Protein-protein interactions (PPIs) create the steps in signaling and regulatory networks central to most fundamental biological processes. It is possible to predict these interactions by making use of experimentally determined orthologous interactions in other species.In this study, prediction of PPIs in rice was carried out by the interolog method of mapping deduced orthologous genes to protein interactions supported by experimental evidence from reference organisms. We predicted 37112 interactions for 4567 rice proteins, including 1671 predicted self interactions (homo-interactions) and 35441 predicted interactions between different proteins (hetero-interactions). These matched 168 of 675 experimentally-determined interactions in rice. Interacting proteins were significantly more co-expressed than expected by chance, which is typical of experimentally-determined interactomes. The rice interacting proteins were divided topologically into 981 free ends (proteins with single interactions), 499 pipes (proteins with two interactions) and 3087 hubs of different sizes ranging from three to more than 100 interactions.This predicted rice interactome extends known pathways and improves functional annotation of unknown rice proteins and networks in rice, and is easily explored with software tools presented here.The online version of this article (doi:10.1186/1939-8433-5-15) contains supplementary material, which is available to authorized users. Saccharomyces cerevisiae  are essential for many fundamental biological processes. With the advent of high-throughput approaches, genome-wide networks of PPIs have been generated in http://www.ebi.ac.uk/intact; Aranda et al., Arabidopsis thaliana was made consisting 1159 high confidence, 5913 medium confidence and 12907 low confidence interactions. This was established using a confidence scoring based method on the number of different data sets in which the interaction was recorded, the number of different types of experiments supported the interactions, and the number of species in which the interaction was discovered . A confidence value  was calculated to estimate the strength of experimental support for each predicted interaction. We have identified 37112 predicted interactions for 4567 rice proteins  had an ortholog that matched at least one of the eleven reference organisms  database.A gold standard of 675 experimentally confirmed interactions was abstracted from IntAct (Drosophila (9 interacting partners) and Arabidopsis (11 interacting partners) and smaller than in yeast (22 interacting partners). Table The rice interacting proteins were divided into free ends (981 proteins with single interactions), pipes (499 proteins with two interactions) and hubs of different sizes ranging from three to more than 100 interactions. The largest class of proteins is that of medium hubs (between 11 to 50 interactions). The interacting proteins had an average of 14–15 partners, which is comparable to Protein-Protein interactions are often mediated by protein interaction domains (PIDs) which bind to other domains (domain-domain interactions), or short conserved amino acid motifs in the partner protein. PIDs that interact most frequently among the rice protein pairs (interologs) were determined using annotation by PFAM domain models (see methods). In rice, the most enriched domains among the interactors included known interaction domains PF00400 (WD40), PF00069 (protein kinase), PF00270 (DEAD), and PF02985 (HEAT). Looking at domain-domain interactions amongst interacting pairs, domain pairs (with one domain in each interacting protein) with extremely high fold of enrichment (vs. random pairing) were often otherwise rare domains among the protein interactions. Among the twenty most frequent PFAM interacting pairs were 8 self interactions between homodomains and 12 interactions between different PFAM domains. Of these 14 (70%) were enriched more than 2 fold, with the highest fold of enrichment (134 fold) for interaction between the homodomains of the proteasome  gene in flowers . By using the graph definition language GraphML, the color of each protein (node) is painted differently based on its predicted subcellular localization , none of the experimental data sets from reference organisms are complete. The interactions contained in this data set are of evolutionarily conserved proteins and pathways. However as the experimental data are based on high throughput studies, the predicted rice interactome does not represent only well studied interactions like the proteosome, ribosome and base transcriptional machinery: there are still some surprising connections and a number of unknown proteins within the data set as well.A rice interactome has been predicted based on conservation of protein interactions across species over the course of evolution. Each interaction has been assigned a confidence level (CV) as an internal quality control. Assignments for subcellular localization and coexpression can be used to further validate the predicted interaction. Coexpression analysis has shown that interacting proteins tend to have similar expression profiles, and tend to be localized to the same or adjacent compartment. By inputting rice proteins of interest, the Rice Interactions Viewer Figure  can be uNote added in revision: we expanded the number of predictions by incorporating Braun et al.’s Arabidopsis Interactome data set (Arabidopsis Interactome Oryza sativa subsp. japonica), Arabidopsis thaliana, Homo sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Caenorhabditis elegans, Saccharomyces cerevisiae, Schizosaccharomyces pombe, Escherichia coli, Bacillus subtilis, Helicobacter pylori were retrieved from TAIR  were retrieved for the prediction of rice interactome.Peptide sequences from rice . A predicted interactome was recorded in an Excel spreadsheet when both interacting proteins in a reference species had orthologs in rice. Duplicates of the same interactions were removed from different interactome data sets and from different reference species to generate unique interacting protein pairs.Interactome databases were obtained from BioGRID  for experimental support of a predicted interaction was calculated using the following formula, CV = N x E x S; where N is the total number of data sets in which the interaction was recorded, E is the number of different experimental methods from which an interaction was inferred, and S is the number of reference species from which the same interaction was recorded. The higher the CV, the more likely an interaction is to be conserved in multiple species and to be more convincing as it is supported by different experimental methods. The interactions were classified according to their CV to three groups: low confidence (CV = 1), medium confidence (CV = 2 to 10) and high confidence (CV > 10).http://geneontology.org). The top 7 enriched GOSlim terms for molecular functions (MF) and cellular component (CC), respectively; were used as node features in the visualization of protein network using Cytoscape 2.6  terms for rice were retrieved from the GO site . Some of the proteins involved in the interactome may possess more than one PFAM domain, thus all possible pairs of PFAM domains of the interacting protein pairs were queried using MySql  of each protein (the number of proteins that interact with it), and the total number of interacting pairs (EE), nab is given byHere Cia = 1 or 0, whether protein i is in compartment a or notCib = 1 or 0, whether protein i is in compartment b or notki,kj = the node degree of protein(node) i,jeij = the probability of protein(node) i and protein(node) j interactsE = total number of interactions(edges)P(multi) = 1 − (1−P)m where for enrichment m equals the number of ab pairs with at least one observed interolog and for depletion m equals the number of ab pairs possible in the ensemble of random networks.The P values are finally subject to a multiple-test correction Coexpression between interacting proteins was determined using the Pearson correlation coefficientith sample, and Y is the expression level for gene Y in the ith sample (−1≤ r ≤1).Where N is the number of expression samples, X is the expression level for gene X in r values indicate a correlation of expression patterns, while low negative r values indicated anti-correlation. A large multi data set compendium consisting of 165 diverse data sets Additional file 1:Table S2. PFAM domains for interacting pairs. (XLSX 3 MB)Additional file 2:Table S3. List of GSM identifiers of microarray expression data. (XLSX 17 KB)Additional file 3:Table S4. Experimental verification of predicted interactions. (XLSX 43 KB)Additional file 4:Authors’ original file for figure 1Authors’ original file for figure 2Authors’ original file for figure 3Authors’ original file for figure 4Authors’ original file for figure 5Authors’ original file for figure 6Below are the links to the authors’ original submitted files for images.


