Accurate modelling of biological systems requires a deeper and more complete knowledge about the molecular components and their functional associations than we currently have. Traditionally, new knowledge on protein associations generated by experiments has played a central role in systems modelling, in contrast to generally less trusted bio-computational predictions. However, we will not achieve realistic modelling of complex molecular systems if the current experimental designs lead to biased screenings of real protein networks and leave large, functionally important areas poorly characterised. To assess the likelihood of this, we have built comprehensive network models of the yeast and human proteomes by using a meta-statistical integration of diverse computationally predicted protein association datasets. We have compared these predicted networks against combined experimental datasets from seven biological resources at different level of statistical significance. These eukaryotic predicted networks resemble all the topological and noise features of the experimentally inferred networks in both species, and we also show that this observation is not due to random behaviour. In addition, the topology of the predicted networks contains information on true protein associations, beyond the constitutive first order binary predictions. We also observe that most of the reliable predicted protein associations are experimentally uncharacterised in our models, constituting the hidden or “dark matter” of networks by analogy to astronomical systems. Some of this dark matter shows enrichment of particular functions and contains key functional elements of protein networks, such as hubs associated with important functional areas like the regulation of Ras protein signal transduction in human cells. Thus, characterising this large and functionally important dark matter, elusive to established experimental designs, may be crucial for modelling biological systems. In any case, these predictions provide a valuable guide to these experimentally elusive regions. To model accurate protein networks we need to extend our knowledge of protein associations in molecular systems much further. Biologists believe that high-throughput experiments will fill the gaps in our knowledge. However, if these approaches perform biased screenings, leaving important areas poorly characterized, success in modelling protein networks will require additional approaches to explore these ‘dark’ areas. We assess the value of integrating bio-computational approaches to build accurate and comprehensive network models for human and yeast proteomes and compare these models with models derived by combining multiple experimental datasets. We show that the predicted networks resemble the topological and error features of the experimental networks, and contain information on true protein associations within and beyond their constitutive first order binary predictions. We suggest that the majority of predicted network space is dark matter containing important functional areas, elusive to current experimental designs. Until novel experimental designs emerge as effective tools to screen these hidden regions, computational predictions will be a valuable approach for exploring them. Many features of biological systems cannot be inferred from a simple sum of their components but rather emerge as network properties The scarce knowledge of biological systems is further compounded by experimental error. It is common for different high-throughput experimental approaches, applied to the same biological system, to yield different outcomes, resulting in protein networks with different topological and biological properties There has been a great deal of work analysing biological networks across different species, giving insights into how networks evolve. However, many of these publications have yielded disparate and sometimes contradictory conclusions. Observation of poor overlap in protein networks across species Increasing the accuracy of networks by integrating different protein interaction data relies on the intuitive principle that combining multiple independent sources of evidence gives greater confidence than a single source. For any genome wide computational analyses, we expect the prediction errors to be randomly distributed amongst a large sample of true negative interactions (i.e. the universe of protein-protein interactions that do not take place). Hence, it is unlikely that two independent prediction methods will both identify the same false positive data in large interactomes like yeast or human. In general, we expect the precision to increase proportionally to the number of independent approaches supporting the same evidence.From the available list of well-known integration methods specifically designed to integrate diverse protein-protein interaction -PPI- datasets . The four different prediction datasets obtained by these methods , were integrated using simple integration and Fisher's method as examples of untrained methods (see the section: Integrating the prediction data). Similarly ab-initio prediction datasets from STRING In this work, we significantly increase the prediction power of binary protein functional associations in yeast and human proteomes by integrating different individual prediction methods using the Fisher integration method. Three different untrained methods are implemented: GECO (Gene Expression COmparison); hiPPI (homology inherited Protein-Protein Interactions); and CODA  run with two protein domain classifications, CATH Homo sapiens (human) and Sacharomyces cerevisae (yeast). We observe how the networks change as the cut-off on the confidence score of the predictions is varied. Results of this PG and KG network comparison demonstrate that PG networks resemble KG networks in many of the major topological features and model a substantial fraction of real protein network associations, as previously observed in some bacterial predicted networks Protein pairs identified by significant Fisher integration p-values were used to build a protein network model for yeast and human proteomes referred to as the Predictogram (PG). Additionally, all the protein-protein associations from several major biological databases, including Reactome There have been frequent observations of low overlaps between different experimental high-throughput approaches By analogy The results are divided into four main sections in which the predicted and experimental PPI models of human and yeast are compared. The first section analyses the performance of the single and integrated methods predicting the protein associations and determines the correlation between the prediction scores and the degree of accuracy and noise in the predictions. The second chapter compares the topological network features of the predicted and experimental PPI models at equivalent levels of accuracy and noise. The third section searches for functional differences between the predicted and experimental models looking for specific functional areas which appear to be illuminated by the prediction methods but elusive to the experimental approaches. Whilst the final fourth section explores whether the predicted PPI network graphs contain additional context-based information on protein associations beyond the sets of predicted protein pairs used to build the networks.The different methods for predicting protein-protein interactions and functional associations were run on the whole yeast and human proteomes, generating four prediction datasets for each organism, GECO, CODAcath, CODApfam and hiPPI  used for validating the prediction methods), were used to assess performance  versus Recall  is plotted for yeast and human Gossr validation , for allWe have performed additional validation of the Fisher method using a set of physical interacting pairs as gold standards in yeast and human  in yeast and human. FisherW integration of the STRING datasets showed a significantly lower performance compared to the GECO, CODAcath, CODApfam and hiPPI Fisher integration  Fisher integration of the GECO, CODAcath, CODApfam and hiPPI predictions was shown to be a powerful combination which significantly increases the prediction power without using any KG trained or supervised algorithms. This premise is crucial if we aim to detect genuine similarities between the PG and KG models, unbiased by overlap between supervised predictions and their training sets (as would occur by using a Bayes integration). Because of this the Fisher weighted predictions were chosen for generating the PG network models used in subsequent analyses of the networks.To test whether the PG networks based on the binary predictions share features with networks built on reliable KG evidences, different topological parameters (see section 7 in 0.001) to p-value≤1.0 (PG1.0). KG network models were also tested at different levels of confidence based on the number of KG evidences supporting the same protein-protein associations. Mutual information calculation on the KG data showed broad independence except for the Goss and Foss (FunCat semantic similarity) datasets, therefore Goss and Foss evidences were summed and considered as a single dataset of KG evidences. Different KG networks were constructed by varying the minimum number of independent evidences required to form an edge/link. Random models were also generated for all the PG and KG networks as described in the section: Network randomisation. The PG, KG and their corresponding randomised networks, built at different significance levels, provide comparable frameworks for examining the topological properties of biological networks.Different PG networks were constructed from the binary predictions by varying the link (edge) p-value cut-off. This was done for a range of p-values from p-value≤0.001 (PGi) with values for the exponent between 2 and 3 i) is plotted for the PG and KG networks, constructed at different confidence levels for yeast and human, we observe a trend towards higher exponents in the fitted power law functions as the network reliability increases  frequencies for all levels of network reliability tested, except for the lowest level  lends further support to the scale-free organization of the KG and PG networks in yeast and human  implying a non-hierarchical organization, since hierarchical organization exhibits a power-law distribution of these two parameters  hubs.KG models represent the known  protein associations while PG models represent sets of associations predicted by 0.01 in yeast – about 90,000 pairs - and PG0.014 in human – about 106 pairs; see section 15 and Table S5 in 0.01 model are backed by experimental evidences in the KG set, which is a highly significant figure compared to any of the random models  PG models  were also present in the KG model . AlthougThe density of the overlap between PG and KG in yeast , whilst dark nodes constitute 71% in human.Although PG and KG models explore significantly different regions of protein binary association space, interestingly, given the small intersections and density values, the PG and KG overlap is still significantly larger than expected by random , indicati_er) was calculated in order to measure the difference in the connectivity (ki) values for a protein in the PG and KG networks . A high PGki_er value indicates the presence of a dark  hub, a protein with many predicted associated proteins in the PG model and few, if any, experimentally validated KG associations. Proteins in the yeast and human PG models were ranked using their PGki_er value, retrieving the top 10 ranked proteins for both organisms , sporulation, and various enzymes see . Enrichmi_er ranked lists was estimated using the GOrilla server ab-initio predictions than to experiments.In order to study possible bias in the functional niches highlighted