function in extended networks ,2. In paFor each protein to interact with its appropriate network neighbors, highly specific recognition events must occur. Interaction specificity results from the binding of a modular domain to another domain or smaller peptide motif in the target protein . For exaProtein-protein interactions can be determined experimentally -12. HoweHigh-throughput protein interaction studies and databases of protein interactions -12,20,21Saccharomyces cerevisiae. Such exhaustive assays measure which protein pairs interact, and which do not; rigorous statistical methods to analyze these datasets have been described  with ubiquitin-conjugating enzymes     . For the 68 organisms we analyzed in DIP, a protein interaction network was defined consisting of all of each organism's proteins known to participate in an interaction with another protein also in that same organism, and the interactions between them. For simplicity, we did not include the 396 cross-species interactions in DIP.τ, that organism's observed network of interactions is defined as:For each organism, τ, the interaction network is assumed to be incomplete: many biologically relevant interactions are surely unknown, and unreported in DIP. For simplicity in incorporating protein interaction data from multiple species, a pair of proteins is defined as potentially interacting if the proteins belong to the same organism. Thus,  is only defined when proteins x and y both belong to organism τ. All proteins x belong to one and only one organism, τ.If we do not have experimental information demonstrating that two proteins interact, we assume that they do not interact. Therefore, for all taxa, E-value threshold of 10-4. Then, if an aligned segment of a SwissProt protein completely encompassed a Pfam domain as defined in the swisspfam file, the domain annotation was transferred to the DIP protein. Domain boundaries were allowed to overlap. By these two methods of domain mapping, 74% of amino acids in DIP proteins were assigned to an interval corresponding to a Pfam domain. The remaining 26% of amino acids remained unannotated, even though it is possible that some of these amino acids contain protein interaction sites.We then define the domains of each DIP protein . Pfam-A and -B domains were defined on DIP sequences in two ways. First, the DIP protein's SwissProt accession number, if available, was mapped to the domain annotations in the Pfam 14.0 version of the swisspfam file . Second,i and j to indicate domains and the indices x and y to indicate proteins. We define D(x) as an unordered collection of one or more domains on protein x. We do not consider multiple instances of a kind of domain on a protein, as in the case of WD40 domains; such a domain is only present once per protein in our model. Domains i and j are defined as potentially interacting if there exists at least one pair of interacting proteins x and y such that i ∈ D(x) and j ∈ D(y).In our model, we use the indices et al.  ,50 and RS, θ and E for all possible domain interactions in the network. Of these three scores, only E unambiguously identifies red ↔ blue as the underlying domain interaction in the hypothetical network . Possible domains were defined as a list of colors: red, violet, blue, azure, green, yellow, and orange. Proteins were initially defined as objects containing at least one domain. Proteins were then arbitrarily linked subject to the constraint that any interacting pair of proteins must contain a red domain in one protein and a blue domain in the other. We thus defined red ↔ blue as the underlying domain interaction in the network. This process was repeated for three organisms, arbitrarily chosen to be yeast, worm and human, with 5, 4 and 3 hypothetical proteins defined for each organism, respectively. DPEA was then applied to compute k Figure .Our DIP dataset contains 11,593 interactions of yeast proteins. Of these, 26 are between proteins with Cyclin_N domains [Pfam:PF00134] and proteins with Pkinase domains [Pfam:PF00069]. To increase the number of interactions between these two classes of proteins by a factor of 3, we picked random pairs of proteins consisting of one member of each class and assigned an interaction if the interaction was not already in our data. This was repeated until the number of interactions between Cyclin_N-containing and Pkinase-containing proteins reached 78 (3 × 26). The DPEA algorithm was then run on both the unmodified DIP yeast interaction set, and the set with added interactions.The following additional data are included with the online version of this article: a table showing the numbers of DIP proteins and protein-protein interactions used per organism (Additional data file Numbers of DIP proteins and protein-protein interactions used per organismClick here for fileDIP proteins used in this studyClick here for fileDIP interactions used in this studyClick here for fileDIP protein accession numbers followed by the Pfam 14.0 domain IDClick here for fileE score > 3.0, and the organisms that contributed evidence3,005 inferred domain interactions with an Click here for fileSaccharomyces. cerevisiaeAll protein interactions from the DIP dataset in Additional data file 3 in which both proteins are from the yeast Click here for file52 randomly-assigned, unrecorded interactions between yeast proteins where one protein has a Cyclin_N domain and the other protein has a Pkinase domain. For all simulated interactions, the field exp_class is given the value 'h' for 'hypothetical'Click here for fileNodes, edges, and node-to-domain mappings in a hypothetical protein interaction network. Format of each respective section is the same as in additional data files for DIP proteins, interactions, and domain mappings.Click here for file


