Proteins interact with other proteins or biomolecules in complexes to perform cellular functions. Existing protein-protein interaction (PPI) databases and protein complex databases for human proteins are not organized to provide protein complex information or facilitate the discovery of novel subunits. Data integration of PPIs focused specifically on protein complexes, subunits, and their functions. Predicted candidate complexes or subunits are also important for experimental biologists.Based on integrated PPI data and literature, we have developed a human protein complex database with a complex quality index (PCDq), which includes both known and predicted complexes and subunits. We integrated six PPI data , and predicted human protein complexes by finding densely connected regions in the PPI networks. They were curated with the literature so that missing proteins were complemented and some complexes were merged, resulting in 1,264 complexes comprising 9,268 proteins with 32,198 PPIs. The evidence level of each subunit was assigned as a categorical variable. This indicated whether it was a known subunit, and a specific function was inferable from sequence or network analysis. To summarize the categories of all the subunits in a complex, we devised a complex quality index (CQI) and assigned it to each complex. We examined the proportion of consistency of Gene Ontology (GO) terms among protein subunits of a complex. Next, we compared the expression profiles of the corresponding genes and found that many proteins in larger complexes tend to be expressed cooperatively at the transcript level. The proportion of duplicated genes in a complex was evaluated. Finally, we identified 78 hypothetical proteins that were annotated as subunits of 82 complexes, which included known complexes. Of these hypothetical proteins, after our prediction had been made, four were reported to be actual subunits of the assigned protein complexes.http://h-invitational.jp/hinv/pcdq/.We constructed a new protein complex database PCDq including both predicted and curated human protein complexes. CQI is a useful source of experimentally confirmed information about protein complexes and subunits. The predicted protein complexes can provide functional clues about hypothetical proteins. PCDq is freely available at  Proteins interact with other proteins or biomolecules to perform their functions, and protein complexes are the fundamental functional units of these macromolecular systems. Comprehensive analysis of PPIs provides a valuable framework for understanding the protein functions required for various biological processes in cells. Moreover, it can provide annotation clues about proteins with unknown function -3.An important issue for the elucidation of the functional organization of the proteome is the extraction of information about protein complex formation and function from the PPI network.http://genomenetwork.nig.ac.jp/index_e.html, NOT http://genomenetwork.nig.ac.jp/ ). In the present PPI data, the main focuses are on protein-binding partners or binary protein interactions. Knowledge about how gene products form complexes, interactions among complexes, or protein interconnectivity in a complex is still scarce. The overlap of PPI data entities across databases is relatively low. The existence of only a partial map of the whole interactome space limits the broad application of systems modeling. Accordingly, it is essential to integrate PPI data in order to fill in as many holes in the interactome space as possible. Some integration of the above PPI data has been conducted by STRING .[category II proteins].[category III proteins]/[Total number of proteins in a predicted complex]." For example, if the CQI of a complex is "5.2.1/8," the complex has eight subunits with five, two, and one protein classified into categories I, II, and III, respectively.The predicted complexes were named based on scientific names from the literature, if the majority of proteins in a complex were common to a known complex and a name  for the complex was available; however, we used artificial descriptions using concatenated gene symbols when not all symbols of proteins were available . Descriptions of complexes were quoted from references with their PubMed IDs. Functional categories and subcellular localizations were added if the descriptions were available in the literature.The visualization tool PPI-Map in PCDq can show protein interconnectivity of a complex, complex-external protein interactions, and CCIs. To the best of our knowledge, PPI view is the first database that can show CCIs in the human interactome with detailed annotation. As shown in Figure The novel human protein complex database, called PCDq, provides three main views: protein complex information in the "protein complex view," integrative overview of a PPI in the "PPI view," and network information including both PPI and CCIs in "PPI-Map." The complex view provides names, functions, protein subunits, subunit roles, and CQL. PPI view provides PPI partners for a specified protein. Finally the new visualization tool PPI-Map allows users to visualize protein interactions graphically: not only PPIs among the protein subunits but also CCIs, via a seamless and detailed annotation of each protein complex and its subunits. These three views have hyperlinks to one other and also to transcript/locus/protein views of the H-InvDB human gene/transcript/protein database. Considering all of these features, PCDq is a useful platform for understanding protein function from the viewpoint of protein complexes as another important functional level, as well as their interactions. The CQI provides unique and reliable clues for inferring some roles of proteins whose functions are unknown.In total, we predicted and annotated 1,264 protein complexes. A list of all annotated complexes is available at the PCDq site. Category I contained 2,106, category II 299, and category III 3,273 proteins, with protein subunit sharing allowed  for the assessment. To construct the manually curated set of protein complexes (PCset2), we collected only category I proteins from perfectly or partially matched complexes (these complexes were defined in the subsection "Statistics of PCDq") and discarded category II or III proteins, which have not been described as subunits of a complex in the literature. PCset2 contained 541 complexes.First, we estimated the enrichment of some GO terms in a complex compared to GO terms assigned to the proteome. The proteome set comprised 36,073 proteins, each derived from a distinct locus or gene of H-InvDB. The enrichment of GO terms was examined against two sets of protein complexes, PCset1 and PCset2. Significance of enrichment of a given GO term in a complex was tested by one-sided Fisher exact test for a 2 × 2 contingency table . "A" represents the number of subunits expressing the given GO term, and "B" is the number of subunits not having the GO term in the protein complex. "C" and "D" represent the corresponding numbers estimated for the entire proteome.To estimate the quality of protein complex annotation, we defined another quality index, the "GO consistency index." This index for a given protein complex is estimated by the following equation:cons is the number of edges that connect two proteins sharing the same GO term and Nall is the number of possible combinations (edges) for all subunits of the complex.where NIt was observed that 450 of 1,264 PCset1 (35.6%) protein complexes had one or more enriched GO term . In contrast, 254 of the 541 PCset2 complexes (47%) had one or more enriched GO term. The ratio of protein complexes having enriched GO terms was greater in PCset2 than in PCset1, suggesting that the reliability of protein complex annotation was refined by manual checking.t test, p-value 2.9E-111) than in the random set, and in PCset2 than in PCset1 . These results are still statistically significant after Bonferroni multiple-testing adjustment, which is relatively conservative. The histogram of consistency indexes for the three sets is shown in Figure The degree of consistency of GO terms among subunits in a complex was estimated; i.e., the homogeneity of GO terms assigned to complex subunits. A consistency index  was used as an indicator of homogeneity. With the object of estimating the degree of GO term consistency expected by chance, 100 sets of randomly selected genes from H-InvDB, all representative transcripts with complex sizes matching our annotation of PCset1, were created and used as a control. Average consistency indexes were estimated to be 0.23, 0.41, and 0.04 for protein complexes of PCset1, PCset2, and the random set, respectively. The value is higher in PCset1  of subunits in the complex, we randomly selected k-genes from genes having expression profiles. We then calculated the averages of the cosines of the expression profiles. We repeated the procedure 100,000 times for every number of subunits (k), and used the results for p-value estimation.Expression profiles of 729 complexes were obtained from the Human Anatomic Gene Expression Library (H-ANGEL) , the satOf 729 complexes, seven were found to have significant gene expression similarity by a false discovery rate (FDR) criterion of 0.05. FDR, the expected proportion of incorrectly rejected null hypotheses, is a widely used statistic for multiple testing . The sevSome of the most interesting complexes are those in which the expression of the protein subunits is similar and tissue specific. We found several such complexes using entropy of gene expression profile. Among these complexes, the fibrinogen complex  was such a case. Other examples are the AK5-CPNE6-TRIM46 complex (complex 540) and the troponin complex (complex 258). Though the FDRs of the two complexes were not significant, 0.22 and 0.68, respectively, the gene expression profiles were very similar with cosines of 0.99 and 0.95, respectively. For troponin, the gene expression of the subunits is specific to that of muscle/heart tissue (average entropy 1.12). The gene expression profiles of the three subunits in troponin complex are shown in Figure As shown above, the gene expression of the protein subunits was not significantly similar in most of the predicted protein complexes. However, we found that the gene expression of protein subunits is more likely to be similar for large complexes.We calculated the p-values of gene expression similarities for each complex and plotted the distribution of p-values for different numbers of proteins in a complex Figure . The figTo investigate the contribution of gene duplication to the establishment of protein complexes, we examined portions of duplicated genes (proteins) or paralogs in the complexes.For all combinations of subunits in a protein complex, we evaluated whether the genes were paralogous  following the method of Gu et al. . Gene moThis paralog assignment method yielded 2,353 duplicated genes in a total of 4,191 genes that were the components of 1,264 complexes. Of the 1,264 complexes, 336 (26.5%) were judged to have at least one paralog pair. Moreover, we obtained 218 complexes (17.2%) in which more than half of the components were judged to be paralogous to another gene in the same complex. Using a less stringent method with BLASTP , these percentages were estimated to be 38.5% and 27.3%, respectively.The replication factor C (RFC) complex (complex 105) is a good example of the formation of a protein complex induced by gene duplication. This complex consists of five RFC subunits and one binding partner, PCNA . The comAn important goal of proteomics is functional assignment for proteins that cannot be annotated by homology alone. Several approaches for functional assignment from PPIs have been developed -3.First, we explain the definition of proteins with no functional assignments, known as "hypothetical proteins." H-InvDB proteins were analyzed with standardized functional annotation by curators who classified the proteins into several categories: i) identical to known human proteins, ii) similar to known proteins (having 50% sequence similarity), iii) interPro-domain-containing proteins, and iv) hypothetical proteins . The "hypothetical proteins" discussed here are of the fourth