Large-scale identification of the interrelationships between different components of the cell, such as the interactions between proteins, has recently gained great interest. However, unraveling large-scale protein-protein interaction maps is laborious and expensive. Moreover, assessing the reliability of the interactions can be cumbersome.Arabidopsis thaliana. A highly reliable set of protein-protein interactions is predicted through this integrative approach making use of existing protein-protein interaction data from yeast, human, C. elegans and D. melanogaster. Localization, biological process, and co-expression data are used as powerful indicators for protein-protein interactions. The functional repertoire of the identified interactome reveals interactions between proteins functioning in well-conserved as well as plant-specific biological processes. We observe that although common mechanisms (e.g. actin polymerization) and components  exist between different lineages, they are active in specific processes such as growth, cancer metastasis and trichome development in yeast, human and Arabidopsis, respectively.In this study, we have developed a computational method that exploits the existing knowledge on protein-protein interactions in diverse species through orthologous relations on the one hand, and functional association data on the other hand to predict and filter protein-protein interactions in We conclude that the integration of orthology with functional association data is adequate to predict protein-protein interactions. Through this approach, a high number of novel protein-protein interactions with diverse biological roles is discovered. Overall, we have predicted a reliable set of protein-protein interactions suitable for further computational as well as experimental analyses. The complex regulation of diverse biological processes acting in eukaryotic organisms is only possible through interactions between different components in the cell. Proteins, for instance, can be part of extensive complexes, such as transcription factor complexes for the combinatorial control of their target genes or proteasome complexes for protein degradation. It is the specific timing and location of the activity of these protein complexes that defines their role in the cell. Several attempts have been made to infer protein-protein interaction maps in diverse model organisms through large-scale experimental methods ,2. In yeTo make full use of the currently available interaction data, computational methods are being developed to assess the quality of experimentally generated protein-protein interactions and to predict new interactions ,21,22. WArabidopsis thaliana, attempts to construct large-scale protein-protein interaction maps as well as the application and critical assessment of computational methods have been rather limited [Arabidopsis thaliana: namely, (1) (some) protein-protein interactions in yeast and animals (source organisms) are conserved in Arabidopsis thaliana (target organism), (2) interacting proteins co-localize, (3) interacting proteins function in the same biological process, and (4) genes encoding interacting proteins show similar expression patterns. Hereby, the relative contribution of these features to the prediction of protein-protein interactions in Arabidopsis thaliana is assessed. The prediction of Arabidopsis protein-protein interactions is performed, exploiting the conservation of these interactions between species on the one hand, and utilizing functional association data on the other hand. Finally, protein complexes are delineated from the predicted protein-protein interaction network and the function and evolutionary conservation of these protein complexes is studied.For the model plant  limited ,46. In tArabidopsis thaliana performed in this study resides in the detection of interologs. Interologs are defined as protein-protein interactions that are conserved between two species and that can be detected through the identification of the orthologs in the target organism of the proteins known to interact in the source organism . Systematic assessment of the evidence codes of the GO terms used for the interaction filtering (BP8 and CC5) shows that a minority of terms are inferred from sequence or structural similarity (ISS)  and the GO biological process (BP) annotation of the interacting proteins were evaluated. To measure the similarity between the GO annotations of two proteins, we calculated the maximum depth of the common ancestor of all pairs of GO terms assigned to both proteins  meet the conditions of the genomic features   of 87 species. From this dataset, we extracted the evolutionary relationships between Arabidopsis, yeast, C. elegans, Drosophila and human genes. The approach taken to build this database has the advantage that in-paralogs (duplicates arisen after speciation) and thus orthologous groups rather than one-to-one orthologs can be identified. Employing these orthologous relationships between yeast, C. elegans, Drosophila and human genes on the one hand, and Arabidopsis genes on the other hand, interologs were detected , while the line width of the edge represents the GO biological process similarity score  and the line style of the edge represents the GO cellular component similarity score . The color of the nodes corresponds to the protein cluster the protein belongs to (see further). Proteins belonging to small or no clusters are colored in grey. TAIR functional descriptions are shown as node labels. Subsets of the interactome that are of interest to the researcher can be visualized easily by querying the interactome for (a) protein(s) or a functional description.We have developed an easy-to-use query and visualization system to represent the inferred interactome. The discussed protein clusters as well as the complete predicted interactome can be observed through a web-start version of Cytoscape that can be found at In an attempt to reveal the functional repertoire of the predicted interactome, we have delineated highly interconnected regions in the protein-protein interaction network . In addition, we tried to assign a function to the identified protein clusters. Finally, we investigated the evolutionary conservation of the protein clusters.Identification of protein clusters is performed using the CAST clustering algorithm (see Methods). This clustering procedure employs the connectivity of the proteins. Overall, 1802 proteins taking part in 16,498 interactions could be identified in protein clusters, accounting for the majority of originally identified interactions  and cation/H+ exchangers . Although not connected to the above-mentioned clusters, a protein cluster containing components of the translocase inner membrane complex (CAST cluster 13) associated with carrier proteins (CAST cluster 94), was retrieved as well. Several links between cell cycle control, protein degradation and related processes are captured in the protein clusters enriched for GO categories containing 'cell cycle' . Whereas ubiquitin-mediated proteolysis regulates the activity of cyclins during the progression through the different phases of the cell cycle, B-type cyclins interact with several microtubule-related components during cytokinesis. Similarly, A-type cyclins, DNA replication proteins  and RBR/WEE proteins are associated during the G1/S transition [As could be expected, well-conserved proteins and functions, such as those involved in transcription, translation, and proteolysis, are overrepresented. Typically, proteasome and ribosomal proteins are identified as highly connected . A number of protein-protein interaction networks involved in particular processes such as organelle organization and biogenesis, lipid metabolism, and ATP binding as well as the biological processes mentioned below can be viewed through our Supplementary data site. In addition, a considerable number of protein-protein interactions with a role in transmembrane transport, membrane receptor activity and vesicle trafficking were detected as previously reported by Geisler-Lee et al.  . Proteins active in these stress responses are DNAJ heat shock proteins, calcium-dependent and MAP kinases (CAST cluster 1), DNA repair proteins , superoxide dismutases CAST 49), glutathione peroxidases (CAST 74) and glutathione S-transferases (CAST 15). These proteins are involved in very diverse biological processes, such as response to heat, response to toxins and response to chemical stimulus, which is reflected in the dashed edges in the network (see Supplementary data). In order to verify that these predicted interactions also reflect actual conserved biological stress responses, we compared the expression patterns of all 500 Arabidopsis genes in these 'response to' protein clusters using a recently compiled comparative stress response matrix [, glutathBesides interactions functioning in well-conserved biological processes, we could also identify the recruitment of well-conserved proteins and protein-protein interactions in plant-specific processes. In the following examples, the regulatory mechanisms such as chromatin remodeling and actin polymerization are common to different lineages. However, these mechanisms are put into play in highly lineage-specific biological processes, such as seed and trichome development.In a first example, we predict that the proteins FERTILIZATION INDEPENDENT ENDOSPERM (FIE), CURLY LEAF (CLF), SWINGER (SWN), MULTICOPY SUPPRESSOR OF IRA1 (MSI1), and MEDEA (MEA) interact and that this protein complex functions in flowering and embryonic development see Fig. . This coIn a second example, we have predicted interactions between different actin-related proteins (ARPs), constituting the ARP2/3 complex involved in leaf development  as filters to increase the confidence in our predictions. As such, a set of highly reliable interactions could be delineated that can be further employed in both computational and experimental studies. In contrast to previous efforts to predict protein-protein interactions in Arabidopsis thaliana, we do not only provide a list of all interologs and their associated genomic feature values, but rather focus on the subset of protein-protein interactions that is supported by the different genomic features . The extensive study of the behavior of the genomic features for experimentally identified protein-protein interactions compared to random protein pairs allowed us to validate the protein-protein interactions adequately.In this study, we have predicted protein-protein interactions in the model plant The setup of this study allowed us to rigorously investigate the functional repertoire and evolutionary conservation of the identified protein-protein interactions. We conclude that although this type of protein-protein interaction prediction is highly dependent on the degree of conservation of protein-protein interactions between Arabidopsis and yeasts or animals, we were able to predict interactions with roles in diverse biological processes. Interestingly, these cover both interactions in evolutionary conserved and plant-specific processes.Future improvements to the prediction of protein-protein interactions are manifold. For instance, it has been shown that not all protein-protein interactions are equally stable. Some interactions are permanent while others are transient, meaning that proteins only come together at certain time points or locations possibly resulting in different expression profiles of the encoding genes (just-in-time assembly) . AlthougIn conclusion, this study showed that the integration of orthology with functional association data, such as localization, biological process and co-expression, is adequate to predict protein-protein interactions. In particular, for organisms with limited existing knowledge on protein-protein interactions, such as Arabidopsis, our approach is very valuable. On the contrary, sophisticated machine learning approaches perform poorly because of the lack of gold standard sets of interactions. We could predict a high number of new protein-protein interactions, and analysis of the functional repertoire of identified protein clusters supports the significance of these putative interactions. The approach described here can easily be adapted for estimating the reliability of experimentally identified interactions. Finally, with the growing availability of expression and gene ontology information, this approach can be applied to the detection of protein-protein interactions in agronomically and economically interesting plants, such as rice, corn and poplar.Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster and Homo sapiens and the target organism Arabidopsis thaliana . We have compared balanced datasets  to estimate the reliability of our genomic feature filtering. The thresholds chosen in this study would correspond to a positive predictive value ) of 95% in a one to one ratio. However, in reality, although difficult to estimate, the positive and negative protein pairs do not occur in a one to one ratio. Therefore, the positive predictive value is actually smaller. In a one to ten or one to 100 ratio, the PPV drops to ~50% or ~30%, respectively. However, these positive predicted values are probably not robust and should be considered with caution due to the small sample size (sparse distributions of genomic features of the positive dataset compared to the negative dataset). The number of positive interactions for which the genomic features pass the thresholds is extremely low, probably due to the fact that so few protein-protein interactions have been experimentally identified and/or possess sufficient gene ontology information. Even more importantly, the calculation of these positive predictive values does not take into account the fact that, through the interolog detection, our initial predictions (before application of genomic feature thresholds) are already enriched for true interactions. Through this step, the PPV increases to 88% and only the most likely interactions remain.Saccharomyces cerevisiae, Caenorabditis elegans, Drosophila melanogaster and Homo sapiens, and the target organism Arabidopsis thaliana. For a certain pair of interacting proteins in the source organism, all combinations between the (co-)orthologous proteins in the target organism were made , are applied -61. In tThe Gene Ontology (GO) consortium provides a structured standard vocabulary for describing the function of gene products . It is d for details on evidence codes) or electronically assigned and less reliably assigned GO terms  are removed. Although the number of proteins with a GO annotation decreases considerably, the reliability of the GO similarity scores increases through this procedure (data not shown). Nevertheless, ISS-based annotations were included. We rigorously assessed the possibility of including annotations based on ISS as this accounts for a considerably number of annotations and could conclude that the low reliability of these annotations does not pose a problem to our approach  was compiled from NASC (Nottingham Arabidopsis Stock Centre) to detect co-expression between Arabidopsis genes . MicroarThe predicted interactome can be represented as a graph of nodes, corresponding to the proteins, and connecting edges, corresponding to the interactions. Protein complexes were delineated from this graph making use of the Cluster Affinity Search Technique (CAST) algorithm . This alThe identified protein complexes are subjected to functional analysis. The assignments of genes to the original GO categories were extended to include parental terms . All GO categories containing less than 20 genes were discarded for further analysis. Enrichment values were calculated as the ratio of the relative occurrence in a set of genes to the relative occurrence in the genome. Overrepresentation of GO categories  was tested using the Fisher exact test. P values were adjusted using the Bonferroni correction for multiple hypotheses testing. GO categories are assumed to be significantly overrepresented when the corrected P value is smaller than 0.01.SB and PR conceived the study. SB and KV designed the study. SP was responsible for data retrieval and predictions. SB performed the assessment of genomic features, predictions and biological interpretation. KV performed clustering and GO overrepresentation analysis of the predictions. SB and KV drafted the manuscript. YVDP and PR critically revised the manuscript. All authors read and approved the final manuscript.False positive rates of different combinations of genomic features. BP = biological process, CC = cellular component, PCC = Pearson correlation coefficient.Click here for fileCalculation of GO similarity score. All possible GO terms of two proteins are compared in a pairwise manner. For each pair of GO terms (in green), the depth of the common ancestor (in blue) of these terms is calculated. The maximum depth of all pairwise combinations of GO terms is considered as the GO similarity score between two proteins.Click here for fileFigure S2. Assessment of combinations of genomic features.Click here for fileTable S2. Assessment of GO evidence codes.Click here for fileTable S3. Possible contradictory annotations.Click here for fileFiltered interactome. Protein-protein interactions that passed the genomic feature filters. BP score > = 8, CC score > = 5 or BP score > = 5 + PCC > = 0.3.Click here for filePredicted interactome. Extension of filtered interactome. Interactions with orthologous group combination already present in filtered interactome are included.Click here for fileOverlap of the different sets of protein-protein interactions. Overlap of the filtered (1) and predicted (2) interactome with previously experimentally shown (A) and predicted protein-protein interactions (B).Click here for fileCAST clusters and GO overrepresentation.Click here for file


Protein complexes play an important role in cellular mechanisms. Recently, several methods have been presented to predict protein complexes in a protein interaction network. In these methods, a protein complex is predicted as a dense subgraph of protein interactions. However, interactions data are incomplete and a protein complex does not have to be a complete or dense subgraph.We propose a more appropriate protein complex prediction method, CFA, that is based on connectivity number on subgraphs. We evaluate CFA using several protein interaction networks on reference protein complexes in two benchmark data sets (MIPS and Aloy), containing 1142 and 61 known complexes respectively. We compare CFA to some existing protein complex prediction methods  in terms of recall and precision. We show that CFA predicts more complexes correctly at a competitive level of precision.http://www.bioinf.cs.ipm.ir/softwares/cfa/CFA.rar.Many real complexes with different connectivity level in protein interaction network can be predicted based on connectivity number. Our CFA program and results are freely available from  Several groups have produced a large amount of data on protein interactions -9. It isHowever, many biological data sources contain noise and do not contain complete information due to limitations of experiments. Recently, some computational methods have estimated the reliability of individual interaction based on the topology of the protein interaction network (PPI network) ,28,29. Tk-Connected Finding Algorithm--to find protein complexes from this graph. Our algorithm is based on finding maximal k-connected subgraphs. The union of all maximal k-connected subgraphs (k ≥ 1) forms the set of candidate protein clusters. These candidate clusters are then filtered to remove (i) clusters having less than four proteins and (ii) clusters having a large diameter. We compare the results of our algorithm with the results of MCL, RNSC, PCP and CMC. Our algorithm produces results that are comparable or better than these existing algorithms on real complexes of  be the induced subgraph of G generated by the vertex set iL, where {L1, L2, ..., kL} is the set of localization groups. Thus each i Lcontains a set of proteins localized to the same cellular component--i.e., they are annotated by the same informative GO term. Let i Cbe the set of all clusters predicted by an algorithm on G.Let L Cand clusters generated on the original PPI network G. Table LC.To evaluate the impact of localization information, we compare the precision and recall of θ = 0.5 are given in Table BiogridPPI, while MCL performs best on PPIGavin6, PPIGavin2, and HoPPI. In the orginal network of kroganPPI, PCP shows better precision against recall compared to other methods, while after cleaning by using localization information almost all methods have similar performance. This table shows that none of these algorithms has the best precision vs recall in all networks.The precision and recall values obtained at the matching threshold PPIGavin2. This cluster contains a four-member protein complex with specific GO cellular component annotation . The other seven proteins in the CMC cluster belong to other localization groups. This cluster is refined in L Cto match well with the same real complex. In Figure KroganPPI. In contrast, only four proteins in this complex are matched to the corresponding PCP cluster predicted on the original network.We present two illustrative examples in Figure G D= 0 and 41 complexes with density value less than 0.1 in BioGRIDPPI. Similarly, there exist 200 complexes among 551 known complexes with density value less than 0.1 in PPIGavin2.We consider the density of known complexes with size at least three for each PPI network. Figure K3, while there are a large number of cliques of size 3 which are not complex. For example, in BioGRIDPPI, there exist 176 known complexes of size three, while the number of cliques of size 3 in BioGRID PPIis 37230. It means that only about 0.47% of them are known real complexes. So, those clusters and complexes with size atmost three are removed in our work, to avoid an excessive number of false positive predictions.Furthermore, almost all complexes which are complete or have high density are of the form BioGRIDPPI. We find that there exist 138 real complexes of size four, while only 54 of them have high