The task is complicated by the fact that while sequence-based features can be readily compared across species, most other data are species-specific. In this paper, we present a multi-view extension to GOstruct, a structured-output framework for function annotation of proteins. The extended framework can learn from disparate data sources, with each data source provided to the framework in the form of a kernel. Our empirical results demonstrate that the multi-view framework is able to utilize all available information, yielding better performance than sequence-based models trained across species and models trained from collections of data within a given species. This version of GOstruct participated in the recent Critical Assessment of Functional Annotations (CAFA) challenge; since then we have significantly improved the natural language processing component of the method, which now provides performance that is on par with that provided by sequence information. The GOstruct framework is available for download at  The availability of a large variety of genomic data relevant to the task of protein function prediction poses a data integration challenge due to the heterogeneity of the data sources. While features based on sequence can be readily compared across species, most other data are species-specific: protein-protein interactions are probed experimentally in a given species, and the expression of a given gene measured in one set of experiments is difficult to compare meaningfully to expression measured in another species, under possibly different conditions.In earlier work we have shown the power of modeling Gene Ontology (GO) term prediction as a hierarchical classification problem using a generalization of the binary SVM to structured output spaces, which allows us to directly predict the GO categories associated with a given protein . Our resIn addition to data that is commonly used in prediction of protein function, namely gene expression and protein-protein interactions (PPI), we report the successful use of large-scale data mined from the biomedical literature, and find that it provides a large boost in accuracy. Together with the text mining data, features based on sequence similarity and PPI account for most of the predictor performance.We examined the tasks of predicting molecular function, biological process and cellular component in isolation. Our empirical results demonstrate that sequence-based data is more suited to inferring molecular function. Conversely, PPI-based classifiers do well in the other two tasks, outperforming predictors based on any other single source of data. Gene expression data and other sequence-based features provide a marginal increase in accuracy.The Gene Ontology (GO)  is the ctransfer of annotation [et al. that was used in the 2011 CAFA challenge [For a long time, the predominant approach to inferring GO function for newly sequenced proteins has been notation , where anotation -10. Nevenotation -15. New hallenge .The transfer-of-annotation approach operates like a nearest-neighbor classifier, and is unable to effectively deal with today's noisy high-throughput biological data. This has led to the recent development of machine learning approaches that typically address the problem as a set of binary classification problems: whether a protein should be associated with a given GO term . The isThe biomedical literature is a resource that has been previously explored for protein function prediction, including as the topic of a shared task . Severacompatibility function f  that measures the level of association between a protein x and a vector of annotations y. Inference of anno-tations is then performed by finding the most compatible label with a given protein: ŷ = arg maxy f . An algorithm aimed at directly inferring complex labels such as GO annotations is called a structured-output method. Structured-output methods have been introduced to the field of machine learning fairly recently and span a number of discriminative and probabilistic approaches [Rather than treating the task as a collection of binary classification problems ("is a particular GO keyword associated with a particular protein?"), the GOstruct method trains a predictor to infer a full set of annotations directly ("what GO keywords are associated with a particular protein?") using the methodology of structured learning . This isproaches . The mosproaches . Structuproaches ,28, predproaches , and preproaches , but aren is the number of examples. Our goal is to construct an accurate mapping f via the arg max operator:Our labeled training data is provided as y most compatible with the input x. The learning objective is then to ensure that the correct label yi yields the highest compatibility score with xi for every training example, as shown in Figure which selects the label yi and all other candidate labels for every training example xi. The compatibility function for the structured SVM is linear in the joint input-output space defined by a feature map ψ: f  = wT ψ. The structured SVM can be formulated as the following quadratic optimization problem [In this work, we utilize the structured SVM , which a problem :i is the slack variable associated with margin violation for xi, C is a user-specific parameter that controls the trade-off between two competing objectives: maximizing the margin through minimization of the norm of w and minimizing the amount of margin violation in the training data, as given by the sum of the slack variables. This is known as the margin-rescaling formulation of the structured SVM [y, yi). Here, we use the kernel F1 loss function [where ξured SVM , becauseF1-loss [which reduces to the F1-loss  when usiψ, and the kernels are functions of both inputs and outputs: K, ) = ψT ψ. In our experiments, we use a joint kernel that is the product of the input-space and the output-space kernels:To make use of kernels, we solve the problem in Equations (2)-(4) in its dual formulation . When deψ is given by all pair-wise combinations of the input-space and output-space features. Different sources of data are combined by adding kernels at the input-space level, and for the output space we use a linear kernel between label vectors. All