Schizosaccharomyces pombe that did not contain RNA–binding domains, we found that ∼38% copurify with mRNAs that encode interacting proteins. For example, the cyclin-dependent kinase Cdc2p associates with the rum1 and cdc18 mRNAs, which encode, respectively, an inhibitor of Cdc2p kinase activity and an essential regulator of DNA replication. Both proteins interact with Cdc2p and are key cell cycle regulators. We obtained analogous results with proteins with different structures and cellular functions . We showed that copurification of a bait protein and of specific mRNAs was dependent on the presence of the proteins encoded by the interacting mRNAs and on polysomal integrity. These results indicate that these observed associations reflect the cotranslational interaction between the bait and the nascent proteins encoded by the interacting mRNAs. Therefore, we show that the cotranslational formation of protein–protein interactions is a widespread phenomenon.Most cellular processes are conducted by multi-protein complexes. However, little is known about how these complexes are assembled. In particular, it is not known if they are formed while one or more members of the complexes are being translated . We took a genomic approach to address this question, by systematically identifying mRNAs associated with specific proteins. In a sample of 31 proteins from  Most proteins do not function in isolation. Instead, they associate with other proteins to form complexes. Little is known about the assembly of protein complexes within cells. One possibility is that proteins are completely synthesised before they bind to each other. An alternative is that proteins attach to each other as they are being translated in the ribosome . To investigate if cells use cotranslational assembly to form complexes, we identified mRNAs associated with specific proteins. The expectation is that if protein A binds to protein B as protein B is being translated, A will associate indirectly to the mRNA encoding B. Indeed, we found that for ∼40% of proteins (out of a sample of over 30) this was the case. Proteins associated with a small number of mRNAs, most of which encoded known or predicted interacting proteins. We found examples of this phenomenon in proteins with different functions and structures, indicating that cotranslational assembly is widespread. Cotranslational assembly might be required for certain proteins to associate, or it might be important in cases where the early formation of a protein complex is beneficial, such as when a protein is toxic or unstable unless bound to a partner. One possibility is that proteins are fully translated and released into the cytoplasm before finding their interacting partners . Alternatively, protein-protein interactions could form as one or several of the interacting proteins are being translated .The majority of cellular proteins function as subunits in larger protein complexes. However, very little is known about how protein complexes form Ribonucleoprotein Immunoprecipitation analysed with DNA chips) can be used to study cotranslational pathways involved in protein biosynthesis Schizosaccharomyces pombeSaccharomyces cerevisiae found that the SET1 mRNA is part of a complex containing four components of the SET1C histone methyltransferase complex. The protein-RNA interactions were dependent on active translation, suggesting that the complex between these proteins was formed cotranslationally There are indications that some cytoskeletal proteins, including vimentin, myosin and titin, assemble cotranslationally into insoluble filaments To address these questions we carried out RIp-chip experiments with 31 proteins with different functions and structures. We found that more than 12 of the proteins interacted specifically with small numbers of mRNAs (between 1 and 3), most of which encoded proteins that are known or predicted to interact with the bait proteins. We examined the protein-RNA interactions of three proteins in detail: in all cases we found that the interactions required the presence of the protein encoded by the associated mRNA as well as active translation. These data demonstrate that these protein mRNA interactions reflect the cotranslational formation of protein-protein interactions, and suggest that this is a widespread phenomenon.S. pombe, we performed RIp-chip experiments with the Tea2p kinesin. Surprisingly, Tea2p copurified specifically with only two mRNAs: tea2 and tip1  or through a sequence-specific RNA-binding protein (model 2). In model 3, Tea2p interacts with the Tip1p nascent peptide, and thus pull downs the tip1 mRNA as part of the whole polysome. In this case, the complex between Tea2p and Tip1p forms cotranslationally. The different models can be distinguished from each other experimentally by their dependence on the Tip1 protein for the interaction: in models 1 and 2 (recognition of RNA sequences), the interaction between Tea2p and tip1 mRNA should be independent of the presence of Tip1p. In model 3 , the association should only occur if Tip1p is present.As part of a project to identify RNAs associated with molecular motors in and tip1 . tip1 en 1 and 2 , Tea2p ctip1 RNA but not Tip1 protein (tip1 (−ATG-tip1), and expressing the resulting construct in cells in which the endogenous tip1 gene had been deleted (see tip1 (+ATG-tip1). While Tea2p and tip1 mRNA copurified in +ATG-tip1 cells, the interaction was completely lost in −ATG-tip1 cells , we carried out RIp-chip experiments with Tip1p. In this case, Tip1p coprecipitated with its own mRNA, but not with that of tea2  inhibitor that associates with Cdc2p and inhibits its kinase activity cdc18, which encodes an essential DNA replication factor (a homologue of budding yeast CDC6) A striking example is provided by Cdc2p, the ortholog of CDK1 in higher eukaryotes. Cdc2p is the only cyclin-dependent kinase in fission yeast and is an essential regulator of cell cycle progression. Cdc2p interacted with two mRNAs: pyp2, cip2 and its own transcript. Pyp2p is a protein tyrosine phosphatase that directly binds and dephosphorylates Sty1p rpn1301, rpn1302) and a protein required for the assembly of the proteasome core and regulatory subunits (ecm29). A second component of the 19S proteasome, Rpt2p/Mts2p, interacted with the ubp6 and rhp23 mRNAs. Ubp6p is a proteasome-associated ubiquitin C-terminal hydrolase, while Rhp23p contains a ubiquitin-like N-terminus. The budding yeast orthologs of these two proteins  copurify with components of the proteasome. We also looked at two transcription factor of the b-ZIP family, Atf1p and Pcr1p, which can form heterodimers with each other pcr1 and its own mRNA, while Pcr1p only pulled down its cognate mRNA. Finally, Mnh1p, the S. pombe ortholog of the Mago nashi protein  associated with the mRNA of mni1 (SPBC19C7.01), which encodes a protein that contains a Mago nashi-binding domain.Another protein kinase, Sty1p, which is a MAP kinase that mediates most stress responses in fission yeast, interacted with three mRNAs, S. pombe nuclear Arps are components of several chromatin-remodelling complexes . Arp6p, a component of the Swr1C chromatin-remodelling complex, copurified with the alp5 mRNA  snf21 and snf22). The Ino80 complex contains three Arps  ino80 mRNA, while Arp5p and Alp5p pulled down only their own mRNAs. Unexpectedly, Arp2p, a member of the family that regulates actin polymerisation, copurified with the mRNAs of two unrelated Arps (arp8 and arp9). These proteins have very different functions and are not expected to interact directly with Arp2p. Therefore, the nature of the protein-RNA interactions of the Arps could allow the assignment of one protein (Arp6p) to one of several related complexes, and the unambiguous allocation of three proteins to a specific complex .These data suggested that the mRNAs associated to a protein can provide insight into their cellular function and the protein complexes they belong to. To test this idea we examined all 10 members of the Actin-related protein family, which are structurally similar to actin but involved in functions as varied as chromatin remodelling, actin polymerisation or microtubular transport (as part of dynactin). These functions are carried out as part of different protein complexes that have been well characterised tip1, these interactions reflect cotranslational formation of the corresponding protein complexes, we characterised in more detail the interactions between Sty1p/cip2 and Cdc2p/rum1. We followed the same strategy described above for Tea2p/tip1. First, we made non-translatable versions of the rum1 and cip2 mRNAs  was unaffected by the mutation. By contrast, neither cip2 nor pyp2 copurified with Sty1p after puromycin or EDTA treatments , the proteins are key regulators of the bait protein. This phenomenon is not limited to a specific type of protein , nor is it restricted to specific cellular processes . In fact, the functional association between bait proteins and their associated mRNAs could conceivably be used to gain insight into the function of the bait protein (if the proteins encoded by the interacting mRNAs have known functions) or to identify interacting partners. Although previous work had hinted at the importance of cotranslationally assembly , this is the first demonstration that cotranslational assembly is a widespread and ubiquitous process.Our data show that around 38% of a randomly selected set of proteins that do not contain canonical RNA-binding domains specifically copurify with small number of mRNAs (between 1 and 3). Remarkably, the majority of these mRNAs encode proteins that are closely related to the proteins used as bait, either as known direct interactors or as members of the same multiprotein complex. In some cases (rum1 but not with cdc13 (which encodes a