The protein-protein interaction (PPI) is one of the most important features to understand biological processes. For a PPI, the physical domain-domain interaction (DDI) plays the key role for biology functions. In the post-genomic era, to rapidly identify homologous PPIs for analyzing the contact residue pairs of their interfaces within DDIs on a genomic scale is essential to determine PPI networks and the PPI interface evolution across multiple species.Position Specific Scoring Matrix (pairPSSM)" to identify homologous PPIs. The pairPSSM can successfully distinguish the true protein complexes from unreasonable protein pairs with about 90% accuracy. For the test set including 1,122 representative heterodimers and 2,708,746 non-interacting protein pairs, the mean average precision and mean false positive rate of pairPSSM were 0.42 and 0.31, respectively. Moreover, we applied pairPSSM to identify ~450,000 homologous PPIs with their interacting domains and residues in seven common organisms .In this study, we proposed "pair pairPSSM is able to provide statistical significance of residue pairs using evolutionary profiles and a scoring system for inferring homologous PPIs. According to our best knowledge, the pairPSSM is the first method for searching homologous PPIs across multiple species using pair position specific scoring matrix and a 3D dimer as the template to map interacting domain pairs of these PPIs. We believe that pairPSSM is able to provide valuable insights for the PPI evolution and networks across multiple species.Our  Many biological processes involve different types of interactions among proteins. Listing the proteins in the cell is not enough to fully understand the cellular machinery and all the interactions between them need to be delineated as well . RecentlSaccharomyces cerevisiae (baker's yeast) are very similar  to the template in Mus musculus and might interact with each other based on the definition of homologous PPIs  of redundancy occur in RefSeq. Therefore, we used BlastCluster to remove redundancy with 90% identical over an area covering 90% of the length of each sequence in the same species. Finally, 2,109,945 protein sequences were selected into our non-redundant protein database.E-value ≤ 10-9. The two proteins of 3D-dimer are used as a template for constructing two multiple sequence alignments, respectively. Any sequence that is > 95% identical to 3D-dimer (template) is purged.To produce a multiple sequence alignment from the PSI-BLAST, we collected all RefSeq sequence segments aligned to the two proteins of 3D-dimer with ij/PijQ), where ij Qis the estimated probability for the contact residue pair i&j to be found in the column; ij Pis the expect probability of i&j to be found in the column. The estimate of ij Qfor a specific contact position should converse simply to the observed frequency of the residue pair i&j in that column. To estimate the ijQ, we implemented the data-dependent the pseudocount method .BMC Bioinformatics Volume 14 Supplement 2, 2013: Selected articles from the Eleventh Asia Pacific Bioinformatics Conference (APBC 2013): Bioinformatics. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcbioinformatics/supplements/14/S2.This article has been published as part of The supplementary information.Click here for file


Computational approaches to protein-protein docking typically include scoring aimed at improving the rank of the near-native structure relative to the false-positive matches. Knowledge-based potentials improve modeling of protein complexes by taking advantage of the rapidly increasing amount of experimentally derived information on protein-protein association. An essential element of knowledge-based potentials is defining the reference state for an optimal description of the residue-residue (or atom-atom) pairs in the non-interaction state.OCKGROUND resource. Each residue was represented by a pseudo-atom in the geometric center of the side chain. To capture the long-range and the multi-body interactions, residues in different secondary structure elements at protein-protein interfaces were considered as different residue types. Five reference states for the potentials were defined and tested. The optimal reference state was selected and the cutoff effect on the distance-dependent potentials investigated. The potentials were validated on the docking decoys sets, showing better performance than the existing potentials used in scoring of protein-protein docking results.The study presents a new Distance- and Environment-dependent, Coarse-grained, Knowledge-based (DECK) potential for scoring of protein-protein docking predictions. Training sets of protein-protein matches were generated based on bound and unbound forms of proteins taken from the DA novel residue-based statistical potential for protein-protein docking was developed and validated on docking decoy sets. The results show that the scoring function DECK can successfully identify near-native protein-protein matches and thus is useful in protein docking. In addition to the practical application of the potentials, the study provides insights into the relative utility of the reference states, the scope of the distance dependence, and the coarse-graining of the potentials. Protein-protein interactions are a key element of life processes. Thus better understanding of these interactions, coupled with our ability to model them, is essential for the fundamental knowledge of their biology and the multitude of biomedical applications.Computational approaches to structural determination of protein-protein complexes (protein-protein docking) typically involve two steps: the global, often low-resolution, search within a computationally feasible timeframe to detect a set of matches that includes at least one near-native structure (scan stage), and the local refinement of the matches from the scan stage that may involve more computationally expensive protocols. Such refinement often includes scoring aimed at improving the rank of the near-native structure relative to the false-positive matches.Knowledge-based potentials ,2, physiIn structure prediction of individual proteins, the knowledge-based scoring functions gained significant popularity -10. It hAn essential element of knowledge-based potentials is defining the reference state for the optimal description of residue-residue (or atom-atom) pairs in the non-interaction state. For protein-protein interactions, generally, there are three methods of defining the non-interaction state. The first one is based on the large-distance cutoffs . To select the optimal reference state, the scoring functions were tested on GRAMM-X decoy set . The sucOur potentials are distance-dependent by design. In the development of distance-dependent potentials, the choice of the distance cutoff is an important consideration. Earlier studies investigated the cutoff effect in protein-protein energy landscapes . For a lα RMSD <2.5 Å) was present in 66 complexes. The tested potentials were: DECK 1 and DECK 2 , Contact Potential (trained on set 2), and DCOMPLEX. The results were compared with ZRANK values from the score file in the decoys set. The success rates are shown in Figure The potentials were tested on the ZDOCK3.0+ZRANK Decoys developed in Weng's lab . ZDOCK3.A test was also performed on RosettaDock  unbound DECK 1 and 2, and Contact Potential were tested and compared with RosettaDock, DCOMPLEX and ZRANK score values. The RosettaDock score values were obtained from the file in the decoy set. The scores of DCOMPLEX and ZRANK were computed locally. With a hit defined as a match with ligand RMSD <5 Å, 28 of 54 complexes had at least one hit. The results are shown in Figure http://www.ebi.ac.uk/msd-srv/capri). This experiment allows a comparison of different computational methods on a set of prediction targets . The community of predictors is provided with the coordinates of the separate components of the complex, which they use for the docking and scoring. After the models are submitted by the docking predictors, they are made available to 'scorer' groups to re-rank them and submit their own 10 best-ranking matches [An important activity in the field of protein-protein docking is a community-wide experiment on Critical Assessment of Predicted Interactions .The knowledge-based potentials improve modeling of protein complexes by taking advantage of the rapidly increasing amount of experimentally derived information on protein-protein association. The distance dependence of these potentials is supposed to provide a more accurate description of protein-protein interactions by taking into account the structural and physicochemical aspects of the interacting proteins within a broader scope than the immediate contact across the interface. The coarse-graining of the potentials makes them less sensitive to the structural inaccuracies of the proteins, which are unavoidable for unbound X-ray and potentially modeled proteins, especially in high-throughput applications to large interaction networks.Five reference states for the coarse-grained, distance-dependent, knowledge-based potentials were used in this study. Similar reference states in earlier studies focused on protein structure prediction and protein folding ,26,27. WOCKGROUND resource. Each residue was represented by a pseudo-atom in the geometric center of the side chain. To capture the long-range and the multi-body interactions, residues in different secondary structure elements at protein-protein interfaces were considered as different residue types. Five reference states for the potentials were defined and tested. The optimal reference state was selected and the cutoff effect on the distance-dependent potentials investigated. The potentials were validated on the docking decoys sets, showing better performance than the existing potentials used in scoring of protein-protein docking results. The study also provides insights into the relative utility of the reference states, the scope of the distance dependence and the coarse-graining of the potentials.Scoring of predicted protein-protein matches is important for identification of near-native structures in a pool of models. Knowledge-based scoring schemes improve modeling of protein complexes by taking advantage of the rapidly increasing amount of experimentally derived information on protein-protein association. A choice of the reference state for the description of non-interacting residue or atom pairs is an essential element of the knowledge-based potentials. The study presents a new potential for scoring of protein-protein docking predictions. Training sets of protein-protein matches were generated based on the bound and unbound proteins from the DOCKGROUND resource [http://dockground.bioinformatics.ku.edu). The bound complexes were from the representative bound set and the bound part of the docking benchmark. The unbound complexes were from the docking benchmark. For all the complexes, the docking decoys were generated by GRAMM-X [The bound and the unbound complexes for the training sets were taken from the Dresource ,29,30  at distance d across the interface, the contribution of binding energy e can be estimated as:It can be assumed that the probability of structural features at protein-protein interfaces follows the Boltzmann distribution ,27,32-37πobs and πexp are the observed and the expected probability of the residue pair  at distance d respectively, and RT is set to 1.where The interaction distance was divided into 21 bins. Comparison with the contact potential Figure  suggestsi and j was defined as the distance between their pseudo atoms. Atomic environment potential [Five reference states from the existing methodologies were defined. Each residue was represented by a pseudo-atom in the geometric center of the side chain . The distance between residues otential  was usedotential ,40  was defined asThe observed probability of residue pair  is 60; gp,m is the total number of i, j pairs at distance d in near-native structure m of complex p.where i is defined as:The mole fraction of residue type N (i) is the total number of type i residues at the near-native interface. The expected probability of residue pair  is defined as:where i, j) is estimated from the near-native matches. The expected probability of residue pair  was calculated in the same way as the observed probability using all decoys instead of the near-native matches in Eqs. (2-5).The expected probability of residue pair  were calculated from near-native matches. The observed probability of residue pair  was defined asThis reference state is based on KBP potential . The obsd is the distance between residues i and j.where i, j) is defined as:The expected probability of residue pair obs, N(d)obs and mole fraction χi are calculated according to Eqs. (3-5).where i, j) are calculated from near-native matches. The observed probability of residue pair  is defined as:This reference state was proposed by Sippl . The obsσ is set to 0.02.where i, j) were calculated from the near-native matches. The observed probability of residue pair  was defined asThe observed and the expected probabilities of residue pair  at distance d was defined as:The expected probability of residue pair obs and N(d)obs are calculated according to Eq. (3) and Eq. (4) respectively.where πobs and the expected probability πexp of residue pair  were calculated from the near-native matches and the non-native decoys according to Eq. (11), respectively. The only difference between the observed probability πobs and the expected probability πexp of residue pair  are the objects of the statistics - the near-native matches for the former and the non-native decoys for the latter.The observed probability IAV conceived the research and both authors designed it. SL carried out the calculations and both authors analyzed the results. The manuscript was drafted by SL and written/revised by both authors. Both authors have read and approved the final manuscript.SL is an associate professor at the Department of Physics at Huazhong University of Science and Technology, and IAV is the director of the Center for Bioinformatics and professor of Bioinformatics and Molecular Biosciences at The University of Kansas.