Many algorithms exist for protein structural alignment, based on internal protein coordinates or on explicit superposition of the structures. These methods are usually successful for detecting structural similarities. However, current practical methods are seldom supported by convergence theories. In particular, although the goal of each algorithm is to maximize some scoring function, there is no practical method that theoretically guarantees score maximization. A practical algorithm with solid convergence properties would be useful for the refinement of protein folding maps, and for the development of new scores designed to be correlated with functional similarity.increase the scoring functions at every iteration. The solutions obtained are critical points of the scoring functions. Two algorithms are introduced: One is based on the maximization of the scoring function with Dynamic Programming followed by the continuous maximization of the same score, with respect to the protein position, using a smooth Newtonian method. The second algorithm replaces the Dynamic Programming step by a fast procedure for computing the correspondence between Cα atoms. The algorithms are shown to be very effective for the maximization of the STRUCTAL score.In this work, the maximization of scoring functions in protein alignment is interpreted as a Low Order Value Optimization (LOVO) problem. The new interpretation provides a framework for the development of algorithms based on well established methods of continuous optimization. The resulting algorithms are convergent and The interpretation of protein alignment as a LOVO problem provides a new theoretical framework for the development of convergent protein alignment algorithms. These algorithms are shown to be very reliable for the maximization of the STRUCTAL score, and other distance-dependent scores may be optimized with same strategy. The improved score optimization provided by these algorithms provide means for the refinement of protein fold maps and also for the development of scores designed to match biological function. The LOVO strategy may be also used for more general structural superposition problems such as flexible or non-sequential alignments. The package is available on-line at http://www.ime.unicamp.br/~martinez/lovoalign. The number of protein structures obtained experimentally becomes larger every year. This large database is the source of data for the study of important problems in structural biology: The classification of protein structures according to their function, and the correlation of sequence and structure. Studies on the classification of proteins available in the Protein Data Bank (PDB)  have alrReliable methods for assessing similarities or discrepancies between structures are thus required. Algorithmic reliability is now most important since the description of the fold space is changing from a discrete to a continuous representation of similarities in terms of geometrical measures . It is nvery likely compute global maximizers and are guaranteed to compute critical points .Studies on the organization of the protein fold universe employ multidimensional scaling -12 or KeSumming up, algorithms for protein structural comparisons that provide theoretical tools for a more profound analysis of the relationships between sequence, folding, and structure must have some desirable properties: They must converge, in the sense that a solution must always be encountered, and the solutions must have known properties in terms of the similarity function being considered. The algorithm must also be versatile (adaptable for the optimization of diverse merit functions) since the study of the correlations between structural similarity and functionality may require specific similarity measures. Furthermore, a good method must be competitive with current algorithms in terms of computer time.α atoms of each structure being aligned and is not sensitive to the presence of gaps  such thatBy (14) and the Mean Value Theorem, for all Moreover, by (2),k ∈ K.for all K1 ⊂ K, s ∈ n be such that Let sk||, and taking limits for k ∈ K1, we obtain:By (16), dividing both sides of the inequality (17) by ||α < 1 and ∇fi(xk)Tdk < 0 for all k, this implies that ∇fi(x*)Ts = 0. Thus, taking limits in (18), we obtain that ∇fi(x*) = 0. Therefore, by (11), x* is critical.Since j and an infinite set of indices k ∈ K such that j = ν(k) and ||∇ fj(xk)|| is bounded away from zero. This implies that j ∈ Imin(x*) and ||∇fj(x*)|| ≠ 0, contradicting the first part of the proof.    □Finally, let us prove (9). If (9) is not true, there exists The program for performing protein structural alignments with these methods is freely available, with source codes, at:online server for pairwise comparison is also available. The methods are implemented in such a way that performing pairwise, single-protein-to-database or all-on-all database comparisons is straightforward.An LM proposed that the alignment problem could be treated by LOVO theory. LM, RA and JMM designed the algorithms. LM and JMM have written the program and LM has performed the numerical experiments. RA and JMM developed theoretical aspects. LM and JMM have written the paper. All authors have approved the final manuscript.