B type cyclin that binds to Cdc2p), despite the fact that rum1 mRNA levels are almost 6-fold lower that those of cdc13 and both mRNAs are associated with similar number of ribosomes (3.7 for rum1 and 4.2 for cdc13) We notice that the protein-mRNA interactions we report are extremely specific. For example, Cdc2p interacts with and is regulated by many proteins, including at least four cyclins, protein phosphatases (Cdc25p), protein kinases (Wee1p) and kinase inhibitors (Rum1p). In addition, Cdc2p is thought to have dozen of targets. However, Cdc2p associates specifically with two mRNAs. This suggests that only a fraction of all protein-protein interactions are formed cotranslationally. Interestingly, both proteins encoded by the Cdc2p-bound mRNAs are phosphorylated by Cdc2p and degraded through the same mechanism It has recently been shown that many proteins that lack RNA-binding domains can interact directly with specific mRNA sets The cotranslational formation of protein complexes could be required for multiple reasons in the cell. It is possible that some interactions can only form before a given member of the complex has folded completely. Indeed, it is relatively frequent that recombinant proteins need to be coexpressed in order for them to form a complex. Second, some proteins are unstable in the absence of their partner. In this case, cotranslational assembly would stabilise the protein by reducing the time during which a protein is susceptible to degradation. This explanation has been proposed for the SET1C histone methyltransferase complex Unexpectedly, we have found that a large fraction of protein-protein interactions are likely to be formed cotranslationally, and we demonstrate that the RIp-chip strategy can provide a genome-wide view of this phenomenon. The protein-RNA networks we present here add another layer of complexity to the formation and regulation of protein complexes in eukaryotic cells.alp5 and arp10, where ∼400 nucleotides of ORF and 3′UTR sequences were cloned into the pFA6a-2xTap-Kan vector arp1 (integrated in pat1 diploids) and alp5 and arp10 (transformed into wild type diploids). Successful tagging was verified by western blot as described below. All epitope-tagged strains grew normally and displayed normal cell shape, showing that the tagged proteins were functional. A complete list of the strains used in this work is presented in leu1 locus was performed as described Standard methods were used for fission yeast growth and manipulation leu1 locus of a strain in which the endogenous copy of the mutated gene had been deleted  region was amplified by PCR as a KpnI/SalI fragment and cloned upstream of the ORF. The leu1 gene was amplified by PCR as a XmaI/NotI and cloned into the vectors above. The plasmids were linearised with NruI before transformation into leu1-32 cip2Δ cells.The tip1 ORF and 154 bp downstream were amplified by PCR from genomic DNA as PacI/AscI fragment and cloned into pFA6a. Two constructs were made: in the wild type control the 5′ primer contained the endogenous ATG, while in the −ATG one the ATG was mutated to TTT. The tip1 promoter region (865 bp) was amplified by PCR as a BamHI/PacI fragment and cloned into the vector above. The whole construct containing the tip1 ORF and flanking regions was cloned into pJK148 (containing a leu1 marker) as a KpnI/BamHI fragment. The construct was linearised and transformed as described above into leu1-32 tip1Δ cells.The rum1 we used site-directed mutagenesis to mutate the first five ATGs of the ORF to CTGs. However, we found that this construct was still able to complement the sterility phenotype of a rum1Δ strain. This suggested that translation was taking place from a cryptic start site (different from ATG). Therefore, we made a construct in which all seven in-frame ATGs in the rum1 ORF were mutated to stop codons. For this construct a DNA fragment was synthesised (GENEART) that contained the whole ORF and flanking sequences (from the NheI to the SphI restriction sites), and in which all 7 in-frame ATGs had been replaced with the stop codon TGA. The digested DNA fragment was used to replace the corresponding section in pJET2.1 containing a leu1 marker and 2.6 kb of rum1 ORF and UTR sequences. −ATG and +ATG rum1 plasmids were linearised with NruI and transformed into leu1-32 rum1Δ cells.For rum1, mutation of the first ATG of cip2 and tip1 was sufficient to create a loss-of-function phenotype indistinguishable to that of the deletion mutant. As both genes contain several ATGs downstream of the annotated initiation codon, we cannot completely rule out the possibility that in the –ATG mutants there is initiation from downstream ATGs (or from other cryptic sites different from ATG), leading to the formation of N-terminally truncated peptides. However, any such peptides would not be functional  to detect the protein A-binding domains of TAP. Myc tags were detected using the 9E11 monoclonal (Abcam).We followed a previously published protocol S. pombe DNA microarrays or to custom-designed oligonucleotide microarrays manufactured by Agilent as described Total RNA purified from the cell extract was used as a reference in all experiments. 20 µg of total RNA and all the RNA from the IP were labelled using the SuperScript Plus Direct cDNA Labelling System (Invitrogen). Labelled cDNAs were hybridised to PCR 10 ratios were used for the analysis. We compiled a list of common mRNA unspecific contaminants, which were removed from the analysis. Selection of enriched RNAs in the RIp-chip experiments was carried out as described −3 to ∼6.25×10−6. All RIp-chip experiments in which mRNAs different from the one encoding the bait were detected were carried out at least twice. Other RIp-chips (negatives or containing only the cognate mRNA of the bait) were performed once or twice. All repeats were independent biological experiments and dyes were swapped for at least one experiment with each protein.Only probes corresponding to coding sequences were considered. Spots with unreliable signals were removed as follows: for the PCR microarrays, spots that did not show a minimum of 55% of pixels above the median background signal plus two standard deviations in the immunoprecipitate and 90% of pixels in the total RNA (or at least 90% in the channel of the IP) were removed; for the Agilent microarrays the corresponding thresholds were 70%, 98% and 98%, respectively. Median logAll raw and normalised microarray data have been deposited in ArrayExpress (accession number E-TABM-1158). Figure S1Non-translatable mRNAs are expressed at levels comparables to wild-type mRNAs. Raw microarray signals of total wild type mRNAs (+ATG) compared to untranslatable mRNAs (−ATG). The data are shown for every probe of the microarray and have not been normalised or filtered to remove weak signals. The number of independent probes varies between 2 and 5 depending on the microarray platform used for the experiment. We detected strong signals for all the −ATG constructs, ruling out the possibility that the mRNAs encoding untranslatable proteins are degraded.(PDF)Click here for additional data file.Figure S22O2. csx1Δ cells are sensitive to oxidative stress. This phenotype is suppressed by deletion of cip2. The presence of the −ATG-cip2 construct did not affect the phenotype of cip2Δ, indicating that no functional Cip2 protein was produced. (B) Morphology of tip1Δ mutants. tip1Δ cells containing the −ATG-tip1 construct or the corresponding wild type control (+ATG) were grown for 48 hours in YES and inoculated into fresh medium. −ATG-tip1 cells showed the characteristic branched morphology of tip1 null mutants, while the wild type control suppressed the tip1Δ phenotype. Scale bars: 10 µm. (C) Sterility of rum1Δ mutants. rum1Δ expressing −ATG-rum1 or the corresponding wild type control (+ATG) were incubated on malt extract plates for 48 hours. −ATG-rum1 did not suppress the sterility phenotype of rum1Δ cells, whereas rum1Δ cells with the +ATG construct mated and sporulated normally. Scale bars: 10 µm.No functional proteins are produced from the untranslatable mRNAs. (A) Spot assays to measure sensitivity to oxidative stress. Fourfold serial dilutions were plated on yeast extract (YES) plates (Control) or YES plates containing 1 mM H(PDF)Click here for additional data file.Figure S310 enrichment ratios in the corresponding RIp-chip experiments Click here for additional data file.Table S1mRNA enrichments in puromycin, EDTA and ΔATG experiments. The numbers display the number of standard deviations above the median enrichment of all mRNAs in the immunoprecipitate (see Methods). Multiple numbers correspond to independent biological replicates of the experiment. Cases where there were not enough mRNAs in the IP to calculate the median enrichment for the background distribution, but in which the corresponding mRNA was clearly present, are denoted as P. mRNAs that were not detectable in the immunoprecipitate are indicated as ND. The mRNAs used for the ΔATG experiments are underlined.(PDF)Click here for additional data file.Table S2mRNA enrichments in all RIp-chip experiments. Only mRNAs considered significant are displayed. The numbers represent the number of standard deviations above the median enrichment of all mRNAs in the immunoprecipitate (see Methods). For experiments performed three times or less the enrichment in each biological replicate is indicated. For other experiments the numbers indicate the average and standard deviation of the enrichments.(PDF)Click here for additional data file.Table S3Strains used in this study. YGRC: Yeast Genetic Resource Center, Osaka City University (Japan).(PDF)Click here for additional data file.Dataset S1Normalised enrichment levels for all RIp-chip experiments.(XLS)Click here for additional data file.