Biological networks are powerful tools for predicting undocumented relationships between molecules. The underlying principle is that existing interactions between molecules can be used to predict new interactions. Here we use this principle to suggest new protein-chemical interactions via the network derived from three-dimensional structures. For pairs of proteins sharing a common ligand, we use protein and chemical superimpositions combined with fast structural compatibility screens to predict whether additional compounds bound by one protein would bind the other. The method reproduces 84% of complexes in a benchmark, and we make many predictions that would not be possible using conventional modeling techniques. Within 19,578 novel predicted interactions are 7,793 involving 718 drugs, including filaminast, coumarin, alitretonin and erlotinib. The growth rate of confident predictions is twice that of experimental complexes, meaning that a complete structural drug-protein repertoire will be available at least ten years earlier than by X-ray and NMR techniques alone. Predicting drug-target interactions is a hot topic, and many efforts have been undertaken to do this, many using large interaction networks. We take a novel approach using protein-chemical interactions derived from 3D structures. The basic premise is that two proteins sharing a common bound chemical will likely share others. We use protein and chemical superimpositions and physical tests of chemical-protein compatibility to identify the most likely candidates among the nearly one million potential interactions. We show for a benchmark that known protein-chemical structures are reconstructed with good accuracy and sometimes via very different proteins and chemicals. We make thousands of confident predictions, including structures for known protein-drug interactions lacking a structure (e.g. topoisomerase-2/radicicol) and many new interactions. The number of confident predictions grows faster than the number of known structures, suggesting that this approach will play a key role in completing the protein-chemical interaction repertoire. Large biological networks have been used previously to suggest protein-protein interactions 1 and P2) binding a common ligand (La) and a third where one protein (P1) binds another ligand (Lb). By superimpositions based on the common protein and the common ligand, we obtain an additional putative complex (P2 with Lb). We then used several criteria to decide whether or not these new complexes were structurally viable and evaluated the statistical significance using a p-value (see 1 and P2) shared ≥80% identity (i.e. where ligand transference would be very likely due to orthology). The predictions include enzyme/substrate, enzyme/product, target/inhibitor and target/activator structures  chemicals or proteins are less interesting as they represent cases where transference is more obvious: for instance testing a chemical inhibitor of a human protein in a mouse orthologue, or inferring that a slightly modified chemical compound might have a similar activity. The number of these complexes is small relative to the total number of predictions owing to these requirements. That is, it is currently relatively unlikely that these combinations exist given the frequency of chemicals solved in complex with multiple distinct protein structures. There were thus only 376 complexes of known structure that were also predicted significant (p≤0.05) by our method using different protein-chemical complexes in the network. For 270 (72%) of them, the ligand in the predicted complex had an RMSD <2 Å when compared to the known structure, which matches standards acceptable for docking solutions e.g. .Pneumocystis carinii dihydrofolate reductase (DHFR) with trimethoprim. This prediction was made since both this DHFR and a remote homolog (36% identity) from Mycobacterium tuberculosis have known structures in complex with methotrexate, and the latter has been solved with trimethoprim .Among predictions involving drugs are several established relationships that lack an experimental structure. For example, we predicted complexes between DNA topoisomerase 2 and radicicol There are 19,578 new predicted interactions, including 7806 involving known drugs. Of the total, 4738 were made using intermediate proteins sharing ≥30% identity to the target protein or one in the same Pfam Several predictions are made using what appear to be convergently evolved binding pockets. That is, the proteins sharing the common ligand share no sequence or structural similarity, and we exploit the common ligand (and thus the two binding pockets) to predict a new ligand for one of the proteins. More exactly, there are 112,546 total protein pairs from the potential predictions that share <30% sequence identity and for which a SCOP E. coliWe predict a complex between the heart-specific fatty acid binding protein (FABP3) and alitretinoin, using structures of FABP3 with stearic acid and of mouse RXRα with strearic acid and alitretinoin. There is only indirect evidence in support of this interaction: proteins in the wider superfamily of lipid binding proteins show some ligand promiscuity E. coli transporter Tsx, mostly based on structures of herpes virus thymidine kinase with thymidine, which also binds to Tsx. The kinase has been solved in complex with 12 anti-viral compounds, of which eight fit well into the Tsx structure . They offered an opportunity to test whether our approach could say anything about inhibitor selectivity. There are currently 766 unique human protein kinase-inhibitor crystal structures (284 kinases and 627 inhibitors). We compared our predictions with systematic screens for 127 human kinases and 33 inhibitors. The overlap of kinase/compound pairs in the screens (i.e. whether interacting or not) and the known or predicted structures is low: only 21 complexes overlap with our set of potential structures, and we get significant predictions for six of these (see Supplementary information). Despite the low overlap, we saw a correlation between the tendency for an inhibitor to be predicted to bind many kinases and the tendency to interact with many kinases in the screens, even if the particular kinases differ . Range estimates as to the average number of proteins to which a typical drug will bind can come from known 3D structures (3 proteins per compound), or drug-target databases such as SuperTarget/Matador The growth in the number of confident predictions is steeper than that for known structures . For a, e, f and g we take the fraction of iξ>ξ, as these terms are detrimental to binding. We sum is for all the parameters and obtain a combined score iS that can range from 0 to 7, greater scores corresponding to better models.We considered seven structural criteria to judge the predicted complexes: a) the clash volume; b) the number of ligand-protein contacts; c) the number of potential hydrogen bonds; d) the number of potential Van der Waals contacts; e) the number of un-satsified ligand hydrogen bond donors and acceptors; f) the number of ligand carbon atoms not involved in Van der Waals contacts; and g) the number of potential hydrogen donor and acceptor atoms exposed to the solvent. The raw values were normalized to ranges from 0 to 1: for a specific value of the parameter iS we calculate p as p = (#σ: σ>iS)/#σ, where σ are the scores from the negative dataset. Note, that only 24% of real structures, when scored by this scheme, have p≤0.05, which we believe not to be due to crystal packing or non-specific binding of buffer components, as a solvent-free subset, and a subset where promiscuous small molecules (those seen in more than 20 structures) were removed gave similar ratios. Instead, we believe this to reflect the stringency of our approach; p<0.2 clearly separates the positive and negative datasets (see below) but produces many false positives and predicted complexes with poor RMSDs in the benchmark . For a given model score We consider all pairwise complexes extracted from the Protein Data Bank a and Lb differs more than two fold.For the benchmark dataset and the dataset of potential complexes, we selected all combinations of three complexes as shown in Text S1Includes a more detailed comparison of the predictions with BindingDB (PDF)Click here for additional data file.