The physical interactions between proteins constitute the basis of protein quaternary structures. They dominate many biological processes in living cells. Deciphering the structural features of interacting proteins is essential to understand their cellular functions. Similar to the space of protein tertiary structures in which discrete patterns are clearly observed on fold or sub-fold motif levels, it has been found that the space of protein quaternary structures is highly degenerate due to the packing of compact secondary structure elements at interfaces. Therefore, it is necessary to further decompose the protein quaternary structural space into a more local representation.Here we constructed an interface fragment pair library from the current structure database of protein complexes. After structural-based clustering, we found that more than 90% of these interface fragment pairs can be represented by a limited number of highly abundant motifs. These motifs were further used to guide complex assembly. A large-scale benchmark test shows that the native-like binding is highly likely in the structural ensemble of modeled protein complexes that were built through the library.Our study therefore presents supportive evidences that the space of protein quaternary structures can be represented by the combination of a small set of secondary-structure-based packing at binding interfaces. Finally, after future improvements such as adding sequence profiles, we expect this new library will be useful to predict structures of unknown protein-protein interactions.The online version of this article (doi:10.1186/s12859-014-0437-4) contains supplementary material, which is available to authorized users. Throub initio  and homob initio  in protes in PDB . Consides in PDB , it is rIn terms of modeling the structures of PPI, there is a further question regarding the nature of PPI space: whether proteins adopt a finite number of quaternary structures . SimilarHere we constructed an interface fragment pair library from the current structure database of protein complexes. For any dimeric complex in the database, an interface fragment pair is defined as a pair of 9-residue-long fragments from each side of the complex. Both residues in the middle of these two fragments are located at the dimer interface and form contacts at the atomic level Figure . All paiThe systematic construction and test of the interface fragment pair library presented here was performed in three basic steps Figure a. First,For a given pair of protein domain I and J that form physical contacts Figure a, we staThe list of all interface fragment pairs for a given PPI was generated by above procedure. Following the same procedure, we collected data from 3did database. The 3did database selects a large number of domain-domain interactions in proteins for which high-resolution three-dimensional structures are available. The database consists of a large group of items called interacting domain pairs (ID). The interacting domain pair could be homodimer, heterodimer, or inter-domain interaction within a single subunit. Information about Pfam index is given for both domains of an ID. Each ID further includes different number of instances. The specific instances are called 3D items in which information about PDB index, chain id and residue range are provided for both interacting protein domains. In order to construct the interface fragment pair library, we selected only one representative 3D item from each ID in 3did database to reduce redundancy. For each selected 3D item, all interface fragment pairs were extracted from the structure of domain-domain interaction by the algorithm described in the previous paragraph. Finally, a total number of 153127 entries were derived.ith picked entry enters the clustering algorithm, the previous i-1 entries form m clusters. We will compare the ith entry with all the previous i-1 ones and find the nearest neighbor that has the smallest RMSD with the ith entry. If the RMSD value is smaller than the cutoff, the ith entry will be assigned to the same cluster as its nearest neighbor belongs to. Otherwise we will generate the m + 1 cluster for this entry. For simplification, we selected a member from each cluster as the representative model of the cluster. The representative model has the most number of neighbors with the other members in the corresponding cluster.All the interface fragment pairs collected from 3did database were structurally clustered by the following simple algorithm. An initial fragment pair was randomly picked from all the entries and was set as the first cluster. We then further selected another entry randomly from the same pool. For this second entry, structure alignment was carried out with the first cluster. Root mean square difference (RMSD) was calculated after the alignment for all the Cα atoms in both fragments between the first and the second selected entries. If the RMSD was smaller than a predetermined cutoff value, the second entry was merged into the first cluster. Otherwise a new cluster was created for the second entry. Similar procedure was iterated for all the rest entries. Assuming before the The cutoff value of RMSD during clustering was empirically adopted. If the cutoff is too low, there will be too many clusters and the library will lose generality. On the other hand, if the cutoff is too high, pairs without structural similarity will be classified into the same group so that the library will lose accuracy. The current cutoff value we used in this study equals to 4.0 Angstrom. This leads to 2135 clusters from original 153127 interface fragment pairs. Clustering using other cutoff values will be discussed in the results. Moreover, the purpose of constructing the library is to decompose the space of protein quaternary structures into lower dimensions. Therefore, only the most representative clusters in which the number of members is higher than a cutoff value were selected into the library. The cutoff value was also empirically determined. If the cutoff is too high, we will lose the coverage for the structural space of fragment pairs. In contrast, if the cutoff is too low, the application of the library will be limited by the large number of clusters. In this study, any clusters with larger than 20 members was selected. Finally, the library includes 459 clusters and each cluster is represented by a structural model of the corresponding interface fragment pair.The stability of our clustering process was tested across different runs. Specifically, five independent clustering runs were carried out. Each run was generated by a random order. The clustering results are listed in Additional file I and NJ residues, respectively, the total combination numbers of fragment pairs is (NI − 8) × (NJ − 8). Under each combination, if the center residues of both fragments are on the protein surfaces, we further compare the structures of these two fragments to all the 459 entries in the interface fragment pair library. The surface residues are defined as any residue with solvent accessible surface area larger than 10 Ǻ2, which was calculated with atomic details of protein structures using a probe size of 1.4Ǻ.Given structures of any two interacting protein domains I and J we explore all possible modes of their binding based on the constructed interface fragment pair library. Without a priori knowledge of binding sites for both domains, we assume that any residue pair from both of their surfaces can be located on the binding interface. Therefore, we first enumerate all potential combinations of fragment pairs in the complex by assigning 9-amino-acid long windows for both domains. The 9 consecutive residues in the window correspond to a fragment in the tertiary structure and the window can slide from N to C terminus. Assuming the two protein domains contain NNI − 8) × (NJ − 8) combinations of fragment pairs. This leads to an ensemble of structural models for binding between protein domains I and J. Structural models include inter-residue clashes are eliminated from the ensemble. We hypothesize that this derived ensemble forms a representative space of quaternary packing between two protein domains of known structures. We will evaluate the likelihood of the native binding mode being in this space by a large-scale benchmark test.If a specific entry in the library includes an interface pair of fragments A and B, we first align the Cα coordinates of residues in the current window of domain I to the fragment A and align residues in the other window of domain J to fragment B. In parallel, we align residues in the window in domain I to fragment B and align window in J to A. In either case, we calculate the RMSD for both alignments. If both RMSD values are smaller than 2Ǻ, we further superimpose the structures of entire domains I and J to their corresponding fragments A or B according to the relative position of windows in each domain /(P(H)P(S))), in which P(HS) is the probability of finding HS motif at binding interfaces, and P(H) is the probability of finding H fragments at binding interfaces. A higher score for a specific motif indicates that it is more favored to form. Consequently, the preference scores for all six motifs are plotted in Figure We further investigated the distribution of protein secondary structures in the 459 representative models of interface fragment pairs. Each fragment in a pair was first divided into three categories: helix (H), strand (S) and loop (L). The criteria that each fragment belongs to one of these three categories depend on the secondary structure type of the residue at the center of the corresponding fragment. The secondary structure type of a residue is determined by the standard DSSP algorithm . After wFigure Figure In order to evaluate the completeness of protein quaternary structural space represented by the interface fragment pair library, we applied the library to a large-scale benchmark set. The protein-protein docking benchmark constructed by ZLAB was used in our study . The mosSome specific examples of our modeling results are shown in Figure In order to estimate the difficulty in finding good candidates from the ensemble of structural model, we have also included the total number of structural models generated for each entry in the benchmark in additional to the lowest RMSD. The distribution of total number of structural models for all 176 benchmark entries is plotted as a histogram in supplemental document as Additional file The physical interactions between proteins play pivotal roles in many biological processes. Understanding the structural features of these interactions is the basis to study protein functions in cells. However, the spatial arrangement between two interacting proteins or protein domains is highly diversified, leading into an interesting question of whether the complexity of protein binding interfaces can be simplified. Similar to the space of protein tertiary structures in which discrete patterns are clearly observed on fold and sub-fold motif levels, it has been found that the space of protein quaternary structures is highly degenerate due to the packing of compact secondary structure elements at interfaces. Therefore, it is necessary to further decompose the protein quaternary structural space into a more local representation. Fragment-based methods have been proved their success in predicting protein tertiary structures. In this article, similar idea has been extended to protein interfaces. Specifically, a library was constructed by collecting the interface fragment pairs from the structural database of protein interacting domains. After structural-based clustering, we found that more than 90% of these interface fragment pairs can be represented by a limited number of highly abundant motifs. These motifs were further used to guide complex assembly. A large-scale benchmark test shows that the native-like binding is highly likely in the structural ensemble of modeled protein complexes that were built through the library. Overall, our study presents supportive evidences that the space of protein quaternary structures can be represented by the combination of a small set of secondary-structure-based packing at binding interfaces.In order to conduct a comparative study between different databases, we downloaded information of protein interactions from iPfam. The iPfam database includes a total number of 8160 intermolecular domain interactions. These interactions belong to either homodomain or heterodomain. For each interacting domain structure in iPfam, we enumerated all interface fragment pairs based on the same criteria introduced in the method. We compared all these interface fragment pairs with our fragment pair library derived from 3did. We calculated RMSD between fragment pairs in iPfam and our library. The RMSD of the closest fragment pair in the library was recorded. The distribution of this closest RMSD for fragment pairs in all 8160 iPfam interactions is plotted as a histogram in the supplemental document (Additional file Although the native-like complex models are among the structural ensembles in most cases of the benchmark test, as shown in Figure