Here, we investigate how the domain interactions from the iPfam database are distributed in protein interactions taken from the HPRD, MPact, BioGRID, DIP and IntAct databases.Protein interactions are thought to be largely mediated by interactions between structural domains. Databases such as E. coli, S. cerevisiae and H. sapiens. These domains are related to essential cellular functions, suggesting that many domain interactions were already present in the last universal common ancestor.We find that known structural domain interactions can only explain a subset of 4–19% of the available protein interactions, nevertheless this fraction is still significantly bigger than expected by chance. There is a correlation between the frequency of a domain interaction and the connectivity of the proteins it occurs in. Furthermore, a large proportion of protein interactions can be attributed to a small number of domain interactions. We conclude that many, but not all, domain interactions constitute reusable modules of molecular recognition. A substantial proportion of domain interactions are conserved between Our results support the concept of domain interactions as reusable, conserved building blocks of protein interactions, but also highlight the limitations currently imposed by the small number of available protein structures. One way to understand a protein's function is to look at its composition of conserved domains. Such families of related sequence regions, collected in the Pfam database , usuallyet al. described an approach to predict domain–domain interactions using literature curation, evolutionary history and the distribution of domains in protein interactions [et al. compared the relationship between this set of predicted interacting domain pairs to the domain coocurrence network [Therefore, domain–domain interactions are often regarded as the currency of protein–protein interactions. Based on this assumption, Ng ractions . Wuchty  network . More re network -10. HoweiPfam database [For complexes with known structure, it has been shown that domains can mediate interactions ,12. Suchdatabase . The strUnfortunately, the selection of complexes in the PDB database of protein structures  is ratheS. cerevisiae is thought to be incomplete and inaccurate [High-throughput experiments -18 and eaccurate -27. GiveiPfam are distributed in experimental protein interactions from five major model species. This allows us to address a number of questions: what proportion of each organism's protein interaction network, its interactome, can be attributed to a known domain–domain interaction? How conserved are domain–domain pairs between species, and how many interacting domain pairs are still unknown?In this study, we investigate how pairs of protein families taken from iPfam domain pairs) in experimentally derived protein interactions . The experimental interactions were filtered to only include interactions with exactly two partners (see Methods). The fraction of experimental interactions that contain at least one iPfam domain pair is referred to as the iPfam coverage. Accordingly, the fraction of experimental interactions that contains any pair of Pfam domains (excluding the iPfam domain pairs) is called the Pfam coverage.We analysed the distribution of Pfam families known to interact from a PDB structure ) = k log x + log a) shown as dotted lines. The slopes k of the H. sapiens and S. cerevisiae distributions are very similar , while E. coli has a markedly smaller slope (-2.78). This suggests that the ratio of specific to promiscuous iPfam domain pairs is very similar in S. cerevisiae and H. sapiens, whereas E. coli features fewer multiply reoccurring iPfam domain pairs.All three distributions in Figure iPfam frequencies implies that the majority of covered protein interactions can be attributed to a minority of iPfam domain pairs. 51.7% of the iPfam domain pairs in S. cerevisiae and 45.3% in H. sapiens are seen in just one experimental interaction. Conversely, 92.4% of H. sapiens and 85.4% of S. cerevisiae covered experimental interactions contain an iPfam domain pair that occurs more than once. Even more, half of the covered experimental interactions in H. sapiens contain an iPfam domain pair that occurs in more than 16 different experimental interactions (5 for S. cerevisiae).The power law distribution of iPfam domain pairs that act as reusable modules in protein interactions, then highly connected proteins should also be more likely to contain promiscuous iPfam domain pairs and vice-versa.We reasoned that if there are H. sapiens and S. cerevisiae