Predicting whole genome protein interaction networks from primary sequence data in model and non-model organisms using ENTS
© Rodgers-Melnick et al.; licensee BioMed Central Ltd. 2013
Received: 5 December 2012
Accepted: 4 September 2013
Published: 10 September 2013
The large-scale identification of physical protein-protein interactions (PPIs) is an important step toward understanding how biological networks evolve and generate emergent phenotypes. However, experimental identification of PPIs is a laborious and error-prone process, and current methods of PPI prediction tend to be highly conservative or require large amounts of functional data that may not be available for newly-sequenced organisms.
In this study we demonstrate a random-forest based technique, ENTS, for the computational prediction of protein-protein interactions based only on primary sequence data. Our approach is able to efficiently predict interactions on a whole-genome scale for any eukaryotic organism, using pairwise combinations of conserved domains and predicted subcellular localization of proteins as input features. We present the first predicted interactome for the forest tree Populus trichocarpa in addition to the predicted interactomes for Saccharomyces cerevisiae, Homo sapiens, Mus musculus, and Arabidopsis thaliana. Comparing our approach to other PPI predictors, we find that ENTS performs comparably to or better than a number of existing approaches, including several that utilize a variety of functional information for their predictions. We also find that the predicted interactions are biologically meaningful, as indicated by similarity in functional annotations and enrichment of co-expressed genes in public microarray datasets. Furthermore, we demonstrate some of the biological insights that can be gained from these predicted interaction networks. We show that the predicted interactions yield informative groupings of P. trichocarpa metabolic pathways, literature-supported associations among human disease states, and theory-supported insight into the evolutionary dynamics of duplicated genes in paleopolyploid plants.
We conclude that the ENTS classifier will be a valuable tool for the de novo annotation of genome sequences, providing initial clues about regulatory and metabolic network topology, and revealing relationships that are not immediately obvious from traditional homology-based annotations.
Proteins do not exist within a vacuum. Much of the startling diversity of living organisms emerges only with the aggregate combinatorial complexity of protein-protein interactions (PPIs). As such, the discovery of physical interactions between proteins is often an essential step in the characterization of protein functions, providing insights into diverse cellular processes such as the fluxes of metabolic pathways, the logic of transcriptional activation, and the kinetics of signal transduction. This in turn forms the basis of understanding biological functions at the organismal scale, including mechanisms of environmental responses and the etiology of disease states.
Despite the biological importance of PPIs and the availability of high-throughput screening methods in recent years, experimentally-verified PPI networks remain sparsely populated, especially with respect to the amount of sequence data currently available. High throughput approaches such as automated yeast two-hybrid screens and tandem affinity purification/mass spectrometry have detected thousands of binary PPIs in animal and fungal model organisms such as Homo sapiens, Saccharomyces cerevisiae, and Drosophila melanogaster, yet the current size of the interactome belonging to the experimental workhorse of the plant kingdom, Arabidopsis thaliana, only constitutes approximately 3% of its expected size. Moreover, the lack of significant numbers of PPIs for non-model species hinders the development of evolutionary studies concerning rewiring within the interactome.
The demand for additional PPIs has led to the development of several methods for computational PPI prediction over the past decade. Several groups have attempted to expand the A. thaliana interactome using statistical learning methodology and/or transfer of interaction annotation based on homology (interologs)[7–10]. Similar methods have been used to expand the number of network connections in the S. cerevisiae and H. sapiens proteomes. Though each of these methods does have the potential to provide useful network information, each approach carries distinct disadvantages limiting its use on non-model species. The interolog-based approaches are limited to discovering PPIs for only the most conserved proteins, as reflected by their relatively low discovery rates in A. thaliana. Other methods rely on an ensemble of functional data, such as genome-wide measures of co-expression and co-localization, which is often not available for non-model organisms.
Although many homologous proteins may evolve to become highly dissimilar at the primary sequence level, they often retain conserved structural and/or functional units known as domains. These domains may directly mediate interactions between proteins, as demonstrated by databases of domain-domain interactions such as DOMINE. However, even in the absence of direct interaction, certain pairwise combinations of domains suggest a high probability of interaction. Thus, domain-based approaches of PPI prediction have the potential to provide the advantages of the interolog-based approaches while maintaining utility for less-conserved proteins, especially if the approach also includes features more sensitive to fine-scale differences in amino acid content. There have been several recent attempts to infer PPIs based on pairwise domain information. For example, Singhal et al. used a genetic algorithm to discover domain-domain interactions that could be used as predictors of PPIs. Chen and Liu used a domain-driven random forest classifier to predict PPIs for S. cerevisiae. However, feature representation for the algorithm required vectors with several thousand entries, making its use computationally expensive for full-genome prediction. Although these approaches are initially promising, there is a lack of publicly-available software that would enable domain-based PPI prediction on a genome-wide scale for non-model organisms lacking large experimental data sets.