These detected cytoplasmic interactionsincluded the self-interaction of Ede1, homolog of mammalian Eps15. Ede1 is the crucial scaffold forthe organization of the early stages of endocytosis. We show that oligomerization of Ede1 throughits central coiled coil domain is necessary for its localization to the endocytic site and we linkthe oligomerization of Ede1 to its function in locally concentrating endocytic adaptors andorganizing the endocytic machinery. Our study sheds light on the importance of the regulation ofprotein–protein interactions in the cytoplasm for the assembly of the endocytic machineryin vivo.Clathrin-mediated endocytosis is a highly conserved intracellular trafficking pathway thatdepends on dynamic protein–protein interactions between up to 60 different proteins. However,little is known about the spatio-temporal regulation of these interactions. Using fluorescence(cross)-correlation spectroscopy in yeast, we tested 41 previously reported interactions  Currently, up to 60 proteins have been associated withdirect functions in the formation of cargo-containing clathrin-coated vesicles. A hall-mark ofendocytosis is the complex orchestration of protein–protein interactions during the variousmolecular steps in endocytosis. These steps include the recruitment of protein and membrane cargo,the assembly of the endocytic coat, actin polymerization, membrane invagination, scission of thevesicle, and concomitant disassembly of the endocytic machinery, which is then reassembled at newendocytic sites , including Las17, Myo3/5, and Bbc1, arrive at the endocytic site. Las17 is the main Arp2/3activator and is regulated by at least four proteins: Sla1, Bzz1, Bbc1, and Syp1  and fluorescence cross-correlation spectroscopy (FCCS) to investigateprotein–protein interactions between the endocytic proteins in the cytoplasm. FCS and FCCSare used to analyze signal fluctuations derived from fluorescently labeled molecules diffusingthrough a small defined observation volume  or of the yeast codon-optimized mCherry (3mCherry), using mating typeTo confirm the correct fusion of the triple tags to the proteins, we validated the size of thetagged proteins by Western blotting. In addition, their characteristic localization to corticalpatches was confirmed by light microscopy. In order to test the functionality of the fluorescentlylabeled endocytic proteins, we used assays that score for growth phenotypes of the tagged strains inrelation to the corresponding deletion strains and a wild-type strain under standard  or stress conditions (37°C or 1 M NaCl). Apl1, Arc18, Las17, Pan1, and Rvs161 couldeither not be tagged with 3myeGFP or their tagging with 3myeGFP led to a strong growth defect. Theseproteins were fused to single myeGFP instead. Under stress conditions, phenotypic growth defectswere still seen in Rvs161-3mCherry (at 37°C and 1 M NaCl), Rvs167-3mCherry, Rvs161-1myeGFP,and Arc18-3mCherry , while these strains behaved normally under the standardconditions used for imaging and FC(C)S.3 for haploid S. cerevisiae cells with a total molecular weight of 267 kDa  of theproteins under investigation, suggesting that it is part of a larger protein complex , the N-terminal EH motifs(ede1ΔEH), or the coiled coil domain alone(ede1ΔCC) or in combination with the C-terminalpart (ede1Δ591–1381)  Fig A. Using rain Fig A. Deletiede1-mutant which contained only the coiled coil domain(ede1CC) could still localize to cortical patches. Thiswas surprising since all known binding sites for endocytic adaptors were deleted and Ede1 has notbeen shown to bind membranes directly. However, the residence time ofede1CC at the endocytic site was much shorter thanfull-length Ede1. We compared the timing of ede1CCrecruitment to the endocytic patch with that of Sla1, a marker of the later stages of endocytosis.This revealed that ede1CC shows very similar temporaldynamics to Sla1, with a peak intensity only a few seconds before the peak intensity of Sla1 showed a strong reduction in molecularbrightness, faster diffusion . Furthermore, FCCS reports on co-mobility behavior of proteins,which is insensitive to the spatial arrangement of the fluorophores (in contrast to FRET-basedmethods), and therefore, FCCS can reliably detect the absence of protein–protein interactionswithin its dynamic range  Las17, the main Arp2/3activator, is partly bound to at least three of its regulators, Sla1, Bbc1, and Bzz1, with thestrongest interaction between Las17 and Sla1  motif-containingadaptor proteins in the cytoplasm , applying the highest confidence score and taking into accountonly physical interactions from both high-throughput and manually curated studies. Data aboutself-interactions were obtained from the S. cerevisiae database (http://yeastgenome.org).Reported interactions between the chosen set of proteins were obtained by STRING  technology asdescribed . GFP fluorescence was recorded using a470/22 nm excitation filter and a 520/35 nm emission filter. For mCherry-tagged proteins, we used a566/20 nm excitation filter and a 624/40 nm emission filter.et al , Alexa 546 (red channel), andRhodamine Green (cross-correlation channel) at a concentration of 2 nM. The intensity tracescollected in the two detection channels were auto- and cross-correlated, and analyzed using customsoftware. Raw data were autocorrelated by:i = j = 1, 2 for the autocorrelations in thegreen and the red channel, respectively, and i = 1 for the green channel andj = 2 for the red channel in the cross-correlation.δF(t) is calculated as the deviation of the present signalintensity F(t) from the mean intensity<F>. The brackets indicate an averaging over time. The average ofthis product for multiple data points is then standardized by the square of the mean intensity,which leads to independence from parameters such as the laser power. The parts of the rawfluctuation traces in which cellular movement or diffusion of vesicles through the observationvolume was apparent were either cut out or the file was discarded. A local average approach was usedto calculate the autocorrelation function corrected for bleaching of the fluorophores :with N, which was obtained from the fitted auto- orcross-correlation curve. The influence of the background can be described by:At the beginning of each measurement session, the average background fluorescence was estimatedusing wild-type cells not harboring any fluorescent protein. The background value was then used tocorrect for the particle number N for the red, green, and cross-correlation channels werecomputationally corrected for bleaching of the fluorophores, background fluorescence, and cross talkbetween the channels. The particle numbers were converted into concentration values by divisionthrough the size of the observation volume. This was determined by calibration measurements withdyes with known diffusion coefficient. Finally, differences in maturation of the fluorophores andthe non-perfect overlap of the two detection volumes were corrected for manually.The particle numbers N yielded thecounts per particle per second [cpps].Dividing the photon counts per second by the number of particles diff, that is their mean dwell time in thefocal volume, is represented as the mean length of the fluctuations. Knowing this time and measuringthe lateral diameter wxy of the observation volumeallow to calculate the diffusion coefficient D according to:The diffusion time of the molecules τGrg is proportional to theconcentration [AB] of complexes found in the observation volume.The amplitude of the cross-correlation Vrg is the effective cross-correlation volume.where KD must be understood as a measure for the interactionstrength under the given condition, which is influenced by the specific concentration of thefluorescently labeled proteins as well as other (sometimes unknown) protein complex members. It istermed therefore apparent or effective KD,using the protein Don1 tagged C-terminally to eGFP and N-terminally to 3mCherry, yielded aet al, where [A]


Advances in high-throughput and advanced technologies allow researchers to routinely perform whole genome and proteome analysis. For this purpose, they need high-quality resources providing comprehensive gene and protein sets for their organisms of interest. Using the example of the human proteome, we will describe the content of a complete proteome in the UniProt Knowledgebase (UniProtKB). We will show how manual expert curation of UniProtKB/Swiss-Prot is complemented by expert-driven automatic annotation to build a comprehensive, high-quality and traceable resource. We will also illustrate how the complexity of the human proteome is captured and structured in UniProtKB.Database URL: www.uniprot.org The human proteome, as we define it in UniProt, is the set of protein sequences that can be derived by translation of all protein-coding genes of the human reference genome, including alternative products such as splice variants. Although curation of human proteins has always constituted the top priority in the UniProt Knowledgebase (UniProtKB), the content of the human proteome in UniProtKB has evolved greatly in recent years, partly due to advances in technologies.The recent rise of big data and high-throughput technologies has shifted a number of paradigms in the scientific community. Although for decades, researchers focused on a single gene and its products, it is now common to work with whole genomes and proteomes. New technologies generate large amounts of data in different fields that can be handled thanks to increases in computing and storage capacities. For instance, next-generation sequencing technologies have accelerated the identification of new genetic variants and disease-associated genes . SimilarBeside high-throughput technologies, we also have to take into consideration that the requirements of our users are quite diverse. Although some users need a summary of knowledge in free text, others need structured and machine-readable information. Some users want a complete set of protein sequences derived from the reference genome, while others need expertly curated variations mapped to the reference genome or a list of variations associated with a phenotype. UniProt has to take into consideration these varied needs to build a comprehensive and high-quality resource.UniProtKB is composed of two sections: Swiss-Prot, the reviewed section containing manually curated records with information extracted from the literature and TrEMBL, the unreviewed section with automatically annotated records. We will describe how they complement each other to offer the scientific community an expertly curated human proteome. Literature-based expert curation is essential to capture the complexity of the human proteome, but we will describe how an expert-driven automatic pipeline can complement it to filter and integrate reliable high-throughput data into UniProtKB.When our project to manually curate all human protein-coding genes was initiated, we were first focusing on proteins that were characterized in the literature, or described in other databases, such as HGNC , OMIM 77 or the http://www.uniprot.org/help/sequences_section).All known human protein-coding genes are expertly curated and described in 20 197 UniProtKB/Swiss-Prot entries (numbers in this publication are from UniProt release 2015_10). Gene-centric versus protein-centric curation of proteomes is a matter of debate in the scientific community (http://www.uniprot.org/) is shown in http://www.uniprot.org/help/human_proteome).With 42 128 curated sequences, it is clear that the human proteome manually annotated in UniProtKB/Swiss-Prot remains incomplete. The expert curation of all functional protein isoforms produced by alternative splicing will require more resources and time. However, a complementary pipeline for the import of predicted human protein sequences in UniProtKB/TrEMBL has been developed in collaboration with Ensembl to complete the set of human isoform sequences. >49 000 additional predicted alternative products are currently available in UniProtKB/TrEMBL. An easy way to access the human proteome annotated by the UniProt Consortium (http://vega.sanger.ac.uk/index.html), Ensembl and RefSeq . We continuously review and update our protein sequences to make sure that they correspond to the latest version of the genome reference assembly and send feedback to the GRC when we identify potential issues within the assemblies. For this, we directly use the ‘Report an issue’ pipeline available from the GRC website which allows us to exchange with GRC curators