protein interaction network, we calculated its degree, defined as the number of adjacent edges (i.e. interactions). At the same time, we counted the number of iPfam domain pairs on the adjacent edges. In Figure iPfam domain pairs relative to the degree of the node.For each node (i.e. protein) in the filtered H. sapiens and S. cerevisiae  between degree and number of iPfam domain pairs on adjacent edges. For the 1.2% of proteins in H. sapiens and 6.4% in S. cerevisiae which have a degree higher than 50, the correlation gradually diminishes.We find that for proteins from a degree of 1 to 50, there is strong correlation in both iPfam domain pairs and their frequencies in the experimental protein interactions, while Additional file iPfam domain pairs, as well as interactions involving recognition domains such as SH2 or SH3. In S. cerevisiae, the Proteasome family  and WD40  are also amongst the five most frequent iPfam domain pairs. As expected, more frequent domains are also more likely to be found as pairs in interacting proteins. It should be noted however that in the PDB structures, some of the observed domain pairs  are only seen to interact within one protein (intrachain interactions) as opposed to interactions between two distinct proteins (interchain interaction). The table in Additional file iPfam domain pair, distinguishing between intrachain and interchain interactions. Looking for example at the covered experimental interactions in H. sapiens on interacting proteins is given in Additional file iPfam domains such as SH2, SH3_1 or Pkinase_tyr frequently occur in the same architecture. Without further experiments, we cannot assign the correct interacting domains with certainty.A possible explanation for the occurrence of purely intrachain iPfam domain pair interact through these domains. This, of course, is not necessarily the case. Although it has been shown that sequence similarity is linked to the mode of interaction [iPfam domain pair is necessarily mediated by exactly this domain pair. To gain a rough estimate of the false positive rate due to this assumption, we counted how many protein pairs in the PDB contain an iPfam domain pair that does not mediate an interaction in one complex structure but does so in another. 3671 out of a total of 5380 interacting protein pairs from the PDB contain an iPfam domain pair that does not interact in one complex structure but does so in another. This means that for more than 32% of the protein interactions in the PDB, the iPfam domain pair assignment is correct. For the remaining 68%, the iPfam domain pair assignments are wrong in one case but correct in another. The real false positive rate is likely to be smaller, because some iPfam domain pairs might still independently mediate an interaction with a different, possibly unknown, partner protein.This highlights a basic assumption of this study that could be a source of error. We assume that interacting proteins that contain an eraction , not eveiPfam domain pairs are enriched in known protein complexes from S. cerevisiae. This is interesting firstly because domain–domain interactions are thought to be more common in obligate interactions. Secondly, the described modularity of known S. cerevisiae complexes lends support to the assumption that the underlying iPfam domain pairs are modular. In fact, we find a two-fold enrichment for iPfam domain pairs in the complexes described by Gavin et al. [iPfam domain pair, which corresponds to a coverage of 8.16% .We tested whether n et al. . From thet al., 157 contained at least one pair of proteins with an iPfam domain pair (31.9%). In total we found 617 pairs of proteins that contained an iPfam domain pair. Interestingly, we find that the distribution of iPfam domain pairs on complexes is uneven. When we drew 617 protein pairs randomly from all possible protein pairs in the complexes, we covered 192 complexes on average, with a standard deviation of 7.22. The probability of covering only 157 complexes is just 6.24 · 10-07. Thus, some complexes contain a greater number of iPfam domain pairs, while other complexes do not contain any at all. This suggests that some sets of domain pairs are specific to certain complexes or pathways. Typical examples are the RNA polymerase II complex (IntAct id: EBI-815049) or the U1 snRNP complex which contain numerous iPfam domain pairs that are specific to these complexes.We also analysed the full dataset of protein complexes. From 491 complexes described by Gavin iPfam, we analysed the conservation of iPfam domain pairs between species. We call an iPfam