Studying protein complexes is very important in biological processes since it helps reveal the structure-functionality relationships in biological networks and much attention has been paid to accurately predict protein complexes from the increasing amount of protein-protein interaction (PPI) data. Most of the available algorithms are based on the assumption that dense subgraphs correspond to complexes, failing to take into account the inherence organization within protein complex and the roles of edges. Thus, there is a critical need to investigate the possibility of discovering protein complexes using the topological information hidden in edges.To provide an investigation of the roles of edges in PPI networks, we show that the edges connecting less similar vertices in topology are more significant in maintaining the global connectivity, indicating the weak ties phenomenon in PPI networks. We further demonstrate that there is a negative relation between the weak tie strength and the topological similarity. By using the bridges, a reliable virtual network is constructed, in which each maximal clique corresponds to the core of a complex. By this notion, the detection of the protein complexes is transformed into a classic all-clique problem. A novel core-attachment based method is developed, which detects the cores and attachments, respectively. A comprehensive comparison among the existing algorithms and our algorithm has been made by comparing the predicted complexes against benchmark complexes.We proved that the weak tie effect exists in the PPI network and demonstrated that the density is insufficient to characterize the topological structure of protein complexes. Furthermore, the experimental results on the yeast PPI network show that the proposed method outperforms the state-of-the-art algorithms. The analysis of detected modules by the present algorithm suggests that most of these modules have well biological significance in context of complexes, suggesting that the roles of edges are critical in discovering protein complexes. Interpretation of the completed biological genome sequences initiated a decade of landmark studies addressing the critical aspects of cell biology on a system-wide level, including gene expression analysis ,2, gene Protein complexes, consisting of molecular aggregations of proteins assembled by multiple protein interactions, are of the fundamental units of macro-molecular organizations and play crucial roles in integrating individual gene products to perform useful cellular functions. It is confirmed by the fact that the complex 'RNA polymerase II' transcribes genetic information into messages for ribosomes to produce proteins. Unfortunately, the mechanism for most of biological activities is still unknown and hence accurately predicting protein complexes from the available PPI data has a considerable merit of practice because it allows us to infer the principles of biological processes.et al. ) data is adopted, which consists of 4,928 proteins and 17,201 interactions. To evaluate the protein complexes predicted by our algorithm, a benchmark set was constructed from the the MIPS [The Database of Interaction Proteins  (DIP), Type II in Eq.(3). The basic information of predictions by various compared algorithms is summarized in Table Figure P-value is adopted here. The functional homogeneity P-value is the probability that a given set of proteins is enriched by a given functional group merely by chance, following the hypergeometric distribution. It is the probability of cooccurrence of proteins with common functions. Accordingly, a low P-value of a predicted complex indicates that the collective occurrence of these proteins in the complex does not merely combine by chance and thus achieves high statistical significance. The values are calculated by the GO::TermFinder [To further investigate the biological significance of the predicted complexes, the rmFinder .P-value above a cutoff threshold. In the experiments, we chose a cutoff of 1 × 10-2 for each protein complex because it offers a compromise between complex-cluster matching rate and a clustering passing rate.We discarded all clusters with P-values, predicted by our method. The second columun is Table Table Because the above experiments are sufficient to prove that the superiority of the proposed bridgeness, we only focused on the Type II method in the forthcoming experiment.P-values of predicted complexes by our algorithm support that the roles of interactions in PPI networks is promising on enhancing the accuracy of prediction. The module size distribution of predicted protein complexes for each compared methods on the DIP network has been shown in Figure The Notice that our algorithm is quite different from those based on discovering the dense subgraphs because it makes use of the weak ties effect. To verify the difference on the densities of the predicted complexes, we compared the Coach algorithm with our method in terms of the graph densities of the predicted complexes, shown in the Figure τ and β used effects the performance. The value of τ controls the size of a core, the total number of cores in the virtual graph, and the connectivity 'strength' of the network involved. Therefore, we studied its effect on the size of the virtual network. Figure τ. From it, we can see that the size of the virtual graph decreases dramatically when the value of τ increases from 0 to 0.4. Specifically, the size is approximately 3 × 104 if τ = 0.02. The reason is that when the value of τ increases, only the edges whose connectivity is strong enough are maintained.This subsection is devoted to investigate how the parameters β controls the weights on the edges. Thus, we study its effect on the accuracy of prediction in terms of F-measure and coverage rate. Figure β increases. A possible reason is that the size of a maximal clique in the virtual network decreases when β increases, resulting in many small cores by dividing the large cores in the virtual graphs with small β. As β increases, more and more proteins in the PPI data are covered because the number of predicted protein complexes increases. For this reason, the coverage rate keeps increasing. To make a good balance between the F-measure and coverage rate, we set β = 0.618.The parameter test graph is constructed by regarding each protein as a vertex and connecting each pair of nodes in the same complexes. The test graph has a poor value for assessing the robustness of the algorithms because each protein complex corresponds to a clique in the test graph. To solve this problem, the altered graphs are constructed from the test graph by adding or deleting the edges in various proportions. For the sake of convenience, the altered graph is denoted by add, delAGwhere add and del show the percentage of added and deleted edges, respectively.The robustness analysis on the proposed algorithm was discussed in this subsection. The benchmark networks adopted here originated from Ref. . In detaIn this experiment, only the MCL and Coach algorithms are selected for a comparison. The reason is that it is reported that the MCL is the most robust algorithms , and theτ.The Figure Figure Figure Protein complexes are key and basic molecular units in cellular functions and computational approaches to discovering accurately the unknown protein complexes hidden in the available PPI data are critical need. At present all these computational algorithms focus on the roles of proteins without taking into account the roles of interactions.In this paper, we investigate the possibility to predict protein complexes with the roles of edges in PPI networks. Firstly, the weak ties phenomenon in the PPI network is proved by using the concept of bridge. Secondly, a reliable and virtual PPI network is constructed making use the relations of topological similarity and bridgeness. Finally, a core-attachment algorithm is designed. The experimental results demonstrate that the roles of edges in biological network is more promising than the roles of proteins, implying the significant importance of the roles of interactions.The possible future research directions are• Because biological network is a special kind of social networks, to uncover the social behaviors hidden in biological networks and make the most of them to discover biological problems, such as protein complex prediction, disease causing genes prediction, are very promising.• The discovery of structure-functionality is a hot and very important topic in bioinformatics. How to associate the social behaviors including the weak ties with the functions is challenge and critical since it provides a deep insight into the biological processes.Thus, designing effective and efficient methods which can solve these problems will be very important and interesting.The authors declare that they have no competing interests.XM designed the study. XM and LG implemented the method, performed the experiments, analyzed the data and wrote the manuscript.


Sequencing the genomes of the first few eukaryotes created the impression that gene number shows no correlation with organism complexity, often referred to as the G-value paradox. Several attempts have previously been made to resolve this paradox, citing multifunctionality of proteins, alternative splicing, microRNAs or non-coding DNA. As intrinsic protein disorder has been linked with complex responses to environmental stimuli and communication between cells, an additional possibility is that structural disorder may effectively increase the complexity of species.We revisited the G-value paradox by analyzing many new proteomes whose complexity measured with their number of distinct cell types is known. We found that complexity and proteome size measured by the total number of amino acids correlate significantly and have a power function relationship. We systematically analyzed numerous other features in relation to complexity in several organisms and tissues and found: the fraction of protein structural disorder increases significantly between prokaryotes and eukaryotes but does not further increase over the course of evolution; the number of predicted binding sites in disordered regions in a proteome increases with complexity; the fraction of protein disorder, predicted binding sites, alternative splicing and protein-protein interactions all increase with the complexity of human tissues.We conclude that complexity is a multi-parametric trait, determined by interaction potential, alternative splicing capacity, tissue-specific protein disorder and, above all, proteome size. The G-value paradox is only apparent when plants are grouped with metazoans, as they have a different relationship between complexity and proteome size. Biological complexity is a feature that increases during evolution, distinguishing us from more primitive forms of life. Whereas it has no straightforward definition, it is generally accepted that it can be measured by the number of different cell types in an organism ranging from 1 (bacteria) to about 200 (humans) . As compIntrinsically disordered proteins exist and function without a well-defined three-dimensional structure, typically carrying out signaling and regulatory functions ,11. ThesHere we carried out a systematic analysis of the possible correlation between proteome size, structural disorder, binding capacity and the complexity of 76 organisms ranging from bacteria to human, which cover the full complexity range of 1 to 200. We used the number of amino acids instead of the number of genes, as average protein length tends to vary a great deal among different organisms (from 282 to 814 in our collection). However, we found no overall, proteome-wide correlation between protein structural disorder and organism complexity, apart from the clear increase in disorder from prokaryotes to eukaryotes, with very large variations between different bacteria and also single-celled eukaryotes - for example, protozoa. When we looked at only those proteins that comprised domains associated with evolutionary expansion [We analyzed another structural disorder-related feature, namely binary interactions in interactomes -14, and Our overall conclusion is that complexity is a multi-parametric trait affected by interaction potential, alternative splicing capacity, tissue-specific protein disorder and, above all, proteome size, thus severely limiting the scope of the G-value paradox.The authors who coined the term 'G-value paradox'  question2 = 0.1333, P-value = 0.0072), if we leave out plants the correlation increases dramatically .We did distinguish plants from other eukaryotes in Figure 2 = 0.262, P-value = 0.0357). Both human and fugu  have strikingly large alternative proteomes, although these are based only on RNA sequence information, which does not necessarily mean that viable proteins are produced from them [We calculated the relationship also for the alternative splice complement of those organisms Figure  for whicrom them .We repeated the calculation using gene numbers in place of proteome sizes . For eukaryotes only, there is no significant correlation between biological complexity and proteome disorder  whether considering individual species or combining them into the same large evolutionary clades as in Additional file Mycobacterium tuberculosis, Myxococcus xanthus and Streptomyces coelicolor. The high disorder of M. tuberculosis may be a result of its high GC content and preference towards amino acids Ala, Gly, Pro, Arg and Trp [Overall, there is correlation between the two measures only if we consider all species  domains correlated  with comFor eukaryotes, the average disorder of proteins with expanding type of SCOP domains  among the eukaryotic species. This correlation may be caused by a lengthening of structural domain-containing proteins during evolution  as a function of biological complexity in Figure Because of these uncertainties, we used predictions to get an unbiased estimate of binding capacity of the different proteomes. We used ANCHOR, a method developed for the prediction of binding regions embedded in disordered regions . We carrP < 0.0001; Figure P = 0.1395); if we exclude plants, the correlation becomes significant but only marginally . However, the correlation between the total number of binding sites within entire proteomes and complexity  and eukaryotes only . These results suggest that an increase in the number of potential disordered binding sites and the concomitant increase in the size and complexity of the interactome have probably contributed to the increase in complexity in evolution.When all species are considered there is a highly significant correlation between the mean number of binding sites per protein and biological complexity  than the number of alternative splicing events per gene . Protein isoforms of more complex organisms also contain more exons .Figure When we plot the two attributes (disorder and interacting partners) against each other . Plotting the predicted binding sites against the observed PPIs in STRING , with a Spearman correlation value of r = 0.9412.Figure P-value = 0.0159 for correlation between the alternative splicing ratio and disorder of the main isoforms; r = 0.6294, P-value = 0.009 between alternative splicing ratio and disorder of the alternative isoforms), they both implicitly reflect a positive correlation between complexity and both alternative splicing and structural disorder in the different tissues.As there is a close link between alternative splicing and structural disorder , it is aAs both organismal complexity and structural disorder increase significantly from prokaryotes to eukaryotes, it is reasonable to assume that the two are related across a wider scope of evolution, especially if complexity (measured by the number of distinct cell types an organism has) cannot be related even to such a basic measure as the number of protein-coding genes of an organism, as reflected in the G-value paradox.However, a simple comparison of proteome sizes measured in amino acids made it clear that if we treat plants as a separate group, complexity does correlate with the information content of the proteome Figure  and lessOn the other hand, there is no correlation between complexity and disorder beyond the already known significant increase between prokaryotes and eukaryotes, despite the critical roles disorder plays in PPIs -14, asseWhile not on a proteomic level, disorder does increase over evolution in structural domain-containing proteins and correlates significantly with complexity , we selStructural disorder of all proteins in a proteome was predicted with the IUPred algorithm , availabSTRAL database [N = 19,326), non-expanding  and all . We ran a BlastP search with sequences of SCOP domains in the three sets against studied prokaryotic and eukaryotic proteomes. Then, we predicted structural disorder of proteins with sequence identity to a SCOP domain above 50%.The entire SCOP domain database was downloaded from   and 12 eukaryotic proteomes  from the STRING database [We extracted data on binary PPIs in four prokaryotic  test and chose two-tailed PIC: proteome information content; PPI: protein-protein interaction; SCOP: Structural Classification of Proteins.The authors declare that they have no competing interests.PT conceived and designed the experiments and wrote the paper. HH conceived and designed the experiments, performed the experiments, analyzed the data and wrote the paper. ES performed the experiments, analyzed the data and wrote the paper. All authors have read and approved the manuscript for publication.Correlation between gene number and complexity in 53 eukaryotic organisms. Protein-coding gene numbers in all eukaryotes in the study excluding plants (red diamonds) and all plants (green triangles).Click here for fileTotal mean proteome information content values for the six phylogenetic groups of the 76 species we used in this study. Total mean proteome information content  values for the six phylogenetic groups of the 76 species we used in this study. After excluding plants we carried out a one-way analysis of variance among the remaining five groups, assuming Gaussian distribution for proteome sizes and used Bonferroni's multiple comparison test to check the significance of pairwise differences among the five phylogenetic clades.Click here for fileProkaryotic and eukaryotic species used in our analysis, their reduced proteome size and their biological complexity. This table also contains data related to disorder, binary protein-protein interactions, protein binding sites located in disordered regions of proteins and alternative splicing.Click here for fileData related to disorder of proteins including SCOP domains in different prokaryotes and eukaryotes.Click here for fileDisorder, protein-protein interactions and binding of proteins expressed in different human tissues. Disorder, protein-protein interactions and binding of proteins expressed in different human tissues. (a) Median disorder of proteins versus median number of interacting partners in STRING. (b)Ttotal number of predicted binding sites versus total number of protein-protein interactions.Click here for file


Escherichia coli and other aerobes and more recently to study the stress response behavior of Desulfovibrio vulgaris Hildenborough, a model obligate anaerobe and sulfate reducer and the subject of this study. Here we carried out affinity purification followed by mass spectrometry to reconstruct an interaction network among 12 chromosomally encoded bait and 90 prey proteins based on 134 bait-prey interactions identified to be of high confidence. Protein-protein interaction data are often plagued by the lack of adequate controls and replication analyses necessary to assess confidence in the results, including identification of potential false positives. We addressed these issues through the use of biological replication, exponentially modified protein abundance indices, results from an experimental negative control, and a statistical test to assign confidence to each putative interacting pair applicable to small interaction data studies. We discuss the biological significance of metabolic features of D. vulgaris revealed by these protein-protein interaction data and the observed protein modifications. These include the distinct role of the putative carbon monoxide-induced hydrogenase, unique electron transfer routes associated with different oxidoreductases, and the possible role of methylation in regulating sulfate reduction.Protein–protein interactions offer an insight into cellular processes beyond what may be obtained by the quantitative functional genomics tools of proteomics and transcriptomics. The aforementioned tools have been extensively applied to study  Desulfovibrio vulgaris Hildenborough as a model anaerobe. Much of the information currently available on this sulfate reducer is based on quantitative transcriptomics analyses of stress response behavior Recent functional genomics efforts have established Saccharomyces cerevisiaeEscherichia coli K12 Many of the problems with the exogenous approach can be alleviated using an endogenous approach that relies on chromosomal modification of the organism under investigation to incorporate an affinity tag at either the amino- or carboxy-ends of the protein of interest. This approach relies on native or near-native concentrations of interacting partners and assumes that the intact and functional complex consisting of the affinity-tagged bait and prey proteins can be recovered provided the tag does not interfere with complex formation. The endogenous approach requires well-functioning genetic tools for chromosomal modification and to some extent depends on the cellular concentrations and compartmentalization of the bait protein. Large-scale protein-protein interaction datasets generated with this approach have been reported for D. vulgaris. Our approach is based on suicide-vector-assisted chromosomal tagging  to the C- terminus of twelve proteins from various functional categories. The Strep-tag approach offers the simplicity and convenience of a single-step method and has been touted for its reliability and efficacy in high throughput applications Strep-tag II), the latter (Strep-tag II) was reported to possess the ideal combination of excellent purification with good yields at a moderate cost Strep-tag II has been successfully employed for purifying functional holoenzyme protein complexes from mammalian cells Strep-tag method does not require the availability of cofactors or the coexpression of modifying enzymes and is unlikely to interfere with complex functionality given its small size thus making it suitable for use in generic protein-protein interaction studies in a variety of cell types E. coli or proteins unique to D. vulgaris energy metabolism. The interacting partners associated with these Strep-tagged baits were identified in replicate using affinity purification followed by liquid chromatography-mass spectrometry (LC-MS)-based analyses.In this paper we describe our efforts to apply the endogenous ‘pull-down’ approach for identifying protein-protein interactions in the sulfate reducer  tagging . We appeD. vulgaris. We validated this network with a series of comparative and functional genomics analyses and statistical tests, and discuss the biological significance of the observations.To date only a single study has reported a protein-protein interaction network based on replicate pull down experiments Strep-tagged bait proteins for identification of potential interacting partners. JW801, a strain of D. vulgaris Hildenborough lacking the native plasmid, pDV1 , was chosen because of its increased transformation efficiency relative to the wild type strain when transformed with E. coli K12-derived plasmids. The lack of pDV1 results in the inability of JW801 to fix