by the PG predictions but absent in the KGs, functional enrichment in the yeast and human PGkGOrilla did not find any significant functional enrichment bias  in the yeast ranked list, but detected enrichment of some GO terms in the human ranked list associated with particular biological processes and molecular function categories in GO see . Dark  to predict associations it has with other proteins sharing a similar context.If the reliable PGIn order to test this hypothesis, functional predictions were generated for additional protein pairs, by comparing the interactions of the respective proteins in these pairs, in the PG networks. The results were then validated using the gold standard KG protein pairs' datasets.0.001&0.0014 pair-wise predictions' datasets used to build the networks in first place are considered the first order predictions in this work , showed close correlation with precision in predicting true, functionally associated proteins  for yeast and human.The scoring functions of the three proteins . The corab-initio  methods. Experimental (KG) and predicted (PG) networks share all of the main topological features explored in this work. In summary the node ki degree distribution, assortativity, clustering distribution, and clustering average coefficient for each of the PG and KG networks demonstrate a trend towards a scale-free organization as network confidence increases. KG and PG are both non-random networks, both in the connectivity and in the statistical weight distributions of their edges  connectivity part of the plots for the least reliable KG and PG models, disappearing in the KG and PG models built at higher levels of reliability  in the KG and PG models, thereby generating analogous frameworks for the KG and PG models built at different reliability levels. In the KG models the associated error is inversely correlated to the number of evidences supporting a given protein-protein association. Reducing error by summing evidences is analogous to the repetition of experiments carried out in standard experimental protocols lity see  and 3. Tions see .Since one of the prediction methods, hiPPI, exploits available experimental data by inheriting experimentally validated interactions between homologous proteins there may be some concern that the dependency of the hiPPI predictions on some of the KG datasets could bias the PG network models so that the features resemble those of experimental KG networks. Addressing this possibility we repeated the main analyses of this work excluding the hiPPI predictions and demonstrated that the similarity of the PG and KG models remained and is therefore not due to any circular information or bias. This confirms our previous observations and conclusions of our work (see section 21 in 0.01&0.014 predictions by KG datasets appears much higher in yeast (18%) than in human (1.34%) for all the analysed cases , and since the PG models show the most important properties of real, biological networks, resembling the properties observed in the KG models, we can conclude that the yeast and the human PG networks are valuable models, akin to the currently more accepted KG models, for investigating the properties of real biological networks, complementing and completing experimental studies in Systems Biology.Overview of the Methods. Homology inherited Protein-Protein Interaction (hiPPI) method, scores potential protein-protein interactions based on their homology to known interacting protein pairs; Co-Occurrence Domain Analysis (CODA) method, looks for and scores protein pairs in a given target genome (e.g. yeast or human) found as fused (Co-Occurring) domain architectures in homologues from genomes of 575 different species; Gene Expression COmparison (GECO) method, measures the correlation of gene expression profiles between protein pairs (detailed explanation of the ab initio methods in section 1 in Homo sapiens) and yeast (Sacharomyces cerevisiae) proteomes  using the curvefit tool from MATLAB. The particular Probability Density Functions (PDF) associated with the score distributions for each of the four methods was calculated in order to translate the scores into p-values. Right tailed Ztests were performed to ensure that the PDF distributions of the PG datasets fit random Gaussian distributions with different means μ (null hypothesis) at 5% significance level for accepting the null hypothesis being false  between pairs of predictors, indicated that the datasets were largely conditionally independent - in order to maximize the overall statistical power.The p-values from each method were integrated using two methods: Simple integration, and Fisher weighted (Fisher_W) th orderMonte Carlo Method of 5Enhanced Simulated Annealing (ESA)The weights for the Fisher_W method were calculated using a MATLAB script. This consisted of simultaneously running a We benchmarked our predictions using the highest quality annotations of yeast and human proteomes in the Gene Ontology (GO) database http://www.geneontology.org/). Sets of protein pairs with significant Goss score  scores were calculated for all versus all protein pairs in human and yeast proteomes as described by Lord et al. 2003 Precision was calculated as the ratio of accumulative TP/TP+FP at different prediction p-values, where TP (True Positives) is the rate of hits predicted within the validation dataset of true protein binary associations , and FP  is the average rate of hits predicted from 1000 random models of the same validation dataset.The FP are the randomly selected PPIs above different scoring thresholds . The FPs are calculated as an average of 1000 random validation iterations to estimate the errors (deviations) associated with the calculation. We then compare the relative differences in the TP and FP rates in the ranked prediction list, obtained by using our predictor and a random approach. For example, a precision ≥90% associated with a p-value≤0,001 means we find 9 times more TPs in the set of predictions with p-value≤0,001 than a random predictor does by chance. In our analyses the precision (ie TP/TP+FP) will always tend to 50% because we select the same number of FPs from our random predictors as given by the integrated prediction method.Using a random model for benchmarking it is possible that a randomly selected PPI could be a known TP, by chance, although the probability is expected to be very low since the space of known PPI (TPs) is much lower than the space of random PPIs pairs considering all possible combinations. It is also likely that any of the gold standard datasets, or combinations of them, do not contain all the true PPIs taking place in nature. Therefore it is not possible to correctly estimate FPs in the ranked predictions, based on pairs absent in the validating datasets (ie many of these FPs may be currently uncharacterised TPs). In any case, the consequence of considering TPs as FPs in the random validation model used in this work is conservative, giving an underestimate of the performance of our predictor  ratio, since not all the true PPI are known in our validation model, we can not reliably estimate the FN rates. Therefore, in this work Recall is calculated as the accumulated number of predicted hits by a given method, at different p-value levels.0.01.Yeast and human PG protein networks were built based on the binary protein prediction data selected at different discrete Fisher_W p-value statistical significance levels. Fisher_W predictions were chosen because these gave the best results from the benchmarking. Various PG networks were generated over a range of predicted p-value cut-offs. The p-value cut-off used to generate a given PG network is specified in the subscript of its name. For example if a p-value cut-off ≤0.01 was used the PG network was termed PGThe construction of KG protein networks for human and yeast proteomes was based on the existence of protein functional links. For the interaction databases HRPD A cumulative score was associated with each edge  to represent the number of independent resources with evidence of the functional link between the two proteins. The KG models statistics are shown in Table S5 in p-values random models and they were built to analyse the distribution of the statistical weights associated with protein edges  compared to random behaviour. The second randomised model, referred to as the adjacency random model, was generated by randomly distributing all nodes, p-values and # of evidences in the PG and KG pairs-wise datasets. Any new self-associations in the PG network datasets produced by the randomisations were removed. The adjacency random models were built to analyse the distribution of edges and p-values in the KG and PG models compared to random behaviour. Both models went through 1000 randomisation iterations.Two different randomisation procedures were implemented. The first method randomised the p-values associated with edges in the PG network and the # of evidences associated with edges in the KG models, whilst keeping the same pairs of connected nodes in the matrices. These models are referred to as i) In order to compare the PG/KG networks generated by this study several different network statistical features were calculated. Topological parameters included the node degree connection (ki) for nodes in the PG network compared to the KG network was calculated for all the nodes (proteins) using the following formula: i_er (pi) = (PGki−KGki)/(KGki+1)PGk where i_erPGk is the PGki enrichment ratio of the protein ip, iPGk is the ki value of the protein ip in the KG network and iKGk is the ki value of the protein ip in the KG network. Yeast and human proteins were ranked using the PGki_er parameter values and the ranked lists were used as input for the GOrilla web server (http://cbl-gorilla.cs.technion.ac.il/). GOrilla is a tool for identifying and visualizing enriched GO terms in ranked lists of genes , were compared using different similarity measures, such as: bits, specific bits and congruence. These similarity scores, which are calculated over the PG network matrices, are termed second order predictions  = b1, where p1 and p2 are the two proteins compared and b1 is the number of shared interacting proteins between the two proteins' interaction vectors in a given PG network matrix. The specific bits score was calculated using the following formula: s = b1·[−log(b1/(b1+b2))], where p1 and p2 are the two proteins compared, b1 is the number of shared interacting proteins, and b2 is the number of non-shared interacting proteins between the two compared proteins in the PG networks. Congruence is a similarity measure between pairs of protein interacting vectors that was calculated as described in Lehner 0.014 network matrix  from the most significant to the least significant. Validation was performed using as true positives (TP) protein pairs from the KG matrices in yeast and human respectively  construction) mapped to pairs in the ranked lists. An extra gold standard dataset of mapped true positive hits was built using those pairs present in two or more KG datasets (KG≥2). False positive (FP) sets were obtained by mapping the same KG gold standard datasets on randomised lists of second order predictions ranked lists, with 1,000 random iterations in yeast and 500 in human .Precision and recall parameters were calculated as described above, the precision mean and error (standard deviation) values were calculated based on the TP and the different accumulated random FP distributions. In order to present representative results values with standard deviations more than 1/3 of the mean were ignored, as they were due to the small size of the TP and FP samples at the beginning of the accumulated distributions (for further details see section 18 in Text S1Supporting information.(4.01 MB PDF)Click here for additional data file.