category.Next, we explain how the functions of those hypothetical proteins can be inferred. In PDBq we found 78 hypothetical proteins (as defined in H-InvDB) in the 82 predicted complexes. Although the majority  were subunits of 67 hypothetical complexes (none of their subunits were reported as complexes in the literature), 13 hypothetical proteins were subunits of 12 complexes whose functions were strongly deduced because at least half of their subunits were annotated as common to known complexes. A protein complex is thought to be a functional unit in which proteins combine to perform biological functions; accordingly, a hypothetical protein can be assigned a function related to that of the complex it joins. For example, two hypothetical proteins HIP000013164 and HIP000053526 were in the "dREAM complex" (complex 24), which is tightly bound to E2F-regulated promoters in G0 and dissociates from these promoters in the S phase of the cell cycle. In addition, some subunits of the complex can also interact specifically with MYB and may be involved in expression of MYB-dependent genes important in G2/M progression . We expeAfter annotation, we found that some of the hypothetical proteins were reported in the literature as actual protein subunits Table . The resPCDq comprises both known and predicted complexes and subunits. The evidence level for each subunit was also determined and summarized as a complex quality index (CQI) for each protein complex.The expected users of PCDq are both experimental biologists and computational scientists. Biologists can seek candidate protein subunits for known or unknown protein complexes and review the information  about a protein complex. Computational scientists can collect integrated PPI network datasets with various levels of reliability using original annotation in the form of protein categories and CQIs. Thus, for users who would like to develop a method for protein complex prediction, PCDq provides different thresholds for dataset assembly using CQI.http://h-invitational.jp/hinv/dataset/download.cgi, see "Results of computational analysis").Users can download the dataset of PCDq, including protein complex list, their subunits (members), and related functional annotation from the H-InvDB download page  than in all the predicted complexes (PCset1) or the random set, indicating the relatively high quality of manual annotation and the advantage of protein complex prediction followed by manual annotation as opposed to only single computational prediction.Next, for each complex, we compared the expression profiles of the protein subunits in the complex based on the idea that proteins in the same complex would have similar functions and that coexpressed genes are more likely to have similar functions. The result showed that the subunits of large complexes tend to be expressed similarly. The ratio of duplicated genes to all the proteins in a complex was evaluated, and the results indicated that the enlargement of a protein complex is mainly mediated by homologous interactions and that gene duplication events markedly contribute to the establishment of protein complexes.Recent statistics of H-InvDB proteins show that 35% of H-InvDB representative transcripts are hypothetical proteins. Assigning functions to hypothetical proteins of unknown function is one of the most important issues in proteome analysis. Since subunits of a complex generally tend to have the same biological function, prediction of a protein complex allows increased confidence in the annotation of hypothetical proteins. After the construction of PCDq by protein complex prediction and annotation, we found that 78 hypothetical proteins were contained in the 82 predicted complexes. Of these 78, 13 were subunits of 12 functionally annotatable complexes. These hypothetical proteins are probably involved in biological processes shared by other subunits of their complexes. Thus complex prediction gives us some clues for inferring their functions. For example, it is suggested that the hypothetical proteins HIP000013164 and HIP000053526 in the dREAM complex function in the cell cycle, and that HIP000177716 (FA core complex), HIP000079962 (INO80 complex), and HIP000024165 (Lamins complex) function in DNA repair, DNA repair and transcription, and nuclear organization, respectively. The remaining eight hypothetical proteins that could be assigned functions are summarized in Table We predicted and annotated 1,264 human protein complexes from integrated PPI data. GO analysis increased the reliability of both complex prediction and manual annotation. The analysis of expression profiles and duplicated genes made it clear that protein subunits tend to be expressed similarly and are mutually paralogous within complexes. Comprehensive protein complex prediction and annotation will provide strong functional annotation clues about hypothetical proteins. We constructed a new human protein complex database with quality index (PCDq) to provide this comprehensive annotation of human protein complexes.http://h-invitational.jp/hinv/pcdq/.PCDq is freely available at the URL BIND: (Biomolecular Interaction Network Database); BLAST: ; CAGE: ; CCI: (Complex-Complex Interaction); cDNA: (Complementary DNA); CQI: ; DIP: (Database of Interacting Proteins); EST: (Expressed Sequence Tag); FDR: ; GNP: (Genome Network Project); GO: (Gene Ontology); H-ANGEL: (Human Anatomic Gene Expression Library); H-InvDB: ; HPRD: (Human Protein Reference Database); iAFLP: (introduced Amplified Fragment Length Polymorphism); MINT: (Molecular INTeraction database); ORF: (Open Reading Frame); PCDq: ; PDB: (Protein Data Bank); PPI: (Protein-Protein Interaction); PSI-MI: (Proteomics Standards Initiative Molecular Interaction Standard); RFC: (Replication Factor C); ROC: (Receiver Operating Characteristic); XML: (Extensible Markup Language).The authors declare that they have no competing financial interests.The first three authors contributed equally to this study. SK and KN initially designed the study, collected and integrated PPI data, predicted complexes by DPClus, developed the complex-annotation system PCDq and PPI-Map, and wrote the manuscript. KM joined designing of the study, analyzed gene expression profiles, arranged PCDq, updated data especially newly found functions of previously hypothetical proteins, and wrote the manuscript. YS was responsible for the analysis of GO terms consistency in complexes and the contribution of gene duplication to complexes. MS directed a collaborative activity for the manual annotation of complexes. AA and SK developed and improved DPClus. TI participated in its coordination and helped to draft the manuscript. All authors read and approved the final manuscript.


Identification of protein complexes and functional modules from protein-protein interaction (PPI) networks is crucial to understanding the principles of cellular organization and predicting protein functions. In the past few years, many computational methods have been proposed. However, most of them considered the PPI networks as static graphs and overlooked the dynamics inherent within these networks. Moreover, few of them can distinguish between protein complexes and functional modules.In this paper, a new framework is proposed to distinguish between protein complexes and functional modules by integrating gene expression data into protein-protein interaction (PPI) data. A series of time-sequenced subnetworks (TSNs) is constructed according to the time that the interactions were activated. The algorithm TSN-PCD was then developed to identify protein complexes from these TSNs. As protein complexes are significantly related to functional modules, a new algorithm DFM-CIN is proposed to discover functional modules based on the identified complexes. The experimental results show that the combination of temporal gene expression data with PPI data contributes to identifying protein complexes more precisely. A quantitative comparison based on f-measure reveals that our algorithm TSN-PCD outperforms the other previous protein complex discovery algorithms. Furthermore, we evaluate the identified functional modules by using “Biological Process” annotated in GO (Gene Ontology). The validation shows that the identified functional modules are statistically significant in terms of “Biological Process”. More importantly, the relationship between protein complexes and functional modules are studied.http://netlab.csu.edu.cn/bioinfomatics/limin/DFM-CIN/index.html.The proposed framework based on the integration of PPI data and gene expression data makes it possible to identify protein complexes and functional modules more effectively. Moveover, the proposed new framework and algorithms can distinguish between protein complexes and functional modules. Our findings suggest that functional modules are closely related to protein complexes and a functional module may consist of one or multiple protein complexes. The program is available at  Recent advances in biotechnology have resulted in a large amounts of protein-protein interaction (PPI) data. Modeling and clustering PPI networks with simple graphs makes it possible for us to understand the basic components and organization of cell machinery from the network level. One of While significant progress has been made in computational methods, there are two major challenges in clustering PPI networks. One of the challenges is that the conventional clustering methods generally considered the PPI network as a static graph and overlooked the dynamics inherent within these networks. This is mainly because that the widely used large-scale technologies for determining PPIs, such as yeast two-hybrid and TAP-MS, do not provide spatial, temporal or contextual information for the predicted PPIs . In factJansen et al.  first inAnother challenge in clustering PPI networks is how to distinguish between protein complexes and functional modules. Up to now, little progress has been made on this point. Most clustering methods based on PPI networks detected both protein complexes and functional modules without distinguishing between them because they disregard interaction dynamics. How closely are functional modules related to protein complexes? What are the differences between them? Spirin and Mirny have argued their differences from the concepts that protein complexes are groups of proteins interacting with each other at the same time, and functional modules, by contrast, are groups of proteins participating in a particular cellular process while binding to each other at a different times. Though S.cerevisiae, we found that many proteins were found in a multitude of complexes rather than a single complex. Here, we would like to ask whether two protein complexes interact with each other through their common proteins. Moreover, what is the underlying machine between protein complexes and functional modules. To answer these questions, we constructed a complex-complex interaction network and proposed an algorithm, DFM-CIN, for detecting functional modules from it.In this paper, we will not go as far as what the conventional clustering algorithms have focused on but rather try to propose a framework to detect and distinguish between protein complexes and functional modules. In other words, we will not only explore protein complexes and functional modules but also study their relationships. Considering the fact that proteins in a complex interact with each other at the same time, we constructed a serial of time-sequenced subnetworks by integrating gene expression data into PPI data. These time-sequenced subnetworks show dynamic changes in the original network. Thus, we call these time-sequenced subnetworks together as a dynamic PPI network. An improved algorithm TSN-PCD, developed from our previous algorithm HC-PIN, is propf -measure reveals that our algorithm TSN-PCD outperforms six other previously proposed protein complex discovery algorithms: MCL, , and  are shown in Table To get insights on the shared, underlying biological processes of the identified functional modules, we use Gene Ontology annotations, downloaded from the Saccharomyces Genome Database (SGD) , to anallog of DFM-CIN is much greater than that of the other five