Compared with the previous version, the number of high-confidence protein–chemical interactions in human has increased by 45%, to 367 000. In this version, we added features for users to upload their own data to STITCH in the form of internal identifiers, chemical structures or quantitative data. For example, a user can now upload a spreadsheet with screening hits to easily check which interactions are already known. To increase the coverage of STITCH, we expanded the text mining to include full-text articles and added a prediction method based on chemical structures. We further changed our scheme for transferring interactions between species to rely on orthology rather than protein similarity. This improves the performance within protein families, where scores are now transferred only to orthologous proteins, but not to paralogous proteins. STITCH can be accessed with a web-interface, an API and downloadable files.STITCH is a database of protein–chemical interactions that integrates many sources of experimental and manually curated evidence with text-mining information and interaction predictions. Available at  Protein–chemical interactions are essential for any biological system; for example, they drive the metabolism of the cell or initiate many signaling cascades and most pharmaceutical interventions. A large collection of such interactions can, therefore, be used to study a variety of cellular functions and the impact of drug treatment on the cell. For such research, it is important to have, as complete as possible, data on protein–chemical interactions. By treating proteins and chemicals as nodes of a graph, which are linked by edges if they have been found to interact , we can Compared with STITCH 3, we use the same underlying set of proteins, containing 1133 species. We updated the set of chemicals , and fini or IC50 i or IC50Database ,11. FromDatabase  and kinaDatabase , GLIDA  and predicted interactions, based on text mining and other prediction methods. In the text-mining channels, interactions were extracted from the literature using both co-occurrence text mining and Natural Language Processing ,22. For Drosophila melanogaster, Escherichia coli, Homo sapiens, Mus musculus, Saccharomyces cerevisiae and Schizosaccharomyces pombe.In previous versions, we have used medical subject headings (MeSH) terms in text mining and when importing external databases. These terms allowed us to expand concepts like ‘alpha adrenergic receptors’ to individual proteins. We used to map MeSH terms to proteins using a combination of automatic and manual approaches, which led to errors in some cases. Furthermore, the mapping was only valid for human proteins. We have, therefore, started to use terms from the Gene Ontology  to defix−1 (i.e. interacting with one protein is not predictive for the other proteins).As the coverage of synonyms is lower than for MeSH terms, we manually added additional synonyms to GO terms to increase the text-mining sensitivity. As one GO term corresponds to multiple proteins, the resulting confidence score for the individual protein–chemical interactions should be down-weighted compared with interactions that are directly associated with a single protein. We, therefore, determined a correction factor through benchmarking (as a function of the number of member proteins in the GO term). For each channel, we looked at the GO terms that are interacting with chemicals. We then checked if the member proteins that are part of the GO terms are in turn interacting with chemicals. For each of these chemicals, we determined the fraction of member proteins that are interacting. For example, if a drug was known to bind two of the three α2-adrenergic receptors, it was added as a data point  to the benchmark data. The data points were then fitted for each channel by the following function:In this version of STITCH, we introduced a fourth channel, namely predicted protein–chemical interactions based on chemical structure. Countless articles on the prediction of drug–target interactions have been published in the last years ; Novo Nordisk Foundation Center for Protein Research. Funding for open access charge: European Molecular Biology Laboratory.Conflict of interest statement. None declared.


The size of the protein sequence database has been exponentially increasing due to advances in genome sequencing. However, experimentally characterized proteins only constitute a small portion of the database, such that the majority of sequences have been annotated by computational approaches. Current automatic annotation pipelines inevitably introduce errors, making the annotations unreliable. Instead of such error-prone automatic annotations, functional interpretation should rely on annotations of ‘reference proteins’ that have been experimentally characterized or manually curated.The Seq2Ref server uses BLAST to detect proteins homologous to a query sequence and identifies the reference proteins among them. Seq2Ref then reports publications with experimental characterizations of the identified reference proteins that might be relevant to the query. Furthermore, a plurality-based rating system is developed to evaluate the homologous relationships and rank the reference proteins by their relevance to the query.http://prodata.swmed.edu/seq2ref.The reference proteins detected by our server will lend insight into proteins of unknown function and provide extensive information to develop in-depth understanding of uncharacterized proteins. Seq2Ref is available at:  Due to the avalanche of protein sequences made available by high-throughput genome sequencing, complete manual annotation is unfeasible, leaving a large fraction of protein functions to be predicted by automatic functional annotation pipelines . HoweverSuch errors and omissions make database annotations insufficient for complete functional interpretation of a protein. A more accurate source of annotations is the ‘reference proteins’ closely related to the protein of interest. We define ‘reference, proteins’ as proteins that have been experimentally studied, manually curated, and reported in the literature. Information about reference proteins is essential for accurate functional interpretation and experimental design. The cross-links between proteins, genes, and associated literature available from National Center for Biotechnology Information (NCBI) provide a basis for reference protein identification. However, it is not trivial to identify a good set of reference proteins and supporting literature because such reference proteins constitute only a small portion of protein databases. Additionally, many proteins linked to large-scale studies (such as genome sequencing) do not provide sufficient functional information.We have developed a web server named Seq2Ref to assist the identification of applicable reference proteins. Seq2Ref employs BLAST  to perfoThe input and output interfaces are shown in Figure The results page  -18. The At each rating score cutoff, coverage and average accuracy were used as parameters to evaluate the performance of Seq2Ref Table . The covDue to its ability to retrieve reference proteins and their relevant information in a ranked order, the Seq2Ref server is useful for finding PubMed references relevant to proteins of unknown function, as well as obtaining a deeper understanding of proteins than that revealed by short annotations, as illustrated by the following examples.Hypothetical proteins of unknown function constitute a remarkably large portion of the database . Novel sMacaca mulatta protein, corresponding to gi|355567738, is annotated as a hypothetical protein EGK_07670 in the NCBI Protein database (Seq2Ref results: http://prodata.swmed.edu/wenlin/server/user_data/seq2ref/S2Rnv4cun/result.html). This hypothetical protein contains three conserved domains of unknown function (two DUF3730 and one DUF3028). Our server detects one close homolog, a hypothetical protein (gi|23345097) in human, which has been experimentally studied. The highly confident statistics in BLAST  and similar protein domain composition support an orthologous relationship between these two proteins. The human protein was recently (in 2012) reported to be a tumor suppressor in gliomas. It was named ‘focadhesin’, due to its cellular localization at the focal adhesion of the cell membrane [M. mulatta protein might also be a tumor suppressor and localized at the focal adhesion. Thus, by finding a homolog with the latest experimental publication not yet incorporated in sequence databases, Seq2Ref can serve as a basis for reliable functional prediction of unknown proteins.The membrane . As a liAlthough conserved domains in proteins usually suggest their functions, overly broad descriptions of domain functions are less informative than more specific descriptions. By presenting reference proteins and associated literature, Seq2Ref can offer more definitive and reliable information about protein functions.Cronobacter turicensis z3032 . A search of the Conserved Domain Database (CDD) merely suggests that this protein contains a ‘haemolytic domain’, with the most similar hit  annotated as a ‘hypothetical protein’ and one possible informative hit as ‘conserved hypothetical protein YidD’. The ‘conserved hypothetical protein YidD’ domain (TIGR00278) shows neither functional studies nor a detailed functional description. The publication [Bacillus subtilis) containing this domain can cause cells to lyse in culture. Unfortunately, this study failed to suggest a specific molecular function. Seq2Ref provided more information by detecting  the experimentally studied protein YidD from E. coli (gi|67476547), which is identical to the NCBI nr database representative protein (gi|16767126) from Salmonella enterica. This orthology is reinforced by the common conserved genomic context [E. coli YidD and C. turicensis hlyA cluster tightly together among their homologs  shown in Figure We developed a plurality-based rating system with scores ranging from 1 to 6, with 6 indicating the most relevant hits  protein and the experimental studied hit (E. coli YidD), respectively. Edges (lines) show BLAST connections between sequences that have an E-value at least as good as 10−33. Lengths of edges indicate that sequences in tightly clustered groups are relatively more similar to each other than sequences with few and distant connections.The protein similarity network of YidD homologs produced by CLANS program. Each black dot represents one protein sequence. Red circle and green asterisk represent the query (Click here for fileThe workflow of Seq2Ref. The whole process can be divided into homologous reference protein detection (Step 1) and homology evaluation (Step 2). Starting from a query sequence, a BLAST/PSI-BLAST search of the NCBI non-redundant database (NR) is performed (Step 1–1) to detect homologous proteins. Seq2Ref detects the reference protein among these homologous proteins by retrieving and checking the information in NCBI databases (Step 1–2). Orange lines represent the reference proteins among the BLAST result. Sequentially, Reciprocal BLAST (RB) and multiple sequence comparison (MSC) will be performed to evaluate the homologous relationships (Step 2–1). Integrating the statistics calculated above, a rating system will assign scores and rank the reference proteins (Step 2–2).Click here for file