density.We have also studied the number of known complexes of size four in The discussions above suggest that the density criterion alone cannot answer the question of finding complexes. We need to introduce another criterion to overcome this problem.We show in this section that connectivity is a reasonable alternative criterion for identifying protein complexes. Although this criterion is simple, it may directly describe the general understanding of the protein complex concept. This criterion is better than density because, while there are a lot of known complexes that are not complete or dense, there are many k-connected subgraphs with low density. For example, Figure locscore, we define kscore of a set of complexes, C, as follows;Similar to the definition of k- connected subgraphs of complex c.where kscore and average density of different PPI networks on MPC are shown. The average density of the set of real complexes are usually low. On the other hand, on average, 99.5% of proteins of each real complex are located in 1-connected subgraphs. Also 78.4%, 53.7% and 37.4% of proteins of each real complex are located in 2-connected, 3- connected, and 4-connected subgraphs respectively. By increasing the connectivity number, this average decreases but there exist some proteins which are located in a subset of a real complex with high k- connectivity.In Table k until k cannot be increased any more. In other words, the algorithm continues until some integer k0 such that there is no k-connected subgraph with k > k0.This suggests that using connectivity number as a criterion of protein complex prediction may be a good approach. Therefore, our algorithm is based on finding maximal k-connected subgraphs in PPI networks by keep increasing To check the validity of CFA, we compare clusters predicted by CFA with the clusters obtained by CMC, MCL, PCP and RNSC, on the seven protein interaction networks of GRID and BioGRID. The networks are first segregated by informative cellular component GO terms before these algorithms are run. MPC and APC are used as benchmark real protein complexes.UetzPPI, none of the algorithms could produce any cluster matched by real complexes in MPC and APC. U etz PPIis a difficult example because, as can be seen in Table In KroganPPI, ItoPPI, PPIGavin2 and PPIGavin6 compared to other methods. In fact, both precision and recall values of CFA are greater than all of the other algorithms in these networks. In HoPPI, RNSC has the greatest precision. However, RNSC predicts merely 26 clusters and, among these predictions, 13 clusters are matched to 5 real complexes in APC and 19 clusters are matched to 21 real complexes in MPC. Thus the recall value of RNSC is very low (0.166 on APC and 0.038 on MPC). In contrast, CFA correctly predicts 13 real complexes of APC and 62 of MPC. The clusters of CFA give the precision value 0.416 (0.166) and the recall value 0.114 (0.433) on MPC (APC), which are generally better than that obtained by RNSC and other methods on HoPPI.Table BiogridPPI. We find that almost all algorithms predict the same number of real complexes in APC. However, CFA matches a lot more complexes in MPC than CMC (18% more), MCL (5% more), PCP (15% more) and RNSC (17% more). Furthermore, this significant superiority of CFA in recall comes with the highest precision value in MPC. The overall precision of CFA on the combined APC and MPC complexes, as can be computed from Table We also study the number of matched clusters and matched complexes of predictions on We find that all complexes predicted by CMC and RNSC are identified by at least one of the other three algorithms. To compare real complexes predicted by CFA, MCL and PCP, Figure Biogrid PPIare derived from two-hybrid technique. Due to the level of noise in two-hybrid experiments, we expect those predicted clusters having the form of a tree structure to have lower reliability compared to other 1-connected subgraphs. Hence, in order to improve the results of CFA, we only use 1-connected subgraphs that are not trees. A tree with n vertices has n - 1 edges; so a connected cluster is a tree if and only if its cluster score is 2. Thus, we consider 1-connected subgraphs with cluster scores greater than 2. Similarly, we can do additional filtering for each k-connected subgraphs by considering the clusters with cluster score greater that k+1. The precision and recall values of the resulting further refined clusters are 0.465 and 0.178 in MPC and 0.347 and 0.838 in APC. So the precision vs recall of CFA, using cluster score filtering, shows significant improvement compared to other methods in Biogrid PPIon APC too.Some interactions in i Sand j Ssuch that Overlap ≥ α. To get a more concise understanding of CFA and the other prediction methods, we also clean up the set of predictions by removing redundant clusters. In the other words, when two predicted clusters show an overlap score above the threshold value (of α = 0.5), we keep the larger one. The precision and recall values after this additional cleaning of the set of predictions are given in Table On the other hand, we observe that some predicted clusters have large overlap with each other. That is, we have some clusters In this section, we present five matched and unmatched clusters predicted by CFA.PPIGavin2. Complex 1 is an eleven- member complex  that contains a protein, Y NL113W, whose interactions with other proteins are missing from PPIGavin2. Complex 2 contains 12 proteins  and there exists a protein, Y LR418C, in this complex whose interactions with other proteins are missing in PPIGavin2. There are four common proteins in these two complexes. Without considering localization annotations, CFA predicts all vertices of this graph (except for Y LR418C and Y NL113W) as a 2-connected subgraph. After segregating the network using GO terms, CFA predicts two clusters . The one additional protein (YDR363W-A) predicted by CFA to be in this cluster turns out to have the same biological process GO term annotation. We think that with more accurate experimental data, this 19th protein may also be a protein of this complex. The smallest cluster in our samples contains six proteins that are predicted by CFA in BioGRIDPPI. The cluster members have the same specific GO annotation , though this cluster is not presented as a known complex in MPC and APC.In Figure To gain further insights into the differences among CFA's clusters and clusters predicted by other algorithms, we consider the first CFA cluster presented in Figure The third cluster shown in Figure In the first part of this work, we study the impact of using informative cellular component GO term annotations on the performance of several different protein complex prediction algorithms. We have shown Table  that exiIn the second part of this work, we study the density of protein interactions within protein complexes. We have shown Figure  that thek-connected subgraphs on an input PPI network that has been seggregated according to informative cellular component GO term annotations on its proteins. Table k-connected subgraphs for various values of k as our set of predicted protein complexes yields the best precision vs recall performance. This combined set constitutes the predicted clusters output by CFA.So in the third part of this work, we propose the CFA algorithm to predict protein complexes based on finding BiogridPPI), the number of complexes predicted by RNSC is very low compared to CFA. In particular, CFA predicts 19 complexes which RNSC is unable to predict, while RNSC predicts 2 complexes which CFA is unable to predict. Furthermore, by varying the threshold on the matching score, we show in Figure F-measure graphs based on protein clusters predicted for various protein interaction networks. We observe that CFA consistently shows the best performance compared to other methods over the entire range.Finally, we compare the performance of CFA to several state-of-the-art protein complex prediction methods. We have shown Table  that CFAk-connected subgraphs (1 ≤ k) on PPI networks segregated by informative cellular component GO terms.In the Observations section we explained that cellular component annotations can help us to improve predictions. On the other hand, by studying the connectivity number of real complexes as subgraphs of PPI network, we showed that the connectivity number could be a reasonable criterion to predict complexes. So we present a new algorithm based on finding k-connected subgraphs for various k are generated as candidate complexes. In the second step, a number of filtering rules are applied to eliminate unlikely candidates.A new algorithm named CFA (k-Connected Finding Algorithm) is presented here to predict complexes from an input (cleansed) PPI network. The CFA algorithm comprises two main steps. In the first step, maximal REFINE, which removes all vertices of degree less than k from the input graph. This is an obvious optimization since, by the global version of Menger's theorem [k-connected subgraphs. The second procedure is COMPONENT, which takes the refined graph and fragments it into k-connected subgraphs. This procedure finds a set of h < k vertices that disconnects the input graph, producing several connected components of the graph. The procedure is then recursively called on each of these connected components. The procedure terminates on a connected component  if it cannot be made disconnected by removing h < k vertices. The correctness of this procedure follows straightforwardly from the global version of Menger's theorem.The heart of the first step of CFA contains two simple procedures. The first procedure is  theorem , such vek until no more k-connected subgraphs are returned. This way, we obtain maximal k-connected subgraphs for various values of k. These subgraphs are then filtered using the following three simple rules: (1) 1-connected subgraphs having diameter greater than 4 are removed. (2) k-connected subgraphs (k ≥ 2) having diameter greater than k are removed. (3) Subgraphs of size less than 4 are removed. The pseudo codes of the CFA algorithm are given in Table In the second step of CFA, we call the procedures defined in the first step on larger and larger values of We choose fixed parameter values for each algorithm Table . The impLW and CE conceived the project and designed the experiments. All authors contributed to conceiving and improving the proposed algorithm. MH implemented the algorithm during all stages of its development and performed all the experiments. All authors contributed to writing the manuscript. All authors have read and approved the manuscript.


Protein interactions are central to most biological processes, and are currently the subject of great interest. Yet despite the many recently developed methods forinteraction discovery, little attention has been paid to one of the best sources ofdata: complexes of known three-dimensional (3D) structure. Here we discuss howsuch complexes can be used to study and predict protein interactions and complexes,and to interrogate interaction networks proposed by methods such as two-hybridscreens or affinity purifications.