The chemical-genetic profile can be defined as quantitative values of deletion strains' growth defects under exposure to chemicals. In yeast, the compendium of chemical-genetic profiles of genomewide deletion strains under many different chemicals has been used for identifying direct target proteins and a common mode-of-action of those chemicals. In the previous study, valuable biological information such as protein–protein and genetic interactions has not been fully utilized. In our study, we integrated this compendium and biological interactions into the comprehensive collection of ∼490 protein complexes of yeast for model-based prediction of a drug's target proteins and similar drugs. We assumed that those protein complexes (PCs) were functional units for yeast cell growth and regarded them as hidden factors and developed the PC-based Bayesian factor model that relates the chemical-genetic profile at the level of organism phenotypes to the hidden activities of PCs at the molecular level. The inferred PC activities provided the predictive power of a common mode-of-action of drugs as well as grouping of PCs with similar functions. In addition, our PC-based model allowed us to develop a new effective method to predict a drug's target pathway, by which we were able to highlight the target-protein, TOR1, of rapamycin. Our study is the first approach to model phenotypes of systematic deletion strains in terms of protein complexes. We believe that our PC-based approach can provide an appropriate framework for combining and modeling several types of chemical-genetic profiles including interspecies. Such efforts will contribute to predicting more precisely relevant pathways including target proteins that interact directly with bioactive compounds. Finding the specific targets of chemicals and deciphering how drugs work in our body is important for the effective development of new drugs. Growth profiles of yeast genomewide deletion strains under many different chemicals have been used for identifying target proteins and a common mode-of-action of drugs. In this study, we integrated those growth profiles with biological information such as protein–protein interactions and genetic interactions to develop a new method to infer the mode-of-action of drugs. We assume that the protein complexes (PCs) are functional units for cell growth regulation, analogous to the transcriptional factors (TFs) for gene regulation. We also assume that the relative cell growth of a specific deletion mutant in the presence of a specific drug is determined by the interactions between the PCs and the deleted gene of the mutant. We then developed a computational model with which we were able to infer the hidden activities of PCs on the cell growth and showed that yeast growth phenotypes could be effectively modeled by PCs in a biologically meaningful way by demonstrating that the inferred activities of PCs contributed to predicting groups of similar drugs as well as proteins and pathways targeted by drugs. The collection of yeast deletion strains has enabled systematic genomewide functional analysis In the previous study, however, valuable biological information such as protein-protein and genetic interactions has not been integrated with chemical-genetic profiles in a single model for drug's target pathway prediction. Also, those profiles have not been yet modeled at the molecular level in terms of biological real entity such as protein complexes. As chemical-genetic profiles of many bioactive compounds are accumulating in many species including yeast, such real molecular entity-based modeling becomes essential to the molecular level understanding of phenotypes of the eukaryotic cell exposed to different chemicals. For example, that will allow us to infer drug's mode-of-action from chemical-genetic profiles more precisely than the previous model-free approach.In order to relate hidden biological activities of some real biological entities at the molecular level to the phenotypes of deletion strains at the organism level, we assume that the growth of a deletion strain is affected by hidden activities of protein complexes in a given condition, which leads to the observable population changes of the strain. This assumption comes from the molecular rationale of protein complexes for gene-to-phenotype relationships Based on the assumption that each protein complex should play a proper role as well as communicate efficiently with each other for cell survival and adaptation in various treatments, we developed a Bayesian factor analysis model. Our model is similar to the network models developed in transcription regulation studies By modeling chemical-genetic profiles in terms of protein complexes using physical and genetic interactions as a priori knowledge, we inferred hidden activities of a collection of PCs in each cell exposed to different chemicals. Based on those PC activities, bioactive compounds with similar mode-of-action were clustered together. It means that the binary association network in our model represents biologically meaningful relationship between knockout genes of strains and protein complexes as hidden factors. In addition, we showed that protein complexes with similar function were clustered together, which implies that the unknown functions of protein complexes can be predicted. Finally, we presented a new effective method to predict drug's target pathway using our PC-based model. For example, we were able to highlight target-protein, TOR1, of rapamycin as well as RUB1, UBA3, UBC12, and ULA1 related to protein neddylation as relevant biological pathway for cellular toxicity of camptothecin.We believe that our protein complex-based approach can provide appropriate framework for combining and modeling several types of chemical-genetic profiles including interspecies. Such efforts will contribute to predict more precisely relevant pathway including target-proteins that interact directly with bioactive compounds.We applied our PC-based Bayesian factor model , (c), (e), (f), (g), (h), (j), (l), and (n), have been already confirmed by the literature survey or experiments in the previous paper of releasing the compendium Many drugs were similarly closely clustered together in both dendrograms (see the blue star in Cluster (b). Doxycycline inhibits mitochondrial enzyme synthesis leading to a lack of oxidative ATP synthesis and so to proliferation inhibition in human leukemic cells Saccharomyces cerevisiaeCluster (d). Wortmannin and caffeine physiologically inhibit the signaling of phosphatidylinositol 3-kinase (PI3K)-related protein kinases, TEL1 and possibly MEC1 by regulating the level of inositol pyrophosphates, decreasing telomeric length and leading to cell death TOR1/2 may participate in the regulation by inositol pyrophosphates of vesicular endocytosis TOR2 localization to membranes of yeast vacuoles TOR1 to the plasma membrane of yeast Saccharomyces cerevisiae was represented in complex-based cluster (d), but not in the strain-based clustering.Cluster (h). The antifungal bioactive compound in extract 00-192, derived from a sea cucumber from the Commonwealth of Dominica, is identical to stichloroside as well as the antifungal compound in extract 00-132, derived from an Indonesian marine sponge, is identical to theopalaumide Cluster (i). A recent paper suggests that loss of vacuolar H+ translocating ATPase (V-ATPase) activity leads to abnormal intracellular acidification, which facilitates the DNA damage mediated by well known DNA-damaging agents, cisplatin, methyl methanesulfonate (MMS) and hydroxyurea (HU) 3-deoxyadenine Saccharomyces cerevisiaeCluster (k). LY294002 is a cell-permeable compound that acts as a potent and selective inhibitor of phosphatidylinositol 3-kinase (PI3K) Cluster (m). Sulfometuron methyl, a sulfonylurea herbicide, blocks growth of bacteria, yeast, and higher plants by inhibition of acetolactate synthase, the first common enzyme in the biosynthesis of branched-chain amino acids, leucin, isoleucin and valine TAT1 and TAT2 in the posttranscriptional level TAT1 in branched-chain amino acid uptake was reported Saccharomyces cerevisiae, respectively. Our complex-based cluster (m) reflected such same physiological effect, but the strain-based clustering did not.We found that the complex-based clusters showed more physiologically meaningful grouping of drugs compared with strain-based clusters in yeast. For example, sulfometuron methyl and FK506 are very different chemicals in their structures, by both of which the same physiological effect of branched amino acid depletion occurs in yeast. What enables PC-based clustering to group together drugs with similar cellular defect in yeast better than strain-based clusters? Possible reasons are as follows:First, 52% of 1114 essential genes currently released in Saccharomyces Genome Deletion Project Second, valuable biological information on physical and genetic interactions are used to associate protein complexes with haploid deletion strains in PC-based modeling. This integration of prior knowledge allowed us to reduce the large dimension of chemical-genetic profiles (4111 by 82) into relatively small dimension of chemical-PC profiles (488 by 82) in a biologically meaningful way. That process makes chemical-PC profiles represent relevant features of chemical-genetic profiles at the minimum information loss. Furthermore, noise reduction by reweighting of irrelevant information is achieved by the dimension reduction in our model. It is indirectly supported that PC-based clustering is more robust than strain-based clustering. For example, removal of 27% of 488 protein complexes with low values did not significantly influence the clustering result made by all of 488 protein complexes, whereas removal of 7% of 4111 strains with low values made different clustering result compared with all of 4111 strains .Z matrix in Taken together, we claim that the binary associations of 488 PC and 4111 strains  supports that the protein complexes of “cluster I” are obviously related to the protein synthesis within mitochondrion.The “cluster II” represented the groups of protein complexes involved in ER-to-Golgi and sequentially Golgi-to-vacuole or Golgi-to-endosome vesicle transports. Especially, the Sec34/35 Golgi Transport Complex (PC 293) in “cluster II” is known for tethering factors that attach the vesicle to the destination organelle prior to fusion to retrograde vesicular trafficking within the Golgi apparatus The TRAPP II complex (PC 182) in “cluster III”, one of two forms in TRAPP complex, functions as the tethering factor at the Golgi. Whereas the other, TRAPP I complex regulates YPT1 related to the entry of cargo vesicles into the Golgi apparatus, TRAPP II complex (PC 182) regulates YPT31 and YPT32 controlling exit of the cargo from the trans-Golgi network, residing in trans-Golgi As shown in the above examples, the hierarchically clustered groups of protein complexes revealed insights into similar biological processes. In other words, unknown functions of protein complexes could be inferred based on those groups closely clustered. Nonetheless, it was preliminary investigation using simple hierarchical clustering method to highlight the biological essential features of protein complex activities. If chemical-PC profiles are applied to other clustering methods such as biclustering or network reconstruction methods, such results could be different in some cases as well as give more essential features on behaviors of protein complexes perturbed by drugs.To find a drug-target pathway, we first selected significantly sensitive protein complexes to each drug , and theRapamycin and its analogues have a unique mode-of-action