Here we present “Elucidating Network Topology with Sequence” (ENTS), a binary PPI classifier that uses a random forest framework. ENTS is capable of efficiently and exhaustively evaluating all potential protein-protein pairs in a large eukaryotic genome using parallelization. We show that the method provides comparable or better predictions on recently experimentally-determined PPIs than several existing methods and that such predictions are biologically plausible using the predicted interactomes of A. thaliana, P. trichocarpa, M. musculus, H. sapiens, and S. cerevisiae. Scripts, instructions for use, and predicted PPIs for several organisms are available athttp://ENTS.as.wvu.edu.
ENTS performance relative to experimental predictions
ENTS performance relative to other classifiers
Whole genome prediction counts
Although most alternative classifiers performed similarly to ENTS on the testing data set (Figure1), this set included interactions that were used to train the alternatives and could thereby inflate their sensitivities at a given specificity value. Therefore, in order to assess the performance of ENTS on whole genome data relative to several alternative classifiers, we examined the frequency of positive predictions among sets of experimentally-determined PPIs that were not used for training, testing, or calculation of pairwise domain odds in ENTS or used for training in the alternative classifiers. We obtained genome-wide predicted PPI datasets in S. cerevisiae, H. sapiens, M. musculus, and A. thaliana[7–10]. The sizes of the predicted datasets varied greatly, so when making comparisons we reduced the sizes of the ENTS predictions to those of the alternative datasets following removal of predictions that corresponded to data used for ENTS training or calculation of pairwise domain odds in order to place bounds on the possible number of positive predictions and thereby provide fair comparisons.
We find that the number of novel interactions predicted by ENTS is similar to those predicted by the Pitre et al. PIPE2 classifier (n = 13,826) in S. cerevisiae and the PIPS classifier (n = 22,687) in human[14, 15] (Figure2A-C), as found through comparisons to a high-throughput yeast two-hybrid dataset in S. cerevisiae (n = 1,337) and high-confidence interactions from two large-scale studies of human PPIs (n = 2,045)[18, 19]. Again, relatively few predictions were shared between each pair of classifiers. The alternative classifiers differed substantially in their prediction methods. The Pitre et al. classifier based its predictions on the pairwise-occurrence of short sequence motifs, while the PIPS classifier used naive Bayes to combine sequence-derived features such as orthology and pairwise domain content with functional data such as gene co-expression, post-translational modifications, and co-localization. In mouse, we find that ENTS predicted many fewer novel interactions from a high-confidence set of literature-curated interactions (n = 1,807) relative to the interolog-based predictor of Yellaboina et al. (n = 36,608) (Figure2D). The high number of mouse interologs inferred directly from human interactions is responsible for most of this disparity, as ENTS predicts more of the novel interactions (36 vs. 25) when the human-derived interologs are filtered out of the Yellaboina et al. dataset.
Although training was performed with the response defined as the presence of an interaction between two proteins, we also repeated the comparisons after restricting to a protein set that did not occur within the data used for calculation of domain odds or training the classifier. We did this in order to assess the ability of ENTS to predict beyond the scope of proteins for which there is interaction data currently available. For the species in which there were large numbers of these proteins among the novel interactions - A. thaliana (n = 2,239) and M. musculus (n = 1,005), we observe a decline in the relative number of novel interactions predicted relative to other classifiers (Additional file1; Figure S1), although the predicted interactions shared between ENTS and the alternatives remain low. The remaining two species contained relatively few proteins with novel interactions that were never used for calculation of domain odds (H. sapiens n = 305; S. cerevisiae n = 208). Neither the truncated ENTS set nor the PIPE2 classifier predicted any interactions within this S. cerevisiae set. ENTS did predict two of the novel interactions within the H. sapiens set, while the PIPS classifier failed to predict any novel interactions within this set.