Semantic similarity scores for protein pairs are widely applied in functional genomic researches for finding functional clusters of proteins, predicting protein functions and protein-protein interactions, and for identifying putative disease genes. However, because some proteins, such as those related to diseases, tend to be studied more intensively, annotations are likely to be biased, which may affect applications based on semantic similarity measures. Thus, it is necessary to evaluate the effects of the bias on semantic similarity scores between proteins and then find a method to avoid them.First, we evaluated 14 commonly used semantic similarity scores for protein pairs and demonstrated that they significantly correlated with the numbers of annotation terms for the proteins . These results suggested that current applications of the semantic similarity scores between proteins might be unreliable. Then, to reduce this annotation bias effect, we proposed normalizing the semantic similarity scores between proteins using the power transformation of the scores. We provide evidence that this improves performance in some applications.Current semantic similarity measures for protein pairs are highly dependent on protein annotation lengths, which are subject to biological research bias. This affects applications that are based on these semantic similarity scores, especially in clustering studies that rely on score magnitudes. The normalized scores proposed in this paper can reduce the effects of this bias to some extent. Many scores for measuring semantic similarity  between proteins have been proposed, based on the Gene Ontology (GO) terms  used to GO protein annotations are known to be incomplete , and sufhttp://www.ebi.ac.uk/GOA/index.html, including versions in November (Nov) 2001, Nov 2002, Nov 2003, Nov 2004, Nov 2005, Nov 2006, Nov 2007 and August 2008. The GO vocabulary data were downloaded from the GO website http://www.geneontology.org in August 2008. Here, we considered only IS-A links in GO  by the Min-Max normalization method r ≥ 0.99,e Resnik  and JianSS) for pairs of term groups with given length combinations did not fit normal distributions . For nine similarity measures, namely the Resnik(AVG), Resnik(BMA), Lin(AVG), Lin(BMA), RS(AVG), RS(BMA), Jiang(AVG), Dice and Kappa scores, over 80% of the power-transformed scores for pairs of term groups with given length combinations followed normal distributions. Then, these power-transformed scores were normalized to the standard normal distribution. Thus, for these nine similarity measures, the normalization method based on the power transformation is largely suitable for comparing scores for pairs of term groups with different length combinations. However, based on each of the other five similarity measures, less than 60% of the power-transformed scores fitted normal distributions. We also analyzed another five simple transformation methods and the results  for paip < 2.20E-16), supporting the model that proteins with similar sequences tend to be functionally similar [r = 0.88, p < 2.20E-16).Then, for two types of applications, by comparing the results based on the original scores and their corresponding normalized scores, we showed that the bias affects certain analysis more than others. One type of applications based on semantic similarity scores for protein pairs study the correlation between functional similarity and similarities on other aspects ,8,14-19.p = 7.3E-13), while 78.4% of "Immunological" genes (blue) were in another (p = 3.96E-13). In contrast, as shown in Figure p > 0.10). As shown in Figure Another type of applications is clustering of functionally similar proteins ,20-22 orr p = 3.9E-13. In Saccharomyces cerevisiae) are also incomplete and suffer from the research bias because important genes such as the homologues of human disease genes tend to be studied more intensively [Saccharomyces cerevisiae data, we also found that the similarity scores between two groups of terms increased significantly with the annotation lengths (data not shown). Thus, our conclusion on the bias of semantic similarity scores for proteins would be applicable to other organisms.In this paper, we found that most semantic similarity scores for protein pairs increased as protein annotation lengths increased. Because protein annotations are likely to be subject to biological research bias, most applications based on current semantic similarity scores for protein pairs will be biased. Without the annotation bias, one could argue that over-annotated proteins might be more likely to be similar than under-annotated proteins, when considering only shared functions, and disregarding differences. However, currently, most semantic similarity scores for protein pairs evaluate the overall functional similarity between proteins. Depending on the available knowledge about domains, and the final aim of the application, different criteria could be used to define similarity between proteins. We note that protein annotations in GO for most model organisms , we could determine that the two proteins are functionally similar [To avoid the influence of annotation bias, other approaches may be attempted. For example, the statistical  similar . Functio similar -85.Current protein semantic similarity scores are highly dependent on protein annotation lengths, which are subject to biological research bias. This bias may affect many current applications based on these scores. The proposed normalization method might solve this problem to some extend.Conceived and designed the experiments: ZG, JW. Performed the experiments: JW, XZ and CZ. Analyzed the data: ZG, JW and JZ. Wrote the paper: JW, JZ and ZG. All authors read and approved the final manuscript.


Dengue virus along with the other members of the flaviviridae family has reemerged as deadly human pathogens. Understanding the mechanistic details of these infections can be highly rewarding in developing effective antivirals. During maturation of the virus inside the host cell, the coat proteins E and M undergo conformational changes, altering the morphology of the viral coat. However, due to low resolution nature of the available 3-D structures of viral assemblies, the atomic details of these changes are still elusive.In the present analysis, starting from Cα positions of low resolution cryo electron microscopic structures the residue level details of protein-protein interaction interfaces of dengue virus coat proteins have been predicted. By comparing the preexisting structures of virus in different phases of life cycle, the changes taking place in these predicted protein-protein interaction interfaces were followed as a function of maturation process of the virus. Besides changing the current notion about the presence of only homodimers in the mature viral coat, the present analysis indicated presence of a proline-rich motif at the protein-protein interaction interface of the coat protein. Investigating the conservation status of these seemingly functionally crucial residues across other members of flaviviridae family enabled dissecting common mechanisms used for infections by these viruses.Thus, using computational approach the present analysis has provided better insights into the preexisting low resolution structures of virus assemblies, the findings of which can be made use of in designing effective antivirals against these deadly human pathogens. Aedes aegypti mosquitoes for their transmission between the vertebrate hosts [3C6R] [1P58] , acidic D][3C6R]  as well ] [1P58] . The str] [1P58] . All theAs mentioned earlier the dengue virus coat consists of two proteins namely E and M. Hence, the interfaces can be classified as homodimeric or heterodimeric where the homodimeric interfaces are formed by interacting either E or M proteins while the heterodimeric interfaces are formed between a chain of E and a chain of M protein. In the present analysis we have traced these interactions as a function of maturation process of the virus.Using the method mentioned above, the residues that are inferred to be participating in the protein-protein interaction interface have been identified. The three structures analysed here consisted of three chains each of E  and M  proteins. Hence, nine different chain pairs were analyzed to obtain the residues contributing to these interactions. The residues from different chains of the E glycoprotein participating in the heterodimeric interactions in all the three structures mentioned above are listed in the Table Figure The homodimeric interactions (either amongst the chains of E or M proteins) were analyzed as a function of viral maturation process. As can be seen in the Table The viruses belonging to the flaviviridae family are known to utilize similar mechanisms for infecting the hosts although they use different arthropod vectors for their transmission and these common features have been targeted for vaccine development against this family of viruses . Hence, The coat proteins E and M of dengue viruses are being targeted for the development of new antivirals. During the maturation process of the viral particles, the two proteins interact closely and undergo conformational changes leading to the change in the morphology of the viral coat. However, details of the interactions between the two proteins have been elusive as most of the structures of the viral assemblies are available at very low resolutions. In the present analysis, by using a newly developed method which combines recognition and prediction, the interface residues of the two proteins in the viral assemblies have been predicted from these low resolution structures with only Cα atom positions available. Good conservation of these residues supports our identification. A comparative analysis of the three different structures of dengue viruses at different phases of their life cycle pointed some of the interesting details of the process of maturation.In the immature viruses, where the coat proteins E and M form heterodimers that are arranged as trimers, the M protein appeared capping the fusion peptide of the E glycoprotein. The two residues of the fusion peptide namely G(102) and N(103) seemed likely to interact with Pr peptide of the Pr-M directly, thereby preventing premature fusion with the host membrane. These likely interactions were found to be present in immature viruses, both before and after the furin cleavage. The two residues were found to be conserved across the flaviviridae members thus reiterating the fact that all the members must be using common mechanism for the fusion with the host membrane for the release of the nuclear material. Although, this mechanism had been reported earlier in the literature , the pre2B6B] and the structure of immature virus [PDB: 1TGE) [2R6P] the above mentioned interactions were found to be disrupted, perhaps owing to the conformational changes brought about by the bound antibody  RG designed the problem and contributed substantially in acquisition and analyzing the data and interpretation of results besides drafting the article and revising it and approving the version to be published. 2] NS contributed substantially in the analysis of data as well as interpretation of results besides contributing to drafting the article and revising it critically for important intellectual content as well as final approval of the version to be published.To investigate robustness of the method used for prediction of protein-protein interaction interface residues. The additional file provides details of the method used to calculate robustness of the method and a table  summarizing the results obtained.Click here for file