as follows: Rapamycin first binds to the 12-kDa immunophiln FK506-binding protein (FKBP12) and this complex inhibits the target of rapamycin (TOR) phosphoinositide-kinase (PIK)-related serine/threonine kinase conserved in all eukaryotes which is rapamycin target as well as subunit of TORC1 complex Camptothecin is a natural product of which Topoisomerase I (TOP I) is the only cellular target. TOP I is essential in higher eukaryotes, as they are required to relax DNA supercoiling generated by transcription, replication and chromatin remodeling. Despite their frequency throughout the genome, their cleavage intermediate steps (referred to as the “cleavable complexes”) are normally so transient that they are not detectable, but it is these complexes that are specifically and reversibly trapped by camptothecin rub1, uba3, ubc12, ula1, and rpn4 strains among all of 303 strains sensitive to camptothecin play important roles in camptothecin-target pathway  among them is a ubiquitin-like post-translational modifier that is covalently linked to cullin (Cul)-family proteins in a manner analogous to ubiquitination S. pombeS. cerevisiae, conjugation of RUB1 is not essential for normal cell growth, but occurs selectively in a small set of substrates, CDC53, RTT101, and CUL3 Based on physical interactions with the most sensitive protein complex to camptothecin, PC 181, our complex-constrained strains suggests that five knock-out genes of  pathway . Their eS. cerevisiae that RUB1-attachment of CUL3 would enhance the degradation of TOP1 cleavable complexes and, therefore, the blocking of RUB1-conjugation pathway could contribute significantly to increase the level of camptothecin toxicity in a cell growth as depicted in Taken together, we propose the model of camptothecin toxicity in MMS and cisplatin themselves are potent damaging agents. Genes of their sensitive strains are related to DNA repair system as expected . In partBenomyl and nocodazole are known for microtubule poisons. As expected, tubulin-folding or binding-related terms were enriched. In particular, the GO term of Gim/prefolding complex resided in top rank only in PC-constrained strains. This complex is known for promoting formation of functional α- and γ-tubulin http://pombe.kaist.ac.kr/CMA/ModeOfAction.pl). By guilty-by-association, the set of genes of drug-sensitive strains specified by drug-sensitive complexes with specific biological function would be involved in the corresponding function although some of them are not severely sensitive to a given drug. It could give rise to enrichment of some of GO terms related to drug's cellular toxicity in PC-constrained strains.Those examples showed that the PC-based approach was able to highlight the subset of important genes in the relevant drug-target pathway more than PC-free approach. How can PC-constrained strains narrow down PC-free strains in this sensible way? A possible reason is as follows: some protein complex itself is composed of proteins representing specific biological function. In camptothecin and benomyl examples, PC181 is known for related ubiquitin activating protein complex, and PC413 known for Gim protein complex promoting functional microtubule formation , which not only allowed us to robustly compute the equation, but also provided a window through which relevant biological information such as protein-protein interactions and genetic interactions can be integrated with experimental data. Second, the estimation of protein complex activities by Bayesian factor model is more robust and stable because estimands are obtained by tens of thousands of samplings. Deterministic factor analysis methods From the purely computational standpoint, our model and Bayesian hidden component analysis (BHCA) developed by Sabatti and James To improve the hierarchical clustering, Parsons et al. also utilized a matrix decomposition method known as probabilistic sparse matrix factorization (PSMF). Both PSMF and our factor model all decompose chemo-genomic profiles into some factors and their weights. These decompositions of the two methods contribute to noise-reduction of original data, and so reveal more essential features. For example, Parsons et al. discussed two cases . If the number of strains is N and the number of experiments is M, the model can be rewritten in a matrix notation as follows:The central assumption of our model was that the observed growth fitness measurements of each strain were determined by combined effects of the activities of a collection of the protein complexes (PCs) in each cell under different treatments. By log-transforming strains' fitness measurements, a linear model was formulated so that E denoted an N×M matrix A was a matrix P denoted the matrix Here, P matrix have a clear interpretation, as they correspond to specific protein complexes. (2) The specific value of the jtp is the primary interest for the prediction of drug's mechanism. (3) The matrix A is known to contain a large number of zeroes, corresponding to the sparseness of the network. This sparseness in our model is very important feature to solve the notorious non-identifiable problem in the factor model, by which multiple sets of different parameter values lead to the nearly identical likelihood. Therefore, the identifiability should be achieved by imposing some sort of constraints in the model. A typical way in biological analysis is to constrain the loading matrix of factors by the same size of the matrix with a specific pattern of zeros. In our factor model, the sparse network topology as such constraints was represented in Z matrix with the same size as the A matrix but with only 0 or 1 values  the “factors”, row vectors in the 1 values .Z matrix was shown in (A) and (C) of ijz were assumed to be independent. If there was the association between the knockout gene of a strain i and at least one of components in a protein complex j, we set ijz = 1. Otherwise we set ijz = 0. By letting ijπ = Pr(izj = 1), we assigned 1 to the ijπ because of limited memory size and computation time. By doing so, we lost the chance to remove false positive associations. The remaining entries of Z were set to zero. The other priors on A, P and σ|Z were also defined as follows:ija, jtp and A, P and σ were mainly used for regularization. To explore the posterior distributions of the four parameter groups Z, A, P, and Z, A, P, and The procedure for constructing S. cerevisiaeWe used the compendium of chemical-genetic profiles for 82 different bioactive compounds by screening them against the yeast haploid deletion collection, of ∼5,000 viable strains A and P matrices in our models using the collapsed Gibbs sampler derived in the BHCA paper α = 0.7 and β = 0.3 for the gamma distribution as a prior of inverse aσ = 1, 10, 30, 60, 70, 90, 100 and pσ = 1, 10, 30, 50 for a priori standard normal distributions of ija and jtp.We obtained the posterior distributions of all the parameters of aσ and pσ. The initial 1000 iterations were discarded as burn-in, and the results of one per ten iterations were recorded, as suggested by Sabatti and James aσ = 30 and pσ = 1, the convergence of SSE was best (posterior (or sample) distribution of each parameter. Then, posterior means and posterior variances of each posterior distribution were used as estimands of each parameter. In this study, we focused on the analysis of posterior mean of each element in P matrix because our goal is to infer the hidden relative activities of protein complexes.We first ran the Gibbs sampler for 11,000 iterations written in statistical language R was best  so that Hierarchical agglomerative clustering was performed by Gene Cluster 3.0 P with rows of 1…i…488 complexes and columns of 1…j…82 compounds, and the reference set of each complex was defined as a collection of activities of each protein complex under different 82 compounds, {iPj = 1‥82,}. For every activities (ijP) of a given protein complex under a given compound, the drug effect (ije) on a protein complex was calculated by subtracting the mean (X̅i) of its reference set from ijP, and the uncertainty (ijP obtained from tens of thousands of Gibbs sampling (Equations 3 and 4).N represented the number of compounds, 82.To estimate the significance of the effect of a drug on a protein complex, we modified the error model used for haploinsufficiency-based direct drug-target identification in Yeast ije) on a protein complex and its pooled variance  analysis for all of 82 compounds were performed, and those results were available at Figure S1The distribution of essential and non-essential genes in protein complexes. (A) The distribution of essential and non-essential genes involved in each protein complex is shown. The protein complex is composed of 5.5 essential and 7.8 non-essential genes in average. (B) All of 488 protein complexes include 1490 non-redundant genes, which consist of 580 essential and 910 non-essential genes. In particular, 52% of 1114 essential genes reported in Saccharomyces Genome Deletion project are included in 488 protein complexes.(0.07 MB PDF)Click here for additional data file.Figure S2Hierarchical clustering according to noise removal. (A) When chemical-genetic profiles of 4111 haploid strains just excluding non-viable and multi-drug sensitive strains were applied to hierarchical clustering, many of clusters are different from those obtained after 17% removal  in text.(0.06 MB PDF)Click here for additional data file.Figure S3Two-dimensional hierarchical clustering. The set of the inferred protein complex activities was visualized by two-dimensional hierarchical clustering. In total, 82 compounds were clustered on the vertical axis, based upon the similar patterns of protein complexes, and 488 PCs were also clustered on the horizontal axis, according to the similar patterns of bioactive compounds(8.71 MB PDF)Click here for additional data file.Figure S4Z matrix. The Z matrix comprises 488 columns (PCs) and 3241 rows (strains). The sparseness of Z matrix is shown in column- and row-wise counting. (A) The x-axis represents the number of strains associated with a protein complex. The y-axis represents the frequency of protein complexes with same number of associations with strains. The minimum, average, and maximum values of strains associated with a protein complex are shown in the histogram. (B) The x-axis represents the number of protein complexes associated with a strain. The y-axis represents the frequency of strains with same number of association with protein complexes. The minimum, average, and maximum values of protein complexes associated with a strain are shown in the histogram.The row (strain)-wise and column (complex)-wise statistics of binary associations of (0.05 MB PDF)Click here for additional data file.Figure S5Plot of the sum of squared error (SSE). The chain of SSE sampled from Gibbs sampler seemed not to be sticky. It is used for monitoring overall convergences of parameters.(0.02 MB PDF)Click here for additional data file.Figure S6Comparison of grouping of drugs by probabilistic sparse matrix factorization (PSMF), protein complex (PC)-based hierarchical clustering and strain-base hierarchical clustering. The red arrow indicates drugs comprising a specific factor obtained by PSMF. (A) Group of drugs comprising factor 6. (B) Group of drugs comprising factor 5. The original factorgram images in the paper by Parsons et al. (0.18 MB PDF)Click here for additional data file.Table S1Functional annotations of protein complexes clustered together.(0.02 MB PDF)Click here for additional data file.Table S2GO analysis of drug-sensitive strains associated with drug-sensitive protein complexes.(0.04 MB PDF)Click here for additional data file.