rapidly and efficiently (http://www.ncbi.nlm.nih.gov/projects/genome/assembly/grc/).The proteome content continues to evolve as new information becomes available. The last major release of the human genome assembly was published in December 2013 (GRCh38), with new centromere representation, better segmental duplication accuracy, fewer assembly gaps and thousands of rare bases corrected (http://www.uniprot.org/help/protein_existence). More than half of them are the predicted translation products of putative pseudogenes and their existence is a matter of debate between collaborating resources involved in gene and protein curation. When a consensus is reached on true pseudogene status, the dubious entries are deleted from UniProtKB/Swiss-Prot. This was recently the case for the CASP16 gene. After reexamination and exchanges between resources, this putative protein-coding gene was reconsidered a pseudogene by Havana, HGNC, RefSeq and UniProtKB, and the corresponding record (UniProtKB P0CB46) was deleted from release 2015_10 of UniProt. Deleted sequences are still available in UniParc (http://www.uniprot.org/uniparc/), the sequence archive of UniProt. On the other hand, the status of ‘uncertain’ proteins can be changed when evidence confirms the existence of the protein. An example is provided by the entry encoded by the NLRP2P gene (UniProtKB P0DMW2), which was considered to be a processed pseudogene until recently when it was shown to encode a functional protein . Although thousands of smORFs have the potential to encode peptides, the number of smORFs actually translated into functional proteins is difficult to predict . For thiLiterature-based expert curation is considered the gold-standard of functional annotation and constitutes a core activity of the UniProt Consortium. It is very reliable and provides high-quality annotations for experimentally characterized proteins across diverse protein families. The human proteome is certainly the best curated corpus of proteins in UniProtKB/Swiss-Prot: human proteins contain knowledge captured from >65 000 unique publications indexed in PubMed, which are curated to UniProtKB/Swiss-Prot standards. In total, if we consider knowledge inferred by similarity to mammalian model organisms such as mouse or rat, the human proteome set contains information extracted from >100 000 unique articles.Expert curation requires in-depth reading of full-text articles before knowledge is summarized in sections dedicated to the different aspects of protein biology including function, subcellular location, or interactions with other proteins. Knowledge is represented both in a comprehensive summary that provides a complete overview of the information available, and in standardized vocabularies to facilitate subsequent retrieval whenever possible. For more information regarding expert curation, read , 19. Evein silico translation of coding sequences (CDS) submitted to the International Nucleotide Sequence Database Collaboration. However, transcriptional and translational events can alter the genetic information and result in the production of a protein with a sequence differing from the predicted one. These events are not easily predictable and information based on experimental results, retrieved from the literature through expert curation, is the only way to reconstitute the accurate protein sequence as it is produced in cells.We attach utmost importance to publications reporting events that affect the protein sequence and increase protein complexity. Most UniProtKB protein sequences come from the RNA editing is an interesting way to introduce post-transcriptional alteration in mRNA coding sequences . In primFor a small, but growing number of mammalian mRNAs, it is known that translation initiates at a non-AUG codon. In this context, CUG, the leucine codon, is the most frequently used for non-canonical initiation. In UniProtKB/Swiss-Prot, we follow the general consensus and translate non-canonical initiator codons as methionine by default. A ‘Sequence caution’ comment in ‘Sequences’ section indicates which initiator codon is used (e.g. see UniProtKB Q8TBF5).The presence of selenocysteine (Sec) in proteins, an amino acid similar to cysteine but that contains a selenium atom in place of sulfur, is difficult to predict. In the appropriate context, some UGA termination codons can be considered by the ribosome as a signal for the incorporation of Sec. In UniProtKB/Swiss-Prot, there are currently 26 human entries describing proteins with Sec in their sequence. These entries can be retrieved with the keyword ‘Selenocysteine’ (e.g. see UniProtKB Q9NZV5).http://www.uniprot.org/docs/ptmlist).Once synthesized, proteins undergo numerous post-translational modifications (PTMs) that can have profound effects on their function and properties. Although hundreds of different types of PTMs are known, from the addition of simple chemical groups or complex molecules onto amino acids to proteolytic processing, a limited number can be accurately predicted. In UniProtKB/Swiss-Prot, 60% of human entries contain PTM information and >72 000 modifications of residues are expertly curated, including >5700 post-translation peptide processing events. Experimental data can be distinguished from data propagated from a related experimentally characterized protein or predicted by a program through the use of ECO codes as discussed above. PTMs are described using a controlled vocabulary: a list of all terms used in UniProtKB is available in the ptmlist.txt document . Although the majority of these SNPs are neutral, i.e. not altering the function of the protein, some dramatically alter proteins and are responsible for phenotypes and diseases .http://www.uniprot.org/docs/humsavar) and for each protein, in the ‘Sequences’ section (http://www.uniprot.org/help/sequences_section).In UniProtKB/Swiss-Prot, priority is given to the curation of single amino acid polymorphisms associated with diseases and phenotypes described in peer-reviewed literature. Currently, 72 960 genetic variants are annotated in UniProtKB/Swiss-Prot entries. 40% of them are associated with a genetic disease and 12% contain information concerning their functional consequence on proteins. This indicates that a large fraction of variants are of unknown significance with respect to protein function and that our biochemical and cellular knowledge is still scarce. All UniProtKB/Swiss-Prot variants can be found in the humsavar.txt table (http://www.uniprot.org/help/pathology_and_biotech_section). For instance, in this section of the entry describing the pseudokinase FAM20A (UniProtKB Q96MK3), we mention that the gene is associated with a form of amelogenesis imperfecta, a disorder affecting the dental enamel .Information related to disease is found in the ‘Pathology and Biotech’ section of entries (l enamel . The disl enamel ]. Strongl enamel . One canhttp://ftp://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/variants/).Recent advances in sequencing technology generate large datasets of variants that provide a comprehensive view of human genetic variation . It is tResults from mass spectrometry-based proteomics experiments also constitute a valuable source of information for the curation of the human proteome. We use them to confirm the existence of proteins and protein PTMs. However, a number of challenges exist for integrating high-throughput proteomics data in UniProtKB. Publications and dataset reports from proteomics experiments exhibit highly variable levels of quality and reliability. This is due to the heterogeneity of proteomics experimental protocols on one side, and to the computational and interpretational stringency of results on the other side. Because the overall number of false identifications increases with the number of incorporated datasets, the integration of peptide identifications that can tolerate up to 1% of false positives may lead to the accumulation and propagation of erroneous annotations. This might be of potentially high cost in time and resources if a false positives protein identification is at the origin of further biological studies and it can undermine the value of databases such as UniProtKB .To limit the number of false positives and only integrate reliable data, we have developed an expert-driven analysis pipeline for integration of proteomics data into UniProtKB/Swiss-Prot . The pipAfter this first evaluation step, we completely reprocess the identified peptide data from publications. Extracting data from publications can be challenging since results are provided in heterogeneous formats . We implemented a robust extraction and filtering system to keep only reliable and consistent information. Mass spectrometry data are not re-analysed but stringent cut-offs are applied on the peptide scores provided in the publications to remove dubious identifications , 29. We et al.  together with a description of how the mappings are generated. This includes peptides from PeptideAtlas . Other publications are either not relevant for curation or present redundant and/or weaker data. Collaborations with other resources are also crucial, because they ensure data consistency, facilitate exchanges, prevent duplication of the curation effort and thereby enhance the efficiency of expert curation. Our collaborations with the HGNC, Havana, Ensembl and RefSeq groups and the GRC on sequence curation in human constitutes an excellent example of a mutually beneficial collaboration and, most importantly, beneficial to the community. In this context, we participated with other annotation resources in defining guidelines for the validation of novel human protein coding loci in a workshop held at the Wellcome Genome Campus in May 2015 .Famiglietti et al., Moreover, expert curation should be in constant evolution to ensure that it meets user expectations. In UniProtKB/Swiss-Prot, we have considerably improved traceability and representation of knowledge to enhance the usability of the resource. We have also restructured a number of annotations into a machine-readable format in order to improve access to this knowledge. We recently modified the format of the ‘Cofactor’ subsection and introduced a controlled vocabulary from ChEBI to describe the cofactors . We are Musmusculus, Rattusnorvegicus, Arabidopsisthaliana, Saccharomycescerevisiae and Mycobacteriumtuberculosis have been curated. In total, from the 77 curated publications, we extracted and verified around 234 000 unique peptides covering 26 236 UniProtKB/Swiss-Prot entries and generating the annotation of 53 519 different PTMs.It is quite clear that high-throughput experiments cannot be ignored and that many studies provide valuable information which is extremely useful for a number of users. Moreover, efforts in the proteomics community in recent years have improved the downstream integration of data, and coordination through the proteomeXchange has shown benefits in the selection of relevant datasets . However(http://www.uniprot.org/docs/humsavar), while variants automatically imported from other resources are available in a distinct file on the UniProt FTP site. Similarly, data from published high-throughput proteomics studies are labeled in UniProtKB/Swiss-Prot with a distinct ECO evidence code (ECO code ECO:0000244) to inform users that such knowledge is derived from a combination of experimental and computational analysis) while automatically generated mappings to peptides in proteomics repositories are available on the UniProt FTP site.Traceability of information is also essential to inform users about the reliability of information, particularly when resources contain a combination of expert and automatic annotations. We always separate expert curation from automatically generated information in UniProt: all protein-coding genes and well-characterized alternative splicing sequences of the human proteome are stored in UniProtKB/Swiss-Prot, while unreviewed sequences corresponding to predicted alternative splicing sequences are stored in UniProtKB/TrEMBL. They complement each other to build a complete proteome. We apply the same principle to protein variants: variants described and confirmed in the literature which have been reviewed by UniProt curators can be found in UniProtKB/Swiss-Prot and in the humsavar.txt tableThe human proteome is prepared by the UniProt Consortium through a process of expert curation that continuously evolves to adapt to changing knowledge, new technologies and the changing needs of its users. The high complexity of the human proteome results from the combination of post-transcriptional and post-translational modifications. Capturing the complexity of the protein world is what we try to reflect in UniProtKB and constitutes a major challenge for proteome curation. From this perspective, the human proteome paves the way for the curation of other proteomes.