Recently, large data sets of protein-protein interactions (PPI) which can be modeled as PPI networks are generated through high-throughput methods. And locally dense regions in PPI networks are very likely to be protein complexes. Since protein complexes play a key role in many biological processes, detecting protein complexes in PPI networks is one of important tasks in post-genomic era. However, PPI networks are often incomplete and noisy, which builds barriers to mining protein complexes.K and a node in the neighbours of K is proposed as well. The performance of our algorithm is evaluated by applying it on two PPI networks which are Gavin network and Database of Interacting Proteins (DIP). The results show that our algorithm is better than Markov clustering algorithm (MCL), Clique Percolation method (CPM) and core-attachment based method (CoAch) in terms of F-measure, co-localization and Gene Ontology (GO) semantic similarity.We propose a new and effective algorithm based on robustness to detect overlapping clusters as protein complexes in PPI networks. And in order to improve the accuracy of resulting clusters, our algorithm tries to reduce bad effects brought by noise in PPI networks. And in our algorithm, each new cluster begins from a seed and is expanded through adding qualified nodes from the cluster's neighbourhood nodes. Besides, in our algorithm, a new distance measurement method between a cluster Our algorithm detects locally dense regions or clusters as protein complexes. The results show that protein complexes generated by our algorithm have better quality than those generated by some previous classic methods. Therefore, our algorithm is effective and useful. Nowadays, with the rapid development of advanced proteomics technologies such as mass spectrometry , microarIn order to solve the problem of detecting protein complexes in PPI networks, many researchers have proposed a lot of computational approaches. The method proposed by Spirin and Mirny  in 2003 K and a node in the neighbours of K as well. In order to estimate the performance, our algorithm is compared with MCL, CPM and CoAch. The results show our algorithm has an improvement in terms of F-measure, co-localization and GO semantic similarity.Kelly et al.  found thG=, in which V represents the set of nodes (proteins) and E represents the set of edges (protein interactions) in the network. Here, self-interactions (loops) and multiple edges between the same pair of nodes are not considered. Before detail description of our algorithm, some terminologies used in the following algorithm section are presented as follows.A PPI network can be modeled as an undirected simple graph uv wof edge  ∈ E is defined as the similarity between u and v. It is obvious that two nodes with an edge between them belong to the same cluster if they have high similarity. The similarity between u and v is measured by Jaccard's coefficient 24], for In this section, the above three categories of evaluation criteria are used to compare the performance of our algorithm with three classic methods which are MCL, CPM and CoAch.cp Nis the number of predicted protein complexes matching at least one real protein complex in B and | P | is the total number of predicted complexes.Table From Table Recall of our algorithm is not very high because the number of protein complexes predicted by our algorithm is not large. And since MCL and CoAch predict large number of protein complexes, they achieve higher recall than our algorithm. The reason that our algorithm predicts a limited number of protein complexes is that our algorithm tries to use robustness degree and edge weight to find reliable protein complexes in incomplete and noisy PPI networks. Thus, protein complexes predicted by our algorithm have very high precision at the cost of not high recall. Although recall is not high, our algorithm still obtains the highest F-measure on both Gavin and DIP dataset. And as F-measure takes both precision and recall into consideration, it can be found that protein complexes detected by our algorithm have good quality.The localization dataset published by Huh et al.  is used Figure Since proteins within a complex tend to be at the same sub-cellular locations, co-localization score can be used to evaluate the biological relevance of predicted protein complexes. And high co-localization score means high biological relevance of predicted protein complexes. Thus, as our algorithm achieve highest scores on both Gavin and DIP dataset, it is obvious that protein complexes detected by our algorithm have relatively high quality from the biological view.The similarity of two different GO terms is determined by their most recent common ancestors in the ontology structure. And the similarity of GO terms related to proteins in a protein complex can demonstrate the biological relevance within the complex. In this section, we also use GO semantic similarity score which can show the similarity of proteins within a complex to evaluate the quality of predicted protein complexes. We still make use of ProCope to calculate the GO semantic similarity score.Figure As high GO semantic score means high biological relevance of predicted complexes, our algorithm which achieves the highest GO semantic scores on both Gavin and DIP dataset can detect protein complexes with relatively good quality.In this paper, a novel algorithm based on robustness to detect overlapping protein complexes is proposed. First, we present a method to measure the functional distance between a cluster and a node in the neighbours of the cluster. Then, we explain our algorithm in details. Our algorithm is a dense-based approach and tries to find reliable protein complexes in PPI networks. Finally, three categories of evaluation criteria are used to compare the performance of our algorithm with MCL, CPM and CoAch. The results show that our algorithm is better than MCL, CPM and CoAch in terms of F-measure, co-localization and GO semantic similarity, which means our algorithm can discover protein complexes with good quality. Therefore, our algorithm is effective and can be helpful in the future biological study.The authors declare that they have no competing interests.The algorithm is designed by SW and FW. And FW fulfills the algorithm and conducts related experiments. Both SW and FW have read and revised this paper.


The published literature indicated experimental evidence for 31 of the top predicted interactions, highlighting the promising nature of our approach. Novel interactions discovered may lead to the drug being repositioned as a therapeutic treatment for its off-target's associated disease, added insight into the drug's mechanism of action, and added insight into the drug's side effects.Repositioning existing drugs for new therapeutic uses is an efficient approach to drug discovery. We have developed a computational drug repositioning pipeline to perform large-scale molecular docking of small molecule drugs against protein drug targets, in order to map the drug-target interaction space and find novel interactions. Our method emphasizes removing false positive interaction predictions using criteria from known interaction docking, consensus scoring, and specificity. In all, our database contains 252 human protein drug targets that we classify as reliable-for-docking as well as 4621 approved and experimental small molecule drugs from DrugBank. These were cross-docked, then filtered through stringent scoring criteria to select top drug-target interactions. In particular, we used MAPK14 and the kinase inhibitor BIM-8 as examples where our stringent thresholds enriched the predicted drug-target interactions with known interactions up to 20 times compared to standard score thresholds. We validated nilotinib as a potent MAPK14 inhibitor  Most drugs are designed to bind to and inhibit the function of a disease target protein. However, drugs are often able to bind to ‘off-target’ proteins due to similarities in the protein binding sites. If an off-target is known to be involved in another disease, then the drug has potential to treat the second disease. This repositioning strategy is an alternate and efficient approach to drug discovery, as the clinical and toxicity histories of existing drugs can greatly reduce drug development cost and time. We present here a large-scale computational approach that simulates three-dimensional binding between existing drugs and target proteins to predict novel drug-target interactions. Our method focuses on removing false predictions, using annotated ‘known’ interactions, scoring and ranking thresholds. 31 of our top novel drug-target predictions were validated through literature search, and demonstrated the utility of our method. We were also able to identify the cancer drug nilotinib as a potent inhibitor of MAPK14, a target in inflammatory diseases, which suggests a potential use for the drug in treating rheumatoid arthritis. The continuing decline of drug discovery productivity has been documented by many studies. In 2006, only 22 new molecular entities were approved by the Food and Drug Administration (FDA) despite research and development expenditures of $93 billion USD by biotech companies and large pharmaceutical companies, and this low productivity has not improved since Many of today's repositioned drugs were discovered through serendipitous observations, including high profile drugs sildenafil by Pfizer - first developed for angina but later approved for erectile dysfunction - and thalidomide by Celgene - first marketed for morning sickness, then approved for leprosy and recently for multiple myeloma By screening compounds against a panel of proteins, there is potential to discover novel drug-target interactions. Drug candidates are routinely screened against a small panel of similar proteins to determine their specificity to the intended target. Large panels with hundreds of kinase proteins have been developed to assess kinase inhibitor specificity Recent computational endeavors to predict novel drug repositioning candidates have used methods incorporating protein structural similarity Large-scale docking of many targets to many drugs is now feasible when run on powerful computer clusters. However, limitations in scoring methods result in high false positive prediction rates A computational pipeline was developed for large-scale molecular docking of drugs to protein targets . BrieflyWe first docked 3570 known protein-drug interactions annotated by DrugBank, between 678 unique human proteins and 1309 small molecule drugs. We used the docking software ICM developed by Molsoft In high-throughput molecular docking, it is common to hold protein structures rigid during the simulation. With this restriction, re-docking a PDB ligand back to its native PDB structure (cognate docking) is a simpler task than docking a different ligand to the structure (non-cognate docking) because in the former case the protein is already in a specific ligand-bound conformation. Cognate-docking situations occur frequently and previous studies show that they can be docked well in 60–80% of cases holo (unliganded) protein structure, 2) an apo (liganded) structure with a same or similar ligand as the drug (the cognate-docking scenario), or 3) an apo structure with a chemically different ligand from the drug. Chemical similarity was defined as having a Tanimoto coefficient less than 0.54. apo protein with the same ligand (Tanimoto coefficient of 0). The majority of drugs docked well to holo structures as well as apo structures with dissimilar ligands. In short, the ICM docking method was able to predict known interactions for both cognate and non-cognate docking scenarios.We analyzed the 1116 known interactions to examine whether those that docked well were only due docking cognate ligands. For each interaction, we observed whether the drug bound 1) a Aside from the docking score, it was also important to verify that the ligands were docked in correct binding conformations. We further examined the 380 cognate dockings and found that the docked drug conformation was close to the known drug conformation  in 69% of cases. The other 31% fell into two categories: 1) partly symmetrical ligands like NAD and 2) ligands that bound to a small pocket. In the first case, the molecule was incorrectly determined to be flipped, causing a high RMSD; however, its central portion was docked correctly due to symmetry. In the second case, the region of ligand bound in the pocket was docked correctly, but the region free in solvent contributed to a poor RMSD value. Overall, this analysis showed that when a known interaction was docked with a good score, the binding conformation was also reasonably predicted.We gathered the known protein-drug interactions into a network  with proWe proceeded to dock the 252 reliable protein set against the database of 4621 drugs. Considering the multiple crystal structures per protein and the multiple binding pockets per structure, there were a total of 1514 crystal structures and 2923 binding pockets. Each drug was docked to all binding pockets of a protein and whichever pocket gave the best docking score for the drug determined the final protein-drug score. This method allowed multiple conformations of a protein to be accounted for during docking and provided a simple model of protein flexibility.In total, we docked 1.2 million protein-drug interactions. 