We are witnessing tremendous advances in our understanding of the organization of life. Complete genomes are being deciphered with ever increasing speed and accuracy, thereby setting the stage for addressing the entire gene product repertoire of cells, towards understanding whole biological systems. Advances in bioinformatics and mass spectrometric techniques have revealed the multitude of interactions present in the proteome. Multiprotein complexes are emerging as a paramount cornerstone of biological activity, as many proteins appear to participate, stably or transiently, in large multisubunit assemblies. Analysis of the architecture of these assemblies and their manifold interactions is imperative for understanding their function at the molecular level. Structural genomics efforts have fostered the development of many technologies towards achieving the throughput required for studying system-wide single proteins and small interaction motifs at high resolution. The present shift in focus towards large multiprotein complexes, in particular in eukaryotes, now calls for a likewise concerted effort to develop and provide new technologies that are urgently required to produce in quality and quantity the plethora of multiprotein assemblies that form the complexome, and to routinely study their structure and function at the molecular level. Current efforts towards this objective are summarized and reviewed in this contribution. Protein-protein interactions (PPIs) are intrinsic to virtually every essential process in the cell. Deciphering PPIs is imperative for understanding the underlying biological mechanisms of living systems. Cellular activities that govern health and disease, such as DNA replication, transcription, splicing, translation, secretion, cell cycle control, signal transduction and intermediary metabolism are controlled by PPIs -5. New dTo date, many thousands of PPIs are known, however, the precise molecular details are available for only a small fraction of these interactions. Structure elucidation can ultimately turn abstract system representations into models that more accurately reflect biological reality. The utility of structural biology is to understand the mechanisms governing biological interactions in living systems for designing strategies to modulate, and interfere with these interactions. However, the large and increasing body of data describing PPIs on a genome-wide scale, and the pace at which it is amassed, is currently at a pronounced disparity with the rate at which the structure and function of representative protein complexes that comprise the identified interactions, are described at the molecular level. Despite considerable advances in contemporary structure determination techniques and significant efforts by structural genomics consortia to streamline the process leading to high-resolution structures, many bottlenecks in the structure determination pipeline remain.Protein complexes are often found in scarce amounts in their endogenous host and remain difficult to isolate in the quantity and quality required for detailed functional and structural analysis. This is often the case already for electron microscopy experiments, although the requirements of this technique in terms of sample quantity are typically less imposing as compared to studies for example by X-ray crystallography or by nuclear magnetic resonance (NMR) spectroscopy. The latter two are the currently most powerful and widely used techniques for providing high-resolution structural information. Multiplexed overexpression experiments by using advanced recombinant production technologies could be instrumental not only for overcoming the sample production bottleneck, but also for compellingly validating proposed interactions in a heterologous setup. Streamlined high-throughput technologies for production of multisubunit protein complexes, however, have been utterly lacking to date. New developments are required to rapidly and reproducibly construct large protein complexes and variations thereof at the rate that they are conceptualized from genome-wide studies.1)). Such techniques have produced a wealth of data and have given rise to a new sphere of research designated “interactomics”. The term “interactome” is used to describe all known interactions present in the cellular gene product repertoire [In recent years, new and powerful methods have been developed which allow complex cellular protein-protein interaction networks to be mapped  method . In thisciations , 9. In oomplexes . In an iet al. have developed a Strep-protein interaction experiment (SPINE) that deals with the inherent false positives otherwise found in TAP tagging experiments [et al. were able to get an in vivo snap-shot of bait interactors in B. subtilis in a single affinity purification step.Methods to optimize the TAP tagging strategy are under way in an effort to obtain larger quantities of tagged protein assemblies. One of the challenges of the TAP method is to gain insight into the more fleeting interactions present in a protein complex. Herzberg eriments . By replet al. described a procedure using the TAP tagging principle to purify protein complexes from C. elegans strains and cultivated HeLa cells [C. elegans kinetochore formation.In the years since the pioneering initial glimpses into the yeast interactome, subsequent affinity purification studies have sought to shed light on the interactomes of multicellular organism. Multicellular organisms are generally less amenable to TAP-tagging approaches due to the challenge of using homologous recombination to insert affinity tags and the difficulties in retrieving sufficient quantities of purified material. Nevertheless, Cheeseman La cells . By modiet al. overcame the problem of low protein yields in TAP tagging experiments in mammalian systems by likewise altering the composition of the TAP tag [Furthermore, Burckstummer  TAP tag . They alin vivo protein localization and endogenous protein interactions.An interesting approach called BAC TransgeneOmics was recently described as a tool for studying protein-protein and protein-DNA interactions in addition to protein localization . BAC Tanet al. estimate that only 8% of the full human interactome has been covered by Y2H screens [Another powerful method for generating interactome maps in a high-throughput manner is the yeast two-hybrid (Y2H) approach . Interac screens . However screens . Y2H scr screens . There a screens . The latS. cerevisiae [D. melanogaster [C. elegans [While the Y2H strategy has the capacity to meet the demands of high-throughput interactome mapping, this approach cannot currently compete with affinity based methods in terms of genome coverage. Nonetheless, Y2H surveys have realized a rich source of high-quality binary interaction maps from a wide range of organisms, including viruses, bacteria , S. cererevisiae , 18, 19, elegans -22 and h elegans , 23, 24.et al. [et al. [Two valuable high-throughput Y2H human PPI maps were generated by Stelzl et al.  and Rual [et al. . These iet al. have attempted to deal with a long standing criticism that Y2H screens are biased towards interactions that occur within the nucleus [et al. performed a Y2H screen in parallel with a yellow flourescent protein complementation assay (PCA) in which the traditional bait and prey peptides are replaced with non-flourescing halves of yellow fluorescent protein (YFP). Once the interacting partners are in close proximity, the fluorescent properties of YFP are reconstituted and thereby create a useful marker that is not limited to reporting on interactions that occur within the nucleus. Using their dual method, Yu et al. were able to validate their own results, which showed a greater degree of correlation than that shown between the Gavin and Krogan TAP studies. Y2H screens are certainly becoming a valuable tool for studying genome-wide protein ineractions and will likely continue to make major contributions to the field of interactomics.In a recent high-quality yeast binary protein interaction study, Yu  nucleus . To counResults from high-throughput interactome studies are being tabulated with increasing clarity. These efforts are resulting in unprecedented amounts of potentially useful data for molecular and structural biologists. On the bioinformatics side, the major hurdles in analyzing high-throughput interactome data sets include managing databases, creating useful clustering algorithms to glean valuable information about protein interactions, and using the resulting clustering to make predictions about biological systems. Results from combined genome-wide interaction studies may contain only partially overlapping datasets, false positives  and false negatives (limited or biased coverage that excludes a true interaction). Such issues hamper a comprehensive portrayal of protein networking . Today’shttp://dip.doe-mbi.ucla.edu), IntAct (http://www.ebi.ac.uk/intact), MINT (http://mint.bio.uniroma2.it/mint), MPact (http://mips.gsf.de/genre/proj/mpact), MatrixDB (http://www.matrixdb.ibcp.fr), BioGRID (http://www.thebiogrid.org), MPIDB (http://www.jcvi.org/mpidb) and BIND (http://www.blueprint.org). Alongside IMEx is MIMIx, the minimum information required for reporting a molecular interaction experiment. MIMIx tackles the lack of community consensus on what information is required to report molecular interaction by setting up an international standard to facilitate the extraction of useable data from PPI experiments by users. Currently, data is exchanged in XML format.Considering that each genome-wide interactome study generates characteristic data and that each existing repository uses characteristic file formats for storing data, the challenge of creating a consolidated resource for a transparent flow of data between datasets is startling. The Molecular Interactions (MI) group of the Proteomics Standards Initiative (PSI) has created an international standard for representing protein interaction data by consolidating existing interactome data sets from individually curated databases to create the International Molecular Interaction Exchange consortium (IMEx) . The conet al. used an unsupervised probabilistic scoring scheme and assigned confidence scores to each interaction. This approach generated a matrix-model interpretation of the yeast interactome datasets [et al. suggested that these studies are approaching saturation of what can be known about the subset of the complexome of yeast grown in rich media. Recently, Krogan indicated that a rough calculation based on the overlap of the two studies suggests that approximately 80% of the interactions capable of detection in yeast by the TAP method have been detected [A major challenge concerning interactome datasets is how to cluster the resulting interactions to accurately report on real protein complexes rather than spurious, or false positive interactions while including more transient members of protein complexes rather than only architectural ones. Based on the Gavin, Krogan and Ho studies, Hart datasets . Unsatisdatasets , Hart etdetected .Another consequence of the upsurge in PPI maps and genome-wide sequencing efforts is the new wealth of data that can be used by the community of scientists who model protein interactions and predict protein function from the gene sequence. With the ever increasing amounts of data about PPIs, it is possible to identify recurring ‘domain signatures’ and to correlate frequent interactions between them, the idea being that the interaction may be mediated by the signature sequence . Knowledde novo characterization of the composition and organization of the cellular machinery. Data derived from these methods indicated that complexes can combine transiently and differentially in a modular fashion thus enabling a diversification of the potential function of individual protein complexes [Mass spectrometry (MS) has emerged as an indispensable tool for studying the interactome , 33. MS omplexes . MS-baseomplexes . MS baseomplexes . A key iomplexes , 37.MS experiments can likewise be used to obtain inventories of biochemically isolated organelles allowing for the characterization of sub-interactomes contained within subcellular compartments. High-resolution methods were applied for accurate protein identification and novel algorithms