Biological plausibility of PPI predictions
Metabolic pathway linkages in P. trichocarpa
The connectivity of PPI networks permits insight into higher-order structures that largely remain hidden under non network-based analyses. Several studies have demonstrated that biological networks are organized into pathways or modules, each of which contain highly connected groups of genes that may act semi-autonomously with respect to the action of the network as a whole. We used the predicted P. trichocarpa PPI network to infer a network of P. trichocarpa metabolic pathways, which we then analyzed for higher order structures.
Significant enrichments in clusters of the P. trichocarpa metabolic linkage network
Phenylpropanoid derivatives biosynthesis (6)
4.105 × 10−11
Secondary metabolites biosynthesis (8)
5.815 × 10−9
Flavonoids biosynthesis (6)
6.486 × 10−9
Carbohydrates biosynthesis (9)
2.185 × 10−8
Sugars biosynthesis (8)
5.65 × 10−6
Nitrogen compounds metabolism (3)
1.077 × 10−5
Inorganic nutrients metabolism (3)
Fatty acids and lipids biosynthesis (8)
2.838 × 10−10
Cofactors, prosthetic groups, electroncarriers biosynthesis (8)
1.741 × 10−7
Phospholipid biosynthesis (6)
2.106 × 10−7
Quinol and quinone biosynthesis (3)
6.301 × 10−6
Vitamins biosynthesis (4)
3.871 × 10−6
Nucleosides and nucleotides biosynthesis (6)
1.831 × 10−13
Purine nucleotide biosynthesis (3)
7.213 × 10−6
Nucleosides and nucleotides degradation (5)
2.283 × 10−21
Purine nucleotides degradation (4)
2.128 × 10−13
3.977 × 10−10
Amino acids biosynthesis (3)
1.642 × 10−6
Inorganic nutrients metabolism (2)
2.583 × 10−7
Sulfur compounds metabolism (2)
1.062 × 10−6
1.256 × 10−5
Predictions of human disease associations
The analysis of PPI networks has great potential for aiding our understanding of heritable disease, as the manifestation of a given pathology may result from the perturbation of entire network modules rather than the abrogation of a single gene. In particular, the physical associations between disease-related genes within a protein interaction network may signify a functional relationship between the corresponding disease states, including co-morbidity or alternative routes to a disease due to disruption of a shared pathway.
As an example of intuitive relationships, one of the connected components consists entirely of associations between 11 cataract disorders (Figure6B), all of which are based on predicted interactions between crystallin proteins without experimental support in public databases. We also find a number of non-intuitive relationships with anecdotal support in the literature. These include associations within one connected component containing several myopathies, including cardiomyopathy, limb-girdle muscular dystrophy, myotilinopathy, and spheroid body myopathy. These three latter disorders have overlapping symptoms and are known to co-occur with cardiomyopathy, as indicated by the network (Figure6C). A disease characterized by progressive extraocular muscle weakness - horizontal gaze palsy with progressive scoliosis - is also associated with both cardiomyopathy and limb-girdle muscular dystrophy based on a predicted interaction between TCAP and ROBO3, though limb-girdle muscle weakness is not directly associated with this disorder in the literature. The last condition within the component, vesicoureteral reflux (VUR), is characterized by developmental abnormalities of the kidney and urinary tract. Its relationship to cardiomyopathy and limb-girdle muscular dystrophy is indicated by a predicted interaction between TCAP and ROBO2. Interestingly, although VUR is not associated with disorders of the striated or cardiac muscle, it has been observed to co-occur with visceral myopathy in cases of Chronic Intestinal Pseudo Obstruction and Berdon Syndrome.
Network properties and duplicate gene evolution
Duplicate retention coefficients
P. trichocarpa Salicoid
A. thaliana α
A. thaliana β/γ
In this study we find that our random forest-based classifier, ENTS, can predict biologically meaningful PPIs both within the species on which we trained the classifier and within species sharing a relatively distant common ancestor with the training species. These results are comparable or favorable to existing methods of protein interaction prediction, including several that used experimentally-obtained functional data as predictors. This demonstrates that researchers may generate a high quality set of probable PPIs prior to performing extensive functional experimentation. Therefore these predicted PPIs may serve as a basis for the development of functional hypotheses in newly-sequence organisms. Notably, even in the cases in which ENTS predictions were outperformed - the A. thaliana PAIR predictor and the M. musculus Yellaboina interolog approach - the majority of interactions predicted by ENTS were not predicted by the alternative approach. This suggests the value of using ENTS as part of an ensemble rather than relying on any single classifier. This may be particularly effective when large amounts of functional data are available, as with PAIR, or an orthologous interactome is well-covered, as with the Yellaboina interolog approach.