algorithms. The absolute percentage of functional modules identified by our algorithm, DFM-CIN, with P-values less than E-15 is 7 times more than that of SPICI and MCL, 2.5 times more than that of COACH and MCODE, and about 1.5 times than that of CPM. Figure As shown in Table th on the identification of functional modules. The values of parameter th are set to be from 0.2 to 0.6 with 0.1 increments. In total, five different sets are obtained by variation of th. For each set, the number of identified functional modules, the average size, the percentage of significant modules, the average -log, and the percentage of modules whose P-value falls within P<E-15, , , and  are shown in Table In this section, we will discuss the effect of parameter th. This is because the larger value of th leads to fewer edges connecting the protein complexes. That is to say, a sparser graph is constructed by using a larger value of th. As a result, more functional modules will be identified with the same criterion for generating modules. From Table th, for evaluation of its biological meaning.The number of the identified functional modules increases with the increase of %of the functional modules consist of at least two identified protein complexes. To avoid the bias of using the algorithm DFM-CIN, we also analyze how many known protein complexes a functional module will include. Given a known protein complex (Kc) and an identified functional module (Im), we say that Kc is part of Im if more than 60% proteins in Kc are members of Im. The results agree closely with the identified protein complexes, as shown in Figure As protein complexes and functional modules are significantly related to each other, we discuss their relationships in this section. Analysis of the identified functional modules shows that they generally include multiple protein complexes. As shown in Figure #15) and module (#235) both consists of only one protein complex  complex, which has been found to support activated transcription in yeast[#166 participate in the mechanism of transcription regulation. More functional modules which consist of multiple protein complexes can be seen in Additional file For a functional module consisting of multiple protein complexes, exploration of its biological processes shows that these multiple protein complexes participate in the same biological process. The illustrated example module  in yeast. The tra in yeast,45. As s in yeast. NuA4 hi in yeast. Four pr in yeast. The CCR in yeast. The alp in yeast. AccordiAn important and challenging task in post-genomic era is to investigate the systematic and dynamic organization of PPI networks and explore biologically significant clusters. This paper introduces a new framework for constructing a dynamic PPI network by integrating gene expression data into PPI data. An important contribution of the framework is that in which protein complexes and functional modules can be distinguished. Few such works have been done before, though many researchers know that protein complexes and functional modules are two different concepts which have different biological meanings. In the proposed framework, the dynamic PPI network is composed of a series of time-sequenced subnetworks, based on the the time that the interactions are activated. Two different clustering algorithms: TSN-PCD and DFM-CIN are proposed for identifying protein complexes and functional modules, respectively.f-measure reveals that our algorithm TSN-PCD outperforms the other five protein complex discovery algorithms. Comparison of the results on static and dynamic PPI networks shows that the combination of temporal gene expression data with PPI data is worthwhile for protein complex discovery.To test and validate the effectiveness of the proposed framework and clustering algorithms, the identified protein complexes and functional modules are compared with those detected by other clustering algorithms: MCL, MCODE, CPM, SPICI, and COACH. A quantitative comparison based on An evaluation of the identified functional modules involved the function enrichment. The evaluation shows that the identified functional modules discovered by DFM-CIN are statistically significant in terms of “Biological Process”. More importantly, the analysis of the relationship between protein complexes and functional modules reveals that a module generally consists of one or more protein complexes, and the protein complexes contained in the same module participate in the same biological process universally.In conclusion, the proposed framework and clustering algorithms, TSN-PCD and DFM-CIN, effectively reveals modular organization of the PPI network, and they distinguish well between protein complexes and functional modules.The authors declare that they have no competing interests.ML and XW developed and implemented the new framework and algorithms. ML drafted the manuscript under the guidance and supervision of JW and YP. All authors read and approved the final manuscript.Protein complexes identified by TSN-PCD and their frequencies. Additional file Click here for fileA supplemental table with the standard names and systematic names paired. Additional file Click here for fileThe matched results of the identified protein complexes of HC-PIN and TSN-PCD with known complexes. Additional file Click here for fileFunctional modules identified by DFM-CIN. Additional file Click here for fileIdentified functional modules consist of multiple complexes and their relationship. Additional file Click here for file


Arabidopsis thaliana interactome. The resultant proteome-wide predictions are available via www.ab.wur.nl/sliderbio and set the stage for further investigations of protein-protein binding sites. To assess our method, we first show that, by using a priori information calculated from protein sequences, such as evolutionary conservation and residue surface accessibility, we improve the performance of interface prediction compared to using only interactome data. Next, we present evidence for the functional importance of the predicted sites, which are under stronger selective pressure than the rest of protein sequence. We also observe a tendency for compensatory mutations in the binding sites of interacting proteins. Subsequently, we interrogated the interactome data to formulate testable hypotheses for the molecular mechanisms underlying effects of protein sequence mutations. Examples include proteins relevant for various developmental processes. Finally, we observed, by analysing pairs of paralogs, a correlation between functional divergence and sequence divergence in interaction sites. This analysis suggests that large-scale prediction of binding sites can cast light on evolutionary processes that shape protein-protein interaction networks.The specificity of protein-protein interactions is encoded in those parts of the sequence that compose the binding interface. Therefore, understanding how changes in protein sequence influence interaction specificity, and possibly the phenotype, requires knowing the location of binding sites in those sequences. However, large-scale detection of protein interfaces remains a challenge. Here, we present a sequence- and interactome-based approach to mine interaction motifs from the recently published  Genotype-to-phenotype relationships are mediated via molecular networks, including protein-protein interaction networks. Hence, understanding how phenotypes are influenced by sequence changes requires understanding how the specificity of protein interactions is encoded in protein sequences. Identifying which sites are involved in the interactions is a necessary step towards studying the underlying molecular mechanisms and the evolutionary processes influencing protein interaction networks. However, accurate automatic detection of protein binding sites remains a challenge when aiming at large-scale identification.Arabidopsis thaliana, there is a gap between the amount of protein-protein interactions experimentally unravelled and the amount of structural information available in the Protein Data Bank Those interaction sites composing the protein interface are directly identifiable given a 3D structure of a complex Recently, the Arabidopsis Interactome map has been released, describing about 6,200 highly reliable interactions between about 2,700 proteins In contrast to protein structures, in which an interaction site is seen as a continuous stretch of amino acids in space, protein sequences show an interface as scattered short sub-sequences. It has been suggested that proteins with common interaction partners also share common functional features We previously developed a method that uses protein-protein interaction networks to find sequence motifs shared by proteins with common interaction partners We start by assessing the performance of our new method. By comparing our predictions against available structural information, we show that the modifications in the method improve its performance. In addition, the assessment provides a basis for determining a set of default parameters for the algorithm. Next, we obtain large-scale predictions of protein interaction sites from the complete Arabidopsis interactome data. We use single nucleotide polymorphism data to obtain evidence that the predicted binding sites are functionally relevant. Subsequently, we analyse available data describing the effect of amino acid mutagenesis to show that our predictions can be interrogated to obtain insight into previously unknown molecular mechanisms underlying the effect of specific mutations. Finally, we analyse the sequences of paralogous pairs to set the stage for further investigations of the molecular mechanisms behind the link between sequence diversity and functional divergence in Arabidopsis proteins.www.ab.wur.nl/sliderbio.We recently developed SLIDER, a method that uses a protein interaction network to locate binding sites in the sequence of interacting proteins For computational details of the SLIDER method, the reader is referred to One critical step in the algorithm consists of verifying whether a short motif is present in a protein sequence. Originally, SLIDER considered that a protein contained a motif if a perfect match was found between motif sequence and a region in the protein sequence. In contrast, the SLIDERBio algorithm makes use of the BLOSUM62 Additionally, to select only those overrepresented motifs that are likely to be located in the interaction interface, filtering steps based on pre-calculated biological information were implemented. SLIDER considered that a protein contained all the motifs that satisfy the sequence match criteria. For SLIDERBio, the region from the protein sequence that matches the motif has to satisfy two extra conditions: (i) it has to show evolutionary conservation greater than a conservation threshold, and (ii) it has to have predicted surface accessibility greater than an accessibility threshold . These rBefore the modifications, SLIDER required as input only protein sequences and protein-protein interaction data. The SLIDERBio algorithm now additionally requires the conservation score and the predicted surface accessibility for all proteins. In addition, SLIDERBio requires the user to set values for parameters that determine the thresholds of degree of similarity, conservation and residue solvent accessibility. The performance of various parameter settings was analysed by comparing our sequence-based SLIDERBio predictions with available protein structure data. This analysis allowed to assess the significance of the inclusion of the biological information in SLIDERBio and, furthermore, to obtain a default set of parameters. Next, we predicted protein interaction motifs for the Arabidopsis interactome and investigated the predicted interaction sites, in particular aiming at applying these towards understanding the effect of sequence variation.Arabidopsis thaliana, for this assessment, given the small number of Arabidopsis proteins with structural mapping, we also used human and yeast protein-protein interaction data  to assess the performance of the algorithm towards large scale predictions of protein binding motifs; (ii) to evaluate the significance of the implemented modifications