Although EM techniques are evolving to the point of directly obtaining structural data at near-atomic resolution, for many molecules the attainable resolution might not be enough to propose high-resolution structural models. However, accessing information on atomic coordinates is a necessary step toward a deeper understanding of the molecular mechanisms that allow proteins to perform specific tasks. For that reason, methods for the integration of EM three-dimensional maps with x-ray and NMR structural data are being developed, a modeling task that is normally referred to as fitting, resulting in the so called hybrid models. In this work, we present a novel application—3DIANA—specially targeted to those cases in which the EM map resolution is medium or low and additional experimental structural information is scarce or even lacking. In this way, 3DIANA statistically evaluates proposed/potential contacts between protein domains, presents a complete catalog of both structurally resolved and predicted interacting regions involving these domains and, finally, suggests structural templates to model the interaction between them. The evaluation of the proposed interactions is computed with DIMERO, a new method that scores physical binding sites based on the topology of protein interaction networks, which has recently shown the capability to increase by 200% the number of domain-domain interactions predicted in interactomes as compared to previous approaches. The new application displays the information at a sequence and structural level and is accessible through a web browser or as a Chimera plugin at  Electron microscopy (EM) is experiencing a revolution with the advent of a new generation of Direct Electron Detectors  and the Many medium resolution EM fitting approaches perform a six-dimensional search to localize the positions where the atomic structures maximize a certain scoring function with respect to the density map. The cross correlation between the atomic density and the EM map has been shown to be a good prediction score . HoweverThe novelty of the work we present is that we do not aim at providing a modeling method per se but, instead, we bring the knowledge on reported protein-protein-interactions (PPIs) to the fitting process, somehow mimicking the role of an expert in the complex under investigation who has at his fingerprints all the relevant published information. The current amount of interactomics data have been proved to be efficient to predict interactions between protein domains  and thushttp://3diana.cnb.csic.es and, also, through Chimera viewer . Also, the 3DID  additional information, such as interactomics data related to binding sites or directly proposes structural templates—if they exist—for protein docking. To that end, we have used the 3DID database  as a souHomologous pairs of proteins often interact using similar interacting areas; however, they are not unique . To encaThis tool allows the evaluation of potential contacts/interactions between the domains of two selected proteins, highlighting which are the most probable domain pairs to interact and, therefore, to be used in more detailed modeling strategies. The application evaluates all possible domain pairs between the selected proteins independently of their structural conformation and physical contacts. The evaluation process is performed with DIMERO , a metho3DIANA can be used to analyze the likelihood of the proposed physical bindings occurring between the proteins and domains of a given candidate hybrid model. Interactions are calculated measuring the distances between nonhydrogen atoms, so that proteins or domains with any pair of nonhydrogen atoms closer than 6 Å are considered to interact. The application displays the physical bindings between the subunits of a protein complex as a color code matrix where each element represents a binary interaction between two subunits of the complex, and its color shows the best scored interacting domain pair between the subunits, as described in DIMERO . FurtherThe Domain Binding Sites tool is used to display known binding sites of protein domains based on the information contained in 3DID structures. The application offers a user-friendly interface to browse all the interactomics information available in 3DID for the different binding sites of the protein domains. The domain binding sites are clustered according to the number of common interacting amino acids and interface geometry, as described in 3DID . The firAnother tool included in 3DIANA offers the possibility to perform docking between protein structures based on DDI structural templates. To this end, when the application finds structural evidence of an interaction between two domains in different chains, the user can access the different structural conformations of the interacting domain pair. The different structures are clustered according to the geometric conformation of the interfaces as described in 3DID . A user-3DIANA integrates the PatchDock package  to perfoThe web application is designed as a desktop-like environment, where the different tools are organized in individual windows or widgets. The application includes different 3D viewers  to displWe envision two scenarios for 3DIANA to work for hybrid models. The first one starts from a user-proposed hybrid model and 3DIANA evaluates the probability for the interactions resulting from the proposed hybrid model to be compatible with the current body of known interactomics information. The second application is aimed at helping the researcher to build a hybrid model maximally compatible with known interactomics data, rather than evaluating a proposed one. In the main text of this work we are going to use two different examples. In the first case (911/FEN1 complex), we will present the two types of scenarios indicated previously, whereas the second example (retinoid X receptor (RXR)/vitamin D receptor (VDR) nuclear receptor) will be used to show how 3DIANA models the interaction between two subunits using the template-based docking approach. Several other examples are presented in the Flap endonuclease 1 (FEN1) is a protein involved in DNA replication and repair contributing to maintain the cellular genome integrity. To perform its function, FEN1 is known to associate with the 911 complex, a heterotrimeric ring that encircles DNA and serves as a mobile platform for different enzymes , 55, 56.3Q8K), the 911 heterotrimeric complex (PDB: 3G65), and the FEN1/911 EM volume (Electron Microscopy Data Bank (EMDB): 2029) with 3DIANA through the Chimera plugin. Both subunits are easily differentiated in the EM volume  is decomposed into their Pfam domains, and a multidimensional table of all possible (known and predicted) DDIs is calculated. This large table is presented to the user just focusing on pairs of proteins, indicating if there are structures that are known to contain interacting pairs of domains and, if not, presenting the probability of interaction based on DIMERO scores. A and C show the structural alignment between all possible interacting conformations of XPG and PCNA domains with the FEN1 subunit. Finally, considering the high fold similarity between the PCNA and Rad1 domains, we used the structural matching tool of Chimera to align the conformations displayed in A and C with the Rad1 domain of the 911 complex. B and D, show the possible conformations of FEN1 and 911 when the XPG and PCNA interacting structures are used as templates to model the interaction.Once potential interactions between the domains of a given complex have been established, 3DIANA offers two additional modules for those cases in which there are instances of experimental structures relating to these domains: domain binding sites analysis and domain-domain template docking. We have previously indicated that there were no experimental structures involving interactions between the domains of 911 and FEN1, but 3DIANA offers the possibility to focus further the query to concentrate into one of the two interacting domains, exploring which are the known interacting partners for which experimental structures do exist. Focusing on the XPG domains, In this way, 3DIANA has accompanied the user all the way, from having the atomic structure of the individual subunits together with a cryo-EM map of the complex, to providing templates of DDIs and, finally, building potential models of the complex. Clearly, 3DIANA has not been designed to build structural conformations close to satisfying the physical restrains of interactions between proteins. Indeed, it aims at proposing initial models that could be further refined by other approaches, such as docking algorithms , 58, molA and B. At this stage 3DIANA evaluates the physical bindings in terms of the DDIs implied by the models; this information is displayed in a matrix where the different elements represent physical bindings between different subunits . Analyzing the two matrices of interactions for the two pseudohybrid models, it is clear that the one in A has interactions that are at least MCPs, whereas the model in B at most has LCPs. On the basis of this information, the first model would be preferred over the second and this was, indeed, the one proposed by the authors.In this case a given hybrid model is proposed by the user without using 3DIANA and then, 3DIANA evaluates its likelihood in terms of the DDIs implied in the proposed hybrid model. In the case of EMDB: 2029, the original authors did not provide a fitting model , but for the sake of argument we would consider that two possible hybrid models could have been submitted, shown in 1DKF , replacKF (1DB1 ) through1DB1 but, instead of using the RXR-LBD mouse protein of the PDB: 1DKF, we selected the human RXR subunit of the PDB: 2P1T structure (red box) allows the user to select only those DDI templates above a certain threshold of sequence identity; thus, the sequence identity between the template domains and the particular domains to be modeled are above the selected threshold. By default, this option is set up at 30%, because it has been shown that above this threshold the interface root mean-square deviation decreases significantly . The cluster panel also shows the highest sequence identity value of the cluster templates calculated against the domains to be modeled. We selected the cluster and DDI template with the highest sequence identity (PDB: 1XV9). When selected, the structure of the template is displayed in a 3D viewer (D) and its sequences are aligned with the domains of interest highlighting the interacting amino acids (B and C). Furthermore, the interacting amino acids are also mapped in the complex subunits and displayed in the main 3D viewer (A); in this manner the interacting residues defined by the particular DDI template can be checked before docking. Once the desired DDI template is selected, 3DIANA can perform docking by aligning the subunits to the template. B shows the model structure of the RXR-LBD/VDR-LBD based on the LBD-LBD template contained in PDB: 1XV9. Finally, this model was manually fitted in the cryo-EM map  to check if the proposed conformation was compatible with the map, with good results.For this demonstration, we have used the same VDR-LBD structure from the PDB: tructure  because tructure , red boxficantly . In thisD viewer D and itno acids , B and CD viewer A; in thIn this work we present 3DIANA, a novel web platform that interfaces EM hybrid models with DDI predictions as well as with current knowledge of experimentally determined protein structures, aimed at guiding the quaternary structure modeling of protein complexes from medium and low resolution structural data. A current amount of interactomics data have been proven to be efficient predicting interactions between protein domains  and modeCurrently, the application can perform different types of analysis and calculations. It allows assessment of potential physical contacts between protein domains, providing an independent measure to evaluate a given model when the user has been able to place the different components within the density map using other sources of information or approaches. In addition, 3DIANA can be used to enrich structural data, annotating experimentally determined biding sites, or modeling the structure of interactions using domain-domain templates. Alternatively, when no information exists on how components could fit or interact because no experimental data is available or the resolution of the map is not enough to place the different subunits, the strategy of DDI prediction can be used to determine which regions of the different interacting proteins may be involved in physical bindings.http://3diana.cnb.csic.es or through Chimera 3D viewer installing the 3DIANA Chimera plugin. All tools integrated in the platform offer a user-friendly and intuitive interface to compute the different scores or to browse the different sources of information; an online help is always available for all widgets. When the Chimera plugin is not used, the web interface integrates Jsmol to display the different 3D structures.3DIANA is accessible through a web browser at Finally, we would like to point out that 3DIANA has been designed to provide a first approximation of how subunits could interact and fit within a density map. For that reason the methodologies integrated in the platform are highly efficient in terms of computational cost but not as accurate as other approaches that have been designed to maximize the modeling accuracy , 66, 67.J.S.: concept, design, acquisition, analysis and interpretation of data, and writing of the manuscript. R.S.G.: design and analysis of data. D.T.M.: design and analysis of data. J.C.A.: design and analysis of data. C.O.S.S.: concept, supervision, analysis, and interpretation of data, and writing of the article. J.M.C.: concept, supervision, analysis and interpretation of data, and writing of the article. All authors read and approved the final manuscript.