domain pair conserved when the same pair is observed in experimental interactions of two different species. The matrix in Table iPfam domain pairs. For each species, a maximum of 40% to 90% of iPfam domain pairs can also be found in another species, although not all overlaps are as large.Within the 3 to 19% of experimental interactions covered by S. cerevisiae and H. sapiens and the prokaryote E. coli. While the eukaryotes share 524 domain pairs, only 158 iPfam domain pairs are shared between S. cerevisiae and E. coli, and only 135 between E. coli and H. sapiens. Remarkably, 53% of the observed iPfam domain pairs in E. coli are also observed in one of the two eukaryotes, and 107 iPfam domain pairs are even conserved amongst all three species. The iPfam domains in these pairs are related to housekeeping activities such as translation, replication or ATP synthesis. Additional file iPfam domain pairs.Figure iPfam domain pair frequencies between H. sapiens and S. cerevisiae directly. We derive a Spearman correlation coefficient of 0.50 for the frequencies of all 524 iPfam domain pairs that are conserved between S. cerevisiae and H. sapiens. To test whether the correlation is an artefact of the distribution of the values, we recalculated the correlation 1000 times, each time shuffling one distribution randomly. From these random results, we derive a P value of 3.6 · 10-30 that the observed correlation is random. This suggests that iPfam domain pairs with a large number of occurrences in one species tend also to be more frequent in the other.We also compared the iPfam domain pairs would eventually cover all protein interactions. This corresponds to the predictions made by Aloy and Russel [Our analysis allow us to estimate how many d Russel . SimilarχS The number of iPfam domain pairs observed in species SθS The number of observed interactions in species S that contain an iPfam domain pairS The total number of observed interactions in species SΘψS The number of proteins from species S that are seen in an interaction screenS The proteome size for species SΨξS The number of Pfam domains observed in all protein of species SΞ The total number of known Pfam domainsiPfam domain pairs in species S with We denote the estimated number of iPfam domain pairs to cover all observed interactions. We then use the relative proteome coverage to estimate the total number of iPfam domain pairs in all proteins.This means we scale the observed number of S indicates the fraction of the protein universe represented in the species. We therefore predict the total number of iPfam domain pairs Finally, we follow the argument of Aloy and Russel that the number of Pfam families seen in species iPfam domain pairs ranges from 33813 to 120511, with an average of 76918.Both parameters and results of the calculation are shown in Table iPfam on experimentally derived protein interactions is low. For S. cerevisiae, the species with the best mapped interactome, only 4.47% of the protein interactions contain an iPfam domain pair. Even in H. sapiens, where we suspect a positive bias due to the overrepresentation of disease-related proteins in both the PDB and protein interaction databases, 81% of protein interactions do not contain an iPfam domain pair. This reveals the limits of our understanding of the molecular structure of protein interactions.The coverage of iPfam quickly.Figure iPfam domain pairs occur significantly more often in experimental interactions than would be expected by chance. This requires that at least a subset of the iPfam domain pairs are reused in several experimental interactions.We find, however, that iPfam domain pairs are found in more protein interactions than would be expected by chance  and Pkinase_Tyr (PF07714) interact, therefore they form an iPfam domain pair. In this study, iPfam version 21 was employed, containing 2837 iPfam domains, forming 4030 iPfam domain pairs. Figure iPfam domain pairs. H. sapiens, E. coli and S. cerevisiae are clearly over-represented compared to the other 1113 species with less than 179 complex structures. Some iPfam domain pairs are seen to form interactions between distinct peptide chains in the structure (interchain), while others form an interaction between two distinct domains within the same chain (intrachain). In iPfam version 21, there are 3407 interchain and 1171 intrachain domain pairs, which means that 548 domain pairs mediate both inter- and intrachain interactions. In this analysis, both types of domain interactions were used equivalently, assuming that intrachain interactions can become interchain