The performance of ENTS does vary significantly between organisms, with the AUC ranging from a low of 0.811 in S. cerevisiae to a high of 0.963 in A. thaliana. These differences likely arise due to functional differences between the positive datasets. The contrast in performance between the A. thaliana and non-plant classifiers may be attributed to biases in the interaction data currently available for these organisms - particularly A. thaliana. With the exception of a recent high-throughput study that was not used for training, A. thaliana studies of PPIs within the literature have focused on testing of specific hypotheses concerning proteins of high a priori importance. This can result in sets of highly clustered, high degree nodes within literature-curated PPI networks[5, 30]. Such clusters have likely led to an enrichment of A. thaliana interacting pairs with high pairwise domain odds (Additional file1: Figure S1). It also explains the decline in relative performance for A. thaliana predictions on proteins that were never used for training or calculation of pairwise domain odds (Additional file1: Figure S2). In contrast, the multiple high throughput studies of binary PPIs conducted on H. sapiens, S. cerevisiae, and D. melanogaster contribute a more unbiased view of the interactome than for A. thaliana. The more comprehensive experimental datasets have yielded a number of interactions that are less amenable to ENTS detection due to either an absence of PFAM domains in one or both of the proteins in the pair or because of the presence of domain pairs infrequently associated with physical interaction. Testing performance based primarily on literature-curated sets may therefore give results that are inconsistent with the true global protein interaction network. This highlights a necessary caveat present for all statistical learning methods - the performance of the classifier on novel data depends on the scope of its training.
Although the most important features for ENTS classification are based on domain composition, we also find subcellular localization information from MultiLoc2 to be highly important for prediction of PPIs. Interestingly, the most important MultiLoc2 features tend to be the SVM amino acid scores, which indicate how well the total amino acid content of a protein matches a particular subcellular compartment. These measures provided ENTS with more sensitivity to subtle changes in amino acid content than the domain-based scores, which rely on the gain or loss of conserved PFAM domains. For instance, the duplicated genes from the Salicoid WGD in P. trichocarpa only share 56% of their predicted neighbors on average in the 0.65 confidence network despite high pairwise similarity in domain content. This allows for the possibility that ENTS may predict rewiring of PPIs within networks following duplication, although confirmation of this would require experimental validation.
Interpretation of ENTS interactions
We trained ENTS using sets of known physically-interacting pairs of proteins. Therefore, proteins predicted to interact by ENTS should be interpreted as having a high potential for physical interaction, given that they are present within the same location at the same time. Each set of predicted interactions represents a more than 1000-fold reduction in the number of total possible interactions for the organism. However, because we want to permit the use of this classifier across a variety of organisms without broad functional data, we do not include expression or proteomics data as features in the set of predictor variables. As such, researchers should confirm all predictions with functional data. Furthermore, ENTS has limited capability for predicting interactions between proteins that either lack conserved domains or that contain domains never before experimentally observed within physically interacting proteins. This precludes the detection of some novel interactions, although other researchers may wish to append functional data to the set of ENTS predictor variables for their organism in order to predict these interactions through a greater variety of evidence sources.
Applications of ENTS networks
The elucidation of PPI networks permits tremendous insight into both cellular and evolutionary processes. In humans, several studies have used experimental PPI data to map relationships between human diseases based on causative similarity and infer causative disease genes within large implicated linkage regions. However, such studies have reported limitations due to the lack of detected interactions with shared disease etiology. Here, we have shown that ENTS is capable of yielding novel predicted interactions with relevance to known co-occurring human diseases. We contend that these predicted interactions may be used by biomedical researchers to narrow the scope of regions implicated in genome-wide association studies in addition to providing predictions on which to base more targeted searches for candidate loci. The latter function may prove especially vital due to the increasingly visible role of rare de novo mutations, particularly CNVs, in the etiology of disease. Furthermore, the generality of our approach across species allows the potential for yielding insight into a variety of agriculturally important diseases in non-model species that are currently only understood through large linkage regions based on QTL and relatively small GWAS studies. We have also shown that ENTS is capable of revealing the higher-order structure of metabolic networks in a plant species without extensive experimental data, P. trichocarpa. ENTS-predicted physical interactions yield a significant enrichment of associations between pathways that share compounds and group sets of coordinated pathways such as pyrimidine and arginine biosynthesis. Such insights may be used to inform targets of selection in breeding programs in order to increase the output flux from key pathways.