nitrogen and slows its growth on LS4D medium; however, pDV1 encodes no essential functions under laboratory conditions.In this study we engineered mutant strains of JW801  to encodStrep-tag II sequence was appended to all genes discussed in this study at the 3′ end. The Strep-tag II Strep-Tactin (Kd = 1 µM) and has been used previously for the identification of protein-protein interactions D. vulgaris.The Strep-Tactin as well as other proteins that interact with these ‘sticky’ proteins. For large protein interaction datasets false positives may be estimated empirically from the protein interaction dataset by measuring the promiscuity of prey proteins Bait-prey interactions described in this study may be divided into two groups. The first group is composed of tagged-bait proteins and their true interacting partners. The second group consists of false positive interactions: ‘sticky’ proteins bound to the bait pull-down column either due to their inherent abundance or some affinity for In total 130 distinct proteins were identified in the control JW801 strain in at least one replicate . We usedWe were able to confirm that the emPAI scores performed reasonably on our data by assessing the emPAI values of the bait protein in each bait pull-down. It was expected that the tag-column specificity would result in enrichment of the tagged bait and its interacting partners and that these would have higher emPAI values than in the control fractions. In fact, the bait proteins were among the highest scoring proteins identified in the pull-down fractions  with theTo assess statistical significance we calculated p-values for each pulled down protein by bootstrap analysis of the bait pull-down and control replicate data . For theD. vulgaris interactions for 6 of the 12 D. vulgaris baits that have orthologs in E. coli, to previously reported interactions in E. coli. In E. coli, these baits had 111 unique non self-self interactions, of which 89 involved prey that have orthologs in D. vulgaris and were pulled down by one of the six E. coli orthologs of the D. vulgaris baits. We identified 13 of these 89 (15%) “expected” interactions with high confidence . Thus, recovering 15% of the ortholog-based expected interactions may be acceptable.One approach to identifying potential false positives and negatives is to consider orthologous protein interactions in a related organism. We compared the nfidence . In addiWe performed an analysis of the confident interactions as well as the control data to assess similarity between the prey pull-down profiles for different bait proteins . The preE. coli were 8% (166 out of 2152) in the endogenous Considering all non-self interactions observed amongst the 12 bait proteins in this study, three reciprocal interactions were detected, giving a 50% (3 out of 6) confirmation rate for the interaction data by reciprocal bait pull-downs. The reciprocal interaction confirmation rates for Interacting protein pairs would tend to be co-expressed as the presence of both proteins is necessary for formation of a complex, and vice versa for non-interacting pairs. The co-expression distribution  of the iE. coli K12 were restricted to only the baits and prey proteins observed in the JW801 dataset, which highlights the low orthology between the two species. The JW801 protein-protein interaction data consisted of 12 bait proteins having an average of 11.2 prey interactions per bait and a total of 90 prey proteins with an average of 1.5 bait interactions per prey, corresponding to a total of 134 interactions. The assignment of TIGR functional role membership to the protein interaction network revealed four functional subnetworks, all of which contained at least one bait protein interacting with other members of the functional role  as well as assigned function of the neighboring protein  suggests a possible regulatory role for DVU1455.Another intriguing observation was the co-elution of putative ATPase domain proteins (DVU2103 and DVU3330) with the heat shock protein DnaK. Finally, DVU2215 showed co-expression with other energy metabolism genes, suggesting that there are additional unknown features of energy generation in these anaerobic organisms that remain to be validated.D. vulgaris interactome. In the following sections, we take a detailed look at individual baits and discuss the functional importance of associated interactions that were observed in this study. We discuss interactions associated with highly conserved proteins as well as those specific to D. vulgaris.The network analysis and co-expression distribution discussed in the previous sections give us a broad view of the E. coli K12 We compared bait-prey interactions reported for D. vulgaris, RpoB and RpoC, are encoded by genes predicted to occur in an eight-member operon with genes encoding other proteins such as preprotein translocase (DVU2922), transcription antiterminator protein (nusG) and ribosomal proteins L11, L1, L10 and L7/L12 Strep-tagged RpoB or Strep-tagged RpoC we observed all of the core catalytic components of RNAP - the α, β, β′ and ω subunits. However we did not observe other components such as sigma factors or accessory proteins perhaps due to the transient nature of those interactions.The tagged components of RNAP in E. coli K12, proteins reported to co-purify with SPA-tagged RpoB (b3987) included the catalytic core composed of the α subunit (b3295) and the β′  subunit in addition to several sigma factors , elongation factors , and accessory factors . Unlike RpoB (b3987) however, SPA-tagged RpoC (b3988) did not pull down sigma factors such as σ32, σ38, σ54 or YacL even though the core subunits were still observed, further suggesting the transient nature of the non-core component interactions in these complexes In Strep-tagged DnaK included HSPs from the ‘Protein fate’ role (TIGR) such as GrpE, two paralogs of DnaJ  and DafA , which did not co-purify with Strep-tagged DnaK. Of the three paralogs, only genes encoding the interacting partners DVU3243 and DVU1876 were over-expressed during heat shock response grpE, dnaJ (DVU1876) and dafA suggest that these genes are not abundantly expressed in D. vulgaris, but their proteins were observed to associate with Strep-tagged DnaK (DVU0811).In addition to serving as a molecular chaperone, the well-conserved protein DnaK also modulates heat-shock response in bacteria and DafA , all of E. coli K12, heat shock proteins (HSPs) that co-purified with SPA-tagged DnaK (b0014) included GrpE (b2614), chaperone protein HscA (b2526), ATP-dependent protease Lon (b0439), and Peptidase B (b2523) but not the chaperones DnaJ (b0015) and GroEL (b4143) E. coli K12, DnaK, GrpE, and DnaJ have been demonstrated to form a chaperone complex for in vivo repair of denatured proteins E. coli K12 genome also features a second DnaJ homolog, CbpA (b1000), which can function as a co-chaperone and regulate the activity of the DnaK system. CbpA activity has been shown to be modulated by a small 11-kDa protein, CbpM (b0999). However, neither CbpA nor CbpM were identified in pull-down fractions of SPA-tagged DnaK even though DnaK itself was observed as prey for both SPA-tagged DnaJ and SPA-tagged CbpA Thermus thermophilus the CbpM analog, DafA (TTHA1488), assembles the corresponding chaperones DnaK (TTHA1491) and DnaJ (TTHA1489) to produce a (DnaK)3–(DnaJ)3–(DafA)3 complex referred to as the KJA complex. DafA (TTHA1488), like its E. coli counterpart, inhibits the chaperone activities of both DnaK and DnaJ by forming the KJA complex and acts as a thermosensor under both heat stress and optimal growth conditions T. thermophilus counterparts leads us to believe that a similar mechanism of DnaK regulation may be operative in this sulfate reducing bacterium (SRB).In E. coli K12 is also characterized by up-regulation of a two-component ATP-dependent proteolytic complex comprised of adjacently encoded HSPs, HslV (b3932) and HslU (b3931) and the corresponding genes regulated by σ32D. vulgaris, HslV (DVU1577) and HslU (DVU1467), however, appear in separate predicted operons that lack σ32-dependent promoters or CIRCE sites that are present upstream of other heat-shock genes in this organism Strep-tagged HslV does not interact with HslU under the conditions we tested. This could be attributed to a weak association between the two proteins as reported previously E. coli K12 however, using SPA-tagged baits, HslU and HslV have been identified in reciprocal tagging experiments 6]2[(HslV)6]2hslV (DVU1577) and hslU (DVU1467) lack σ32 or CIRCE sites there is some evidence that these genes are co-regulated as both are highly over-expressed during heat shock and air stress The heat shock response in E. coli K12, Pnp (b3164) is a component of the degradosome complex that plays an important role in messenger RNA processing and is composed of the following additional proteins: Ribonuclease E , RNA helicase , polyphosphate kinase  and enolase . The suggested component stoichiometries in the complex are [(Ppk)4][(Rne)4][(RhlB)2][(Pnp)3][(Eno)2] (www.ecocyc.org). While the assembled degradosome mediates the decay of transcripts in E. coli K12, the individual components have been suggested to be active in their unbound state as well E. coli K12 is enabled by the C-terminal half of Rne, which provides a scaffold for other components of this protein complex, whereas the N-terminal half of Rne provides the catalytic function D. vulgaris (DVU3055) and its E. coli K12 counterpart (b1084) confirmed that only the N-terminal portion of Rne exhibits conservation (42% sequence identity) between the two species. Even though the D. vulgaris Hildenborough genome encodes several homologs to components of the E. coli K12 degradosome – Rne (DVU3055), RhlE (DVU1982) and Eno (DVU0322) – it is not entirely surprising that these potential interacting partners were not found complexed with Strep-tagged Pnp.Polynucleotide phosphorylase (Pnp) is a 3′-to-5′ exonuclease and a 3′-terminal oligonucleotide polymerase. In Strep-tagged Pnp included several members of the large and small subunits of the ribosome as well as DNA and RNA binding proteins. Similar interactions were also observed for SPA-tagged Pnp (b3164) in E. coli , DVU0403; DsrD, DVU0404; and DsrC, DVU2776; respectively) D. vulgaris has been reported to be an α2β2γ2 structure D. vulgaris genome sequence reveals the presence of six possible membrane bound complexes involved in electron transfer – HmcABCDEF, TmcAB, OhcBAC, RnfABEDG, QmoABC and DsrMKJOP of which the last two are suspected to be involved in electron transfer to the sulfate reduction pathway Strep-tagged ApsA, we identified most of the cytoplasmically localized enzymes predicted to be involved in the sulfate reduction pathway described above , inorganic pyrophosphatase , the αβ heterodimeric adenylylsulfate reductase , and the dissimilatory sulfite reductase composed of α, β, δ and γ subunits . The methylated lysine residues observed in this study appear to be very well conserved in close homologs of ApsB, ApsA, DsrC, and RplL, suggesting conservation of functionality  in the form of methylated amino acids . Proteinionality  & S6 for1 scan, two pieces of evidence support these identifications as trimethylations. We observed mono- and di-methylation, +14 Da and +28 Da respectively, of the same peptide in fractions co-purified with Strep-tagged ApsA, suggesting that the +42 Da peptide is indeed tri-methylated and not acetylated. In addition, the MS/MS spectra revealed the presence of fragment ions corresponding to a neutral loss of 59 Da  cycle pathway (http://biocyc.org/META/NEW-IMAGE?type=NIL&object=PWY-5041) known to be implicated in methyl group transfers were observed as interacting partners of Strep-tagged ApsA. These included the S-adenosylmethionine synthetase  and adenosylhomocysteinase   as the methyl (Me) donor, adding methyl groups to Lys or Arg DVU0607) .Strep-tagged ApsB, we observed the larger subunit, ApsA, as an