Information about protein interaction networks is fundamental to understanding protein function and cellular processes. Interaction patterns among proteins can suggest new drug targets and aid in the design of new therapeutic interventions. Efforts have been made to map interactions on a proteomic-wide scale using both experimental and computational techniques. Reference datasets that contain known interacting proteins (positive cases) and non-interacting proteins (negative cases) are essential to support computational prediction and validation of protein-protein interactions. Information on known interacting and non interacting proteins are usually stored within databases. Extraction of these data can be both complex and time consuming. Although, the automatic construction of reference datasets for classification is a useful resource for researchers no public resource currently exists to perform this task.Saccharomyces cerevisiae. Both positive and negative cases for a reference dataset can be extracted, organised and downloaded by the user. GRIP also provides an upload facility whereby users can submit proteins to determine protein complex membership. A search facility is provided where a user can search for protein complex information in Saccharomyces cerevisiae.GRIP (Gold Reference dataset constructor from Information on Protein complexes) is a web-based system that provides researchers with the functionality to create reference datasets for protein-protein interaction prediction in Saccharomyces cerevisiae. Manual construction of reference datasets can be a time consuming process requiring programming knowledge. GRIP simplifies and speeds up this process by allowing users to automatically construct reference datasets. GRIP is free to access at .GRIP is developed to retrieve information on protein complex, cellular localisation, and physical and genetic interactions in  Saccharomyces cerevisiae (S. cerevisiae) and other organisms, such as Drosophila melanogaster and Homo sapiens from experimental high-throughput methods. However, even the best studied model organisms contain a large number of proteins whose interactions and functions are currently unknown [et al. [S. cerevisiae. The Gold Standard utilized in this study was derived from the biological process Gene Ontology [et al. [et al. [S. cerevisiae localisation data. These studies produced accurate PPI networks providing a comprehensive view of the S. cerevisiae interactome. A recent study by Collins et al. [S. cerevisiae into a single reliable collection of experimentally-based PPIs by analysing the primary affinity purification data using a novel Purification Enrichment (PE) scoring system. Using a Gold Standard obtained from MIPS [Saccharomyces Genome Database (SGD) their study demonstrated that the consolidated dataset is of greater accuracy than the individual sets and is comparable to PPI defined using more reliable, small-scale experimental methodologies.A fundamental problem in the post-genomic era is the determination of protein-protein interactions (PPI). Efforts have been made to map interactions on a proteomic-wide scale. Several large-scale protein-protein interaction maps have been produced for  unknown . This hi unknown ,3 have u [et al.  construcOntology . The Gol [et al.  applied  [et al. . The negs et al.  merged trom MIPS  and SaccInformation on protein interactions is usually extracted from sources such as databases to construct Gold Standards. This manual process is time consuming, complex and requires programming knowledge. We have constructed GRIP (Gold Reference dataset constructor from Information on Protein complexes) which removes the complexity of this process and reduces the time required by automatically constructing Gold Standards for researchers.S. cerevisiae, which is an important model organism for systems biology. GRIP provides three main types of functionality:GRIP has been developed for Both positive and negative reference datasets are constructed by GRIP. The reference datasets are constructed using data obtained from two sources: the MIPS Comprehensive Yeast Genome Database (CYGD) – Complex Catalogue and Localisation Catalogue  and the GRIP provides the functionality to verify protein complex membership. Using a Web browser, a user can upload a text file containing a defined set of proteins to GRIP. Validation is performed by the system to ensure that a text file containing proteins has been submitted by the user. These proteins are validated against known protein complexes from MIPS stored in MySQL database tables. SQL queries are implemented to perform the search function. All protein complexes that contain one, some or all of the proteins in the user defined text file are selected. Using this information, complex ids and proteins found within each complex id are returned. These results are displayed on the GUI and a download facility is provided to the user whereby results can be downloaded as a text file. If no matches are obtained by the search function, a message is displayed to users on the GUI.The protein complex membership verification is a valuable function for users to validate possible protein complex membership predictions. GRIP not only returns the complex details in which all the proteins may exist, it also returns complexes where one or some of the defined proteins exist.GRIP provides the utility to search and retrieve protein complex information related to a complex id or complex name  input by the user. GRIP accepts either protein complex name or complex id as a search term. The complex matching algorithm will search for protein complexes containing the search term. The protein complex search function provides the flexibility of allowing partial names and ids to be entered. Using SQL queries, protein complex information stored in MySQL database tables are scanned to match the search term. If matches to the search term are found, complex description, complex id and protein information are displayed via the GUI. A message on screen also informs the user if no results are obtained.S. cerevisiae. Classification techniques have been implemented to integrate diverse sources of functional genomic data for the computational prediction of PPI networks [The functionality for constructing a reference dataset, protein complex membership and protein complex matching has been integrated into a web-based system. To the best of the authors knowledge, no tool currently exists that incorporates together these different functions. Additionally the user has the option of construct a reference dataset from either the MIPS complex catalogue or BioGRID repository. GRIP is a useful resource for the task of computational inference of PPI networks in networks ,3. ReferGRIP provides a GUI interface which can be used to construct reference datasets for computational PPI prediction, verifying predictions and searching for protein complex information. The GRIP tool has been developed using the PHP programming language  in an obThe reference dataset constructor provides the functionality to create positive and negative cases. For creating either a positive or negative reference dataset, users can select the number of cases required (between 1 and 10000). Initially an upper limit of 10000 has been placed on number of cases due to an increase in processing time. Users can download the complete positive and negative reference dataset for pair-wise interactions from either the MIPS complex catalogue or BioGRID. The number of proteins (between 2 and 11) to be included (i.e. pair-wise interactions or module-based interacting proteins) can also be selected when the MIPS complex catalogue is selected as the data source. Once GRIP has constructed the reference dataset, the results can be viewed in the browser or downloaded to users' local machine.n (where n is the number of proteins required for a case). A positive case consists of proteins which belong to the same complex. Only MIPS complex sub-classes are considered. For instance, the MIPS class 'Transcription complexes/Transcriptosome' contains the subclasses 'mRNA guanylyl transferase', the 'DNA repair complexes' and other subclasses related to transcription complexes. Complexes that match these criteria are randomly sampled to ensure that no bias occurs in selecting the complexes. Proteins from each complex will be randomly chosen to form a case. Users can select the BioGRID as the data source for constructing a positive reference dataset. Only protein pairs that have been verified as having a genetic or physical interaction are considered as 'positive' cases. To use the Gold Standard Constructor for Positive Cases, a user firstly selects the data source . Secondly the user enters the number of positive cases they require in a positive dataset. The number of cases can lie between 1 and 10000. If the user selects the MIPS complex catalogue then the number of proteins that make up a case can be entered. This number must lie between 2 and 11. The positive cases are written to a text file which can be viewed in the browser or downloaded to the user's computer. Additional checks are performed to ensure that two cases will not contain the same proteins. Therefore, the user obtains a dataset containing unique cases. Figure Using the MIPS complex catalogue as a data source, GRIP constructs a positive dataset by performing a search to find complexes that contain a number of proteins greater than or equal to For the construction of a negative reference datasets the user has the option to select either the MIPS complex catalogue or BioGRID as the data source. To construct a negative case using the MIPS complex catalogue as the source, a user can select from two different generation criteria. For the first criteria – different sub-cellular locations and complexes, GRIP randomly selects proteins belonging to eleven different sub-cellular locations . Further analysis is performed by GRIP to ensure that each protein in a case belongs to a different complex and each case is unique. Each protein is required to come from different sub-cellular locations and complexes when constructing negative cases for the pair-wise and module-based approaches. The second criteria – randomisation, GRIP randomly selects proteins in the eleven sub-cellular locations. When the BioGRID is selected as the source, random protein pairs are selected by GRIP. To use the Gold Standard Constructor to create negative cases, a user firstly enters the total number of negative cases that they require in the dataset. The number of cases must lie between 1 and 10000 . The user then enters the number of proteins in a case if the MIPS complex catalogue is the data source. This must lie between 2 and 11. The negative cases are written to a file which can be viewed in the browser or downloaded to the user's computer. Additional checks are performed to ensure each case is unique and each case contains unique proteins.Figure Figure S. cerevisiae. Seven functional genomic data ranging from co-expression to essentiality were integrated using the classification techniques. The predicted interactions were verified using a reference dataset constructed by GRIP as the MIPS complex catalogue as the data source. The reference dataset consisted of 10802 positive cases  and 330GRIP integrates the functionality for constructing reference datasets, protein complex membership matching and protein complex matching into one public, user-friendly environment. Currently, no other tool exists that performs all these functions. GRIP provides the user with two validated data sources  to construct the reference datasets. GRIP has been developed as a web-based system to allow ease of access and sharing of these resources. GRIP can support the development and evaluation of computational models for PPI prediction as it provides researchers with a resource to create reference datasets (Gold Standards). The automatic construction of these reference datasets removes programming required thereby simplifying and saving time for researchers. Recent research by ,3,13 demWe plan to extend GRIP to provide Gold Standard datasets for other organisms, including humans. Future versions of GRIP will allow users to construct Gold Standards using additional diverse, validated reference datasets.Project Name: GRIPProject Home Page: Operating system(s): Platform independentProgramming language: PHPOther Requirements: Apache server, Linux Server, MySQL severRestrictions: NoneRequirements: Web-browser , internet connection.GRIP: Gold Reference dataset constructor from Information on Protein complexes; GUI: Graphical User Interface; KNN: K-Nearest Neighbour; MLP: Multi-Layer Perceptron; NB: Naïve Bayesian; PHP: Hypertext Pre-Processor; PPI: Protein-Protein Interactions; SQL: Structured Query Language.The authors declare that they have no competing interests.FB developed the source code of the GRIP Web-system. HW, HZ and FA contributed to the system specification, design and evaluation. All authors contributed towards the manuscript and documentation. All authors read and approved the final manuscript.