interactions and vice-versa as a result of a gene-fission/fusion events.The involved . Every pThere are many types of experiments used to derive protein interactions, with different properties and error rates. For this analysis, solely the properties of physically interacting proteins is of interest. Therefore, only interactions between exactly two proteins per experiment were considered. That means all protein complex data that were derived by co-purification methods were removed, unless a particular experiment had identified exactly two binding partners. All genetic interactions were also removed.Randomised protein interaction networks with identical degree distributions were generated from the original filtered experimental interaction data for each species. In each randomisation step, a mapping is created that assigns every node a randomly chosen replacement node. In this way the edges of the network remain in place, while the nodes are shuffled randomly. It should be noted that the degree distribution per node is not maintained. Instead, this behaviour simulates a network with a high false positive rate.x were calculated as P(X ≥ x) = f, where f is the probability density function of the normal distribution with mean μ and standard deviation σ. μ and σ are estimated through the randomisation experiments. The density function thus provides the probability that a value less than or equal to x is observed by chance, given the distribution estimated by a random resampling method. Where appropriate, the inverse probability P(X > x) = 1 - f was applied.P values for observations BSB wrote all software and carried out all the analyses. AB contributed to the design and interpretation of the study.iPfam_pair_frequencies. En Excel file containing the frequencies of all iPfam domain pairs for all five species, i.e. the number of interactions that contain the respective iPfam domain pair.Click here for fileiPfam_architectures. Two Excel sheets containing the frequencies of all iPfam domain architectures in S. cerevisiae and H. sapiens experimental interactions, i.e. the number of proteins that are composed of a certain set of iPfam domains.Click here for fileiPfam_pair_conservation. An Excel spreadsheet containing the 107 iPfam domain pairs conserved amongst E. coli, S. cerevisiae and H. sapiens and their frequencies.Click here for fileiPfam_frequencies. An Excel file containing the frequencies of each of the iPfam domains for all five species, i.e. the number of proteins containing the respective domain.Click here for fileiPfam_inter-intrachain. An Excel file listing the number of interchain and intrachain interactions in all PDB structures for each iPfam domain pair.Click here for file


We identify these residues using a novel, highly sensitive co-evolution detection algorithm called CRoSS . The residue pairs selected by CRoSS are involved in direct physical interactions in a docked-domain NMR structure. A single PKS system can use docking domain pairs from multiple classes, as well as domain pairs from multiple subclasses of any given class. The termini of individual proteins are frequently shuffled, but docking domain pairs straddling two interacting proteins are linked as an evolutionary module. The hierarchical and modular organization of the specificity code is intimately related to the processes by which bacteria generate new PKS pathways.Polyketides, a diverse group of heteropolymers with antibiotic and antitumor properties, are assembled in bacteria by multiprotein chains of modular polyketide synthase (PKS) proteins. Specific protein–protein interactions determine the order of proteins within a multiprotein chain, and thereby the order in which chemically distinct monomers are added to the growing polyketide product. Here we investigate the evolutionary and molecular origins of protein interaction specificity. We focus on the short, conserved N- and C-terminal docking domains that mediate interactions between modular PKS proteins. Our computational analysis, which combines protein sequence data with experimental protein interaction data, reveals a hierarchical interaction specificity code. PKS docking domains are descended from a single ancestral interacting pair, but have split into three phylogenetic classes that are mutually noninteracting. Specificity  Biomolecular interactions can be extraordinarily specific. In many instances, a protein can select its single correct binding partner from among a large array of closely related candidates. For polyketide synthases (PKSs), a family of bacterial enzymes, such specificity is