and; (iii) to obtain a set of default values for the parameters. For these investigations, we used a subset of protein-protein interactions such that for the proteins involved, their sequences could be mapped to available structures of protein complexes; hence the interface residues could directly be identified for assessment of our predictions. Hereafter, these subsets are referred to as “structurally mapped datasets”. Although we focus our application on data see  and S2. data see . Both meFirstly, we observed that for most of the parameter settings, SLIDERBio obtains better results than the previous SLIDER, in terms of both Accuracy and Coverage . Note thSecondly, scatter diagrams and Pearson's correlation coefficients (PCC) were used to determine whether F-scores obtained for the same parameter settings are correlated among the three structurally mapped datasets. A strong correlation implies here that the same set of parameters would give results with similar quality in different datasets. A good correlation is particularly important, because we based our assessment on structurally mapped datasets of three species in order to determine the best parameter setting for further predictions on the complete Arabidopsis interactome data. When comparing the F-scores obtained for the same parameters but networks from different species  predicted to be responsible for the interaction is given. This set of predictions, which is comprised by motifs that are overrepresented in pairs of interacting proteins, conserved across species and predicted to be located in the surface of the protein structure, was used for further analysis.Turning now to the complete Arabidopsis interactome data, our method predicted protein-protein binding motifs that could be mapped See  to 1498 Arabidopsis thaliana). If our predicted interaction sites are indeed functionally important, one would expect less variability in their positions compared to the rest of the protein sequence. To test this hypothesis, we calculated the percentage of predicted interface residues in which a non-synonymous SNP (nsSNP) is found (1.6%); this is significantly lower than the percentage of all protein residues in which a nsSNP is found  data , alanine mutagenesis of the residues 200 or 213 located in the F-box domain eliminates the interaction with ASK1 (AT1G75950), in the yeast-two-hybrid system and A similar case is obtained by analysing available annotation of the protein CXIP1 , which is thought to activate CAX1 (AT2G38170) through a direct interaction. In CXIP1, alanine mutagenesis of two highly conserved motifs  has been shown to lead to loss of ability to activate CAX1, presumably by abolishing the interaction between these two proteins shy2-2, in which a proline in position 69 is mutated to a serine; and shy2-3, in which a glycine in position 67 is mutated to a glutamic acid. Although both mutations are known to interfere with auxin-related developmental processes, i.e. root growth, gravitropism and lateral root formation Additionally, analysis of available mutagenesis data indicates a number of cases in which mutations are known to affect certain phenotypes, but the molecular mechanism behind this is unknown. Our predictions, together with the Arabidopsis interactome, allow us to generate hypotheses for these unknown mechanisms, which could in principle be experimentally tested. For example, for two naturally occurring mutations in SHY2  the phenotypic effects have been identified: cis-regulatory elements and its coding sequence. In the latter case, mutations in protein-protein binding sites may either abolish existing interactions or create new interaction sites. These mutations lead to interaction rewiring as one of the mechanisms for functionalization Gene duplication is a major driving force of evolutionary novelty For the examined paralogous pairs, the sequence identity of the predicted binding sites was better able than the identity of the whole protein sequence to distinguish the three functional divergence groups . The weaThe potential for exploiting the sequence of binding sites towards predictions of functional divergence may be illustrated by examining the two paralogs FT (AT1G65480) and TFL1 (AT5G03840). Both genes mediate signals for floral transition in an antagonistic manner: whilst the knockout mutant of FT strongly induces late flowering, the knockout mutant of TFL1 induces early flowering et al., submitted to Trans Comp Biol Bioinf) although this does not yet use the biological information sources applied in the current study.Efficient bioinformatics strategies are crucial to retrieve information encoded in biological networks, in particular to support the formulation of hypotheses on evolutionary processes and molecular mechanisms linking genotype to phenotype. Here, we addressed the challenge of locating, at a large scale, protein binding sites in the Arabidopsis proteins. For this task, we defined a strategy that exploits information encoded in the Arabidopsis interactome and the sequences coding for the interacting proteins. Our sequence- and interactome-based approach enabled the prediction of binding motifs in 985 (36%) of the proteins represented in the interactome. Although this number represents only a small percentage of all Arabidopsis proteins, it is much higher than would be expected from methods that rely on protein structure information. One possible way to achieve higher coverage would be by using a different set of parameters controlling the thresholds of evolutionary conservation and surfaces accessibility of the motifs. Alternatively, predictions based on additional protein-protein interaction datasets We used our predictions to investigate evolutionary aspects of binding site variability. By assessing the frequency of synonymous and non-synonymous SNPs either in the whole protein sequence or only in the predicted motifs, we found that, overall, our predicted sites are under stronger evolutionary constraints than the rest of the protein. Additionally, we identified non-synonymous SNPs that may be correlated with changes in the protein interaction specificity between different Arabidopsis ecotypes.www.ab.wur.nl/sliderbio and can be used to pinpoint residues which should be mutated in order to interfere with specific interactions, or to interpret the results of obtained phenotypic changes upon mutations in a molecular and mechanistic way. They also enable to perform large scale studies on the effects of various types of naturally occurring sequence variation on protein interactions, similar to what we recently demonstrated for the MADS domain protein family Previously, we employed sequence-based approaches cis-elements. Here, Arabidopsis paralogous pairs that have previously been classified, based on morphological changes observed upon mutation, into functional divergence groups It has been debated whether constraints placed on binding sites play a major role in functional divergence Homo sapiens (human) and Saccharomyces cerevisiae (yeast) protein-protein interaction data used in this work are described in Arabidopsis thaliana interaction data were obtained from the recently published Arabidopsis interactome map The One of our assessment procedures aims to verify whether the predicted motifs are located in the protein-protein interface, which is a straightforward task when the structure of the complex is available. However, few complex structures deposited in the PDB correspond to the proteins listed in PPI data used in this work. To overcome such a lack of structural information, we used a strategy to assign sequences to known protein structures based on homology. To link a query sequence to a target sequence with a known 3D structure, we used PSI-BLAST After mapping protein sequences to known structures, the interface residues were identified in the complex structures that were assigned to pairs of interacting proteins. To determine these interface residues, we used NACCESS We extended the original SLIDER algorithm by adding filtering steps based on evolutionary conservation and surface accessibility as predicted from protein sequences, and by implementing an approach to define the presence of a motif in a sequence based on a substitution matrix. Below, we describe these adjustments to the algorithm.Calculating residue conservation requires three sequential tasks: to select a group of homologous proteins, to align the protein sequence with these homologs, and to quantify the conservation of each residue in the alignment. To select groups of homologs we used OrthoMCL  of an amino acid residue in a protein indicates its level of solvent exposure. To predict the RSA based on protein sequences, we used the SABLE To quantify the overrepresentation of a given motif in the network, our method verifies in how many sequences that motif is present. Instead of searching for perfect matches, SLIDERBio uses a modified version of the BLOSUM62 similarity table to calculate the “degree of similarity” of a given motif for a protein sequence. In this modified similarity table, a perfect amino acid match has value 1, and a non-perfect match has value ranging from 0 to 1 directly proportional to the BLOSUM62 score . Our method calculates the residue similarity score and it averages the value over the motif length. Only if this average is greater than or equal to the “degree of similarity” threshold, SLIDERBio considers the motif present in the protein sequence.To assess the quality of the SLIDERBio results, we defined two measures that use the structures of the proteins in the above-mentioned structurally mapped datasets. Here, the ‘Accuracy of predicted motifs’ is defined as the number of motifs correctly predicted to be in the interface as a fraction of all motifs predicted to be in protein–protein interface. A motif is said to be in the interface, if at least one of its residues is identified to be in the interface of its assigned complex structure. The ‘Coverage of protein-protein interfaces’ stands for the number of protein pairs that contain at least one motif mapped to their interface, as fraction of the total number of interacting pairs in the interaction data. Thus, the ‘Accuracy of predicted motifs’ reflects the predictive power of the algorithm toward finding motifs that are indeed located in the interface, and the ‘Coverage of protein-protein interfaces’ reflects its predictive power towards finding motifs explaining the largest number of interactions. The overall performance of the predictions was measured via the F-score, which equals 2*‘Accuracy of predicted motifs’*‘Coverage of protein-protein interfaces’/(‘Accuracy of predicted motifs ’+‘Coverage of protein-protein interfaces’).5×6×6) of these values were tested. SLIDERBio predicts a set of N motif pairs. For each combination of parameters, we executed SLIDERBio on the structurally mapped datasets for the three species using the following configuration: length of predicted motif l = 8; number of allowed wildcard-character d = 5; maximum execution time t = 60 minutes; number of predicted motif pairs N = 1,000. We then mapped the resultant motif pairs in the sequence of pairs of interacting proteins, in such a way that each of the interacting proteins contains one of the motifs in the pair. Subsequently, the ‘Accuracy of predicted motifs’, ‘Coverage of protein-protein interfaces’ and F-score were calculated for all the results. For the analysis of the complete Arabidopsis Interactome, maximum execution time was set to t = 24 hours.For the threshold of the allowed degree of similarity between motif sequence and protein sequence, we tested five different values . For the thresholds of conservation and residue surface accessibility, we tested six different values  each. In total, 180 combinations .l, and randomly arranged d wildcard-characters in the substring. The same procedure was repeated to create the second motif in the pair, which resulted in a motif pair. Then, we repeated this step till N motif pairs were created. In this way, we created 1,000 sets of N motif pairs for each of the structurally mapped datasets , using the same set up of parameters controlling the length of the motifs (l = 8 and d = 5), and the same number of motif