interacting partner, but none of the other enzymes involved in the sulfate reduction pathway or the SAM cycle components. However, differences in interacting members from reciprocally tagged baits are observed even for highly conserved complexes such as RNAP D. vulgaris although the exact biological implication of this finding remains to be elucidated.Using D. vulgaris Hildenborough reveals the potential presence of two membrane-bound, cytoplasmically-oriented, hydrogenases, EchABCDEF and CooMKLXUH that could be involved in energy metabolism of this organism ech genes (average relative expression rank of 34) is much lower than that for the coo genes (average relative expression rank of 90) during growth on LS4D (The genome sequence of  on LS4D . We infewww.microbesonline.org reported proteins with conserved COG assignments and synteny information for this predicted operon in δ-Proteobacteria (D. vulgaris Hildenborough and D. vulgaris DP4), α-Proteobacteria , and Clostridia (Carboxydothermus hydrogenoformans Z-2901). Thus, these data indicate a horizontal gene transfer event among these clades  and COG3640 (carbon monoxide dehydrogenase accessory protein) are key enzymes involved in conversion of carbon monoxide to carbon dioxide and hydrogen when carbon monoxide is used as the sole energy source Desulfovibrio species have coo operons or the CooA regulator (DVU2097).We tagged the cytoplasmically localized hydrogenase from the Coo complex to identify interacting partners of this protein. CooH is predicted to be located in an eight-gene operon regulated by a CO-sensing activator, CooA e clades . In R. rD. vulgaris, COG3261 and COG3640 are located in an operon separate from that containing CooH and it is also apparently regulated by CooA C. hydrogenoformans, the CO-oxidizing:H2-evolving enzyme complex activated by CO was shown to be composed of seven subunits – two catalytic sites, a CO-oxidizing site and a H2-forming site (COG1151), which are connected via different iron–sulfur cluster containing electron transfer subunits  D. vulgaris (CooS (DVU2098), CooH (DVU2291), CooU (DVU2290), CooF (DVU2293), CooX (DVU2289), and CooL (DVU2288)) might be expected to form a similar complex. However, under the conditions we tested for protein complexes, only the CooU subunit of the hydrogenase from this putative complex was pulled down by CooH. The membrane-bound components of this complex may not have been observed also due to the nature of the extraction protocol used for bait purification. In contrast, several members of the energy metabolism network were observed to interact with CooH notably desulfoviridin, the αβ adenylylsulfate reductase, and their interacting partners , rubredoxin (Rub), and desulfoferrodoxin  are part of one such oxidative stress defense system. Recent work on Roo and Sor/Rbo knock-out mutants suggests an important role of the former under microaerobic conditions and the latter under aerobic conditions sor, rub and roo under anaerobic conditions are among the highest (percentile rank >95%).In this study we tagged three oxidoreductases from Based on operon organization it has been suggested that Sor/Rbo and Roo may collaborate in the reduction and the detoxification of oxygen entering the cytoplasm through the use of Rub as a common electron donor D. desulfuricans B-1388 has been shown to be induced under low oxygen partial pressures Desulfitoacterium hafniense, Clostridium difficile). In JW801, however, under normal growth conditions we did not find tagged Nox to interact with energy metabolism proteins to a significant degree. Unlike Sor/Rbo, Nox appears to be isolated from the energy metabolism network of this SRB (NADH oxidase (Nox – COG446) acts on NADH and transfers electrons to an acceptor and has been suggested to contribute to antioxidant activities in anaerobes. Biochemical characterization of purified Nox (DVU3212) suggests that its flavin mononucleotide (FMN) cofactor reduces oxygen to hydrogen peroxide and transfers electrons to adenylylphosphosulfate (APS) reductase from NADH this SRB . Based oD. vulgaris. Highly conserved proteins between D. vulgaris and E. coli K12, such as RpoB, RpoC, and DnaK, display several conserved interacting partners. In contrast, structural differences between the ribonuclease, Rne, from D. vulgaris and E. coli K12 may explain why only a subset of interactions are conserved for polynucleotide phosphorylase, Pnp, even though the corresponding bacterial genomes encode for most partners of the degradosome complex. The interaction network contrasts the vastly different energy generation schemes of JW801 with E. coli K12 and this difference clearly contributes to the absence of many orthologs in the latter. Interestingly, proteins from the sulfate reduction pathway  are found to be methylated, which may be attributed to SAM cycle components observed to co-purify with these proteins. The methylated lysine residues from these proteins are highly conserved in other bacterial species suggesting a potentially conserved functionality of this modification. In the absence of added carbon monoxide and during growth on LS4D, CooH is a constitutively expressed hydrogenase and does not appear to interact with CooS. This result is in opposition to observations made for the corresponding orthologs from R. rubrum and C. hydrogenoformans. The oxidoreductases Sor and Rub are characterized by their high constitutive expression levels as compared to other chromosomally encoded proteins implicated in oxygen reduction and ROS detoxification, and interact with many other redox enzymes.For organisms with no neighboring species for which protein-protein interaction data have been collected, the accepted ‘gold standard’ comparison approach to assess data quality is problematic. If in addition the dataset in question is relatively small, there is no accepted way to estimate the number of false positives and false negatives. We present a method designed to directly assign confidence to protein-protein interactions based on experimental data from pull-down experiments. A series of functional genomic and comparative analysis confirm that the high confidence subset of our data is of high quality, including a high reciprocal interaction index compared to previous studies, a significant co-expression of the interacting proteins, and a higher enrichment for functional role interactions compared to random. Our protein-protein interaction data from this study highlight several metabolic features that appear unique to in vivo span a wide range of binding affinities and they can be finely regulated in a condition-dependent manner. Thus to obtain high coverage of the protein interactome, it will be necessary to analyze multiple growth conditions and to devise ways to uniformly collect data both for weak, transient interactions as well as constitutive complexes. In addition a complete view of the interactome will require distinguishing between protein isoforms and post-translational modifications. Finally, the affinity purification followed by MS method has a key limitation, namely the inability to directly distinguish direct physical interactions from secondary interactions, e.g. interactions through other proteins. Further work on experimental design and computational methods is necessary to address these shortcomings.The single-crossover approach we describe in the current study is restricted to monocistronic operons or genes located relatively close to the terminal ends of their respective operons. The complete chromosomal integration of the plasmid bearing the tagged gene as currently configured can cause polar effects on promoter-distal genes. A non-integrative double crossover approach is being perfected to tag any gene on the chromosome regardless of its operon location that will contribute to a complete protein network of this model organism. While our approach represents progress towards the confident identification of protein-protein interactions by setting a rigorous standard for experimental design, data collection and data analysis, a number of obstacles remain. Protein interactions Escherichia coli (TOP10 or α-select) strains were cultured in SOC medium  or LC medium  at 37°C. For solid media, 15 g agar were added per liter. To select for kanamycin-resistant E. coli cells, kanamycin was added to LC medium to a final concentration of 50 µg/ml. Chemicals and antibiotics were obtained from Fisher Scientific . Plasmids bearing the tagged targets were constructed using established cloning techniques and then electroporated into competent JW801 cells followed by selection for G418 resistance (described below). A 202-Kb native plasmid, pDV1, containing 157 predicted ORFs, is found in wild type D. vulgaris Hildenborough and has been lost, generating strain JW801. The aforementioned genes do not affect the ability of strain JW801 to grow on LS4 medium containing 0.1% yeast extract or LS4D medium, which is completely defined D. vulgaris Hildenborough for E. coli K12-derived plasmids. This may be due to the loss of a type II restriction endonuclease (DVUA0020) predicted to be encoded in pDV1; hence this host was chosen for this study.Strains used in this study are listed in 2SO3 as the terminal electron acceptor. To identify putative JW801 affinity-tagged constructs, cells were plated into molten sulfate-containing medium, LS4D Following electroporation, JW801 constructs were allowed to recover at 30°C in an anaerobic growth chamber  in LS3 medium, which is LS4 modified by elimination of sulfate and addition of 40 mM NaStrep-tagged  D. vulgaris genes and their introduction into the sulfate-reducer, pKASK was constructed by digestion of the pASK-IBA3 plasmid  with MfeI  for insertion of a kanamycin resistance cassette. The neomycin-kanamycin resistance gene, neo, located on the 1.8-Kb EcoRI fragment from pUC4-KIXX  was gel purified with the QIAEXII Gel Extraction kit  and ligated with the MfeI-digested pASK-IBA3 generating pKASK.For construction of http://www.biotech.missouri.edu/dnacore/). The sequences returned were aligned with the published D. vulgaris Hildenborough genome sequence .The pKASK vector or pCR4Blunt-TOPO  was used to introduce tagged genes into the chromosome of JW801. Three different cloning schemes were used to generate the plasmids introduced by electroporation, as described below. Tagging plasmids were sequenced to verify that the correct fragment was amplified and that no errors were introduced during the cloning procedure. All sequencing was performed at the University of Missouri DNA core facilities , the amplicon was captured in a plasmid , and the resulting plasmid transformed into prepared E. coli K12 cells  (E. coli K12 cells (α-select), and purified from kanamycin resistant transformants.In heme one , primersheme one , S8. Thebad, CA) . The plascheme two  and rub (DVU3184). This scheme was developed to tag a gene in the middle of an operon (when scheme two was not permissible) and was designed to allow wild-type expression of the downstream genes. In order to place the tag on the 3′ end of a complete copy of the gene, a three PCR approach was used. PCR-1 amplified a region of DNA upstream of the gene that we presumed should contain the promoter. PCR-2 amplified the gene with the in-frame C-terminal tag. Finally, PCR-3 generated a composite of the first two PCR products using Splicing by Overlap Extension  from 1.5 ml of culture grown anaerobically to early stationary phase in Wall LS3 medium. DNA was quantified with a ND-1000 spectrophotometer . Genomic DNA (2–5 µg) from wild-type cells and those with putatively tagged genes were digested at 37°C for 3 h with 5–10 units of a restriction enzyme  . Restric2EDTA (0.5 mM), pepstatin (10 µM), bestatin (0.13 mM), and Pefabloc SC plus  (0.4 mM). Soluble protein extractions were prepared from these cells by sonication as described previously Three one-liter cultures of each JW801 strain producing tagged