Detecting protein complexes within protein–protein interaction (PPI) networks is a major step toward the analysis of biological processes and pathways. Identification and characterization of protein complexes in PPI network is an ongoing challenge. Several high-throughput experimental techniques provide substantial number of PPIs which are widely utilized for compiling the PPI network of a species.Here we focus on detecting human protein complexes by developing a multiobjective framework. For this large human PPI network is partitioned into modules which serves as protein complex. For building the objective functions we have utilized topological properties of PPI network and biological properties based on Gene Ontology semantic similarity. The proposed method is compared with that of some state-of-the-art algorithms in the context of different performance metrics. For the purpose of biological validation of our predicted complexes we have also employed a Gene Ontology and pathway based analysis here. Additionally, we have performed an analysis to associate resulting protein complexes with 22 key disease classes. Two bipartite networks are created to clearly visualize the association of identified protein complexes with the disorder classes.Here, we present the task of identifying protein complexes as a multiobjective optimization problem. Identified protein complexes are found to be associated with several disorders classes like ‘Cancer’, ‘Endocrine’ and ‘Multiple’. This analysis uncovers some new relationships between disorders and predicted complexes that may take a potential role in the prediction of multi target drugs.The online version of this article (doi:10.1186/s13015-015-0056-2) contains supplementary material, which is available to authorized users. Recent advancement in biotechnology produces lots of information about protein–protein interactions. Those information act as a potential source to construct the protein–protein interaction network (PPIN) for a single species. Protein complexes are generally described as molecular aggregation of a set of proteins connected by multiple protein–protein interactions. Protein complexes play different functions in the cell. It can serve as cellular machines, rigid structures, and posttranslational modification systems. In general cellular functions and biochemical events in cell are coordinately performed by a groups of proteins interacting with each other in protein complexes. Identifying such protein complexes is important for understanding the structure and functions of these biochemical events. Moreover, changing of interaction pattern of proteins is the consequence of many diseases. Identifying such interactions through protein complexes predominantly lead to applications in disease diagnosis.In usual representation, protein complexes take the form of a dense clusters of proteins connected through multiple interactions. Different computational methods for finding dense regions in the PPI network are available in literature. Several techniques based on graph clustering, finding dense regions, or clique finding have been proposed to discover protein complexes in PPI networks –4. In 55 MoleculIn  a clusteIn  an algorIn  a multioS. cerevisiae). Although, there have been several studies on determining and analyzing protein complexes in a single organism, computational analysis of human protein complexes is not studied in extensive manner. Some studies have analyzed human protein complexes based on a particular disease association [All these methods primarily focus on the detection of protein complexes in PPI network of model organism yeast   is emploHere a protein complex (or a subgraph of human PPI graph) is encoded as a chromosome. It is represented as For starting from a reasonable position we construct the initial population as a set of modules with high density. For this purpose we randomly chose some substructure consisting of all 1s from adjacency matrix. To find out all 1s substructures we apply a biclustering technique  and randHere two categories of objective functions are built, one is based on the topological characteristics of the network and the other captures gene ontological similarity of proteins. To define the two objective functions belonging to the first one we have incorporated some graph-based properties of the PPIN. For the other category, we employed GO based semantic similarity measure.We have defined two objective functions in this category. One is based on the density of protein complexes, and other is based on closeness centralities. The density of a graph is defined as ratio of the number of edges present in a graph to the possible number of edges in a complete graph of same size. Protein complexes generally represent high dense area in the PPIN. Large number of interactions (or edges) among proteins (or nodes) in the complex is the possible reason behind that. Thus, using density as an objective function and maximizing it for individual subgraphs will yield much denser complexes.C. Now the contribution of a protein cluster can be calculated as the summation of these values which can be formulated as:For choosing the next objective we calculate the contribution of a node as follows: C represents a protein cluster and Closeness centrality of a vertex in a graph is defined as the reciprocal of average shortest-path distance to other vertices. It can be considered as the efficiency of a node  in spreading information to others in the network. Higher value of it indicates that most of the nodes are closer to that node. Here we maximize the objective function:s is the chromosome consisting of nodes E is the set of edges and w is a weight function defined as Proteins within the protein complexes are functionally similar to each other. This suggests that these proteins have high semantic similarity among themselves. This is measured by computing the semantic similarity between GO-terms they are annotated with. As proteins are annotated with multiple GO terms, so, the similarities are calculated by averaging the similarities of the GO term cross pairs which are associated with them . We haveThe usual genetic operators are selection, crossover, and mutation. Here crossover operation is not performed as it produces large number of disconnected graphs. For selection the conventional crowded binary tournament selection in NSGA-II is used here. As whole subgraph is encoded as a chromosome, so a perturbation in node is performed by means of mutation with a high probability , and Accuracy .T) with n rows and m columns where rows and columns represent predicted and real protein complexes, respectively. The value of each cell We have built a Contingency Table  of a clustering result is the weighted average of complex-wise PPV represents a tradeoff between sensitivity and the positive predictive value and it is defined as:In Table J represents the overlap between a benchmark complex and predicted complex. It is defined as j is greater than some threshold value. In this respect Recall (coverage) and precision (sensitivity) can be defined asWe have also performed an analysis to compare the performance of different existing algorithm with the proposed one. Let t from 0 to 1. This is shown in Fig. We evaluate the performance of the existing methods by plotting the precision versus recall curves for the predicted complexes. These curves are plotted by tuning the threshold value In Table Columns 3 and 4 of Table terature –31 it isMost of our predicted protein complexes are associated with SMAD complexes like transforming growth factor–SMAD complex, FOXO–SMAD complex, SMAD1–SMAD4–ECSIT2 containing complex etc. SMADs are intracellular proteins that transduce extracellular signals from transforming growth factor beta (TGF-feration .From Table Here, we discuss the involvement of predicted protein complexes in different disease/disorder classes. The list of disorders/diseases, disorder associated genes and association between genes and disorder/disease classes are obtained from Goh et al. . In Goh To show to what extent the proteins of our predicted complexes are involved in specific disorder/disease classes we plot a bar diagram showing the proportion of protein complexes involved in each of the disorder classes. We assume that a protein complex is associated with specific type of disorder if all the proteins associated with this disorder are belonging to that protein complex. The bar diagram is shown in Fig. We observe from Fig. To identify the overall association between predicted complexes and disease classes we have created a bipartite network between predicted protein complexes and the associated disease classes. One partite set constitutes the complexes, whereas other partite set represents the associated disease classes. Each partite set is connected with other by edges depending on the association between predicted complexes and the disorders involved in disease classes. The network is shown in Fig. We have also performed an analysis to describe the association among protein complexes and disease classes. For this purpose, we have collected PPI information and GO based semantic similarity information of all the genes associated with 22 disease classes. The PPI and GO semantic similarity informations are subsequently converted into 22 PPI and GO semantic similarity networks.We have computed density and average semantic similarity scores from these networks and showed in Table To identify the association among disorders and protein complexes, we have also created another bi-partite network. As depicted in Fig. From Table S1 (Additional file In most of the cases we observe that similar type of disorders have a tendency to get involve in same protein complexes. So we can conclude that protein complexes not only provide a better understanding in molecular evolution but it can also unveil several information of human disorders and uncover new strategies for therapeutic intervention. This may lead to development of new potential strategies to deal with key diseases by giving more importance in protein complex formation information rather than targeting individual proteins.This study introduces a multiobjective approach for detection of protein complexes in human PPI network. Integrating topological features along with GO features, we are able to group functionally similar proteins in same clusters which serve as protein complexes. The algorithm progresses with two primary classes of objectives. Graph based objectives preserve the topological properties of complexes whereas GO based semantic similarity between protein pairs control the accumulation of functionally similar proteins in the same cluster. Moreover the predicted complexes show consistently better result in context of some performance metrics.We have also built an association between predicted protein complexes and 22 primary key disease classes to study the relationship between complexes and disorders associated with these classes. For finding the association of predicted complexes in different disease classes, we have extensively searched the involvement of proteins implicated by different disorders in the predicted complexes and built two bipartite networks between complexes and disorders. Interestingly, we have found most of the predicted complexes are associated with disorders belonging to the ‘Cancer’ disease class. Additionally, the disorders belonging to ‘Endocrine’ and ‘multiple’ disease classes have also shared a significant proportion of proteins involved in multiple predicted complexes.Protein complexes are now considered as potential targets for intervention of new therapeutics to treat against new diseases. It is possible to integrate the drug–disease association information along with the complex–disease association. The new paradigm in drug discovery analysis is now given emphasis on the polypharmacological properties of drugs. Polypharmacological drugs are generally targets multiple cellular function for the treatment of complex diseases. By incorporating protein complexes with drug–disease association, it may be possible to uncover some relationship between protein complexes and targeted drugs.Moreover we can potentially merge the time series gene expression profiles affected by a specific disease with our complex–disease bipartite network structure. This can offer a new way to exploit new topological features and complex modular structure in protein complex–disease and protein complex–disease–drug network. We are now working in this direction.