We have demonstrated that ENTS has great potential for yielding insight into network evolution in non-model species across the plant, animal, and fungal kingdoms. Networks have come to play an increasingly central role in evolutionary studies, but a rigorous analysis of network evolution requires the development of whole-genome networks for many non-model species. Here, we have shown that the topological properties of ENTS-predicted networks, including the first whole-genome interactome for P. trichocarpa, are related to the probability of WGD duplicate gene retention following independent duplication events in a manner consistent with the predictions of the gene balance hypothesis. Specifically, we have shown that the duplication state of a gene’s neighbors has a strong impact on the probability of retention following WGD and that this effect is enhanced at higher connectivity, which is consistent with the hypothesis of a selective drive to maintain stoichiometric balance between interacting proteins. Interestingly, the degree centrality main effect was only significant for the P. trichocarpa Salicoid WGD and the A. thaliana α WGD. In the case of P. trichocarpa, the negative degree centrality coefficient leads to an inverse relationship between connectivity and the probability of duplicate retention in the presence of few duplicated neighbors. This decrease is, again, consistent with the gene balance hypothesis, as the effects of stoichiometric imbalance are likely to become more extreme at higher connectivity and would therefore favor a singleton state when interconnected genes are also singletons. Intriguingly, this effect is also present in the A. thaliana duplications, though it is highly diminished with respect to P trichocarpa and shows inconsistent statistical significance. One explanation is that A. thaliana has undergone a greater degree of fractionation following its last two WGDs than has P. trichocarpa following the Salicoid WGD, so the influence of connectivity is more apparent in the latter. This suggests that the P. trichocarpa duplicate genes most at risk for future nonfunctionalization include those with low degree centrality and few duplicated neighbors.
We have introduced an efficient new approach that enables prediction of protein-protein interactions on a whole genome scale based entirely on information that can be derived from primary sequence data. This is a potentially groundbreaking addition to the standard toolbox for newly-sequence non-model genomes, which are rapidly proliferating. The networks derived from our protein-protein interaction predictions are realistic from the standpoint of consistency with co-expression and shared functional annotations of connected genes. Furthermore, we have shown that our predictions can reveal supported relationships among emergent phenotypes such as human disease states and the coordination of metabolic pathways. Finally, we have demonstrated that our inferred networks can reveal subtle details of genome-scale evolution. Because the method can readily be applied on a large scale to phylogenetically-diverse organisms, we anticipate that large-scale comparative analyses will provide insights into the mechanisms of network structure evolution.
We obtained experimentally verified physical interactions for H. sapiens, S. cerevisiae, M. musculus, D. melanogaster, and A. thaliana from the PPI databases DIP (10/27/2011 Release), IntAct (01/01/2012 Release), and BioGRID (v. 3.1.84). Additional experimentally-verified interactions for A. thaliana were taken from the TAIR database (05/27/2009 Release). We also collected known and predicted domain-domain interactions and their associated confidence scores from the DOMINE database (version 2). All protein annotations were taken from Ensembl core databases with the A. thaliana and P. trichocarpa annotations corresponding to Ensembl Plants release 12, the S. cerevisiae annotations corresponding to Ensembl Fungi release 12, and the H. sapiens, M. musculus, and D. melanogaster annotations corresponding to Ensembl release 65. Only canonical versions of proteins were used; splice variants were not considered in this analysis.
Calculation of domain pair odds
where n(D x ,D y ) is the number of times the domain pair D x , D y was observed among experimentally-verified protein interactions, n p is the total number of domain pairs, and n(D x ) is the number of times D x was observed among the set of proteins with experimentally-verified interactions. Because the absence of a protein pair within the experimentally-verified data set may result from either non-detection of existing interactions or the absence of any such interactions, we chose to assume an LOD of 0 for all domain pairs that were not observed in any interacting protein pairs.