104,625 (0.9%) had ICM docking scores (icm-score) of −30 or better, encompassing all1116 known interactions in the reliable data set. Since the fraction of known interactions in the predicted set was so low, we assumed that the vast majority of predictions were false positives. Though we believed that novel drug-target interactions existed and were enriched within these 104,625, there was clearly a need for more stringent score thresholds.We investigated various methods of selecting top drug-target interactions. The standard software-recommended icm-score is based on a weighted sum of various binding energy terms To assess performance, we measured the positive predictive value (PPV), defined as the proportion of predicted interactions that are known binding interactions. The premise is that a better threshold would yield a set of predictions more enriched with known interactions, and novel interactions that are more likely be true binding events. The protein-rank and pmf-score thresholds appeared to be the worst based on both the PPV plot  and on eAnother threshold method is to use the scores of known binders as the score cut-off for each protein. We investigated this using the best and worst icm- and pmf-scores of known drugs. Overall, the combination of consensus score with the two ranks gave the highest PPV and enrichment values: in the top 50 predicted interactions, 49% are known. This gave us confidence that many of the other 51%, all novel interactions, are real.Two examples are presented to illustrate the utility of combining rank and scoring criteria. The first is for the signaling protein MAPK14 , an integral component in numerous cellular processes. It is a drug-target for inflammatory diseases The consensus score is based on the observation that when docking a large number of diverse compounds to any target, most compounds have poor icm- or pmf- scores, and few compounds have both good icm- and pmf- scores. Therefore, we chose a linear threshold that eliminated the densest area of points in the poor scoring region (top-right) of a score plot like Previous high-throughput studies have shown varying results regarding nilotinib-MAPK14 inhibition. Some enzymatic assays to MAPK14 showed weak inhibition: 570 nM or 2.2 µM depending on the assay type Despite their appeal as an inflammatory disease target, MAPK14 drug candidates to date have failed due to drug toxicity issues A second example is the Protein Kinase C inhibitor BIM-8. We docked BIM-8 to the set of 252 reliable targets, and the results are plotted in We compared our results to three previous studies. Two studies performed protein kinase assays with radioactive ATP and substrate peptides, where inhibitor binding decreases the amount of radioactive peptide produced For a global and quantitative review of the predicted protein-drug interactions, we plotted the icm scores of drugs docked to established drug targets . Each prOverall, the known drugs (black crosses) had better scores than other drugs for a given target. This was expected, as many of these known drugs were chemically optimized for their targets. For a number of targets, the known drug was the only predicted interaction. None of the approved and experimental drugs from DrugBank were able to dock well, despite a reliable protein structure, suggesting that virtually screening larger chemical databases may be the only way to discover novel inhibitors by docking. For most targets, at least one experimental drug showed a better score than the known drugs; however, experimental drugs are often unavailable for purchase or experimental testing. Instead, we were most interested in cases with approved drugs such as the MAPK14-sorafenib example which was verified by the literature, and the MAPK14-nilotinib example which we verified with an in vitro kinase assay.Through literature search, we found experimental support for many of our top drug-target predictions that scored better than known interactions . These aOne type of validated interaction includes drugs that are close analogs of known drugs for that target; for example, the estrogen analog ERA-923 is a known selective estrogen receptor modular (SERM) Overall, we were able to find literature support for 30 of our top predicted interactions, which validated our computational method as useful for finding novel drug-target interactions.The binding of a small molecule drug to its target protein in a cell is much more complex than a single docking calculation. For example, an ATP-competitive kinase drug would have hundreds of ATP-binding sites to choose from due to the large size of the kinome. Cancer drugs such as sunitinib are now known to potently inhibit many more kinase targets than previously expected Our strategy was to find novel drug targets of existing drugs by computationally screening the druggable proteome. For this purpose, we chose molecular docking due to its speed, low cost, and detailed three-dimensional simulation. Moreover, docking can evaluate any protein with a solved structure due to its virtual nature, without the need for tailoring enzymatic assays or collecting drugs in solutions. However, docking is known to have a high false positive prediction rate, due to limitations such as incomplete binding pocket prediction, inadequate ligand conformation sampling, inaccurate scoring functions, lack of protein flexibility, and lack of water and cofactor molecules during the simulation. As evidenced in this study, only 31% of the 3570 known interactions docked with a good score. One review states that 10–50% of a set of diverse compounds can be expected to be docked correctly for a given target Our method attempted to address these limitations. First, we manually included binding pockets that were present in PDB structure complexes but not predicted by the binding pocket search. Second, we docked each interaction 10 times to better sample ligand conformations. Third, we applied consensus score and rank criteria to further narrow down top scoring docking hits. Fourth, we used all available structures of a protein (versus choosing one representative structure), to allow a simple view of protein flexibility. We did not incorporate water and cofactor molecules in our docking simulations due to the computational complexity involved. However, by selecting proteins for which at least one known drug docked and scored well, we selected proteins for which the limitations of the docking software were not critical for a good prediction. In short, assuming the docked conformation of the known ligand was correct, we used only proteins for which the binding pocket was genuine, the scoring functions were adequate, the protein was in a conformation amenable for drug inhibition, and the lack of water or cofactor molecules didn't drastically affect the prediction.et al. had 14% hit rate from 50 tested compounds et al. had a 36% hit rate from 56 tested compounds Virtual screening studies typically involve docking large chemical databases to one protein target, selecting compounds that score within the top 0.5–1% of the database and then further prioritizing them by visual examination. When experimentally validating these top candidates, a 5% hit rate can be considered a successful endeavor  In our case, both the standard scoring threshold and the known-inhibitor score were not sufficient. With a normal score threshold of −30, docking 4621 drugs against 252 proteins resulted in 104,625 predicted interactions. This is roughly 1% of the docked interactions, so even selecting the top 1% of the docking hits for validation becomes prohibitive for large-scale studies. It is important to note that each protein has different physiochemical properties: for some proteins, hundreds of compounds pass the −30 cut-off, while for other proteins none pass. Thus, using the known-inhibitor score as a cut-off allows for a threshold that is tailored to each protein. However, this method still predicted ∼8000 interactions at the most stringent. Our consensus threshold allowed us to pick the top 1% (or any x%) of docked compounds with the best icm- and pmf- scores for each protein and further filter from there. Through testing many combinations, we found that using the consensus score with rank information allowed us the highest PPV – nearly 50% - and enrichment factor – 50 times better than standard −30 score threshold and 490 times better than random selection. This high enrichment for known interactions suggests that many of the other predictions that have not yet been experimentally tested may be true binding interactions.There are limitations to this scoring scheme. Since the pmf-score is a statistical score comparing the docked interaction to known interactions in PDB, a chemical with a different scaffold or novel binding conformation may have a poor pmf-score and become predicted as a false negative. However, our foremost goal in this study was to eliminate as many false positive predictions as possible and obtain a high enrichment of true positives in our predicted interaction set. Thus, it was acceptable to miss some false negative predictions. In addition, the consensus score is quite simple with a linear separation method, and may not be as informative as a machine-learning algorithm that trains on known ligand docking scores. However, we desired an automated scoring method that did not depend upon the existence of known ligands. That is, if a protein structure had just one, or no known binders, our method would still be able to select the top 1% of docking hits.et al. docked 40 targets against 40 compounds to check whether their docking method could distinguish between a target's cognate ligands and the other targets' cognate ligands To date, cross-docking of proteins to compounds has generally been used for small datasets. As an example, Huang in vitro kinase assays that use a specific panel of proteins. In the case of gefitinib, two such studies had distinct differences in their proposed cellular targets in vitro experiments. For MAPK14, the experimental results for nilotinib also varied. We experimentally tested two purchasable approved drugs against MAPK14 and found that nilotinib was a strong nanomolar inhibitor, and zafirlukast was also an inhibitor, though not as potent. Thus, interactions that are predicted to be very likely inhibitors computationally may merit extra study even if experimental tests are initially negative.High-throughput computational screening of drug-target interactions represents a parallel approach to high-throughput experimental screening. Due to differences in experimental methods, assay settings, and protein panels, different studies may present differing results. For example, small molecule affinity purification methods that use whole cell lysates would give different results from In short, we have developed a computational pipeline that can run large-scale cross-docking of compounds to targets. We developed stringent criteria to filter a large proportion of false positive interactions. The two case studies presented were selected based on known experimental binding assay data, so as to demonstrate the notable enrichment of known interactions using our scoring and ranking criteria. We hypothesized that predicting a set of interactions with a higher PPV (enrichment of known interactions) would also lend confidence to the other novel interactions in the set. This appears to have worked, as we were able to find validation for 31 predicted drug-target interactions that were not previously annotated in DrugBank, as well as validate two other inhibitors of MAPK14. Other drug-target interaction predictions are currently undergoing experimental validation; novel interactions discovered are potential drug repositioning candidates, but also provide insight into a drug's mechanism of action