In this context, yeast two-hybrid system appears as a promising approach to efficiently reconstruct protein interaction networks at the proteome-wide scale. This protein interaction screening method generates a large amount of raw sequence data, i) to establish a standardised system to analyse and to annotate ISTs generated by two-hybrid technologies with high performance and flexibility and (ii) to provide high-quality protein-protein interaction datasets for systems-level approach. This pipeline has been validated on a large dataset comprising more than 11.000 ISTs. As a case study, a detailed analysis of ISTs obtained from yeast two-hybrid screens of Hepatitis C Virus proteins against human cDNA libraries is also provided.We develop pISTil, a bioinformatics pipeline combined with a user-friendly web-interface: (.We have developed pISTil, an open source pipeline made of a collection of several applications governed by a Perl script. The pISTil pipeline is intended to laboratories, with IT-expertise in system administration, scripting and database management, willing to automatically process large amount of ISTs data for accurate reconstruction of protein interaction networks in a systems biology perspective. pISTil is publicly available for download at  Interaction Sequence Tags [Molecular interactions data related to human and model organisms are currently being integrated in generalist databases, such as INTACT , MINT 22 or STRInce Tags . Dedicatnce Tags . Howeveri) systematic and fast assignment of ISTs to a unique protein accession number; (ii) annotation of "in frame" ISTs  or "not in frame" ISTs ; (iii) sequence quality filtering, manual checking and visualisation of annotated ISTs through a user-friendly web interface and (iv) export of protein-protein interactions in multiple formats, such as MIMIx standard format [In this paper, we present pISTil, a fully automated pipeline combined to a web-based interface, which are specifically devoted to ISTs identification and analysis. The pISTil system is highly flexible and allows:  or SCF (Standard Chromatogram Format) formats - are filtered by Phred-pregap4 software ,10 in orAn experimental dataset of 11.658 ISTs obtained from more than 300 Y2H screens was tested in order to validate the pISTil pipeline .i.e. <300 bp), filtering ISTs based only on sequence length appears in this context inadequate. In Figure We statistically assessed the stringency of our filter parameters to define optimal thresholds that maximise the true positive rate associated to virus-host protein-protein interactions. One major drawback related to high-throughput sequencing of ISTs is the generation of sequences of poor quality . Indeed,i) an identity threshold superior or equal to 80% with an e-value threshold inferior or equal to 10e-10 and (ii) a protein product defined as "in frame".In this study, ISTs were thus considered as highly significant if they follow these criteria: . As described above, this low retention rate is commonly observed when extracting ISTs directly from yeast by PCR. This success rate dramatically increased when rescuing DNA templates extracted from yeast by bacteria transformation, but this later procedure was much more time consuming (data not shown). Even if 77% of ISTs passing through the first filter are "in frame", the remaining 23% ISTs are "not in frame" and might be considered as "true positives" because of translation mechanisms existing in yeast that allow stop-codon reading through and frame-shift correction for a significant fraction of the preys [Analysis showed that 50% of the sequences passed through the first filters  standard as file format for molecular interaction output and by carefully following MIMIx guidelines, efforts have also been made to facilitate unified exchange of protein-protein interaction data with the main public interaction data providers, such as those belonging to the IMEx consortium.We have developed pISTil, a pipeline for large-scale identification and analysis of ISTs data generated by yeast two-hybrid approach. This application is dedicated to laboratories willing to automatically process, easily visualise and efficiently share yeast two-hybrid data. The use of such a standard approach will facilitate comparisons of datasets and will improve quality of protein-protein interaction network reconstruction in systems biology projects. Finally, next generation sequence tags project relying on cDNA libraries may also take advantage of this open source and efficient pipeline. pISTil is available under the GNU General Public License and may be downloaded from its project website.Project name: pISTil• Project home page: • Operating system(s): Running on Mac OS × 10.4× or higher, Linux (Linux 2.6.18-1.2798.fc6) and Unix Solaris systems (SunOS 5.10)• Programming language: Perl 5.0 or higher, PHP (php4 or php5), PostgreSQL 8. × or higher• Other requirements: Phred, Apache 2.0, Staden 1.6.0, NCBI BLAST Toolkit• License: GNU General Public License• Any restrictions to use by non-academics: License require• bp: base pairs; EST: Expressed Sequence Tag; IST: Interaction Sequence Tag; nt: nucleotide; PSI-MI: Proteomics Standards Initiative-Molecular Interaction; Y2H: Yeast Two-Hybrid; ABI: Applied Biosystems INC; SCF: Standard Chromatogram Format; HTML: Hypertext Markup Language.The authors declare that they have no competing interests.VN designed the study and drafted the manuscript. JP developed the pISTil system, the pISTil web interface and wrote the pISTil documentation. LM contributed to the development of the pISTil web interface, the PSI-MI XML export; tested the pISTil software, corrected the pISTil documentation and manuscript. BdC, LT, POV contributed to the design of pISTil algorithm and corrected the manuscript. VL and CRC provided funding and corrected the manuscript. All authors read and approved the final manuscript.The pISTil documentation. This documentation gives detailed information on how to install, run and use the pipeline as well as its associated web interface.Click here for file


So far, a majority of published interactions have been detected using an Y2H screen. However, with the massive application of this method, also some limitations have become apparent. This review provides an overview on available yeast two-hybrid methods, in particular focusing on more recent approaches. These allow detection of protein interactions in their native environment, as e.g. in the cytosol or bound to a membrane, by using cytosolic signalling cascades or split protein constructs. Strengths and weaknesses of these genetic methods are discussed and some guidelines for verification of detected protein-protein interactions are provided.A key property of complex biological systems is the presence of interaction networks formed by its different components, primarily proteins. These are crucial for all levels of cellular function, including architecture, metabolism and signalling, as well as the availability of cellular energy. Very stable, but also rather transient and dynamic protein-protein interactions generate new system properties at the level of multiprotein complexes, cellular compartments or the entire cell. Thus, interactomics is expected to largely contribute to emerging fields like systems biology or systems bioenergetics. The more recent technological development of high-throughput methods for interactomics research will dramatically increase our knowledge of protein interaction networks. The two most frequently used methods are yeast two-hybrid (Y2H) screening, a well established genetic  This development has been driven by: (i) a huge amount of genomic and proteomic data already available, (ii) the need to understand complex cellular systems or multifactorial diseases such as cancer or the metabolic syndrome, and (iii) emerging technologies which allow high-throughput screening of complex mixtures of biomolecules or non-invasive studies of live cells or entire organisms. In addition, evolution in this field would have been impossible without the parallel development of bioinformatics tools to analyze the large amounts of data generated.Multiprotein complexes, not individual proteins, are increasingly recognized as the molecular basis of cellular fluxes of molecules, signals and energy. Thus, technologies which enable us to decipher cellular interactions between biomolecules (interactomics) together with those measuring metabolite fluxes  and signalling cascades  have taken center stage in systems biology .Interactomics can be applied in a global, unbiased cell systems approach, or in a more targeted approach to study a specific set of proteins . While tTo date, the cellular interactome has mainly been explored for interactions involving proteins in the fields of cell signalling and cell architecture to understand the wiring of cellular data processing. However, it is also becoming increasingly important in many other fields.1.2.Bioenergetics has known several decades of intense research, starting with the discovery of the main biochemical pathways and energy conservation in a chemiosmotic gradient in the 60s and 70s of the last century. After being a quiescent field for more than a decade, several developments during the last 15 years have put bioenergetics and mitochondria back to the forefront of scientific development   systems. These Y2H tools now allow access to the almost entire cellular proteome for interaction screening, including membrane proteins, transcriptionally active proteins and proteins localized in different subcellular compartments. Massive application of such tools can be expected, since they are comparatively inexpensive as compared to others, do not need specialized large equipment and can be performed in any molecular biology laboratory with reasonable throughput.2.in vitro handling of protein extracts. Further limitations of these techniques include restricted sensitivity and bias towards high affinity interactions. Once a partner has been detected, identification by mass spectrometry (MS) is generally straightforward, although rather costly. Cloning of corresponding cDNAs may be time-consuming, but clone repositories such as RIKEN or IMACE can be a convenient alternative. More recently, surface plasmon resonance (SPR), a biophysical technology, has been adopted for screening protein-protein interactions. Purified cellular extracts are injected onto a sensor chip covered with an immobilized binding partner. The instrument setup combines capture of the binding partner with a quantitative readout of the binding event, such that putative partners can be eluted and identified by MS [in vivo. For studies on a genomic scale, highly parallel and automated processes are needed. However, only few detection methods for protein-protein interactions can be easily adapted for a high-throughput strategy. These include in particular yeast two-hybrid (Y2H) and affinity purification coupled to MS (AP/MS).Protein-protein interactions are involved in all cellular processes. Mapping of these interaction networks to elucidate the organization of the proteome into functional units is thus of prime importance for systems biology. A large number of methods have been developed for screening protein interactions. The more classical biochemical approaches, such as copurification, affinity purification or coimmunoprecipitation of protein complexes require ed by MS ,15. Anoted by MS ) such as2.1.Saccharomyces cerevisiae [Drosophila melanogaster [Caenorhabditis elegans [The Y2H technique allows detection of interacting proteins in living yeast cells . As descrevisiae ,20, Drosnogaster , Caenorh elegans  and huma elegans ,24. Expe elegans ,26 or to elegans . Two scra for baits and mating type α for preys). This approach is easily automatable and has been used in yeast and human genome-scale two-hybrid screens. In yeast, 6,000 ORFs were cloned and over 5,600 interactions were identified, involving 70% of the yeast proteome [In the matrix approach, all possible combinations between full-length open reading frames (ORFs) are systematically examined by performing direct mating of a set of baits versus a set of preys expressed in different yeast mating types  and their interaction partners (preys) present in cDNA libraries or sub-pools of libraries. An exhaustive screen of libraries with selected baits can be an alternative to a matrix approach. Here, preys are not separated on an array but pooled (reviewed in ), and li2.2.et al. [et al. [et al. [The value of MS for high-throughput screening of protein interactions has been recognized only more recently. This analytical technique is based on the determination of the mass-to-charge ratio of ionized molecules. Already introduced in 1948, sensitivity and implementation range of MS has been largely extended by technological advances. These include Nobel prize crowned methods for ionization like electrospray ionization , generatet al.  expresse [et al. ,37 and K [et al.  purified [et al. ,40). Rec2.3.MS is less accessible than Y2H due to the expensive large equipment needed. Thus, a large amount of the data so far generated from protein interaction studies have come from Y2H screening. For example, more than 5,600 protein interactions have been so far reported for yeast ,20,28,413.3.1.Saccharomyces cerevisiae [In 1989, Fields and Song revolutionized protein interaction analysis by describing a genetic system to detect direct protein-protein interactions in the yeast revisiae . Until trevisiae . HoweverGAL1-lacZ fusion gene. This reporter gene encodes the enzyme beta-galactosidase which labels the yeast cell when using a colorimetric substrate [Inspired by these findings, Fields and Song exploited the modular properties of the transcription factor Gal4 to monitor protein-protein interactions. The basic idea was to