proteins  were groStrep-Tactin® Sepharose column  as per the manufacturer's recommendations and briefly outlined here. All steps were carried under gravity flow at 4°C. After the Strep-tactin® column was equilibrated, cell lysates (10 ml) containing the protease inhibitor cocktail were added to the column. Total protein mass loaded on to the Strep-tactin® column was between 60∼70 mg. After the cell extract had completely entered the column, the loaded column was washed 5 times with 1 ml of Buffer W to remove unbound proteins. Tagged targets and associated proteins were eluted from the column using 3 ml of buffer containing desthiobiotin, which competes with the binding of Strep-tag II to Strep-Tactin, the engineered streptavidin. Six 500-µl fractions were collected and stored at −80°C until further use.Protein complex purifications were performed using a 1-ml Strep-Tactin were injected onto a PepMap100 trapping column from a Famos Autosampler . Peptide separation took place on a Dionex PepMap 100 column (75 µm×15 cm) at a flow rate of 300 nl/min. Following a 7 min wash period with buffer A (2% (v/v) acetonitrile, 0.1% (v/v) formic acid), the sample was eluted with a gradient, 5 to 35% buffer B (98% (v/v) acetonitrile, 0.1% (v/v) formic acid) in 30 min, followed by 35 to 80% (v/v) buffer B in 10 min, and then 80% (v/v) buffer B for 10 min. The column was re-equilibrated by a decreasing gradient of buffer B, 100 to 5% (v/v), in 5 min, that was maintained for 20 min.To determine the presence of the affinity-tagged target and any associated proteins, eluted protein fractions were subjected to MS analysis after in-solution tryptic digestion as follows. To 50 µl of the eluted fractions, 2 µl of 100 mM DTT was added. The tubes were heated to 95°C for 15 min and then placed on ice for 10∼15 min. Five µl of Trypsin Gold (100 ng/µl)  was added to each sample and the mixture incubated at 37°C overnight. Digested peptides were then analyzed by reversed-phase LC-MS/MS on an Eksigent nanoLC-2D system  coupled to a Quadrupole-Time Of Flight (Q-TOF) mass spectrometer  described previously 1] fibrinopeptide D [M+2H]2+ prior to analysis.The LC system was interfaced to the QSTAR mass analyzer via a nanospray source equipped with a 15 µm Picotip emitter  operating in the positive ion mode (2300–2400 V). Data were collected with Analyst™ QS 2.0  and Information Dependent Acquisition . The three most abundant multiply-charged ions from a 0.25-s MS survey scan (350–1600 amu) above a threshold of 50 counts were selected for IDA analysis. Selected ions were isolated in Q1 (resolution = LOW) and were fragmented with rolling collision energy. MS/MS scans were collected over a mass range of 100–1600 amu set with a fragment intensity multiplier of 4.0 and maximum accumulation time of 2.5 sec. Parent ions (within 100 ppm) and isotopes were excluded from subsequent IDA selection for 60 s following one repeat analysis. The mass spectrometer was tuned and calibrated from the product ion spectrum with [Glum/z range 400–1200, 2+ and 3+ charge states. The resolution of both Q1 and Q3 were set to “unit”, and each transition was measured for 50-ms dwell time per cycle. No more than 100 transitions were used for each LC run to limit the total MRM cycle time to five seconds. An IDA method, triggered above 500 counts/s, was used to verify the peptide identity via collision-induced dissociation (CID) for each MRM transition.For several of the pull-down samples we verified the proteins identified by one peptide in the LC-MS (Q-Star) analysis by a multiple-reaction-monitoring (MRM) LC-MS experiment on an Applied Biosystems 4000Q-Trap mass spectrometer using similar column conditions. Since we did not have protein standards from which to optimize MRM transitions, a list of MRM transitions was generated by the MIDAS program  for each protein. The MRM transitions were limited to D. vulgaris Hildenborough (JW801) (Mascot Distiller (v 2.1) was used to sum similar precursor ion scans from each LC-MS/MS run and generate product ion peak lists for subsequent database searches. A Mascot MS/MS Ion Search  was performed for each dataset against a protein database consisting of all putative ORF sequences of  (JW801)  appendedPost-translational modifications (PTMs) were identified by searching the data with ProteinPilot 2.0 . ProteinPilot was chosen for the PTM search over Mascot because the Paragon search algorithm Strep-tag II sequence and thus serves as a control for proteins pulled down by any tagged bait protein. In order to assess the false positives introduced in the protein-protein interaction data by proteins with potential for non-specific interactions, we tested a Strep-Tactin column with cell lysate from JW801 and identified proteins eluting from the column from three fractions that were collected in the same way as the tagged-bait pull-down fractions. Pseudo-confidence scores for each interaction pair or protein observed in the no-bait pull-down control were computed by first taking the maximum observed emPAI value in any elution fraction for a given bait pull-down biological replicate. Taking the maximum value accounts for the fact that the same elution fraction in different replicates may represent different parts of the elution profile. In order to account for proteins interacting non-specifically with the column, this maximum emPAI value was adjusted by subtracting the median of the maximum values for the corresponding protein in the no-bait control experiment. Next, the median of these adjusted maximum emPAI-values was computed across the three biological replicates; we call these the median-max emPAI values. Finally, the adjusted median-max values for all prey proteins in each bait pull-down were divided by the median-max value observed for the bait protein or the maximum value of any prey in the bait pull-down if the bait was not observed. We call this normalized adjusted median-max emPAI value the pseudo-confidence score for observing a prey protein in a bait pull-down. The prey protein pull-down profile is a vector of pseudo-confidence scores across the series of bait pull-down experiments.The total emPAI data for all bait pull-down experiments were collected into a matrix, where the columns were bait pull-down experiment fraction replicates and the rows prey proteins . Each prThe high confidence subset of protein interactions included only proteins observed in all three biological replicate experiments for each bait pull-down. To produce conservative estimates, we used all of the median-max emPAI values of the protein observations from the no-bait control regardless of how many no-bait control replicates the proteins were observed. In all of the no-bait control samples, RoO (DVU3185) was observed with the highest overall emPAI value. On comparing the median-max data for prey proteins associated with tagged RoO to the data for proteins present in the control we observed that 85% were in common. Proteins, which were present in all three replicates of the control and at equal or higher emPAI values in the tagged RoO pull-down data, were considered to be interacting with RoO. The same rules were applied to ApsA (DVU0847), which was also observed in the control and with the second highest emPAI value of bait proteins from this study. Tagged ApsA had a 63% overlap of prey proteins with the control. Thus each high confidence prey protein was observed in all three biological replicates for at least one bait pull-down and with a non-zero median-max emPAI value greater than or equal to the median-max emPAI value observed for that protein in the control.D. vulgaris Hildenborough protein-protein interaction network was visualized using Pajek The We computed replicate pull-down experiment agreement as the fraction of prey in common between pairs of pull-downs. We report the agreement for the total emPAI dataset and for the dataset after control subtraction . For theTo study similarity between the bait protein pull-down fractions a Pearson correlation coefficient was calculated for each pair of bait proteins, treating the pseudo-confidence scores  of proteins observed in the pull-downs as vectors of corresponding values. This bait-bait prey profile correlation analysis heatmap was rendered with JColorGrid n that the median of the bait values was higher than the median of the ‘control’ values and reported the p-value as 1−n/10,000  and B pulls down A (B-A). A reciprocal interaction confirmation rate was computed by dividing the number of reciprocal bait-prey interactions that were observed by the number of reciprocal interactions that were possible to be observed in the dataset . Positive correlations indicate that the bait proteins have similar protein pull-down profiles.(PDF)Click here for additional data file.Figure S2E. coli K12 D. vulgaris between proteins that have D. vulgaris orthologs in E. coli and which involve the D. vulgaris bait proteins used in this study. The edge thickness corresponds to protein interaction confidence in E. coli . Black dotted edges indicate interactions observed in E. coli but not in D. vulgaris. Thick black solid edges indicate interactions observed in E. coli and in D. vulgaris. Thin solid edges indicate interactions detected in D. vulgaris but not E. coli.Interactions in (PDF)Click here for additional data file.Figure S3D. vulgaris Hildenborough. Shown are the gene co-expression Pearson correlations for the confident protein interactions identified in this study Click here for additional data file.Figure S4E. coli K12 D. vulgaris JW801.CLUSTAL 2.0.8 multiple sequence alignment of Ribosomal protein L7/L12. Note: Boxed region shows conserved lysine that has been observed to be methylated in RplL from (PDF)Click here for additional data file.Figure S5Multiple Sequence Alignment of ApsA (DVU0847), ApsB (DVU0846), DsrC (DVU2776) and RplL (DVU2927).(PDF)Click here for additional data file.Figure S6Hidden Markov Model Alignments. Note: Boxed region indicates position of methylated lysine residue observed in this study.(PDF)Click here for additional data file.Figure S7Q-Star MS/MS data for ApsA, ApsB, and DsrC post-translational modifications.(PDF)Click here for additional data file.Figure S8Replicate agreement for pull-down experiments.(PDF)Click here for additional data file.Figure S9Cloning schemes used for suicide vector construction.(PDF)Click here for additional data file.Table S1Strep-tagged baits chromosomally integrated in JW801.(XLS)Click here for additional data file.Table S2Bait-Prey Interactions in JW801.(XLS)Click here for additional data file.Table S3E. coli K12 corresponding to orthologous baits from JW801.Bait-Prey Interactions in (XLS)Click here for additional data file.Table S4Functional protein-protein interaction subnetworks in JW801.(XLS)Click here for additional data file.Table S5Evidence for hypothetical protein functional associations.(XLS)Click here for additional data file.Table S6Strains and plasmids used in this study.(XLS)Click here for additional data file.Table S7Primers used for PCR amplification, Southern probe, and sequencing.(XLS)Click here for additional data file.Table S8E. coli strain used to make each construct.Primer set, plasmid(s), restriction enzyme(s), and (XLS)Click here for additional data file.Table S9D. vulgaris Hildenborough (JW801).Proteins identified from control sample of (XLS)Click here for additional data file.Table S10Relative transcriptional abundance ranking.(XLS)Click here for additional data file.Table S11Bootstrap Analysis.(XLS)Click here for additional data file.Table S12emPAI values for all pull-down experiments in this study.(XLS)Click here for additional data file.Data S1Gene expression correlations for interacting pairs in this study.(TXT)Click here for additional data file.Data S2D. vulgaris.Protein sequence data for (FASTA)Click here for additional data file.Methods S1Supplementary Methods.(DOCX)Click here for additional data file.