and adverse effects profile.We downloaded the DrugBank 2.5 database http://www.bcgsc.ca/downloads/yli/. To predict pockets, or potential binding sites, we used the PocketFinder We prepared protein structures for docking using Molsoft's ICM software version 3.4-9c The receptor was defined as the box 3.5 Å surrounding the pocket. If the pocket overlapped well with the ligand but the ligand extended out of the protein structure, we defined the receptor be the box 3.5 Å around the pocket but also including 2.0 Å around the ligand. This ensured that known ligand binding sites not predicted by our automated method were also included in our pocket database.We docked drugs to target receptors using the ICM virtual library screening (VLS) module. This method performs rigid-receptor flexible-ligand docking using a two-step Monte Carlo minimization method and energy scoring function to sample ligand conformations and select the best docking hits. MMFF partial charges and ECEPP/3 force-field parameters are used. Docking one interaction required on average 30 seconds to 1 min per processor. A given protein may have several structures, each of which with more than one pocket; in such cases we dock all pockets to a drug, and the best scoring interaction is selected to be the representative protein-drug score.To ensure a sufficient coverage of the docking energy landscape, we docked each drug-target interaction 10 times in the known docking analysis and 5 times in the large-scale cross-docking analysis. Docking was performed on a Linux cluster with 1000 processors – this level of throughput allowed us to complete 1–3 million dockings per day.8867 known interactions between human protein targets and drugs were culled from the DrugBank Drugcards database. Of these, 3570 interactions with protein target crystal structures present in our database were docked. Due to the Monte-Carlo nature of the ICM method, each interaction was docked 10 times to better cover the docking energy landscape. After 10 iterations, the best scoring prediction was retained.If the protein structure was solved in complex with a ligand, a Tanimoto coefficient was used to determine if the docked drug was similar to the complexed ligand. A coefficient less than 0.54 represented similar molecules Cytoscape We applied several methods of score thresholding: applying cut-offs of the ICM docking score ranging from [−25 to −100]; applying cut-offs of the ICM potential of mean force score ranging from [−80 to −200]; applying a drug rank cut-off ranging from [1 to 4500]; applying a protein rank cut-off ranging from [1 to 252]; applying a combined docking score and mean force score cut-offs. For the consensus score thresholds, all slopes (from −1 to −40) and intercept (from 0 to −400) combinations were tested. For each line, we calculated the density of the points eliminated in a trapezoidal area delineated by the consensus line, the best icm- score for this protein, and the best pmf-score for this protein, the midpoint between the worst icm-score and its mean, and the midpoint between the worst pmf-score and its mean. For two consensus thresholds that predicted the same number of interactions, we used the one that eliminated a denser cloud of points.While evaluating PPV for combination thresholds, it was often observed that two sets of thresholds resulted in the same number of predicted interactions but different PPVs. In such cases, we considered only the threshold combination that gave us the higher PPV.1,164,492 interactions between 252 proteins and 4621 drugs were docked using ICM. Though there were actually 4854 drugs small molecules, some were excluded being too small or too large for docking (molecular weight under 100 or over 1000 g/mol). Due to the multiple binding pockets per protein and multiple crystal structures per protein, there were a total of 2923 binding pockets. Each interaction was docked 5 times to better cover the docking energy landscape and the best scoring conformation was retained. Overall there were 2923×4621×5 dockings or 68 million docking calculations. The icm and pmf scores of each interaction were gathered into large matrices for further analysis.33P-ATP at 25 µM, the protein kinase, peptide substrate, assay buffer, and the drug. Blank assays without substrate or drug, and assays without the drug, were used as controls. Staurosporine at 1 µM was used as the positive control drug.Protein inhibition assays were performed by SignalChem . Kinases assays consisted of


The mitochondrion is an essential organelle which plays important roles in diverse biological processes, such as metabolism, apoptosis, signal transduction and cell cycle. Characterizing protein-protein interactions (PPIs) that execute mitochondrial functions is fundamental in understanding the mechanisms underlying biological functions and diseases associated with mitochondria. Investigations examining mitochondria are expanding to the system level because of the accumulation of mitochondrial proteomes and human interactome. Consequently, the development of a database that provides the entire protein interaction map of the human mitochondrion is urgently required.InterMitoBase provides a comprehensive interactome of human mitochondria. It contains the PPIs in biological pathways mediated by mitochondrial proteins, the PPIs between mitochondrial proteins and non-mitochondrial proteins as well as the PPIs between mitochondrial proteins. The current version of InterMitoBase covers 5,883 non-redundant PPIs of 2,813 proteins integrated from a wide range of resources including PubMed, KEGG, BioGRID, HPRD, DIP and IntAct. Comprehensive curations have been made on the interactions derived from PubMed. All the interactions in InterMitoBase are annotated according to the information collected from their original sources, GenBank and GO. Additionally, InterMitoBase features a user-friendly graphic visualization platform to present functional and topological analysis of PPI networks identified. This should aid researchers in the study of underlying biological properties.InterMitoBase is designed as an integrated PPI database which provides the most up-to-date PPI information for human mitochondria. It also works as a platform by integrating several on-line tools for the PPI analysis. As an analysis platform and as a PPI database, InterMitoBase will be an important database for the study of mitochondria biochemistry, and should be particularly helpful in comprehensive analyses of complex biological mechanisms underlying mitochondrial functions. The mitochondrion is an essential organelle in eukaryotic cells that plays important roles in a variety of important processes such as apoptosis, signal transduction and cell cycle . MitochoThe extensive use of experimental approaches including 2D gel electrophoresis and mass spectrometry, has led to the construction of many databases for mitochondrial proteomics, such as MitoCarta , MitoP2 Although several PPI databases have been distributed, such as HPRD , BioGRIDHere, we have developed a database termed InterMitoBase, which covers the biological pathways mediated by mitochondrial proteins and the PPIs between mitochondrial and mitochondrial/non-mitochondrial proteins. The interactions in InterMitoBase are integrated from a wide range of resources including PubMed, KEGG , HPRD, BInterMitoBase is designed as a web-based database providing graphic visualization of annotated PPI interactions that relate to human mitochondrial functions. It integrates the data from diverse sources such as MitoCarta, HPRD, the KEGG pathway database, PubMed and Gene Ontology . Severalth, 2010) by querying " AND humans [MeSH Terms] AND English [Language]". The interactions embedded from KEGG were then derived by collecting the biological pathways that contain at least one mitochondrial protein. Finally, the interactions between mitochondrial proteins and mitochondrial/non-mitochondrial proteins in the four PPI databases of BioGRID, HPRD, DIP and IntAct were integrated.The general process of data collection is illustrated in Figure The current version of InterMitoBase contains 5,883 PPIs of 2,813 proteins  collected from PubMed, KEGG, HPRD, BioGRID, IntAct and DIP. A comparison between the PPI databases of HPRD, BioGRID, IntAct and DIP, InterMitoBase is specifically designed for human mitochondria, which aims at all the possible PPIs that are closely related to human mitochondrial functions. This new database covers: (i) PPIs involved in biological pathways mediated by mitochondrial proteins; (ii) the PPIs between mitochondrial and non-mitochondrial proteins; and (iii) PPIs between mitochondrial and mitochondrial proteins. 1,640 PPIs in InterMitoBase are new ones that have not been covered by any of the four PPI databases  the basic information  of the interacting proteins are annotated referring to GenBank and GO; 2) the information about subcellular location of every protein is provided according to MitoCarta, MitoProteome, MitoP2, HMPDb and UniProt; 3) the information about the direction and regulation type of each interaction is provided; 4) the full context that contains the interaction  is supplied.InterMitoBase provides an intuitionistic visualization of a protein-protein network formed by a set of PPIs. It also integrates a tool to analyze the degree distribution of the network. These will be helpful to uncover the global structural features and key elements of the network.InterMitobase also embeds a tool that can evaluate the enriched functions of the proteins involved in a specific protein-protein network based on the Gene Ontology (GO) terms. The significance of the enriched functions is represented by the p-value that is evaluated following the Fisher's exact test. The false discovery rate (FDR) is controlled by the Benjamini-Hochberg process . The anahttp://www.graphviz.org/) and the statistical analysis is processed by R 2.12 (http://www.r-project.org/).The architecture of InterMitoBase is composed of three layers Figure , the stoInterMitoBase is designed for quick retrieving, visualizing and analyzing PPIs supporting human mitochondrial functions. It is composed of three sections, i.e., the searching section, the interaction retrieving section and the network analysis section Figure .InterMitoBase provides a search engine for users to source the proteins of interest. The queried keywords include various types of protein IDs, GO IDs and terms of biological functions/processes. 58 types of gene/protein identifiers used in the public databases such as GenBank, HGNC  and UniPThe PPIs are navigated through the outcome page of proteins. Two retrieving processes are supported. One approach is to obtain all the interactions of a selected protein. The other approach is to get the interactions between all the outcome proteins. The retrieving returns a page where the outcome PPIs associated with related sources, directions and regulation types are also recorded  are given to judge whether the degree distribution is exponential or power-law (see Figure InterMitoBase is designed for quick retrieving, visualizing and analyzing PPIs that contribute to human mitochondrial functions. It integrates the most up-to-date PPIs for human mitochondria from diverse resources. Several on-line tools are also embedded to uncover the underlying biological properties of PPIs. Besides performing as an analysis platform and a PPI database, InterMitoBase will aid researchers aiming to obtain a comprehensive understanding of complex biological mechanisms underlying mitochondrial functions.http://mcube.nju.edu.cn/bioinfo/intermitobase/ for academic or non-academic users. The client web browser should support JavaScript and Ajax to ensure full usage.InterMitoBase is freely accessible at PPI: Protein-protein Interaction; KEGG: Kyoto Encyclopedia of Genes and Genomes; GO: Gene Ontology; FDR: False Discovery Rate.ZG and JL participated in the construction of the database and the draft of the manuscript. SG, MG and JW and XH carried out the manual curation on the data in InterMitoBase. JW and CZ conceived the study, design and coordination, and helped to draft the manuscript. All authors read and approved the final manuscript.