fuse the two proteins of interest X and Y to DBD and AD of Gal4, respectively, such that interaction between X and Y reconstitutes a functional transcription factor that could then drive reporter gene expression . In the ubstrate .For a genome-wide screen for interactors of given baits, a cDNA library is used to construct an entire library of preys. From a methodological point of view, any such Y2H screen implies the transformation of yeast cells with bait and prey cDNA on different vectors under the control of yeast promoters. Expression levels will depend on the promoter used and may affect sensitivity and specificity of the screen. Once expressed in the cytosol, bait and prey must be able to enter the nucleus to activate transcription, a limitation of the original Y2H approach further discussed below.E. coli repressor protein LexA), transcriptional activators (e.g. AD of Herpes simplex virus VP16) and various reporter genes. A suitable reporter gene must encode a protein whose function provides a simple readout. Thus, besides the colorimetric reaction with the lacZ gene, the most commonly used are auxotrophic markers  that allow growth on minimal media. In the current state-of-the-art, more than one reporter gene is assayed in parallel to increase the stringency of Y2H screens [HIS3 reporter, imidazole glycerol phosphate dehydratase, is competitively inhibited by increasing concentrations of 3-aminotriazole.This classical Y2H system has been extended to exploit different other DNA-binding proteins -protein X hybrid is itself not a potent activator”. These limitations would exclude almost half of all proteins, explaining the great interest for developing alternative Y2H variants.3.2.More recent Y2H-based techniques access almost the entire cellular proteome see . Almost 3.2.1.The classic Y2H system is based on reconstitution of a transcription factor and thus not adapted for interaction analysis with proteins that can directly activate transcription. Such transactive baits would trigger transcription in absence of any interaction with a prey. Two alternative Y2H systems have been developed to analyze the interaction network of such proteins. One is based on repression of transactivation, while the other uses the alternative polymerase III transcription pathway. Also methods mentioned under 3.2.2 (e.g. the split-ubiquitin systems) are suitable to screen transactive baits.repressed transactivator (RTA) system (In the ) system , inverseHIS3 is activated. Strength of the inhibition is translated into expression levels of HIS3 which can be probed by increasing amounts of 3-aminotriazole, a competitive inhibitor of the HIS3 gene product. Compared to the classic Y2H, this assay has the advantage that inhibition of interaction does not result in a loss but in a gain of reporter gene transcription and thus in a positive signal facilitating screening procedures. Thus, the RTA Y2H can not only be used to identify interaction partners of transcription factors, but also as a reversed Y2H to screen small molecule libraries e.g. for potentially novel therapeutic compounds acting as inhibitors of a given protein-protein interaction.Recently, this system has been extended to screen for molecules which inhibit protein-protein interaction, for example between the immunophilin FKBP12 and the transforming growth factor β receptor (TGFβ-R) C terminus . FKBP12 RNA polymerase III based two-hybrid (Pol III) system -mediated transcription. If now the bait interacts with the prey containing τ138p, the TFIIIC complex is bound to DNA and recruits a second transcription factor (TFIIIB) and Pol III. This will activate transcription of the SNR6 reporter gene to produce U6 snRNA [The ) system  is anothU6 snRNA . In a yeU6 snRNA , this reU6 snRNA , but app3.2.2.The classic Y2H and the two alternative systems presented above require the translocation of the interacting proteins into the nucleus and are thus not suitable for membrane associated proteins, integral membrane proteins and many other soluble cytosolic proteins or proteins localized in other subcellular compartments. To circumvent these limitations, truncated versions of these proteins have been used for Y2H screens –71. HoweSOS- and the RAS recruitment systems (SRS and RRS) (The and RRS)  are bypaIn the SOS recruitment system : SRS Y2HIn the Ras recruitment system : RRS Y2HBoth SRS and RRS allow the analysis of interactions between soluble baits and soluble or membrane preys. Specifically for the use of membrane localized baits, the reverse Ras recruitment system . Such a transcriptional readout leads to an amplification of the response following protein interactions and offers more sensitivity, more convenient for transient interactions. This system was successfully used to detect interactions involving different kinds of membrane proteins [In the membrane transactivator split-ubiqitin (MbY2H) system, an artificial transcription factor (LexA-VP16) has been used as a cleavable reporter protein to analyse interactions between membrane proteins of the endoplasmic reticulum (ER) : MbY2H . Once ubproteins . Split-uproteins –81 and lproteins .Recently, an adaptation of the MbY2H system to screen cytosolic proteins has been published : CytoY2HOther Split-protein sensors  system  system  publishef the ER . This syf the ER , as wellf the ER .3.3.Its relative methodical simplicity, its diversity, and its high-throughput capacity make the Y2H system the most popular analytic and screening method for interactomics. Nevertheless, all Y2H methods face the problem of false negatives and false positives.False negatives in Y2H are protein-protein interactions which cannot be detected due to limitations of the screening method. In the classic Y2H, for example, protein interactions involving membrane proteins are mostly undetectable. Thus, the Y2H strategy has to be chosen carefully, depending on the cellular sub-proteome of interest. Further, the interaction between the two proteins assayed in Y2H is often not symmetric (permutation-independent), meaning it depends on whether a given protein is used for fusion in the bait or the prey construct. The fused yeast reporter proteins or anchors may cause steric hindrance that impedes interaction, thus causing false negatives. Another reason for false negatives can be different or lacking post-translational protein modifications in the yeast system when analyzing interactions between proteins of higher eukaryotes. In this case, the modifying enzyme may be coexpressed in yeast together with bait and prey. This possibility has been used with success to identify tyrosine-phosphorylation dependent interactions [Pichia pastoris [ractions . Very trractions . The expractions . The lacpastoris , but it False negatives mainly cause problems in reproducibility of Y2H screens. Two independent large-scale Y2H screens using the same Y2H method showed less than 30% overlap in the identified interactions and only 12,5% of known interactions were found in each of both . These dFalse positives in Y2H are physical interactions detected in the screening in yeast which are not reproducible in an independent system. They are of diverse origin and often depend on the Y2H system used. Among possible reasons for false positive interactions in yeast may be a high expression level of bait and prey and their localization in a compartment which does not correspond to their natural cellular environment. Another source of false positives is interaction of prey with the reporter proteins (e.g. LexA in the classic Y2H) or the membrane anchors (e.g. Ost4p in the cytoY2H) fused to the bait. Proteins which allow yeast to overcome nutritional selection when overexpressed are also often scored as false positives. Finally, proteins that are known to be “sticky” or that are not correctly folded can show unspecific interactions. In general, for each Y2H system, a list of recurrent false positives can be established. A list created by Golemis and co-workers for the classic Y2H can be found at http://www.fccc.edu/research/labs/golemis/InteractionTrapInWork.html.et al. [Despite these limitations, the Y2H system remains a powerful tool for large-scale screening in interactomics. The comparative assessment of high-throughput screening methods by von Mehring et al.  revealedTo evaluate the quality of a generated interaction data set, coverage and accuracy need to be considered together. In fact, a large interaction network cannot be a solid base for systems biology if confidence in the data is low. In a quantitative comparison of interaction data sets, von Mehring estimated the accuracy of a classic high-throughput Y2H screen to be less than 10%. Thus, the question remains how to increase accuracy of Y2H interaction data sets.As mentioned before, there are two different screening approaches: the targeted library screening approach and the global matrix screening approach. To increase accuracy of a library screen, a bait-dependency test can be performed ,94. In tin vivo/in situ methods . The former methods allow the study of physical protein interactions, but pull-down assays require a certain stability of the protein complex or, in case of Biacore, even need purified interaction partners. It may be also difficult to validate transient protein interactions or protein interactions with transmembrane proteins in these assays. The in vivo/in situ methods allow insight into possible coexpression and colocalization of the two proteins involved, but generally do not provide conclusive evidence for direct interaction. However, an advantage of in situ hybridization would be its adaptability for high-throughput. The FRET method has been developed to go beyond protein colocalization in vivo to study the spatio-temporal occurrence of the interaction and its physiological significance. FRET can only occur when the distance separating the two different fluorophores is in the low nm-range, a condition that occurs if fluorophores are coupled to two directly interacting proteins [It is advisable to use more than one method to validate an identified protein-protein interaction, preferentially coupling biochemical methods  with proteins . HoweverValidation of results from high-throughput matrix studies is much more difficult to achieve. Using the mentioned validation methods would be experimentally extremely difficult. Given that both interaction partners are randomly selected, the large amount of generated interaction data would already render a bait dependency test impossible. To handle the problem of false positives in such large-scale approaches, help is coming from computational biology. Confidence scores can evaluate the biological significance and probability of a given interaction. One possibility is to relate screening results to known data like RNA expression levels (expression profile reliability (EPR) index), or interaction networks of protein paralogues ) . Another4.Once a protein-protein interaction has been identified and validated, the physiological function of a given interaction remains to be established in a biological system. The main questions in this respect are: (i) Where and when in the system the interaction does occur? (ii) Which parameters influence the interaction? (iii) What is the effect of the interaction? To answer these questions, the main strategy relies on varying different system parameters that mainly affects the proteins of interest. Combination of a panel of complementary methods is generally able to unveil the physiological significance of an interaction identified in a targeted approach.in vivo by expressing interaction-deficient mutant proteins or using inhibitory peptides.Colocalization experiments in cell culture under different conditions can give information about the spatiotemporal dynamics of the protein-protein interactions. For example, choosing different time points during the cell-cycle may reveal transient colocalizations. In the case of the reported interaction between brain type creatine kinase (BCK) and the cis-Golgi matrix protein (GM130), a transient colocalization during early prophase was observed . The autThese experiments can be carried out for defined interactions of a small number of proteins, but again it would be quite difficult to transfer them to the large interaction network generated by global screens. So far, interactome approaches concentrate on a characterization of the nodes in the interaction network, which may be the major determinants of a phenotype.5.Since systems biology aims at a complete representation of cellular complexity, thus avoiding any reductionism, the applied experimental strategies have to provide non-biased, complete data sets. In this context, the yeast two-hybrid technologies presented here are a starting point rather than a complete solution to the elucidation of interaction networks. However, Y2H has demonstrated its power by its methodological diversity and technical simplicity to rapidly generate a large amount of reliable protein-protein interaction data. More recent Y2H technologies, in particular those based on split proteins, allow to probe protein-protein interactions in their native cellular compartment and to access almost the entire cellular proteome. Y2H is rather complementary in respect to emerging AP/MS techniques, since it identifies direct interactions and also detects interaction of lower affinity that are rather transient.Developing high throughput approaches at the cellular level and further progress in bioinformatics will be necessary to make interactomics a fully integral part of a systems biology approach. Major efforts will be necessary for the challenge of modelling the large and dynamic interaction network of a cell. Only a combination of different approaches  will eventually lead to an accurate description of large interaction networks.