were developed to assign genuine components from co-purifying proteins in these experiments . This hoMS based interactome wide studies are often met with skepticism concerning the reproducibility of results . The TesThe link between interactome research and structural biology is made by native mass sepctrometry of large protein assemblies, an emerging, very promising technology. Native mass spectrometry techniques allow sensitive analyses of endogenously expressed protein complexes with high speed and selectivity , 42. ImpA recent technological advance is ion mobility seperation coupled to mass spectrometry (IM-MS), which has been particulary useful to establish mass spectrometry as a powerful tool for structural biology applications , 43. In Requiring relatively small amounts of protein sample compared to other MS techniques, nanoelectro-spray ionization can achieve the maintenance of a solution structure in the gas phase. Using collision-induced dissociation (CID), even very large protein complexes can be selectively dissociated by collision with neutral gas atoms. Each collision event results in the accumulation of internal energy by the ion in question. Upon accumulation of sufficient internal energy, this ion may undergo dissociation. This approach can be used to dissociate protein complexes into subcomplexes and subunits which are then analyzed with TOF instruments. CID has been used to analyze virus capsids and entire ribosomes with a molecular mass of 2.5 MDa . The comvia high-throughput and automated approaches starting from molecular cloning of the genes to structure elucidation of the encoded proteins. Based on structures deposited in the Protein Data Bank (PDB), structure determination by single crystal X-ray diffraction analysis is currently the predominantly used technique, in addition to structure determination in solution by NMR. By means of comparison with structures of well-characterized proteins and domains, the biological function of uncharacterized proteins can often be discovered or proposed. Until the beginning of 2008, the combined effort from structural genomics consortia worldwide contributed about 50% of the newly-deposited structures in the PDB. One of the largest structural genomic projects is the Project Structure Initiative (PSI) in the United States, which is sponsored by the National Institute of Health (NIH). Several other large consortia exist in Japan, Canada, and Europe [The description of the 3-D structure of biological macromolecules, at near-atomic resolution, is imperative for understanding their function at the molecular level. The elucidation of the DNA sequence of the entire genome of many organisms, including humans, revealed the gene repertoire present in cells. This set the stage to address the proteome, which is the comprehensive assemblage of all known gene products in an organism. The elucidation of the 3-D structure of all encoded proteins, at high resolution, is the goal of structural genomics efforts. Structural genomics aims at building up a high-resolution library dedicated to cataloguing the protein complement of different organisms d Europe .In addition to the very large number of structures to be elucidated for describing a proteome, structural genomics approaches were confronted with a multitude of challenges. Successful structural determination by X-ray crystallography typically requires iterative optimization of protein encoding sequences for expression and purification of the specimens. Several to many expression vectors, host organisms and host strains need to be integrated into the experimental work-flow, in addition to covering a large space of conditions suitable for crystallization. All steps involved require considerable investment in labor and materials and a very significant through-put of experiments. Entire proteomes are addressed most often at the single protein or protein domain level. Consequently, structural genomics intensively stimulated and fostered the implementation of automation and high-throughput approaches, which now result also in considerable benefit for classical, hypothesis driven structural molecular biology. Many laboratories are now in the process of integrating high-throughput approaches at varying levels in their research .via bioinformatics methods. This is followed by cloning, insertion in one or several expression vectors, expression and purification, and finally structure determination. Researchers at centers engaged in structural genomics integrated automated cloning strategies based on restriction/ligation [Structural genomics projects generally start from target selection, which is based on evaluation of a large amount of candidate genes ligation , 51, ligligation , 53, or ligation , 55. Amoligation .E. coli as expression host. Autoinduction is based on a defined medium containing glycerol, glucose and lactose as inducer, which makes use of promoters containing lac operators. Glucose prevents induction by lactose until it is consumed. Upon glucose depletion in the culture, lactose is metabolized and heterologous induction occurs by means of the lac operator. Autoinduction thus simplifies the expression procedure: it alleviates the requirement for monitoring the density of cell cultures, as glucose depletion auto-regulates the time of induction. Further, auto-induction does not require the addition of inducer chemicals facilitating means for automation [Autoinduction procedures were found to be particularly useful for automated high-throughput approaches for expression of the target specimens in tomation .in vivo expression in structural genomics pipelines due to several advantages [Increasingly, cell-free (CF) protein synthesis methods emerge as a viable alternative to vantages . Proteinvantages . CF exprvantages , 57. CF vantages , and mayvantages .Many particularly exciting targets in the proteome will require expression in eukaryotic systems. Baculovirus expression vector systems (BEVS) increasingly become the method of choice for many of these targets. While considerable effort is being invested into automation and high-throughput protein expression by using BEVS -63, contHierarchal multiplex expression and purification strategies utilized by the core Protein Production Platform of the Northeast Structural Genomics Consortium (NESG), foster an increase in the production of protein samples and also the solution of many 3-D protein structures . InitiatStructural genomics has decisively accelerated automation and the development of robust high-throughput methods. Nonetheless, critics claim that structural genomics consortia have gone after the “low-hanging fruit”, such as soluble single proteins of prokaryotic origin which are comparatively easy to express and purify . ActuallE. coli still remains to date the expression system of choice in most laboratories. Nonetheless, eukaryotic expression is also being implemented for production of samples that can not be produced in E. coli. In particular the baculovirus/insect cell system has been streamlined significantly, and detailed protocols have become available that considerably simplify handling, thus alleviating some of the uncertainties regarding this system that impeded its routine application by non-specialist users [The interactome can not be rationalized on the basis of elucidating single protein structures. It is now increasingly clear that the proteins in the cell function as interlocking machines containing ten or more interaction partners, that associate stably or transiently to realize cellular activities . Structust users , 73, 74.2)). MultiBac uses an engineered deletion baculovirus with improved protein production properties including reduced proteolysis and a delayed onset of cell fragmentation in the late phase of viral infection [E. coli cells containing also a small plasmid with four genes encoding for the Tn7 transposon, similar to the widely utilized Bac-to-Bac system from Invitrogen, and essentially all other baculovirus systems that rely on Tn7 transposition of a transfer vector in vivo in an E. coli host strain.Our laboratory has contributed to some of these developments, with particular focus on the production of multicomponent protein complexes for structural biology applications. We are interested in the structural molecular biology of eukaryotic complexes. For recombinant overproduction of these complexes, a system for multiprotein expression in insect cells, called MultiBac, was introduced , 73 ).The transfer vectors that we developed for MultiBac contained elements that made it particularly straight forward to arrange into multigene expression cassettes several to many expression units containing ORFs encoding for example for members of a protein complex of choice. One transfer vector was designed to provide these multigene cassettes between Tn7L and Tn7R DNA sequences for integration into the Tn7 site of the MultiBac baculovirus. A second transfer vector contained a LoxP sequence thus enabling integration of multigene cassettes into the LoxP site of the MultiBac virus in the presence of plasmids . SelectiThe MultiBac system as conceived in 2004 was surprisingly well received in the community, probably indicating the present and growing interest in researching eukaryotic interactomes and multiprotein complexes. Many laboratories requested the MultiBac reagents, many proteins were expressed, and X-ray crystal structures based on specimens produced by MultiBac are now being reported , 76. IntE. coli.In our view, the genuinely useful contribution in conjunction with MultiBac, was not only the creation of yet another baculovirus and a few transfer vectors. We had realized in the process of our experimental work that the parameters of virus generation are not really compatible with routine application of an expression method in laboratories focusing on structural analysis. Baculovirus expression is constrained by certain requirements that need to be met to assure that the recombinant DNA cargo is properly maintained in the baculoviral genome during virus amplification and eventually protein production -81. We fThe combination of many genes encoding for subunits of a protein complex into vectors used for expression will remain a rather laborious task, in particular if it relies on restriction digestion and pasting together of DNA fragments by ligase in a serial, one-gene-at-a-time mode. This approach is essentially refractory to automation. Structural genomics consortia have strived to address the problem by implementing recombination methods for gene insertion. These methods have the advantage that they always use the very same reagents and reaction conditions, and therefore can be scripted into a robotics routine. The emphasis of most systems currently was mainly placed on offering a multitude of expression options for the one ORF of choice. For instance, the Gateway system from Invitrogen, defines an Entry vector for the gene of interest, which is inserted by any suitable means. This Entry vector is then used to introduce this gene into a wide range of Destination vectors providing a large assortment of purification or solubility tags for expression in a variety of hosts. The situation presents itself in reverse for multiprotein complex expression: here, the challenge is to introduce an assortment of genes into probably one expression system of choice to start with. This needs to be achieved in a way that ideally, the genes encoding for the multiprotein complex to be studied can not only be assembled fairly easily, but also options need to be provided to modify the individual subunit components rapidly and in a flexible way by mutation, truncation or replacing of affinity tags. Already for single proteins, altering the wild-type sequence for example by removing low complexity regions is often a prerequisite for successful high-resolution structural analysis, and introducing mutations is commonplace for elucidating the function and activity. This is equally valid