Because of the pharmacological fact that related drugs can bind to receptors without obvious sequence or structural similarity, it is appropriate to categorize proteins based not only on their sequence or structures but also on the chemical structure and the phenotypic side-effect of their ligands. In chemogenomic studies where the complete set of ligands for a protein is not known We present the Weighted Infinite Relational Model (WIRM) that jointly detects biologically sensible ligand groups and protein groups by integrating the clustering of various data types including chemical compound descriptors, protein sequences, ligand-target bindings and pharmaceutical effects. WIRM takes advantage of the Bayesian nonparametric paradigm for integrating multiple data types, for allowing for missing values (e.g. unknown ligand-target interaction) in the data, for automatically inferring the number of clusters without explicit model comparison, and for predicting the ligand-target interactions. Because some of these data types, to varying degrees, may suggest relationships having no implication for ligand-target interactions or for biological sensible ligand and protein groups, WIRM allows different types of data to have different weights based on prior knowledge of their quality or relevance.We apply WIRM to the ion channel proteins and G-protein-coupled receptors. We validate its performance using functional annotation and ligand-target interaction. We also test the relationship among multiple data types by varying the weights which indicate the impact of each data type on the model. The categories and interactions inferred by WIRM both confirm known biology and suggest novel predictions.


Comprehensive protein interaction maps can complement genetic and biochemical experiments and allow the formulation of new hypotheses to be tested in the system of interest. The computational analysis of the maps may help to focus on interesting cases and thereby to appropriately prioritize the validation experiments. We show here that, by automatically comparing and analyzing structurally similar regions of proteins of known structure interacting with a common partner, it is possible to identify mutually exclusive interactions present in the maps with a sensitivity of 70% and a specificity higher than 85% and that, in about three fourth of the correctly identified complexes, we also correctly recognize at least one residue (five on average) belonging to the interaction interface. Given the present and continuously increasing number of proteins of known structure, the requirement of the knowledge of the structure of the interacting proteins does not substantially impact on the coverage of our strategy that can be estimated to be around 25%. We also introduce here the Estrella server that embodies this strategy, is designed for users interested in validating specific hypotheses about the functional role of a protein-protein interaction and it also allows access to pre-computed data for seven organisms. The possibility of retrieving information about protein-protein interactions by high throughput experiments has led to the development of a number of methods for their analysis Here we address the problem of distinguishing whether, when more proteins interact with the same partner, they can do so simultaneously, i.e. whether their interaction is mutually exclusive, and show that this is possible by taking advantage of the continuously increasing information available on the three-dimensional structure of proteins. Most protein-protein interaction map studies have not considered the structural and chemical aspects of interactions; only a few authors have proposed to enrich protein networks with structural information of proteins Here, we follow a rather different general strategy based on the hypothesis that, if two proteins interact with the same region of a common protein and therefore their interactions cannot be simultaneous, they might share a common surface region mediating the interaction.To reliably identify these cases, we extract from a PPI all instances  where at least three proteins of known structure interact with a common protein partner (the hub), compare their surface residues to identify structurally similar substructures comprising at least three residues using the FunClust public server We applied this strategy to the interactomes of seven organisms. Using as a test set several (152) complexes of known structure included in the sub-networks, we show that Estrella allows the identification of mutually exclusive interactions with accuracy higher than 77%. The procedure also allows us to predict which residues are likely to be in the binding interface of the nodes, and in a significant number of cases (between 63% and 75%) we correctly identify at least one of them (5 on average) and this has obvious implications for helping to reduce the search space in docking procedures.The percentage of sub-networks containing a sufficient number of proteins of known structure in the PPI maps, and therefore the coverage of the method, varies substantially for different organisms, as it could be expected, however it does reach 42% for human and more than 36% for yeast averaging at about 25%. . These fGiven a PPI, the Estrella procedure consists in several steps. First the map is analyzed to retrieve sub-networks in which at least three proteins of known structure interact with a central protein. The structures of the proteins  are retrieved from the PDB database http://bl210.caspur.it/ESTRELLA/home.php.We analyzed the publicly available PPI maps for seven organisms In total, we could analyze 8817 sub-networks and in 7310 of them we could identify the presence of structurally similar regions in proteins interacting with the hub, which are candidates for being mutually exclusive interactions. For each sub-network we obtain several cluster, i.e. putative groups of three or more proteins sharing a structurally similar exposed region comprising at least three amino acids, and rank them according to the number of similar residues and the number of aligned pairs of structures.bona fide mutually exclusive interactors exists and used them to evaluate the effectiveness of the method.To test the effectiveness of the method, we extracted all sub-networks (152) where the experimentally determined structures of the complexes between the hub protein and more than one of the Let us assume that there is a sub-network where a central protein interacts with M + N proteins where M are experimentally known to interact with overlapping regions of the central protein and N are not and that, for the same sub-network, Estrella produces a cluster of m proteins predicted to establish mutually exclusive interactions while n are not predicted to do so.The True Positives (TP) are M As it can be appreciated from Clearly, while the similarities that we detect indicate that the interactions can be mutually exclusive, we cannot exclude that other proteins binding to the same hub protein also cannot do so simultaneously since they could impair each other binding by steric hindrance.It should also be mentioned  that rarThe identification of the common substructures often provides a correct prediction of the node binding sites as well. As shown in http://bl210.caspur.it/ESTRELLA/home.php.As mentioned above, the results for the seven analysed interactomes obtained from iRefIndex The database can be searched both with an organism and a protein name . It is also possible to replace the coordinates for a protein included in the database, if a new structure becomes available.The combination of the results of high throughput experiments and of their computational analysis is undoubtedly a powerful strategy for transforming the ever-growing amount of information that we are accumulating into knowledge.Protein-protein interaction maps can be effectively used to transfer functional annotation from one protein to its interacting partners. The challenge consists in understanding at which level of granularity can the annotation be transferred, which also depends upon the mode in which the proteins interact. To this end, it is relevant to understand which interactions are mutually exclusive and which are the specific regions involved in the recognition process. To help solving these issues we developed and made available a system that we believe can effectively speed up the process of understanding the role of the gene products in a biological system.In this paper we describe the implementation and the results of the strategy for identifying mutually exclusive interactions in a protein-protein interaction map based on the hypothesis that, if two or more proteins interact with the same region of a common partner protein, they might share similarity in their binding region.bona fide mutually exclusive interactions in order to further validate the prediction. Equally important is, in our view, that only in a tiny fraction of the cases (less than 0.1%) the method incorrectly identifies more than one protein as part of a mutually exclusive interaction in a sub-network, and this implies that the number of necessary experiments to validate the results is greatly reduced.We tested the idea using seven different interactomes from different organisms. The data are stored in a publicly available database, which we hope will be useful to life scientists. The method provides very satisfactorily results, especially since it has a rather high specificity (above 85%), thereby ensuring that scientists interested in a given biological process can retrieve essentially all of the When the simultaneous interaction of more than one protein with a common partner is correctly detected, the residues identified to be structurally similar among the nodes are very often the correct interface residues, and this implies that they can serve as constraints in docking experiments to reduce the search space.Another observation that can be made from the results presented here is that the coverage of experimentally determined structures starts to be sufficient to allow their use in combination with different types of high throughput experiments.We believe that the ever growing number of experimentally determined structures and of protein-protein interaction experiments, combined with the strategy presented here, that only requires the structure of the interaction partners and not of their complexes, also implemented in a completely automatic fashion and publicly accessible, is likely to add significant value to data produced in high-throughput experiments.Homo sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Caenorabditis elegans, Escherichia coli and Saccharomyces cerevisiae.Protein-protein interaction data were retrieved from iRefIndex release 7.0 (May 18th 2010) From these, we selected binary interactions with both partners annotated in the UniProt database We define a sub-network as an interaction where a central protein is directly connected to at least three partners. To allow the user to select non-redundant interactions, we run PISCES Solvent exposed residues were defined as those with more than 50% exposed surface with respect to the computed accessibility of the same residue type in an extended ALA-x-ALA tripeptide. Solvent accessibility was calculated using NACCESS using the isolated protein chains.Structural similarity among solvent exposed residues of proteins interacting with the same hub was computed using FunClust FunClust consists of a two-step procedure. In the first step, the Query3D algorithm  identifies all the pairwise similarities among the chains within the sub-network. Query3D is a structure comparison method that searches for the largest subset of matching amino acids between two protein chains, regardless of whether hey are continuous in the chain, and only requiring them to be neighbors in space. It also takes into account their structural and biochemical similarity Click here for additional data file.