All data features were defined from pairwise-domain information and predictions of subcellular localization. An exhaustive list of all features can be found in Additional file1: Table S1. Domain-based features included the sum of all odds scores, the highest odds score, the lowest odds score (ceilinged at 0), the number of pairwise domain pairs not observed in any of the interacting proteins used to calculate the odds scores, the number of domain pairs found among the pairs with odds scores, the number of domain pairs predicted or known to interact in DOMINE, and the highest DOMINE confidence score assigned to a domain pair. All other features were outputs of the high-res MultiLoc2 subcellular predicted program, which used protein sequence data and computer-generated GO categories as input. These features included the probabilities of localization to each of the possible subcompartments - cytoplasm, nucleus, peroxisome, ER, mitochondria, chloroplast (plant only), vacuole (plant and fungus), and the lysosome (animal only) - along with the raw output from each of the MultiLoc2 subprograms - SVMTarget, SVMSA, SVMaac, PhyloLoc, GOLoc, and MotifSearch.
Training and prediction
Classifiers were trained using 1,330 randomly selected protein pairs with experimental verification of interaction to serve as positive examples for each organism. We also included 101,300 randomly drawn pairs of proteins without any known or predicted interactions in the Reactome version 39 database to serve as negative examples. In order to avoid potential over-fitting based on the predicted subcellular characteristics of proteins in the positive set, we spiked the negative set with 1,300 randomly selected pairs of proteins without known or predicted interactions drawn from the positive set, wherein the proteins in the pair were drawn in proportion to their representation within the interacting pairs. Additionally, we included each protein pair twice within the dataset but switched the subcellular features between the proteins in the second set (reversed set). We trained the classifier using 400 fully-grown trees with the R randomForest package (http://cran.r-project.org/web/packages/randomForest/). Prediction also used both the forward and reversed set, with the final predictions taken from the union of the two. We trained a total of 3 ENTS classifiers - one on H. sapiens, one on S. cerevisiae, and one on A. thaliana. The H. sapiens-trained classifier was used to predict on H. sapiens and M. musculus; the S. cerevisiae-trained classifier was used to predict on S. cerevisiae; and the A. thaliana-trained classifier was used to predict on A. thaliana and P. trichocarpa.
where TP is the number of true positives (predicted interactions with experimental support), FN is the number of false negatives (non-predicted interactions with experimental support, TN is the number of true negatives (non-predicted interactions without experimental support), and FP is the number of false positives (predicted interactions without experimental support). We also calculated these values for the other predictors using the same set of testing data to provide single points on the ROC curve.
Comparisons to other predictors
Predicted protein interaction datasets were taken from the Pitre et al. PIPE2 S. cerevisiae classifier novel interactions, the H. sapiens PIPS classifier, the NIA Mouse Protein-Protein Interaction Database, the predicted A. thaliana interactome of De Bodt et al., the AtPID A. thaliana interactome, the Geisler-Lee A. thaliana interactome, and the PAIR high-confidence A. thaliana interactome. We obtained protein interaction datasets without any overlap to the interactions used to train ENTS or calculate domain odds scores. We also limited the overlap to training data from the alternative classifiers by choosing datasets containing PPIs that were either published after the alternative classifier’s publication date or were not present within the datasets reportedly used for training. In S. cerevisiae these were taken from a high-throughput yeast two-hybrid study. In humans we used high confidence sets of human protein interactions from a recent study of protein complexes (confidence of at least 0.9) and a high-throughput mass-spectrometry study of human PPIs (confidence of at least 0.3). In M. musculus we used interactions taken from the HitPredict database (05/01/2012) that were published after October 2008. Finally, in A. thaliana we used high-throughput yeast two-hybrid and literature curated interactions from a large study of network evolution.
Prior to performing each comparison we removed any predicted pairs from ENTS and the alternative classifiers that were used for training of the ENTS random forest or calculation of domain pair odds. ENTS predictions were then sorted by decreasing confidence and truncated to sets of the same size as the alternative classifiers. We then calculated the sizes of the intersections with the experimental datasets in addition to the sizes of the three-way intersections between ENTS, the alternative classifiers, and the experimental datasets. We then performed the same comparisons after limiting the predicted datasets to pairs of proteins in which neither protein was present within the training data or the proteins used to calculate domain-pair odds.
Evaluation of functional similarity
For each measure of functional similarity, we first narrowed the protein network (predicted or experimentally-verified) to the set of proteins with the given annotation present. For instance, we narrowed the networks to those with at least 1 KEGG annotation when evaluating KEGG similarity. Similarity was then calculated as the number of pathways present in the intersection of two proteins’ annotations divided the by the number of pathways in the union.