for multiprotein complexes, however, the tasks at hand are considerably more complicated to achieve as the number of interacting subunits increases.3)). For matters of simplicity, we first created ACEMBL in a version suitable for multiprotein complex production in E. coli as an expression host, although, the same robotic scripts can likewise be applied for generating multigene constructs for protein complex expression in eukaryotic hosts. We decided to consequently adapt recombination methods at every step of the process of gene insertion and gene combination into multigene expression cassettes, and to implement already existing, robust robotics protocols for small scale expression and protein extraction by using affinity purification [These deliberations and underlying experimental necessities prompted us recently to introduce ACEMBL, an automatable system for multiprotein expression making use of multigene recombineering by using a robot , 83  and each plasmid contains the LoxP inverted imperfect repeat. Donors contain a conditional origin of replication which makes their existence and propagation in regular cloning and expression strains dependent on Cre-LoxP mediated fusion with Acceptors, which in turn have a regular origin of replication derived from the classical ColE1 origin.Building on our positive experiences using E. coli machinery upon transformation with the annealed DNAs. We found that efficient procedures could be established for integrating single genes or polycistrons into the ACEMBL Donors and Acceptors by SLIC, and scripted into robust routines, which could be readily carried out by a robot [We settled on sequence and ligation independent cloning (SLIC) as the method of choice for inserting genes into Donors and Acceptors, as detailed protocols for this methods became available recently . Nonethe a robot . Gene in a robot .Cre recombinase and the LoxP site present on each vector. Acceptors like Donors can contain one or several genes, polycistrons or a combination thereof. Several Donors can be fused with each Acceptor. Selection for multiple resistance, each of these characteristic for one Donor or one Acceptor, then identifies the Donor-Acceptor fusions in a combinatorial fashion. By using this approach, we could easily generate in a single reaction a series of multigene expression vectors expressing protein complexes as well as all possible combinations of genes contained on the individual vectors, revealing subcomplexes [Cre recombinase to previously generated Donor-Acceptor fusions. This is possible due to the equilibrium reached between the fusion and excision activities of the Cre enzyme. Thus, defined parts of a multigene construct, encoding for subunits of a protein complex, can be excised by our procedure, altered for example by truncation, mutation, or replacement of the encoding genes, and then reintegrated into the multigene expression construct of choice by applying Cre fusion. This provides useful combinatorial options, also for robotics applications [E. coli, a large prokaryotic translocation complex consisting of six transmembrane proteins, from a 16 kb multigene plasmid [Donors thus charged with recombinant DNA cargo, each containing single genes, polycistrons or multiple expression cassettes, are then fused with one Acceptor by using omplexes . Interesications . By empl plasmid .Genome and proteome-wide studies have clearly revealed the key role of macromolecular complexes in most, if not all vital cellular processes. Protein complexes display activities that are entirely different from the activities of each subunit studied independently, as interaction partners often dramatically influence recognition propensities and likewise biological activities. In addition, protein complex composition in particular in higher eukaryotes can depend on tissue type and cell state. Importantly, covalent posttranslational modifications such as phosphorylation, acetylation, methylation and many others can have a critical impact on the formation of protein complexes and their activity. Due to all of the variables that need to be controlled when attempting to assemble protein complexes recombinantly, it is important to have a robust system that allows rapid testing of many different constructs.In the current environment, in which valuable information about interactomes, complexomes and other genome-wide studies is pouring in at an ever increasing pace, structural biology as it is performed to date simply cannot keep up with the increasing demand for the validation that only 3-D structures can provide. Protein structures can offer insights into the details of a protein interaction at the molecular or near-atomic level, and it is imperative for structural biologists to move into the arena of protein complex interactions. Despite recent colossal efforts in obtaining 3-D structures at near atomic resolution by X-ray crystallography, greatly fostered by structural genomics consortia, obtaining diffraction quality crystals of protein complexes remains a significant challenge and often takes on the order of years achieve. This technological state-of-the-art is simply incompatible with the speed at which new data is accumulated through high-throughput research addressing the interactome, and a major effort towards the development of new technologies is urgently required to close this gap.3-D structural information can be gained from purified material extracted in small amounts from native source by electron-microscopic techniques which have significantly matured in recent years -87. In pUndoubtedly, great benefit could be derived from the development of advanced techniques and reproducible protocols for micropurification of endogenous complexes. Purification of protein from biological material present in limited amounts will certainly be necessary in particular for the identification of complexes, or variations of complexes, that are present in specialized cells or specific tissues, and for a thorough validation of interactome data. This requires highly efficient methods to recover the quantities of protein required for biophysical methods. Due to the considerable increase in sensitivity of mass spectrometers achieved in recent years, it is now possible to routinely identify subunits of protein complexes from pico- to femto-mole quantities of material. It is critically important now to develop new strategies for the micropurification of protein complexes that will allow the simultaneous processing of several samples from limited amounts of source material. Such micropurification techniques, in conjunction with process automation for endogenous sample preparation will decisively improve current research approaches both in terms of throughput and also quality of analysis. Size-exclusion chromatography (SEC) is often a rate limiting step in the preparation of protein complexes. New purification strategies involving native gels, capillary electrophoresis or absorption onto membranes could possibly mature into genuine alternatives to SEC, thus allowing parallel processing of many samples and increasing sample homogeneity.Recombinant expression most certainly had a decisive impact on life science research, and is to date the major technique for successful production of well-defined macromolecular specimens in the quality and quantity required for many applications. Apart from notable examples such as ribosomes or RNA polymerase -91, nearA further consideration in protein complex biology are those complexes that contain protein subunits as well as RNA components which may need to be co-expressed for proper complex assembly and folding. Protein-RNA complexes such as telomerase, snRNPs or RNAi containing complexes are a focus of contemporary research efforts aimed at elucidating mechanisms of health and disease. The recent 3-D structure of a human spliceosomal U1 snRNP compellingly demonstrates the power of recombinant reconstitution of such a complex for structure elucidation . TechnolAutomation is essential for accelerating contemporary protein science. Automation depends on standardization and simplification of protocols that are robust and reproducible. These requirements must be addressed by the development of easy-to-use, affordable reagents that are ideally compatible with robotic procedures. Automation has already had a considerable impact on cloning, DNA preparation, protein purification by affinity tags and assaying protein activities. Protocols optimized for automation have at times superseded earlier, more laborious procedures even in laboratories not applying robots routinely, as manual procedures generally also benefit considerably from the standardization and robustness inherently required for methods that can be used by robots. Automation will be particularly important for reconstitution of macromolecular complexes by heterologous multigene expression as probably a large number of constructs will need to be tested for many cases until a satisfactory reconstitution is achieved, yielding specimens suitable for detailed studies. The number of possible combinations increases dramatically with the number of subunits. This is particularly true if the pipeline is geared towards X-ray crystallography.In single crystal structure determination by X-ray diffraction, a vital prerequisite is the ability of a specimen to arrange into a highly ordered crystal lattice that diffracts the incident X-ray radiation to near-atomic resolution. Often, this challenge can only be met by introducing variation into the wild-type sequence until a crystallizable specimen is obtained. Limited proteolysis, in conjunction with mass spectrometry, has been particularly useful for defining regions of low-complexity that can often interfere with crystallization. Such regions are then typically removed by introducing truncations or deletions in encoding DNA sequences, and recombinant overexpression of the resulting variant can then result in sample more amenable to crystallization. Corresponding procedures are now being introduced in more elaborate structural genomics pipelines. Nonetheless, it is clear that implementing such limited proteolysis procedures, often already laborious for single proteins, will be vastly more complicated when several to many ORFs need to be diversified concomitantly in a multiprotein complex. Recent advances in mass spectrometry, including quantitative, multiplexed techniques , 94 may High-resolution structure determination, in particular by X-ray crystallography, has developed into an indispensable technology which can be readily applied to elucidate molecular function in near-atomic detail. While the field of X-ray crystallography has achieved considerable advancements in recent decades, namely in the design of automated crystallization platforms, robotics and greater access to high-brilliance synchrotron radiation sources, there is still a considerable distance to be covered before X-ray crystallography can tackle the number of challenges presented by interactome wide studies and complexomics. Miniaturization and standardization are now indispensable components of high-throughput crystallization platforms. High-throughout methods will continue to provide many exciting possibilities for crystallization experiments aided by the arrival of technologies requiring unprecedented small amounts of sample for screening a very large space of crystallization conditions. Structural genomics consortia have played an indispensable role by installing automated pipelines for solving 3-D structures of individual proteins and protein domains. The discovery of a vast plethora of multicomponent assemblies that form the interactome, their modifications, overlaps and variations poses a challenge for similar efforts that may appear seemingly unmanageable at the moment. What is now required is a concerted effort to advance current technologies as well as to develop and implement new methods and procedures for addressing the complexome of organisms.