PPIs can be detected through different experimental approaches and are collected in several expert curated databases. These databases are used by researchers interested in examining detailed information on particular proteins. In many analyses the reliability of the characterization of the interactions becomes important and it might be necessary to select sets of PPIs of different confidence levels. To this goal, we generated HIPPIE (Human Integrated Protein-Protein Interaction rEference), a human PPI dataset with a normalized scoring scheme that integrates multiple experimental PPI datasets. HIPPIE's scoring scheme has been optimized by human experts and a computer algorithm to reflect the amount and quality of evidence for a given PPI and we show that these scores correlate to the quality of the experimental characterization. The HIPPIE web tool (available at  Protein function occurs or is regulated by protein interactions and therefore knowledge on the partners of a given protein can give us important information regarding its activity. For instance, specific protein-protein interactions (PPIs) can be involved in diseases see e.g. . PPIs caPPIs are collected in several databases that make the data and the evidence behind it easily accessible and allow different mechanisms to query and display the data Computational use of PPI datasets often requires selecting PPIs at particular levels of confidence. For example, the quality of a novel PPI dataset may be evaluated by its overlap with known interactions defined with high reliability, whereas a statistical analysis might require a large number of interactions therefore benefiting from a less restricted set of PPIs. The flexible selection of PPI datasets at various confidence levels requires a continuous scoring scheme for PPIs reflecting the reliability of their experimental characterization.With the objective of creating a resource allowing the selection of PPIs by experimental confidence cut-offs, we generated HIPPIE (Human Integrated Protein-Protein Interaction rEference), a scored human PPI collection integrated from multiple sources. Following http://cbdm.mdc-berlin.de/tools/hippie. The scored dataset includes information on the data we used to build it so that modifications of the scoring mechanism can be easily achieved. We illustrate the usefulness of HIPPIE in increasing the coverage of novel PPI datasets and demonstrate that its scoring scheme reflects the reliability of the reported interactions.A web tool to browse the data as well as the scored PPI dataset are provided at Interactions were retrieved from the following public databases: BioGRID  Where available, we retrieved the information on the originating study and the experimental methodology used to measure each interaction from the source databases and also assigned an experimental category to interactions from the additionally included studies. As a result, more than 99% of all interactions in HIPPIE are associated to at least one of the methods listed in To add to the confidence scoring of experimentally verified human PPIs a component based on experimental evidence in non-human organisms we included data from three databases that map interactions between non-human protein pairs to their human orthologs: HomoMINT  Different public PPI databases and datasets use different types of gene or protein identifiers. We aimed at mapping all protein pairs listed in HIPPIE to Entrez Gene and UniProt identifiers. For this purpose we applied the database identifier mapping tables curated by UniProt Mapping PPIs to the genes encoding the interacting proteins is affected by certain ambiguity since the same protein sequence may be encoded by duplicated genomic loci. In the flat file version of HIPPIE these ambiguous PPIs are expanded such that a given PPI is represented by all possible combinations of gene identifiers.S between 0 and 1 was calculated reflecting the reliability of its combined experimental evidence. This score was calculated as a weighted sum of three different subscores which are ss (a function of the number of studies in which an interaction was detected), ts  and os (a function of the number of non-human organisms in which an interaction was reproduced). Each of these three subscores is was calculated with a non-linear saturating function of the form:i(0)s = 0 and i(∞)s = 1, where the ai are constants that control the steepness of the function.For each interaction a score ss, n is the number of different studies where the interaction was reported (number of PubMed identifiers associated), regardless of whether multiple experimental evidence was provided in each study.For subscore os, n is the number of species where orthologs of the interacting proteins could be defined and were found experimentally to interact .For subscore ts, n is a sum of scores from different experimental techniques by which an interaction was verified (even if used in the same study). Most PPI databases use controlled vocabulary descriptors for these experimental techniques as defined by the PSI-MI ontology S was calculated as a weighted sum of the three subscores:For subscore It is important to note that our dataset does not include interactions not experimentally verified with human proteins: no interaction received a score alone from its verification in non-human organisms. We also remark that this scoring scheme does not consider computational evidence other than the definition of orthology relations from human proteins to proteins in other organisms.sa, oa, ta, sw, ow and tw) were optimized by performing a grid search in the parameter space. We performed the search in the range  for the ia and in the range  for the iw. We chose a step width of 0.1 for both ia and the iw. The step width was chosen sufficiently small such that selecting neighboring parameter combinations resulted only in small changes in the interaction scores which decreased the probability of missing an optimal solution. Constraints were set on the weights iw by requiring that they sum up to 1. Parameter combinations leading to only few discrete scores were excluded .The six free parameters of the scoring formula . This dataset has explicit associations between studies and experiments, and the experimental information is annotated following the PSI-MI format.red, found the set of PPIs described both in the study j and in IntActred, 3Q is the upper quartile of the score distribution of IntActred.The assessment of performance of a parameter set was done by successively removing each one of the 109 studies in IntAct that contain at least 10 interactions and more than two PPIs found in multiple studies. For each study j, we recalculated the scores of the remaining dataset, IntActf of the weighted mean of the logarithm of devi over all studies:iv were chosen proportional to the overlap size between IntActred and studyj and n is the number of studies. The best parameter combination maximizes f.To measure the overall performance of a parameter combination we chose a function f (max(f) = 1.023). From the equally well performing parameter combinations we chose the set of parameters that resulted in the largest spread of the distribution of scored interactions. For that purpose the scores of the entire HIPPIE were repeatedly calculated for each of the optimal parameter combination and for each score distribution the interquartile range (iqr) was determined. We found that the parameter set  maximized both f and iqr.We found several parameter combinations  maximizing the function HIPPIE is a dataset of experimentally measured human PPI derived from several publicly available PPI datasets . For refhttp://cbdm.mdc-berlin.de/tools/hippie) that allows for querying the interactions by a gene symbol, Entrez gene id or UniProt identifier (id and accession). On the result page a confidence score is listed with each interaction partner of the query protein and detailed information on the evidence contributing to the confidence score can be accessed. Links to the original studies are provided.The network is accessible via a web tool  that will do the computation on the local machine of the user for large input sets or neighbours of neighbours. A confidence threshold to control the reliability and size of the constructed network can be also applied. Additionally, we provide a filter option for the PSI-MI interaction type annotation provided by most of HIPPIE's source databases. This feature allows for selecting direct physical interactions from HIPPIE. The thereby generated HIPPIE subnetworks can be exported from HIPPIE for further analyses or can be visualized using the tool Cytoscape Web To facilitate this analysis, HIPPIE can be queried with a set of proteins and/or interactions between them from which a network of known data around the proteins of interest is constructed. The online tool will identify interactions between the proteins submitted (layer 0 network), or their interactors not contained in the query set (layer 1 network). The computation of networks with more layers might be lengthy if hundreds of protein partners have to be analysed. For this we provide a Java command line tool  we calculated an overall score reflecting the reliability of each interaction See .We note that the different experimental methodologies behind the PPIs in HIPPIE are able to detect direct physical interactions between proteins to a varying degree. Even though some of them are in fact measuring co-membership in larger protein-complexes we will refer to all types of associations detected by these methods as interactions or PPIs. The HIPPIE score tries to reflect both the reliability of the various methods as well as the ability to detect direct rather than indirect interactions.The number of PPIs derived from different experimental system types was very variable. HIPPIE integrates various datasets dealing with different experimental systems and thus contains a larger amount of interactions than each of those sets separately . Values To illustrate the benefit of using a large dataset such as HIPPIE, we compared it with novel high-throughput PPI datasets not used for its production. We chose two high-throughput PPI datasets from the recent literature: a Y2H dataset, Y2He We evaluated the usefulness of the HIPPIE score using the two novel datasets. The HIPPIE database was divided in a high quality subset containing the top 25% highest scoring interactions (score > = 0.73) and a lower quality subset  that require contrasting results with interactions selected for a particular level of reliability.HIPPIE currently integrates 72 916 interactions from several public PPI resources scored according to confidence. For comparison, the complete human interactome map has been estimated to contain between 200 000 and 400 000 interactions  UniProt identifier and (2) Entrez Gene identifier of the first protein partner, (3) UniProt identifier and (4) Entrez Gene identifier of the second protein partner, (5) score and (6) a comment field summarizing the origin of the evidence. Evidence is arranged in three types: experiments, pmids, and sources. Experiment types are indicated in http://cbdm.mdc-berlin.de/tools/hippie/hippie_v1_2.txt.(TXT)Click here for additional data file.