In the case of more than one pair of GO terms between a pair of proteins, we set the similarity score as the maximum of all pairwise similarity scores.
Metabolic pathway linkages
We made a network of linkages between metabolic pathways downloaded from the PoplarCyc v. 3.0 database using protein interactions predicted in the P. trichocarpa 0.65 confidence network. Pathways were linked to one another if one or more protein pairs between the two pathways were predicted to interact. We also did not include predicted self-interactions when constructing the network. We tested the significance of all pathway linkages by creating 10,000 randomized PPI networks in which each node had the same degree as the ENTS network but randomized connections. We retained pathway linkages for which the number of supporting interactions was greater than that of at least 99.9% of randomized networks. We found the number of pathway linkages that connected two pathways sharing at least one metabolic compound between them, discounting all compounds that were present in more than 15 pathways in order to prevent less meaningful associations due to common compounds such as ATP. We then assessed the significance of the compound sharing by generating 10,000 randomized pathway linkage networks in which each pathway had the same degree as in the observed network.
where M g is the number of ontology annotations to the cluster, M gt is the number of ontology annotations to the cluster on the ontology branch of interest, M t is the number of pathways annotated to the branch of interest, and M tot is the total number of pathway annotations made to the ontology graph. We assessed significance using a 0.05 family-wide type I error rate under a Bonferroni correction, such that a term was considered significant if where n C is the number of clusters found and n O is the number of nodes in the ontology.
OMIM disease network
We created a network of OMIM diseases and disorders by creating an edge between two diseases if their underlying causative loci in the OMIM database were predicted to interact within the ENTS predicted human network. We did not count self-interactions when creating the network. We tested the significance of the disease-disease associations by creating 10,000 randomized PPI networks in which each node had the same degree as the ENTS network but randomized connections. We then retained disease associations for which the number of supporting interactions was greater than that of at least 99.9% of randomized networks.
where the function refers to the number of PMIDs in the set, and P x , P y are the sets of PMID ids associated with the MeSH terms M x and M y . Only the most significant MeSH term for each OMIM disease was used for comparison. In order to assess the significance of the observed network, we compared the distribution of literature scores to those in 250 random disease-disease networks in which disease nodes were sampled in proportion to their degree in the observed network, thus generating networks with similar degree distributions.
WGD logistic regression
where n G is the number of nodes in the graph, and d(n x ) is the degree of node n x . We assessed the significance of the model coefficients by resampling 10,000 times with replacement, fitting the model to the resampled data, and generating 95%, 99%, and 99.9% confidence intervals using the corresponding quantiles of the coefficient distributions. Additionally, because the degree centrality constrained the possible range of the duplicated neighbor fraction such that a gene with one neighbor could only have a duplicated neighbor value of either 0 or 1, we fit two models for each duplication: one using the full set of genes with at least 1 interaction in the predicted network and one using only the subset with at least 10 predicted interactions.
ENTS is implemented using a combination of Python and R. The user provides a tab-delimited list of proteins and their constituent PFAM domains along with high-resolution output from the subcellular localization prediction program Multiloc2. The user also specifies a set of two tab-delimited files. The first file contains the pairwise domain odds for pairs of PFAM domains potentially involved in interactions, and the second provides PFAM pairwise scores from the DOMINE database. We provide both of these flat files for convenience, along with R workspaces with random forests trained on A. thaliana, H. sapiens, and S. cerevisiae.
ENTS is run by calling a Python script from the command line. The Python script then splits the pairwise comparisons to be performed among a number of subprocesses specified by the user at the command line. These are run in parallel, and each subprocess makes calls to the random forest present in the R workspace through Rserve using the pyRserve package as an interface. The random forest is implemented using the efficient R randomForest package. Protein pairs with confidence scores above a user-specified threshold are then saved to tab-delimited files, which are combined into a single file at the end of the run.
Availability and requirements
Project Name: ENTS
Operating system(s): Windows, Unix-like (Linux, Mac OSX)
Programming language: Python >= 2.7, R >= 2.15
Dependencies: Python - Numpy and pyRserve, R - randomForest and Rserve, MultiLoc2 (Optional)
This work was supported by funding from the BioEnergy Science Center, a U.S. DOE Bioenergy Research Center supported by the Office of Biological and Environmental Research in the DOE Office of Science, and through a United States Department of Agriculture National Institute of Food and Agriculture pre-doctoral fellowship.
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