High-throughput methods for detecting protein-protein interactions enable us to obtain large interaction networks, and also allow us to computationally identify the associations of proteins as protein complexes. Although there are methods to extract protein complexes as sets of proteins from interaction networks, the extracted complexes may include false positives because they do not account for the structural limitations of the proteins and thus do not check that the proteins in the extracted complex can simultaneously bind to each other. In addition, there have been few searches for deeper insights into the protein complexes, such as of the topology of the protein-protein interactions or into the domain-domain interactions that mediate the protein interactions.Here, we introduce a combinatorial approach for prediction of protein complexes focusing not only on determining member proteins in complexes but also on the DDI/PPI organization of the complexes. Our method analyzes complex candidates predicted by the existing methods. It searches for optimal combinations of domain-domain interactions in the candidates based on an assumption that the proteins in a candidate can form a true protein complex if each of the domains is used by a single protein interaction. This optimization problem was mathematically formulated and solved using binary integer linear programming. By using publicly available sets of yeast protein-protein interactions and domain-domain interactions, we succeeded in extracting protein complex candidates with an accuracy that is twice the average accuracy of the existing methods, MCL, MCODE, or clustering coefficient. Although the configuring parameters for each algorithm resulted in slightly improved precisions, our method always showed better precision for most values of the parameters.Our combinatorial approach can provide better accuracy for prediction of protein complexes and also enables to identify both direct PPIs and DDIs that mediate them in complexes. Recently developed high-throughput methods such as yeast two-hybrid or mass spectrometry to obtain protein-protein interactions (PPIs) have provided a global view of the interaction network -5. As a There are four steps in characterizing a protein complex . The firThe predicted results of existing methods to predict protein complexes include significant numbers of false positives , becauseThe key idea of our approach is to eliminate the false positives by considering the exclusiveness of the binding domains. Figure An overview of our analytic approach is shown in Figure First, the dense regions in the PPI network are extracted by using existing graph algorithms, assuming that nodes and edges correspond to proteins and interactions, respectively. Since proteins participating in the complex are likely to interact with each other frequently, dense regions are likely to correspond to protein complexes ,9. Thus,Second, the initial candidates are verified by integrating the DDIs into the candidates by considering the physical binding domains based on two assumptions: (1) a protein participating in the candidate can bind to another protein within the same candidate if there is a potential DDI between these two proteins, and (2) each domain can participate in only one DDI at a time. The second assumption is based on the tendency of the binding interfaces to be exclusive ,19, sincThe initial candidate will be accepted as a final complex prediction if three or more proteins in the candidate are predicted to be connected by DDIs. In this way, we can consider the physical bindings in the protein complex.n possible combination of the DDIs in a predicted protein cluster, where n is the total number of DDIs in the candidate protein complex. To verify the candidates much more efficiently, we formulated this step as a problem in binary integer programming. Binary integer programming is a restricted form of linear programming in which each variable in the constraints is required to be 1 or 0. Let Pi,j ∈ Ω be the potential PPI between protein pi and pj in the candidate where Pi,j = 1 denotes the case in which the PPI actually takes place and otherwise 0. The symbol Ω denotes the set of all potential PPIs that are obtained from PPI experiments. Our objective is to maximize the number of total PPIs denoted by this equation:Time complexities are problematic when adopting a brute force approach to determine the physical binding domains for protein complex prediction. The most naïve approach is the enumerations of all 2Di,j = {Di,j,1, Di,j,2, ... } be the potential DDIs that connect a pair of proteins, pi and pj. The value of Di,j,k = 1 if the pair of domains in Di,j,k actually interact. A PPI variable Pi,j becomes 1 if any of the DDIs in Di,j is 1, as described by this equation:This maximization is rationalized by the fact that the proteins in the complexes are generally densely connected ,9. Then dl be one of the domains in the candidate and D(dl) be a set of DDIs, where each DDI contains domain dl and another domain. This constraint is denoted as:Domain-based constraints guarantee that each domain participates in a single DDI. Let P1,2 = D1,2,1 + D1,2,2 and P1,3 = D1,3,1 + D1,3,2. In this example, both D1,2,1 and D1,3,1 contain domain d1 but can only participate in one of the DDIs. Therefore the DDI-based constraint is D1,2,1 + D1,3,1 ≤1 and, similarly, D1,2,2 + D1,3,2 ≤1. From this, we suggest that proteins pi and pj actually interact if there are combinations of variables that satisfy Pi,j = 1. This implies that there is exactly one k such that Di,j,k = 1 and this value of k denotes the domain-domain interaction Di,j,k which connects the protein pair. Any subgraphs that contain more than two proteins connected by more than one domain-domain interaction are assumed to be verified protein complexes.For example, in the case of the candidates in Figure We downloaded 35,353 yeast PPIs from the BioGrid database . We usedThree types of DDI datasets were used. The first dataset was taken from the iPfam database. We used the Pfam-A dataset, which contains 4,498 interactions . The sechttp://yeast-complexes.embl.de/ because it is the most comprehensive yeast protein complex database.To evaluate prediction performances of protein complexes, we used 763 known yeast protein complexes from the EMBL database ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/ on October 28, 2008, were used to find proteins whose functions are unclear. Proteins that have "GO:0003674" or "GO:0005554" for their ID are regarded as uncharacterized proteins, since they indicate "molecular function unknown" in the Gene Ontology annotations.The Gene Ontology annotations that were downloaded from We used two existing algorithms, MCL and MCODE, to detect dense regions in given PPI networks as a first step for our method. Clustering coefficients were also calculated to detect dense regions. We selected configurable parameters optimized for precision for MCODE and the clustering coefficient Table . For MCLTo evaluate the predictions, precision recall analysis and functional enrichment analysis were used. The precision and recall of each method were computed according to these equations:V as defined by Bader et al. to evaluate the overlapping protein components of two complexes:where "predicted", "known", and "matched" are the numbers of predicted protein complexes, known complexes, and predicted protein complexes that match with known complexes, respectively. Unlike conventional prediction problems, predicted complexes rarely match perfectly with known complexes. We therefore used the matching criterion Np and Nk are sets of proteins in a predicted complex P and a known complex K, respectively. We regarded a predicted complex as matching with a known complex if the overlapping criterion was greater than the threshold of 0.25, which was taken from the work of Chua et al. [where a et al. . In thisTo investigate the enriched level of specific protein function in each predicted complex, we calculated the ratio of the protein pairs that have the same function for each predicted result with our method. We regarded a protein pair as having the same function if their molecular functions are the same in the Gene Ontology annotations.In our approach, protein complexes are predicted by combining a graph algorithm to find dense regions followed by a verification of the detected protein clusters. The results of protein complex prediction are summarized in Table As the complex size increases, the number of interactions among the member proteins in the complex may also increase. Such large complexes require many domains to bind to each other simultaneously if the bonding capability of the domains is limited. In other words, a cluster that contains many proteins is unlikely to have all of the possible interactions simultaneously active because the ability of each protein to bind is limited by the binding interfaces . TherefoFigure On average, our approach was twice as precise as the existing algorithms. Although configuring parameters for each algorithm resulted in slightly improved precision, most of the precision values remained lower than the precision of our approach. Our approach showed better precision with all parameter values except when the number of predicted candidates was 0. In contrast, the recalls of our approach were lower than those of the existing algorithms . In fact, our approach drastically reduced the number of candidates (87% of the candidates). However, the reduction for recall was comparatively small (a 69% reduction). Specifically, the recall reduction of our method applied after performing MCL analysis (Inflation = 3.6) was only 80%, whereas the reduction rate of candidates was 98%. In addition, the recall of our approach improved as the number of DDIs used for the verification increased , (A + B), and (A + C). Each algorithm ran with the optimized parameters shown in Table Click here for filePerformance of existing algorithms and our method with three types of datasets. Each graph is a precision-recall plot in which the vertical axis shows the precision and the horizontal axis shows the recall. Precision-recall graphs for MCL, MCODE and clustering coefficient. Each graph includes the results of existing algorithms and our method for all three types of DDI datasets, (A), (A + B), and (A + C). Each algorithm used the optimized parameters shown in Table Click here for file