Accurate determination of protein complexes has become a key task of system biology for revealing cellular organization and function. Up to now, the protein complex prediction methods are mostly focused on static protein protein interaction (PPI) networks. However, cellular systems are highly dynamic and responsive to cues from the environment. The shift from static PPI networks to dynamic PPI networks is essential to accurately predict protein complex.The gene expression data contains crucial dynamic information of proteins and PPIs, along with high-throughput experimental PPI data, are valuable for protein complex prediction. Firstly, we exploit gene expression data to calculate the active time point and the active probability of each protein and PPI. The dynamic active information is integrated into high-throughput PPI data to construct dynamic PPI networks. Secondly, a novel method for predicting protein complexes from the dynamic PPI networks is proposed based on core-attachment structural feature. Our method can effectively exploit not only the dynamic active information but also the topology structure information based on the dynamic PPI networks.We construct four dynamic PPI networks, and accurately predict many well-characterized protein complexes. The experimental results show that (i) the dynamic active information significantly improves the performance of protein complex prediction; (ii) our method can effectively make good use of both the dynamic active information and the topology structure information of dynamic PPI networks to achieve state-of-the-art protein complex prediction capabilities.The online version of this article (doi:10.1186/s12859-016-1101-y) contains supplementary material, which is available to authorized users. Prediction of protein complexes from protein-protein interaction (PPI) networks has become a key problem for revealing cellular function and organization of biological systems in post-genomic era. In a cell, proteins are central part of life activity. However, most of proteins are functional only after they are assembled into a protein complex which carry out almost all of the biochemical, signaling and functional processes in a cell. Protein complexes are of great importance for understanding the principles of cellular organization and function –3.With the development of high-throughput techniques, such as yeast two-hybrid and mass spectrometry, a large amount of PPI data has been generated , 5. As aHowever, these methods described above only focus on the static PPI networks. In reality, the PPI network in a cell is not static but dynamic, which is changing over time, environments and different stages of cell cycles . GeneralThere are mainly two ways to construct dynamic PPI networks based on gene expression data and high-throughput PPI data. One major methodology to construct dynamic PPI networks is based on gene expression variance of each protein. In general, if a protein is at active time point, the expression level of the corresponding gene is at the peak point. Based on this assumption, Wang et al.  inject gCellular systems are highly dynamic and responsive to cues from the environment , 19. BotThe gene expression data is very valuable to reveal the dynamic properties of proteins and PPIs. We integrate gene expression data with high-throughput PPI data to construct dynamic PPI networks. Based on gene expression data, we use both gene expression variance information and co-expression correlations information to calculate the active time point and active probability of each PPI in dynamic PPI networks.et al. [k-sigma  threshold for each gene expression data p.Since the gene expression level of a protein will decrease after the protein has completed its function, different peak time points of gene expression value may represent the dynamic changes of protien activities. In general, a protein is active at the time point, when its related gene expression value is at the high level. A simple idea is to use a single global threshold for identifying the active time point of each protein. If the gene expression value of a protein is higher than the global threshold at a time point, the protein is active in the time point. Actually, it is very difficult to use a global threshold to identify the active time point of proteins. There are at least two reasons. On the one hand, the expression level of different protein in activity period is different. On the other hand, there is inevitable background noise in gene expression data. To solve these problems, Wang et al.  propose α(p) and σ(p) are the arithmetic mean and the standard deviation (SD) of the gene expression data p, respectively. Threshk is determined by the values of α(p), σ2(p) and k (the times of sigma). Let X be a real random variable of normal distribution N. For any k > 0, P{|X-α| < kσ} = 2Φ(k)-1, where Φ(.) is the distribution function of the standard normal law. In particular, for k = 1,2,3 it follows that P{|X-α| < σ} = P{α-σ < X < α + σ} ≈ 0.6827, P{|X-α| < 2σ} ≈ 0.9545 and P{|X-α| < 3σ} ≈ 0.9973. Similarly, In the equation  be the gene expression value of the gene p at the time point i. For instance, based on the three-sigma rules, when Gi(p)> α(p) + 3 ⋅ σ(p), the probability that the protein p (product of gene p) is active at the i time point is 99.7 %. But when Gi(p)> α(p) + σ(p), the probability that the protein p (product of gene p) is active at the i time point is only 68.3 %.where i to represent the active probability of proteins at the time point i. Based on the above empirical rules, the active probability Pri(p) of protein p at the time point i can be calculated as follows:We use a column Pri.If the value of Gi(p) is lower than Threshk(p), the active probability is 0. It indicates that the protein p is not active in the i time point, when Pri(p) is equal to 0. This method not only identifies the active time points for each protein, but also distinguishes the active level of the protein by its active probability, which is more reasonable than both global threshold methods and active threshold methods. But we also note that, in some extreme cases, our method still cannot accurately identify the active time points of proteins. The whole activity PPI networks Acti are built for each time point:Thus, we can use four levels  to represent the active probability of each protein at the time point i is a column vector representing the activity of all proteins at time i and PriT is the transpose of the column vector Pri.where PrCoe. Coei denotes the coexpression protein networks Coe at the time point i. Calculation of correlation coefficient requires multiple sequential expression data that cover a period of time. We set a time window on the original expression data, which covers three sequential time points. When i is the current time point, the time window covers three time points including i-1, i and i + 1. We use a predefined threshold to filter the small value of correlation coefficient in Coei as follows:Coexpression correlation coefficient is used as a measure of coexpressed genes having the same expression variance patterns across different conditions, which is a strong indicator of protein functional associations. Zhang et al. use the Pearson correlation coefficient  to calculate the coexpression correlation of gene expression data and build coexpression protein networks at different time points . We use P_Correlation is the Pearson correlation coefficient between the protein m and protein n at the time point i. Pre_thresh is the predefined threshold, and we can choose the optimal value for Pre_thresh by preliminary experiments. In our experiments, we set Pre_thresh as 0.5.where adj_SPN denote the static PPI networks adjacency matrix. Integrating Act, Coe and adj_SPN, we can calculate the dynamic PPI networks adjacency matrix adj_DPN at the time point i as follows:The high-throughput PPI data can construct a static PPI networks. Let where ○ represents element-wise multiplication. Equation  integratv1 has two active time points (T1 and T3), and its active probability is 0.99 at T1 active time point. A PPI in the static PPI networks  in DPNTi is defined as follows:The edges in the active PPI subnetworks Nu and Nv denote the neighbors of protien u and protein v respectively. |Nu∩Nv | denotes the common neighbors of u and v. In the respect of topology structure of an active PPI subnetworks, the more common neighbors u and v share, the closer the interaction of two proteins u and v is. Cluster score can effectively balance the effect between the topology closeness and the active probability of each PPI in the active PPI subnetworks.where Candidate_complex. In the second phase, our method filters the candidate complexes set Candidate_complex to remove the highly overlapped protein complexes.A protein complex is a group of proteins assembled by multiple PPIs at the same time and place , 21. MorCluster_Score of all edges in DPNTi based on equation . The top ranked seed edge S1 was then inserted into the Temp_Candidate_core set CORE and removed from Seed_list. We augment the seed edge S1 to generate the core structure by adding the suitable neighbor proteins one by one at line 13-17. If the Density value of the core structure is not less than Complex_thresh when adding the neighbor protein p, it will be added into the core structure. To ensure that the core structure are non-overlapping, the overlapped seed edges are removed from Seed_list at line 19-21. Finally, the attachment proteins are detected for each core structure based on the Attach_Score that is calculated by equation ) at line 2-5. The candidate protein complex associated with highest Density value in Candidate_list is added into Complex_set and removed from Candidate_list. For any other candidate protein complex cci ∈Candidate_list, we check the overlapped degree between cci and cc1. If the overlapped degree is larger than the Overlap_thresh