Certain amino acids in proteins play a critical role in determining their structural stability and function. Examples include flexible regions such as hinges which allow domain motion, and highly conserved residues on functional interfaces which allow interactions with other proteins. Detecting these regions can aid in the analysis and simulation of protein rigidity and conformational changes, and helps characterizing protein binding and docking. We present an analysis of critical residues in proteins using a combination of two complementary techniques. One method performs in-silico mutations and analyzes the protein's rigidity to infer the role of a point substitution to Glycine or Alanine. The other method uses evolutionary conservation to find functional interfaces in proteins.We applied the two methods to a dataset of proteins, including biomolecules with experimentally known critical residues as determined by the free energy of unfolding. Our results show that the combination of the two methods can detect the vast majority of critical residues in tested proteins.Our results show that the combination of the two methods has the potential to detect more information than each method separately. Future work will provide a confidence level for the criticalness of a residue to improve the accuracy of our method and eliminate false positives. Once the combined methods are integrated into one scoring function, it can be applied to other domains such as estimating functional interfaces. Proteins and protein complexes play a central role in a large number of cellular processes such as cellular organization and function, ion transport and regulation, signal transduction, protein degradation, and transcriptional regulation . Since tIn this work we use two different methods to analyze the relative importance of amino acids in a protein - one measures evolutionary conservation and one uses graph-based analysis to estimate the effect of single point mutations on protein rigidity. These two methods use different input data and measure relative importance in two different ways, and thus we hypothesize that combining them will allow us to obtain information about critical residues in a more comprehensive way.et al. have designed and analyzed many mutants of lysozyme from bacteriophage T4, and concluded that the unoccupied volume that is caused by some mutations induces a collapse of that region, while in other cases the cavity remains empty [One way in which a residue can be identified as critical is by performing a mutation in a physical protein and measuring the effect of the mutation on the protein. Matthews ns empty . Therefons empty , and thaet al. [et al. [Gilis et al. , estimat [et al.  used Sup [et al. , which h [et al. . That toet al. have developed force fields to help predict protein stability, and to provide a fast and quantitative estimation of the importance of the interactions contributing to the stability of molecules and protein complexes [In another work, Guerois omplexes . They coThus, progress has been made in predicting whether a residue is critical. However, many such methods rely on experimentally measuring the effect of mutations in the physical protein, or rely on techniques that are computationally intensive, which makes their use on large protein datasets impractical. To complement these existing methods, we seek to apply efficient methods to measure rigidity and evolutionary conservation to identify critical residues. This work extends promising initial studies . In the We use two methods that follow different approaches. One method uses evolutionary conservation information among homologue proteins. The other is a rigidity analysis method that uses a graph-based algorithm to detect residues that play a role in protein flexibility. In what follows we explain the two methods in detail.Proteins bind through a specific site on their interfaces, through a combination of geometric complementarity and specific chemical interactions. In many cases the binding site is not known experimentally and therefore docking algorithms have to scan the entire protein surface for possible binding sites on the protein interface, or use methods that try to detect the binding site. Identifying functional interfaces in interacting proteins can greatly reduce the search time for correct rigid-body transformations, as the only geometric transformations that need to be considered are those that match features residing only on predicted interfaces, while the rest of the monomeric interface is not considered.One can estimate the relative importance of amino acids in a protein through evolutionary conservation. Some amino acids in a protein, play a much more important role in the functionality of the protein than others - for example, amino acids that reside on binding interface and play a role in protein-protein interactions, and hence tend to be highly conserved. The Evolutionary Trace (ET) method  ranks reet al. [The ET Server  provideset al.  devised residueRank is the ET rank value of the residue, µ and σ are the mean and standard deviation of ET rank values of residues in the chain, respectively. A low ET rank value represent lower mutation rates for a given amino acid which leads to higher conservation value and vice versa. The more positive the conservation score, the more critical an atom is. In this work we considered all residues whose conservation score is positive (above average) as critical. Using this conservation value, Akbal-Delibas et al. [where s et al.  defined 1stn). The visualization of its rigidity properties calculated using KINARI-Web are shown in Figure Rigidity analysis  is an efin vitro, because it is not always possible to express a protein with a specific amino acid substitution. The publicly available KINARI-Mutagen tool simulates the mutation of a residue to glycine by removing its side-chain hydrogen bonds and hydrophobic interactions from the molecular model and measuring the effect of the removal on the stability of the protein structure. A new, not yet publicly released, feature of KINARI-Mutagen that was developed specifically for this study was its ability to in-silico mutate residues to alanine, as well as to glcyine. Doing so allowed us to compare the rigidity results against a richer dataset of proteins, for which experimental data about the role of mutations to alanine is known. This new feature of in-silico mutating a residue to alanine will be made publicly available during an upcoming update to the KINARI web server. KINARI-Mutagen identifies critical residues based on the degree to which an in silico mutation to glycine affects the protein's rigidity. It has been demonstrated in identifying critical residues in Crambin. Also, its predictive capabilities to identify critical residues were evaluated on a dataset of 48 mutants from 14 proteins; predictions made by KINARI-Mutagen were correlated against experimental stability measurements [In this study, we used KINARI-Mutagen , which iurements .While the two methods described above use two different approaches and measure different properties, they have one important feature in common - both aim to discover highly important residues in proteins.Therefore, we hypothesize that combining them can give us richer, more accurate information about the relative importance of residues in a protein, than when only one of the methods were used. Extending prior work , We testWe compared the two methods to detect the locations of critical residues in proteins to measure the correlation between the results. Later, we compared our results to experimentally available data and discuss how the two methods complement each other. Our goal is to show that a combined approach provide better prediction about critical residues than any of the methods separately.To perform an in-depth comparison of the two methods, we first analyzed 42 PDB structure files of mutant proteins . In part• residues identified as critical by both methods.• residues identified as non-critical by both methods.• residues identified as critical by only one method.Table The data in Table Crambin (PDB ID: 1crn) [In order to validate the method and obtain more insight about the difference between the two methods, we used the methods on proteins for which experimental data exists. The first protein that we tested was the 46-residues plant protein D: 1crn) . FourteeLysozyme from bacteriophage T4 (PDB ID 2lzm). We retrieved the experimental data about Lysozyme from the ProTherm Database [The other protein that we examined is Database , which pFigure We searched the ProTherm Database  for protin silico mutations to alanine in addition to mutations to glycine. Three proteins were analyzed, for which there was ample experimental ΔΔG data in the ProTherm database.We also performed rigidity analysis and conservation score analysis to predict critical residues, for proteins which had mutations to alanine. For this purpose, KINARI-Mutagen was modified, to permit For the 58-residue Bovine Pancreatic Trypsin Inhibitor, the ProTherm database contains 29 experimentally derived ΔΔG measurements, tabulating how the protein is destabilized in response to a point-mutation to alanine. The change of the stability of the protein ranged from -3.3 to -0.1 kCal/mol, in response to the mutation. We used these experimental values as true predictors of whether a residue is critical. From among the full 29 mutations of the protein, a combined approach of rigidity analysis or conservation score analysis Table  detected2abd, an NMR structure file, whose first model was used). The results of our experiments on this protein are shown in Table 2, which means that the residue is highly exposed, which would make it one of the more difficult residues to identify as critical, using rigidity analysis.The second protein that we studied, for which there is ample experimental data on the effect of mutations to alanine, was the 86-residue acyl-coenzyme A binding protein (PDB ID B. subtilis major cold-shock protein (PDB ID 1csp). The rigidity and conservation score analysis results for that structure  has six different binding partners and ten binding states; and Human Lysozyme (PDB ID 1lz1) has two binding partners and three binding states. The number of binding partners for each known critical residue is shown in the last columns of Table In order to test this hypothesis, we searched the PiSite Database . A proteSome regions in a protein are especially important for the structural stability or functionality of the protein. Mutating critically important amino acids can have a large impact on the correct structure, function or binding ability of the protein. Finding these regions and evaluating their importance can be very useful in facilitating the analysis of protein structures, simulating protein motions and discovering protein-protein interactions and binding modes.In this work we investigated whether combining two different methods for evaluating the importance of residues gives better results than either method alone - one method performs rigidity analysis through systematic mutation to discover critical residues that alter the rigidity of a protein, and the other method uses evolutionary conservation to discover functional interfaces in proteins. Our results show that combining the information obtained by the two methods can detect more information than each method separately.sensitivity versus specificity - the number of false positives increases with the number of true positives detected. However, the actual i cvalues computed by the conservation analysis and the ΔΔG values computed by the rigidity analysis are not boolean; and using these continuous values to provide a confidence level for the criticalness of a residue can be a better way to address this problem.Setting a criticalness threshold for both methods result in boolean data. Such binary classifications introduce the problem of balancing Future work includes incorporating the conservation analysis into KINARI software so that KINARI-Web presents residue conservation values as additional data. Also, we plan to do a detailed analysis using not only information on whether a residue is critical or not but also its level of criticalness to improve the accuracy of our method and eliminate false positives. Finally, we aim to integrate the combined methods into one scoring function which can be applied to other domains such as estimating functional interfaces.The authors declare that they have no competing interests.B. Akbal-Delibas and F. Jagodzinski conducted the research. N. Haspel supervised B. Akbal-Delibas' research. All three co-authors participated in writing the paper.