pairs .In order to assess the significance of the SLIDERBio results, we created sets of random motif pairs by applying the following strategy: First, we randomly selected a sequence in the input sequence set; next, we randomly sampled from the selected sequence a substring of length SNPs were obtained from the currently available 80 accessions from the Arabidopsis 1001 Genome Project We retrieved and analysed the field “Experimental info” from the section “Sequence annotation” as deposited in the UniProt database n = 32). Next, we used Needle To classify the paralogous pairs as having “no”, “low” or “high” functional divergence, we used data from p-values:  and ; for  of “whole protein sequence identity” and “motif sequence identity”, respectively), suggesting that the data is normally distributed.For each functional divergence group , we created two density functions by fitting a normal distribution to the calculated values of either “whole protein sequence identity” or “binding site sequence identity”. Prior to the analyses, we tested the normality of each group of values using Lilliefors test for normality with no significant results  networks and their respective structurally mapped subsets. To create the basis for comparison and assessment of our predictions, we used the structures of protein complexes in order to identify residues that are located in the protein interface. Because the number of complex structures mapped to Arabidopsis proteins is low, we used two other datasets from which more structures are available; the human and yeast protein-protein interaction networks. (A–C) Graphical representation of the human (A), yeast (B) and Arabidopsis (C) interactome. Nodes represent proteins, edges represent interactions. (D) number of proteins and (E) number of interactions in the PPI datasets. Black, proteins and interactions from which structures could be mapped; grey, complete PPI data.(TIF)Click here for additional data file.Figure S2Comparison of the topology of the protein-protein interaction networks and their respective structurally mapped subsets.x-axis represents the number of protein partners (degree) and y-axis represents the frequency. The Figure allows quantitative comparison of the network composed by the subset of interacting proteins from which structural information is available against the complete set of interactions. By using the degree distributions, we observe that the similarity between the structure mapped subsets for the human and yeast interactomes is high, while the Arabidopsis subset has a quite different degree distribution. In addition, the similarity between the yeast and human structurally mapped datasets and the complete Arabidopsis interactome is higher than the similarity between the Arabidopsis subset and the complete Arabidopsis interactome.(TIF)Click here for additional data file.Figure S3Assessment of the SLIDERBio performance for different values for the thresholds of Degree of similarity, Conservation and surface accessibility. The box plots group the F-score results (y-axis) based on each used threshold value for the SLIDERBio parameters:  show the results grouped based on threshold values for the Degree of Similarity between motif and protein sequence;  for the Conservation threshold values; and  for the Residue surface accessibility threshold values. The results for the Human, Yeast and Arabidopsis structurally mapped datasets are shown, respectively, in ,  and . The boxes labelled as ‘none’ contain the F-score results when SLIDERBio did not use the modification in its calculation. The grey horizontal dashed lines touch the boxes in the group that has given greatest 75th percentile. We then tested whether there is statistical difference in the F-score results when SLIDERBio uses or does not use the modification. The figures show the p-value (P) when the results from the group ‘none’ are compared against the results from the group with greatest F-score 75th percentile. All p-values (P) shown in the figures are calculated using a two-tailed paired t-test. At significance level 0.01, we reject the null hypothesis that the means are equal.(TIF)Click here for additional data file.Figure S4Determination of a default set of SLIDERBio parameter values. The figure shows the p-values calculated by comparing F-scores obtained from the SLIDERBio results against those from random results. y-axis represents the p-value; x-axis indicates which combination of parameters has been used. For legibility, only results for which the p-value is less than 0.05 are shown. The vertical dashed grey line indicates the single parameter setting that showed p-values less than 0.05 simultaneously for all the three structurally mapped dataset. This combination of parameters  is used to predict binding motifs on the full Arabidopsis interactome.(TIF)Click here for additional data file.Table S1Human, yeast and Arabidopsis protein-protein interaction networks used in this work.(XLSX)Click here for additional data file.Table S2Structures of protein complexes mapped to sequences of interacting proteins.(XLSX)Click here for additional data file.Table S3Predicted interaction motifs for Arabidopsis proteins.(XLSX)Click here for additional data file.Table S4List of interacting proteins in which a nsSNP overlaps the binding site of both proteins.(XLSX)Click here for additional data file.Table S5Functional divergence classification and sequence similarity analysis of paralogous pairs with predicted motifs.(XLSX)Click here for additional data file.


We optimized the selection of template proteins to cover a broad spectrum of protein molecular function: ligand, metal, inorganic cluster, protein, and nucleic acid binding. In large-scale benchmarks, we demonstrate that the recognition rates in identifying templates that bind molecular partners in similar locations are very high, typically 70–80%, at the expense of a relatively low false positive rate. eThread also provides useful insights into the chemical properties of binding molecules and the structural features of binding. For instance, the sensitivity in recognizing similar protein-binding interfaces is 58% at only 18% false positive rate. Furthermore, in comparative analysis, we demonstrate that meta-threading supported by machine learning outperforms single-threading approaches in functional template selection. We show that meta-threading effectively detects many facets of protein molecular function, even in a low-sequence identity regime. The enhanced version of eThread is freely available as a webserver and stand-alone software at http://www.brylinski.org/ethread.Protein threading is widely used in the prediction of protein structure and the subsequent functional annotation. Most threading approaches employ similar criteria for the template identification for use in both protein structure and function modeling. Using structure similarity alone might result in a high false positive rate in protein function inference, which suggests that selecting functional templates should be subject to a different set of constraints. In this study, we extend the functionality of  Currently, the most accurate and consequently the most widely used methods for protein structure and function prediction build on homology, i.e., they use information educed from related proteins. As demonstrated in the recent Critical Assessment of Protein Structure Prediction (CASP) experiment, the top performing groups in tertiary structure and function prediction categories use various template-based methods . TypicalZ-score filter to identify these templates that are likely structurally similar to the target. Similar criteria are commonly used for the template identification in protein function prediction. For example, to collect functional templates, FINDSITE . For protein–protein complexes, we assess the local structural similarity of binding interfaces using iAlign with a significant interfacial score, IS (interface similarity)-score, of ≥0.191 -score of ≥0.4  and bindf ≥0.191 . AccordieThread  binds its partners in a similar location, and (2) binds chemically similar molecules and/or the binding mode is similar. We refer to these models as Location and Features, respectively. We use a Naïve Bayes classifier (NBC) to combine individual threading scoring functions into a single probabilistic score. In this classifier, the real-value attributes are modeled by a Gaussian distribution, i.e., the classifier first estimates a normal distribution for each threading component by computing the mean and standard deviation of the training data in that class, which is then used to estimate the posterior probabilities during classification. Both classifiers, Location and Features, are independently trained on threading scores. The accuracy of template selection is assessed using twofold cross validation; Pearson’s chi-squared test applied to each individual scoring function confirmed that both subsets are characterized by the same central tendency and dispersion measures as the whole dataset. Thus, considering a maximum sequence identity of 40% between any two dataset proteins, this protocol provides a sufficient cross validation. Furthermore, the imposed pairwise sequence identity threshold automatically excludes close homologs from benchmarks.To identify functional templates, we use eThread , which ieThread , COMPASSeThread , HHpred eThread , HMMER , amino acid acyl-carrier protein ligase 1 from B. japonicum bound to zinc (PDB-ID: 3mey), NADH-quinone oxidoreductase from T. thermophiles complexed with Fe4S4 (PDB-ID: 2fug), transcription factor PU.1 from mouse bound to DNA (PDB-ID: 1pue), and a homodimer of hypothetical transposase from S. tokodaii (PDB-ID: 2ec2). In parentheses are the PDB IDs of weakly homologous (<40% sequence identity) templates identified by eThread for 2zgm ; 3mey ; 2fug ; 1pue ; and 2ec2 .We selected the following representative examples: recombinant eThread in template selection using five datasets that comprise ligand-, metal-, and iron/sulfur-binding proteins as well as protein–protein and protein–DNA complexes. For each target protein, we identify in the PDB library structurally similar proteins that produce statistically significant structure alignment with a TM-score of ≥0.4. These templates that bind molecular partners in similar locations are considered positives with the remaining categorized as negatives. In the subsequent analysis, we examine another requirement for being a positive, which is either a significant chemical similarity of bound molecules for small ligands and metal ions, a similar nucleotide composition for protein–DNA complexes, or a similar interfacial geometry for protein–protein assemblies. The extended version of eThread employs highly tuned machine learning models to provide a set of probability estimates for various aspects of protein molecular function. These are primarily used to assess whether a particular template binds its partners in a similar location; moreover, another set of probabilities estimate whether the template-bound compounds are chemically similar and/or the binding mode between macromolecules is the same. It is important to note that we explore remote evolutionary relationships between protein, excluding those templates that share>40% sequence identity with the target.We evaluate the performance of Figure 1 shows the accuracy of template identification by eThread for all considered aspects of molecular function. First, we assess the recognition of these templates that bind their partners in similar locations [green ROC (receiver operating characteristic) curves]. The recognition rates are very high, typically 70–80%, at the expense of a relatively low false positive rate with tight 95% confidence bounds  of 0.783 and 0.619, respectively], SP3 and SPARKS in detecting DNA-binding aspects , and COMPASS in recognizing protein–protein interfacial geometry (AUC of 0.732). Table 2 extends this analysis further by accounting for the so-called “early recognition problem.” By analogy to virtual screening, where one requires active compounds to be ranked as high as possible, template identification approaches also should rank good templates early in an ordered list. BEDROC (Boltzmann-enhanced