essential. Like workers on an assembly line, PKSs function as multiprotein chains, each enzyme modifying its substrate before passing it along to the next. And like a well-designed jigsaw puzzle, the overall multiprotein chain is correctly ordered precisely because each component protein can only bind to specific nearest neighbors. A PKS multiprotein chain is held together by sticky “head” and “tail” domains found at either end of each protein, the head of one protein binding to the tail of the next. We looked for patterns in the amino-acid sequences of these domains that could explain why certain head–tail pairs bind, while others do not. We discovered that heads and tails each come in three very different varieties. Mismatched head–tail pairs do not bind at all, while the binding of a matching head–tail pair is governed by the amino acids found at a few key positions on the physical interface between these domains. The extraordinary biosynthetic capabilities of polyketide synthases (PKS), some of the largest known bacterial multi-enzyme complexes, have been extensively investigated over the past decade. Using a combination of biochemical and genetic techniques, researchers have developed a detailed understanding of the organization and structure of these complex enzymes, as well as of the biochemical reactions they catalyze . The assBiochemical studies of PKS systems have recently been complemented by powerful computational tools. These tools allow results from well-characterized PKS systems to be extended to the rapidly growing set of putative PKS gene clusters in fully sequenced bacterial genomes. Computational analysis of sequence data has been used to identify catalytic domains and to predict their substrate specificity, both for PKS systems ,8 as welspecific ρ, which –F) Interactions between ACP and KS domains have been shown to contribute to specificity [H1–T1 docking domain pairs in our dataset, precisely as we had done for the docking domains themselves. Starting with a multiple-sequence alignment, we applied CRoSS to detect co-evolving residues by comparing interactors with noninteractors (ijρ). We also applied CRoSS on a control dataset, comparing random pairings with noninteractors (ρ0ij). The ACP-KS CRoSS interaction matrix shows very low signals  CRoSS entries in the interaction (red) and control (blue) matrices are comparable, indicating that there are no significant co-evolving pairs.(1.6 MB PDF)Click here for additional data file.Figure S2minN is varied from high (TP = FP = 0) to low (TP = FP = 1). Curves of different colors correspond to different partitions of the data into training and test sets.(A) Receiver operating characteristics (ROC) for predictions based on Monte Carlo clustering. Each curve shows the TP rate versus the FP rate, as the clustering threshold B) Interpolated ROC curves were used to calculate the FP value at each TP value, for each of the 15 training and test sets. Here we show the mean ROC value (black) along with one standard error on each side (gray) as a function of TP values. A random classifier would trace the curve TP = FP (red), while the ROC of a classifier that performed better than random would lie above this diagonal. The performance of our code is marginally better than random, but statistically significant. The performance tends to be better at higher FP values. The mean area under the ROC is 0.55 ± 0.02, and at FP = 0.5, we have TP = 0.6.–tail pairs of the multiprotein chain. These are represented as six 4 × 4 matrices with a single entry in each row and column.) This procedure was repeated for 1,000 trials. The figure shows the fraction of trials in which each of the six possible permutations was selected as being most likely. We see that the correct permutation is chosen in 42% of instances. Random guessing would pick the correct permutation in 16.7% of instances, so our performance is 2.5 times better than random.(C) Performance of the code as a predictor of PKS multiprotein chain order. Consider a hypothetical five-protein chain for which the termini  are specified, so the three internal proteins  can have six possible permutations. Using FP = 0.5 and TP = 0.6, we generated a possible prediction of pairwise docking domain interactions based on the correct underlying multiprotein chain permutation. We then used Bayesian inference to assign a posterior probability to the six possible permutations given the prediction, and selected the permutation with the maximum posterior probability. Click here for additional data file.Text S1(281 KB PDF)Click here for additional data file.