that is a predefined threshold parameter, cci is directly removed from Candidate_list at line 9-12. After preliminary experiments, the Overlap_thresh is set as 2/3 in our experiments. These steps are repeated until Candidate_list is empty. Consequently, the final protein complex set Complex_set is generated.The candidate protein complexes in Complex_thresh parameter is assessed and discussed. Then, our method is compared with current state-of-the-art protein complex prediction methods. Finally, we present an example of predicted protein complex to illustrate the advantages of our method. This implement of our algorithm and the experimental datasets are avialable in the Additional files In this section, the datasets and evaluation metrics used in the experiments are described. The impact of the In our experiments, we choose four high-throughput yeast PPI datasets including Gavin dataset , Krogan T1,T2,…,T12) for each gene in a cycle. We construct four dynamic PPI networks to integrate high-throughput PPI data and gene expression data. DPN_Gavin, DPN_Krogan, DPN_MIPS and DPN_STRING are constructed by integrating gene expression data GSE3431 with the Gavin dataset, Krogan dataset, MIPS dataset and STRING dataset, respectively.The gene expression data used in our experiment is GSE3431  downloadThe benchmark protein complex dataset CYC2008  includesP be a predicted complex and B) be a known complex. We define the neighborhood affinity score NA between Pand B) as follows:To assess the quality of predicted protein complexes, we match generated complexes with the benchmark complex set CYC2008. Let NA is 1, it means that the identified complex P has the same proteins as a known complex B. On the contrary, if NA is 0, it indicates no shared protein between P and B. We considered P and B to match each other if NA was larger than 0.2, which is the same as most methods for protein complex identification [If fication .F-score have been used to evaluate the performance in most of previous complex prediction studies, which are defined as follows:Precision, recall and Nci is the number of identified complexes which match at least one known complex, and Ncb is the number of known complexes that match at least one identified complex. Identified_Set denotes the set of complexes identified by a method and Benchmark_Set denotes the gold standard dataset. Precision measures the fidelity of the predicted protein complex set. Recall quantifies the extent to which a predicted complex set captures the known complexes in the benchmark set. F-score provides a reasonable combination of both precision and recall, and can be used to evaluate the overall performance. To keep our evaluation metrics as the same as the most studies, we choose F-score as the major evaluation metrics.where et al. [Recently, sensitivity (Sn), positive predictive value (PPV) and accuracy (Acc) have also been used to evaluate protein complex prediction tools. Acc represents a tradeoff between Sn and PPV. The advantage of the geometric mean is that it yields a low score when either Sn or PPV are low. A high degree of accuracy thus requires a high performance for both criteria. These definitions have been described in detail by Li et al. . In our Complex_thresh for protein complex prediction task on different dynamic PPI networks. As described in Algorithm 1, the Complex_thresh determines the number of seed edges in the Seed_set, as well as the Density value of the core structure. The range of Complex_thresh is from 0 to 1. We can choose the optimal value of Complex_thresh by the experimental approach.In this experiment, we evaluate the effect of the threshold parameter Complex_thresh on DPN_Gavin. The detailed experimental results on DPN_Gavin with different Complex_thresh are shown in Table Complex_thresh takes from 0 to 1. When Complex_thresh = 0, our method can predict 623 protein complexes on the DPN_Gavin. On the contrary, our method cannot predict any protein complexes on the same DPN_Gavin when Complex_thresh =1.0. Based on the equation (Seed_set if we set Complex_thresh =1.0. Overall, with the increase of Complex_thresh, the recall, Sn and Acc are decreased in Table Complex_thresh =0.7, and the PPV achieves the highest value of 0.906 when Complex_thresh =0.9. The major metrics F-score is ranged from 0.048 to 0.524. When Complex_thresh =0.1, the F-score achieves the highest value of 0.524.We first evaluate the effect of equation , the CluComplex_thresh on the DPN_Krogan and DPN_MIPS. The detailed experimental results with different Complex_thres are shown in Tables Complex_thresh on the DPN_Krogan and DPN_MIPS are similar to the experimental results on the DPN_Gavin. When Complex_thresh =0.1, our method achieves the highest F-score of 0.52 and 0.372 on the DPN_Krogan and DPN_MIPS, respectively. Based on these experimental results on three DPNs, it can be seen that our method can achieve high performance for protein complex prediction by setting Complex_thresh = 0.1.Then, we evaluate the effect of Complex_thresh =0.1. To equally compare the performance, we test all comparison methods on the Gavin, Krogan and MIPS dataset, respectively, and choose the optimal parameters. The highest value in each row was shown in bold.In this experiment, we compare our method with the following established leading protein complex prediction methods: CSO , ClusterF-score of 0.524, recall of 0.434 and PPV of 0.617, respectively, which significantly outperforms other methods. CSO achieves a high F-score of 0.411 and the highest Sn of 0.476, which exploits the GO annotation data to improve the performance of protein complexes identification. HUNTER achieves the highest precision of 0.87. But the recall of HUNTER is only 0.206, which leads to a low F-score of 0.333. Cluster ONE achieve the highest Acc of 0.524. We also note that our method can predict more protein complexes than other methods. For example, our method can predict 447 protein complexes on the Gavin dataset. In contrast, MCODE and HUNTER only identify 66 and 69 protein complexes on the Gavin dataset, respectively.Firstly, we compare these methods on the Gavin dataset. As shown in Table F-score of 0.52, recall of 0.591, Sn of 0.448 and Acc of 0.551. CSO also achieves a high F-score of 0.455, which is only inferior to our method. HUNTER and Cluster ONE achieve the highest precision of 0.865 and PPV of 0.681, respectively. On the MIPS datasets, our method also achieves the highest F-score of 0.372, recall of 0.576 and Acc of 0.483, respectively. HUNTER, CMC and Cluster ONE achieve the highest precision of 0.654, Sn of 0.389 and PPV of 0.668, respectively.Secondly, we compare these methods on the Krogan dataset and MIPS dataset. On the Krogan dataset, it can be seen that the results on the Krogan dataset are similar to the results on the Gavin dataset. On the Krogan datasets, our method achieves the highest F-score of all comparison methods except for our method on STRING dataset are clearly inferior to the F-score on other three datasets. The compared experiments were conducted on a 3.3GHz four-Core Intel I5 CPU and 8GB main memory. Actually, CSO and CMC methods cannot output the results on STRING dataset, because the clique mining algorithms used by the two methods are very memory and CPU cycle consuming in such large PPI networks. Compared with other methods, our method firstly use STRING dataset and gene expression data to construct a whole dynamic PPI networks DPN_STRING which consists of 12 active PPI subnetworks, {DPNT1, DPNT2, …, DPNT12}. Then, our method predicts the protein complexes from these active PPI subnetworks in turn. Since each active PPI subnetwork is much smaller than the whole static PPI networks, our method is more suitable to deal with very large PPI dataset such as STRING than other methods. From Table F-score of 0.417 and recall of 0.586 on STRING dataset.Thirdly, we compared these methods on the STRING dataset. STRING dataset is much larger than other three PPI datasets, which contains 99786 PPIs. Due to the complexity of the PPI network constructed by STRING dataset, it is much more difficult to predict protein complex on STRING dataset than other three datasets. From Table In summary, our method not only effectively integrates gene expression data and high-throughput PPI data to construct dynamic PPI networks, but also makes good use of dynamic information of dynamic PPI networks to improve the performance of protein complex prediction. Our method is competitive or superior to the current protein complexes identification methods, and achieves the state-of-the-art performance on different yeast PPI datasets.T7. This indicates that all these proteins will be active in the active PPI subnetwork DPNT7. Eventually, our method exactly predicts the RNA polymerase I complex from the PPI subnetwork DPNT7 rather than from the whole PPI network. Furthermore, this result suggests that the life period of the RNA polymerase I is at T7 time point. Compared with other methods, our method can predict the RNA polymerase I exactly from the very large PPI dataset STRING, as well as the active time point of the complex.Figure In Fig. We integrate gene expression data and high-throughput PPI data to construct dynamic PPI networks. Based on gene expression data, we calculate the active time point and the active probability of each protein and PPI. Compared with static PPI networks, dynamic PPI networks can effectively represent both the dynamic active information and the topology structure information of PPI networks. Using dynamic PPI networks, we develop a novel method for protein complex prediction. Experimental comparisons on different PPI datasets show that our approach achieves the state-of-the-art PPI performance. In the future, we will cooperate with biomedical experts to further validate the protein complexes identified by our method. We will also attempt to apply our method to analysis other organisms.