discrimination of ROC) is a generalization of ROC developed specifically to analyze methods that need to segregate positives toward the front of a rank-ordered list  and DNA feature prediction, which is close to random for all methods, eThread most efficiently tackles the problem of the early recognition of functionally related templates across all binding datasets. These results are finally supported by Z-statistics calculated by the Wilcoxon rank-sum (aka Mann–Whitney U) test. Table 3 shows that only SP3 in the identification of DNA-binding sites (Z-score of 23.18) gives better Z-statistics than eThread.The area under ROC is a commonly used method to assess the overall performance of ranking approaches. In red list . As showeThread, which uses meta-threading and machine learning models constructed separately for different facets of protein molecular function, systematically outperforms single-threading methods providing higher overall accuracy.Tables eThread pick out binding sites, we selected five representative examples, one for each aspect of molecular function: recombinant lectin (rAAL) complexed with lactose, amino acid acyl-carrier protein ligase 1 (aa:CP) bound to zinc, NADH-quinone oxidoreductase (NDH-1) complexed with Fe4S4, transcription factor PU.1 bound to DNA, and a homodimer of hypothetical transposase (tnpA). In Figure 2, upon the global template-to-target superposition, these templates that are predicted to have a binding site in a similar location are shown in green. Red spots indicate template binding sites, which are below the optimal probability threshold, i.e., are predicted to bind molecules in different locations. In all cases, there are very few false positives and false negatives; the consensus amongst templates predicted to bind their molecular partners in a similar location precisely identifies the correct binding site. Note that for aa:CP and NDH-1, a simple majority voting would result in the incorrect prediction of the binding site location.To illustrate how weakly homologous templates identified by eThread, a recently developed meta-threading approach (We extended the functionality of approach , to addrapproach  and Patcapproach ,2012 ideeThread is freely available to the community at http://www.brylinski.org/ethread as a webserver and a stand-alone software distribution. The latter can be installed in a local environment for high-throughput computations and the integration with existing structure-based protein function inference pipelines. The documentation includes detailed installation instructions and illustrative examples.http://www.frontiersin.org/Bioinformatics_and_Computational_Biology/10.3389/fgene.2013.00118/abstractThe Supplementary Material for this article can be found online at Click here for additional data file.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The need to retrieve or classify protein molecules using structure or sequence-based similarity measures underlies a wide range of biomedical applications. Traditional protein search methods rely on a pairwise dissimilarity/similarity measure for comparing a pair of proteins. This kind of pairwise measures suffer from the limitation of neglecting the distribution of other proteins and thus cannot satisfy the need for high accuracy of the retrieval systems. Recent work in the machine learning community has shown that exploiting the global structure of the database and learning the contextual dissimilarity/similarity measures can improve the retrieval performance significantly. However, most existing contextual dissimilarity/similarity learning algorithms work in an unsupervised manner, which does not utilize the information of the known class labels of proteins in the database.ij dby considering the contextual information of the proteins. The context of a protein is defined by its neighboring proteins. The basic idea is, for a pair of proteins , if their context ij dby a factor learned from the context In this paper, we propose a novel protein-protein dissimilarity learning algorithm, ProDis-ContSHC. ProDis-ContSHC regularizes an existing dissimilarity measure Supervised learned Dissimilarity measure, we update the Protein Hierarchial Context Coherently in an iterative algorithm--ProDis-ContSHC.Moreover, we divide the context to hierarchial sub-context and get the contextual dissimilarity vector for each protein pair. Using the class label information of the proteins, we select the relevant (a pair of proteins that has the same class labels) and irrelevant (with different labels) protein pairs, and train an SVM model to distinguish between their contextual dissimilarity vectors. The SVM model is further used to learn a supervised regularizing factor. Finally, with the new We test the performance of ProDis-ContSHC on two benchmark sets, i.e., the ASTRAL 1.73 database and the FSSP/DALI database. Experimental results demonstrate that plugging our supervised contextual dissimilarity measures into the retrieval systems significantly outperforms the context-free dissimilarity/similarity measures and other unsupervised contextual dissimilarity measures that do not use the class label information.Using the contextual proteins with their class labels in the database, we can improve the accuracy of the pairwise dissimilarity/similarity measures dramatically for the protein retrieval tasks. In this work, for the first time, we propose the idea of supervised contextual dissimilarity learning, resulting in the ProDis-ContSHC algorithm. Among different contextual dissimilarity learning approaches that can be used to compare a pair of proteins, ProDis-ContSHC provides the highest accuracy. Finally, ProDis-ContSHC compares favorably with other methods reported in the recent literature. Proteins are linear chains of amino acids. The polypeptide chains are folded into complicated three-dimensional (3D) structures. With different structures, proteins are able to perform specific functions in biological processes -14. To stf × idf) scoring scheme commonly used in document retrieval systems r and retdistance , and corposes in , which lposes in , a numbemilarity , Jaccardrd index , Tanimotfficient , and EucThe basic components of a protein retrieval system includes a way to represent proteins and a dissimilarity measure that compares a pair of proteins. Most of the aforementioned studies focus on the feature representation of the proteins, while neglecting the comparison of the feature vectors. Such studies usually apply a simple similarity or dissimilarity measure for the comparison of the feature vectors, such as Euclidean Distance Measure used in ,23,24,26x0 and a database protein i xas d. It does not consider other proteins in the database, neglecting the effects of the contextual proteins. If we consider the distribution of the entire protein database X = {jx}, j = 1 ... N when computing the dissimilarity as d, the retrieval performance may benefit from the contextual proteins {jx}, j ≠ i.• The dissimilarity measure is a pairwise distance measure, which is computed only considering the query protein L = {jl}, j = 1 ... , N in the database. Although we may have no idea about whether x0 and i xbelong to the same class  or not (l0 ≠ il), we do know some prior information about other proteins L. In all of the previous studies, prior class labels L were not adopted to calculate the dissimilarity d.• The dissimilarity measure is computed in an unsupervised way, which does not use the known information of the class labels Due to these two bottlenecks, traditional protein retrieval systems using pairwise and unsupervised dissimilarity measure usually do not achieve satisfactory performance, even though many effective protein feature descriptors are developed and used. In this paper, we investigate the dissimilarity measure and propose a novel learning algorithm to improve the performance of a given dissimilarity measure.d which only considers the two refereed proteins x0 and ix, contextual dissimilarity also considers the contextual proteins X when computing the dissimilarity d. The existing contextual similarity learning algorithms can mainly be classified into the following two categories:Recent research in machine learning points out that contextual information can be used to improve the dissimilarity or similarity measures. This kind of algorithms are called contextual or context-sensitive dissimilarity learning -34. UnliThe first contextual dissimilarity measure (CDM) was proposed by Jegou et al. in ,31. TheyIn ,33, Bai L, and thus work in an unsupervised way. The only one used L is ⊤, as shown in Figure L, we further label each pair γ =  as a relevant pair γ y= +1 if i l= jl, or an irrelevant pair γ y= -1 otherwise.We try to utilize the label information riterion  for protdγ, γy)}, γ = 1, ... ,N C2, we train a binary SVM N+1)×(N+1)(: matrix of size (N+1)×(N+1) of pairwise protein feature distances, where x0 is the query protein and {x1, ... , Nx} are the database proteins;Require: Input κ: size of the hierarchical sub-context;Require: Input P: number of the hierarchical context;D(1) = D;Initialize dissimilarity matrix: for t = 1, ... , T doUpdate the hierarchical context for each protein k' = (p - 1) × κ, k'' = (p - 1) × κ + κ, and where i, j), i, j ∈ {0, ... , N}:Compute the contextual proteins dissimilarity vector where γ y= +1 and γ y= -1 respectively, train an SVM model for their contextual dissimilarity vectors Select relevant and irrelevant protein pairs and label them as Compute the distance to the SVM margin boundary for the contextual dissimilarity vector Update the pairwise protein dissimilarity measures:for i = 0, 1, ... , N dofor j = 0, 1, ... , N doend forend for.end forDt+1). On the other hand, if i l≠ l0 and i ≤ k, i xis defined as a false positive (FP). If i l≠ l0 and i > k, i xis defined as a true negative (TN). Otherwise, i xis a false negative (FN). Using these definitions, we can then compute the true positive rate , the false positive rate (FPR), recall and precision as follows:SCOP  fold clakTPR, kFPR, kRecall, and k Precisionare calculated for all 1 ≤ k ≤ N , where N is the size of the database. The ROC defines a curve of points with k FPRas the abscissa and k TPRas the ordinate. Precision-recall defines a curve with k recalland k precisionas abscissa and ordinate respectively. We use the area under the ROC curve (AUC) as a single-figure measurement for the quality of a ROC curve [OC curve , and useTo demonstrate the contribution of the supervised learning idea, we also compare ProDis-ContSHC with its unsupervised counterpart, i.e., contextual dissimilarity algorithm based on the unsupervised learning, i.e., ProDis-ContHC. ProDis-ContHC is also applied to improve the cosine similarity. We also compare with the widely-used contextual dissimilarity measure ,31 (CDM)k of returned proteins to each query is very small, the returned proteins are not enough to "represent" the class features of the query, which then causes the low TPR. Meanwhile, in this situation, most of the returned proteins are highly confident of belonging to the same class as the query, resulting in a low FPR. Moreover, the TPR is almost 100% when the FPR>50%. It is clear that the ROC curve of ProDis-ContSHC completely embodies the ROC curves of the other three methods, which implies ProDis-ContSHC is the best method among the four. That also means that supervised learning is better than unsupervised learning for this purpose. ProDis-ContHC, on the other hand, is the second best method among these four, which demonstrates the contribution of the hierarchical sub-context idea to the traditional contextual dissimilarity measures. The overall AUC results are listed in Table The performance of different methods are compared, as shown in Figure Regarding the efficiency of the method, in this experiment, the learning time of the ProDis-ContSHC is longer than that of the ProDis-ContHC and CDM. This is because in each iteration of the learning algorithm, a quadratic programming problem with many training protein pairs have to be solved to train the SVM. In addition, the computation of the regularization factor of supervised similarity learning algorithm needs more function evaluations.We also compare the proposed