Extracting and visualizing of protein-protein interaction (PPI) from text literatures are a meaningful topic in protein science. It assists the identification of interactions among proteins. There is a lack of tools to extract PPI, visualize and classify the results.We developed a PPI search system, termed PPLook, which automatically extracts and visualizes protein-protein interaction (PPI) from text. Given a query protein name, PPLook can search a dataset for other proteins interacting with it by using a keywords dictionary pattern-matching algorithm, and display the topological parameters, such as the number of nodes, edges, and connected components. The visualization component of PPLook enables us to view the interaction relationship among the proteins in a three-dimensional space based on the OpenGL graphics interface technology. PPLook can also provide the functions of selecting protein semantic class, counting the number of semantic class proteins which interact with query protein, counting the literature number of articles appearing the interaction relationship about the query protein. Moreover, PPLook provides heterogeneous search and a user-friendly graphical interface.http://meta.usc.edu/softs/PPLook.PPLook is an effective tool for biologists and biosystem developers who need to access PPI information from the literature. PPLook is freely available for non-commercial users at  It goes without saying that manual extraction is both error-prone and time consuming. Moreover, it becomes potentially impossible to manually extract PPI information in a high throughput manner. Therefore, the quick and easy extraction and visualization of PPI relationships from biomedical text is an attractive research goal.Protein-protein interactions (PPI) execute many critical biological activities, including signal transduction and metabolic activity. Understanding these interactions can help in disease diagnosis, prevention and treatment. Although many efforts have been made to create databases that store the verified PPI information in a structured form, much PPI interaction information still remains unmined as unstructured text. While biomedical literature databases, such as MEDLINE databases Thus far, more than 19 million citations of articles, journals, books and technical reports are available in the MEDLINE database. Many other databases, such as the DIP , IntAct Several international systems now exist which can analyze literature databases  to provide users with a summary of relevant biological information services -12, suchIn this paper, we introduce a new PPI extraction tool, PPLook, which is based on the OpenGL graphics interface technology and uses a keywords dictionary pattern matching approach  as its cPPLook is a four-tier information extraction system based on a full-sentence parsing approach. Conceptually, it contains four modules: (i) submission authentication which aims to identify whether the query word is a protein name or not; (ii) an article parser which acts as a collector to pick up sentences containing the query protein name; (iii) a full-sentence parser which determines the PPI by keywords dictionary pattern-matching and (iv) PPI visualization which displays the PPI in the form of a 3-D graph. Figure http://www-tsujii.is.s.u-tokyo.ac.jp/GENIA/home/wiki.cgi?page=GENIA+corpus.Annotated corpora are important to the development and evaluation of protein-protein extraction systems, and there are several available annotated corpora. As our simulation dataset, we chose the GENIA V3.02 Corpus , which chttp://www-tsujii.is.s.u-tokyo.ac.jp/GENIA/home/wiki.cgi?page=GENIA+corpus.Before extracting PPI information, we need to verify whether the input words represent a legitimate protein name. The protein name authentication module uses the GENIA Tagger tool to verify the input words . A two-wGENIA Corpus is an XML-tagged text, which has been structured and annotated. Based on rules of the XML editor, an XML text parser was developed to read out the protein names and protein catalog.Parsing occurs in two steps. In the first step, the article parser checks whether the dataset contains the given protein. In this procedure, the GENIA Tagger tool  is used In the second step, the full-sentence parser identifies PPI from documents classified by the article parser. In order to find more and exact PPI patterns, we carried out statistical analysis of word frequency with our developed statistical tool of word frequency based on the statistical principles related to the verb.Using the rules of pattern matching and part-of-speech , we testAccording to the taxonomy of GENIA Ontology, some entities (proteins) involved in reactions were classified semantically for the GENIA corpus. We adopted this semantic classification to the PPLook system. After the users select the protein semantic class which the users want to search interacting with a query protein, the PPLook can give the count number of how many this semantic class proteins interact with the query protein. In addition, the PPLook can also give the literature count number of the articles appearing the interacting relationship about the query protein. The literature count number supports the reliability of search results. If the literature count number is bigger, the confidence of this PPI is higher.PPLook system employs OpenGL technology for PPI visualization. The OpenGL graphics system is hardware for the GL graphics library software interface ,21. It pFigure Using OpenGL programming, PPI results returned are displayed in 3-D style. The user can either zoom in/out or rotate the PPI network in the main window. The user can also change the size and the color of balls or the length and color of lines according to their needs.Assume that A is the first query protein. In this case, PPLook provides a circular extraction function that can help users extract PPI information of another protein B which interacts with protein A based on the outlink to B within the first query. The user just needs to click on protein B listed on the right side output window. The PPI information of protein B will immediately return on the main window.Normally, users not only want to know PPI information of interest proteins, but also related information about the query protein, such as structures and published articles.In the PPLook system, we developed heterogeneous search engines which include PPI search engine, PDB search engine and Google search engine. Users can get a unique answer that satisfies conjunctive queries where each query can be routed to a specialized engine. By heterogeneous search engineer, PPLook enables cross-references when users require PPI information, protein structures and published articles simultaneously. Otherwise, users have to use several specialized search engines to get what they want. Figure PPLook provides many kinds of output data in a format that meets users' needs. The interaction protein names and corresponding literature appeared in the results can be exported as text files. The 3-D PPI network can also be saved in a design format (PPLook format) or in bitmap format. All PPI information can be printed whenever necessary.The recall, precision and F value for assessment of the PPLook tool are respectively defined as:TP is the number of PPI extracted correctly by PPLook, TP+FN is the number of PPI in the dataset, TP+FP is the number of PPI retrieved by PPLook.Where To evaluate the reliability of PPLook tool, we used the following search query on the PubMed web interface: "Humans" [MeSH] AND "Blood Cells" [MeSH] AND "Transcription Factors" [MeSH] to retrieve the corresponding records from MEDLINE database, then constructed a test dataset which concludes 415 abstracts. Based on interacting domain, gene compression profile and gene ontology (GO) annotation, we annotated the test dataset and identified the PPI by hand, and the annotated dataset which concludes 116 abstracts is shown in Additional file In this paper, we introduced a useful tool, PPLook, which uses an improved keywords dictionary pattern-matching algorithm to extract protein-protein interaction information from biomedical literature. Based on the OpenGL graphics interface technology, visual methods were adopted to show the results of protein-protein interaction in three dimensional stereoscopic displays. PPLook can also provide users with more interactive features, such as the count of the semantic class protein and the number of articles appearing PPI information of query protein, the MEDLINE access number, title and abstract of related literature, as well as integrated resources and heterogeneous search functions.Prospectively, PPLook will add more functions that include extracting PPI information for users submitting text articles or constructing complex PPI networks, both directed and undirected. In addition, based on the new MEDELINE database, we will develop a more complete XML-tagged text corpus in order to enhance the robustness of PPLook's search results.http://meta.usc.edu/softs/PPLook.PPLook is freely available for non-commercial users at Operating system(s): Windows  32-bit, 64-bit, requires .NET Framework version 2.0.Hardware requirements/recommendations: Pentium III or later for Windows, Color display capable of 1024 X 768 pixel resolution.Programming language: C++SWZ developed and implemented the tool and drafted the manuscript. YJL designed the software module, implemented and tested the codes. LX and QP participated in design and implementation. All authors read and approved the final manuscript.An annotated PPI dataset.Click here for file