algorithms with seven other protein retrieval methods, i.e., tableau search , QP tablTo further evaluate the robustness of our method, we test the behavior of ProDis-ContSHC and other contextual similarity learning algorithms on different similarity measures. A group of experiments are conducted on the ASTRAL 1.73 95% dataset with the following similarity measures:• The cosine similarity  as intro• The Jaccard index : it is d• The Tanimoto coefficient : it is a• Squared Euclidean distance : it is aix(m) is the m-th element of vector ix.where ProDis-ContSHC, ProDis-ContHC, and the CDM algorithms are applied to improve each of these similarity measures, respectively. The AUC values of the corresponding retrieval systems are plotted in Figure x0, the pairwise Euclidean distances, di0, i = 1, 2, ... , N , are ranked. The top k proteins are returned as the retrieval results. To evaluate the performance of the proposed algorithms, we test them on both the protein retrieval and the protein classification tasks, following [Unlike the similarity measure used in the last experiment, here we use the Euclidean distance  to compaollowing ,21.i x∈ X of the dataset is used as a query x0 and the retrieved proteins are ranked according to the shape dissimilarity dj0to the query, where j = 1, 2, ... , i - 1, i + 1, ... , N. We also use the precision-recall curve to demonstrate the performance of the proposed methods, where precision is the proportion of the retrieved proteins that are relevant to the query and recall is the proportion of the relevant proteins in the entire dataset that are retrieved as the results.The efficiency of the proposed dissimilarity learning algorithm is first evaluated in terms of the performance on the protein retrieval task. In this case, each protein FT02, Kraw00, and FT02&Kraw00 [Kraw00 &FT02. This is because this vector not only employs the context, but also their relevant information to predict the relationship between the query and the database proteins.To test the robustness and consistency of our methods, we apply our methods to three different protein descriptor vectors, i.e., Daras et al.'s 2&Kraw00 ,21 geome2&Kraw00 ,21, theri xin the database, a dissimilarity measure is applied after removing that protein from the database . A clasFT02, Kraw00, and FT02&Kraw00. The overall classification accuracy is shown in Table Kraw00 &FT02. Among the four dissimilarity measures, ProDis-ContSHC has the highest accuracy, whereas ProDis-ContHC is the second best one. Therefore, this conclusion has been demonstrated on both similarity and dissimilarity measures on different datasets with different descriptor vectors.We again conduct this experiment with the three descriptor vectors, i.e., We have introduced in this paper a novel contextual dissimilarity learning algorithm for protein-protein comparison in protein database retrieval tasks. Its strength resides in the use of the hierarchical context between a pair of proteins and their class label information. By extensive experiments, this novel algorithm has been demonstrated to outperform the traditional context-based methods and their unsupervised version.We formulate the protein dissimilarity learning problem as a context-based classification problem. Under such a formulation, we try to regularize the protein pairwise dissimilarity in a supervised way rather than the traditional unsupervised way. To the best of our knowledge, this is the first study on supervised contextual dissimilarity learning. We propose a novel algorithm, ProDis-ContSHC, which updates a protein's hierarchical sub-context and the dissimilarity measure coherently. The regularization factors are learned based on the classification of the relevant and the irrelevant protein pairs. The algorithm works in an iterative manner.Experimental results demonstrate that supervised methods are almost always better than their unsupervised counterparts on all the databases with all the feature vectors. The proposed method, even though mainly presented for protein database retrieval tasks, can be easily extended to other tasks, such as RNA sequence-structure pattern indexing , retrievThe authors declare that they have no competing interests.JW: designed the algorithm, carried out the experiments, analyzed the results, and wrote the manuscript. XG: designed the algorithm and the experiments, improved the manuscript. QW: carried out the experiments, analyzed the results, improved the manuscript. YL: improved the manuscript. All authors read and approved the final manuscript.


Proteins in organisms, rather than act alone, usually form protein complexes to perform cellular functions. We analyze the topological network structure of protein complexes and their component proteins in the budding yeast in terms of the bipartite network and its projections, where the complexes and proteins are its two distinct components. Compared to conventional protein-protein interaction networks, the networks from the protein complexes show more homogeneous structures than those of the binary protein interactions, implying the formation of complexes that cause a relatively more uniform number of interaction partners. In addition, we suggest a new optimization method to determine the abundance and function of protein complexes, based on the information of their global organization. Estimating abundance and biological functions is of great importance for many researches, by providing a quantitative description of cell behaviors, instead of just a "catalogues" of the lists of protein interactions.With our new optimization method, we present genome-wide assignments of abundance and biological functions for complexes, as well as previously unknown abundance and functions of proteins, which can provide significant information for further investigations in proteomics. It is strongly supported by a number of biologically relevant examples, such as the relationship between the cytoskeleton proteins and signal transduction and the metabolic enzyme Eno2's involvement in the cell division process.We believe that our methods and findings are applicable not only to the specific area of proteomics, but also to much broader areas of systems biology with the concept of optimization principle. Saccharomyces cerevisiae (S. cerevisiae). From the vast amount of PPI data, protein interaction networks (PINs) have been vigorously investigated. In PIN, proteins are represented as nodes, and two proteins are linked if they interact with each other. Basic topological measures, such as a degree (the number of neighbors a node has), and their correlations are adopted to explain various properties of proteomes. For instance, a degree in PIN is the number of partner proteins with which an individual protein interacts. One of the earliest achievements is the heavy-tailed degree distribution  and its importance in characterizing the essentiality of proteins  in case of all proteins, and from this comparative analysis we can observe that core proteins are much less promiscuous than the attachment ones. The degree distributions of proteins for both cases are closer to the exponential distribution, as shown in Figure Different roles of core and attachment proteins are also reflected in the clustering structure of complex-mode projection network. If only the core proteins are considered, the complex-mode projection network is composed of 42 separate connected components, which is a highly fragmented configuration. Thus, core proteins are important as a stable component for individual complexes, rather than a mediator which "binds" complexes together. On the other hand, if only the attachment proteins are used to bond complexes, one single giant component emerges, except for a single tiny component with only two complexes. This small component is also merged into the single giant component if we consider both core and attachment proteins. As a representative example, we present the exosome 3'-5' exoribonuclease complex ] and functions [minimizing Eq. (4)] of complexes and their component proteins. As the input datasets for individual proteins, we use condition-dependent abundance (arbitrary units) of proteins  and the To check the accuracy of our method, we first divide the set of proteins with known values of abundance into training and test sets. Assuming that the values of abundance for the proteins in the test set are unknown, we estimate the abundance for those proteins in case of rich media (YEPD)  and compα values, compared to the ones for random pairing of estimated abundance and real abundance in Additional file Then, we plot the ranked p or at least comparable to each other for small p. The only case worse than CYC2008 is the 'fraction of functions for our raw result' in the fifth column for small p, but this can also be overcome by using HC dataset in the sixth column. Therefore, we conclude that our global optimization method is much better at guessing the unknown protein functions, in terms of sensitivity or recall rate, let alone the problem of dependence on an arbitrary parameter, which is the wholesale E-value threshold for the CYC2008 method.For the function assignment, we also divide the set of proteins with already-known functions into training and test sets. Assuming all the protein functions in the test set are unknown, we assign those functions based on our function assignment method; the results are shown in Table First, we show the average change of complexes' abundance depending on the condition , classified as each MIPS functional category in Figure From Figure In contrast, many complexes with functions of cell cycle and protein synthesis tend to show decreased abundance in minimal media, indicating that many proteins induced in rich media are involved in cell growth and division. A fraction of the complexes with each functional category, for three different ranges of abundance ratio changes, shown in Figure From our results about the functional annotations for complexes, we find a number of intriguing examples. For instance, both the complex 276, whose core proteins are Ste11 and Ste50, and the complex 312, whose core proteins are Bcy1 and Tpk2, are assigned with the cytoskeleton formation and signal transduction. The relationship between the cytoskeleton proteins and the cell signal transduction is discussed in recent literatures ,44, argui.e., by actually tracing the complex to which the function is assigned and looking for another component protein participating in the complex, as the "source" of the function.Our optimization scheme does not only give the systematic estimate of complexes' abundance and function assignment, but also conjecture previously unknown new functions to individual proteins, as stated in the 'Methods' section. Table sufficient condition for the direct involvement in the cell division process, but can be a necessary condition which hints the candidate targets for further investigation in a more elaborate way. With such procedure, we find quite a number of examples indeed, as shown in Table In case of the protein Eno2, it participates in the complex 289 (the same complex index is used as in Ref. ) as a coIt is indispensable for the future of proteomics to understand the composition structure and interactions of protein complexes. However, relatively fewer works about the protein complexes have been done in the genome-wide level, except for a few cases -18, compAs results of the analysis, the degree distribution of the bipartite network asymptotically follows the exponential distribution. The same is true for the degree and strength distributions of the one-mode projection networks as well. Interestingly, the resultant exponential degree distributions are different from the heavy-tailed distribution observed in most pairwise PPI networks. Many complexes and proteins with largest degrees and strengths are affiliated to the ribosome, allowing for the stable formation of large ribosomal complexes. Furthermore, we have shown that the core and attachment proteins in the datasets show different participation patterns in the formation of complexes, which illustrates the different roles in the complexes.The most valuable contribution of our work is presenting the new optimization method without external tuning parameters to determine the abundance and function of protein complexes, along with the previously unknown properties of individual proteins, based on the information of their global organization. The effectiveness of our optimization method is proved by various biologically relevant examples compared with experimental results. Since we provide the full results from our results as Additional files, we hope our result can encourage other researchers to utilize it as their new dataset for deeper understanding of proteomics.S. cerevisiae, many genome-wide researches have been achieved. Analyses based on the protein complexes can open a door to a new understanding of how proteins interact and work in organisms. Important future works include more investigation about our bipartite (and one-mode projections) network topology, along with more systematic comparison with the conventional PIN to see the detailed origin of the different form of the degree distribution. Ultimately, it is essential to perform the small-scale experiments to validate the results claimed by our new optimization method. We hope this work can make a substantial contribution to this direction of research.Thanks to the rich proteomics datasets of The authors declare that they have no competing interests.SHL, PJK, and HJ conceived and designed the study. SHL collected the data. SHL and PJK performed the numerical simulation and statistical analysis, and analyzed the results. SHL, PJK, and HJ wrote, read, and approved the final manuscript.Strength distributions of (a) complex-mode projection and (b) proteins-mode projection . Here, the strength in (a) corresponds to the sum of number of proteins shared with the neighboring complexes for each complex, and the strength (b) corresponds to the sum of number of complexes shared with the neighboring proteins for each protein. The blue squares correspond to the cumulative strength distribution P(s) = ∑s′ ≥ s p(s′), and the pink lines and gray curves are the best exponential and power-law fittings, respectively.Click here for fileProjected network's degree distribution from the generating function approach . Assuming that both protein and complex's degree distributions follow the exponential degree distribution p(k) ~ exp(-k), the projected network's degree distribution is numerically calculated with the generating function approach mentioned in the main text. One can clearly observe that the degree distribution follows the exponential tail for k ≫ 1.Click here for fileE. coli protein complex data in (a) semi-log scale and (b) double-log scale Degree distribution of complexes in the bipartite network for . Here, the degree corresponds to the number of component proteins for each complex. The blue squares correspond to the cumulative degree distribution P(k) = ∑k′ ≥ k p(k′).Click here for fileDegree distribution of complexes and proteins in the bipartite network for human protein complex data, in comparison with the protein-protein interaction network . Here, a complex's degree (blue square) corresponds to the number of component proteins for each complex, and a protein's degree (red circle) corresponds to the number of complexes in which a protein participates as a component. The degree distribution of proteins in the binary protein-protein interaction is shown as pink triangles, as a comparison.Click here for fileList of all the protein complexes with MIPS functions assigned by our method (Table S1). List of all the protein complexes with MIPS functions assigned by our method, where the core and attachment components are taken from Ref. [rom Ref.  (and therom Ref. ). We claClick here for fileList of all the proteins with MIPS functions newly assigned by our method (Table S2). List of all the proteins with MIPS functions newly assigned by our method (bright yellow), along with the ones from the MIPS database (sky blue) which is used as "input function." HC outcomes are selected as in Additional file Click here for fileStatistical validation of abundance estimation . For each value of fraction of training set p, the relative deviationis ranked and shown compared to its random counterparts . The real deviation values are always on the left side  of the random counterparts outside the error range, which implies its statistical significance.Click here for fileStatistical validation of the function assignment, in comparison with the hypergeometric test in Ref. [(CYC2008) (Table S3). Here p is the fraction of training set. †Fraction of test proteins with at least one function assigned, which has at least one assigned function on the original MIPS functional datasets. *Among all the newly assigned functions to the entire test set proteins, fraction of functions which are also on the original MIPS functional datasets. Raw outcomes correspond to the original outcomes from our method, and high confidence (HC) outcomes are the selected subset of them whose function assignments are invariant even if the multiple solutions are considered. in Ref. (CYC2008)Click here for file


It provides pre-calculated sequence similarities interconnecting the entire known protein sequence universe, complemented by pre-calculated protein features and domains, similarity clusters and functional annotations. SIMAP covers all major public protein databases as well as many consistently re-annotated metagenomes from different repositories. As of September 2013, SIMAP contains >163 million proteins corresponding to ∼70 million non-redundant sequences. SIMAP uses the sensitive FASTA search heuristics, the Smith–Waterman alignment algorithm, the InterPro database of protein domain models and the BLAST2GO functional annotation algorithm. SIMAP assists biologists by facilitating the interactive exploration of the protein sequence universe. Web-Service and DAS interfaces allow connecting SIMAP with any other bioinformatic tool and resource. All-against-all protein sequence similarity matrices of project-specific protein collections are generated on request. Recent improvements allow SIMAP to cover the rapidly growing sequenced protein sequence universe. New Web-Service interfaces enhance the connectivity of SIMAP. Novel tools for interactive extraction of protein similarity networks have been added. Open access to SIMAP is provided through the web portal; the portal also contains instructions and links for software access and flat file downloads.The Similarity Matrix of Proteins (SIMAP,  Protein sequences are ubiquitous study subjects in molecular biology. They are determined in large quantities by sequencing of genomic DNA followed by the computational prediction of coding regions or mapping of additional data from functional genomics. During the past decades, protein sequence databases accumulated many millions of different protein sequences, representing blueprints of the function and structure of the encoded gene products. However, many protein sequences are ‘hypothetical’ by nature as their sequences have never been experimentally confirmed and their cellular functions cannot be rationally predicted except by information transfer from known and evolutionary related proteins. Therefore, the comprehensive computational characterization of an increasing proportion of the protein sequence universe is a never-ebbing spring of experimentally testable research hypotheses and one of the central tasks of computational biology .Basic approaches such as pairwise sequence similarity-based searches [e.g. BLAST ] or compThe Similarity Matrix of Proteins (SIMAP) database solves the computational dilemma described above by incrementally pre-calculating the sequence similarities interconnecting the entire known protein sequence universe . SIMAP iInteractive exploration of the protein sequence universe: Individual users identify proteins of interest by text- or sequence-based searches on the SIMAP web interface. For each protein, SIMAP immediately lists potential homologs based on sequence similarity or domain architecture similarity. Homologs can be restricted to selected taxa and sequence databases; results can be displayed according to their scores as well as in a taxonomic tree. Every match found can be used as starting point for subsequent SIMAP queries.Acceleration of large-scale sequence similarity calculations, e.g. in genome annotation: Genome-wide sequence similarity searches against different databases can be replaced by SIMAP database lookups, using the Web-Service or DAS programmatic interfaces. The search space of each query is specifically defined by selection of taxonomic lineages and protein databases. Sequence similarities, sequence alignments, protein domain annotations, cluster and function predictions can be retrieved. If a query sequence is not already known to SIMAP (occurs rarely due to its high coverage), either the rapid SIMAP SeqFinder  or primary database IDs as used in SIMAP internally. These database IDs can be fetched by an own Web-Service method. The client retrieves an XML result string that can be either parsed for the information wanted or be processed using an XML transformation system as XSLT for, e.g., display on a web page.User-defined selection of protein sequences: The nodes of the network are selected according to a protein file, containing either names or sequences. This mode is beneficial for users who have specifically pre-clustered proteins and selected representatives.User-defined selection criteria: The nodes are selected automatically by SIMAP based on selection criteria such as taxonomic affiliation and originating database. This mode is able to generate genome-wide similarity networks [e.g. between all human proteins from the NCBI RefSeq database ].User-defined central protein: The user defines the central node by its protein name or sequence. Optionally, further nodes can be restricted by taxonomic and database selection criteria. SIMAP determines the direct and indirect neighbours (up to a user-defined number of links) of the central node and constructs the sequence similarity network between them.An increasing number of computational tools make use of sequence similarity networks between selected collections of protein sequences. These networks are shaped by evolutionary processes and are crucial for the inference of protein functions. For small numbers of proteins, similarity networks can be calculated on-the-fly . The comThe density of the resulting sequence similarity network can be individually controlled by different parameters  in all modes. If, according to the requested parameters, the resulting network would be too large for direct access via the Web Portal, an information page is displayed suggesting contacting the SIMAP staff to request the project-specific generation of the subnetwork.The representation and specific arrangement of domains in protein sequences provide additional highly valuable information for the evolutionary and functional analysis of protein sequences and ideally complement pairwise sequence similarities. Despite their bias towards protein families well represented in public databases, domains are used for both fast and sensitive protein similarity searches [e.g. ]. SIMAP SIMAP pre-calculated Interpro  domains SIMAP provides domain architecture similarities for interactive exploration in its Web portal. All proteins in SIMAP are linked to their domain architecture similarity report, listing domain-based homologs according to the user-defined selection of InterPro member database and e-Value threshold. To facilitate large-scale projects, we now also provide full dumps of the non-redundant domain architectures for all InterPro member databases linked to all SIMAP proteins for download.The SIMAP database will continue to update its contents monthly. The computational costs for the resulting sequence similarity calculations are considerable, but can be well covered by the BOINCSIMAP project . FurtherFurthermore, we have already planned to increase the sensitivity and accuracy of the SIMAP matrix using composition-based score adjustment ,34 that University of Vienna.Funding for database development and operation, University of Vienna. Funding for open access charge: Conflict of interest statement. None declared.