kernels were normalized according toOur intuition is that two example-label pairs are similar if they are similar in both the input and the output spaces. The corresponding feature map to ensure consistent contribution across different feature spaces. Multiple sets of features were combined via unweighted kernel summation.cross-species view which will have a sequence-based kernel species-specific view whose kernel fc)x1,x associatchain classifier. In this approach, the predictions made by the cross-species classifier are incorporated into the species-specific feature map by adding a feature for each GO term. In other words, arg maxy fc) becomes a set of features for the training of the species-specific classifier. The inference made by the species-specific classifier is then reported as the overall prediction. This approach is related to the method of Clark and Radivojac, which trains a neural network over GOtcha scores [In addition to the multi-view method outlined above, we investigate an approach we call the a scores . The chaTo make inference via Equation (1) feasible we limited the output space lgorithm , but explgorithm , which sindividual GO terms. We compute a score ic(x) for GO term i on protein x according to:Performance in hierarchical classification can be measured either at the level of individual GO terms, or by collectively comparing the discrepancy between the structured labels . For easth ivariable set to 1. The subset ith variable being set to 0. This score measures the difference in compatibility values between the most compatible label that includes GO term i and the most compatible label that doesn't; higher values of this difference reflect stronger confidence that GO term i is associated with protein x. Given this score, we can generate ROC and precision-recall curves in the usual way; in our results we quantify performance using the area under the ROC curve (AUC), and the precision at a recall level of 20% (P20R).where H. sapiens. In the interest of keeping the run times down, we randomly subsampled the external set down to 5000 proteins for molecular function and cellular component experiments and down to 3000 proteins for biological process experiments. Since sequence information was used, cross-validation folds were randomly selected such that no two proteins from different folds have more than 50% sequence identity. To select appropriate values for the SVM parameter C, we ran nested four-fold cross-validation on the training data. The value of Performance was evaluated using five-fold cross-validation on mouse proteins that have species-specific in-formation and valid annotations. Additional proteins, with cross-species features only, were obtained from the external species M. musculus, and use H. sapiens as the external species that participates in the cross-species view. We choose the external species that is reasonably close to the target species and have a significant number of experimentally derived GO annotations. We obtained annotations from the Gene Ontology website (http://www.geneontology.org) and excluded annotations that were predicted through computational means to limit classifier assessment bias [As a target species we focus on ent bias . Table 1http://uniprot.org). In the cases where a gene has multiple splice forms, the longest one was used. Sequence features were extracted as follows, and a linear kernel was used for the input space kernel for the cross-species view.We used features based on protein sequence to construct the cross-species view. Protein sequences for all species were retrieved from the UniProt database , gene expression, and protein-GO term co-mentions extracted from the biomedical literature.M. musculus protein-protein interaction (PPI) data from version 8.3 of the STRING database [i provides the STRING evidence score of an interaction between protein i and the given protein. Preliminary experiments indicate that the resulting linear kernel performs as well as the more sophisticated diffusion kernel.We extracted database . A proteSimilarity of expression was measured using a linear kernel across a compendium of 14,696 microarray experiments provided by the authors of Platform for Interactive Learning by Genomics Results Mining (PILGRIM) . Experimhttp://bionlp-uima.sourceforge.net/) which consists of the following steps: 1) splitting the abstracts into sentences 2) protein name tagging using the LingPipe named entity recognizer  with the CRAFT model [If a protein and a gene ontology term are mentioned in close proximity in a paper, this can be evidence that the corresponding function is associated with the protein. A set of 11.7 million PubMed abstracts, all Medline abstracts on 9/8/2011 that had title and body text, were used to create a protein-GO term co-mention kernel. The abstracts were fed into a natural language processing pipeline based on the BioNLP UIMA resources (FT model  3) Gene FT model  and fit Overall we extracted a total of 146,947,306 protein-GO term co-mentions. However, only 1,392,023 of those were unique - many GO-term protein pairs co-occur many times. An extreme example is interleukin 6, which is mentioned 426,031 times in conjunction with interleukin-6 receptor binding. Across the dataset, each protein co-occurred with a median of 50 molecular function, 117 biological process, and 42 cellular component GO-term mentions. Some basic statistics are presented in Table any document in the collection that might mention the protein; [While it is clear from previous research that exact term matching is inadequate for good recall of Gene Ontology terms in text , it is aprotein;  associatWe trained cross-species, species-specific, multi-view and chain models and assessed their performance in prediction of mouse protein function using cross-validation as described above. The cross-species classifier uses only sequence information, the species-specific classifier uses PPI, gene expression, and protein-GO term co-mentions. The results in Table The cross-species SVM outperforms the species-specific SVM in molecular function, which is consistent with the literature demonstrating that molecular function annotations are the easiest to infer from sequence . In the To assess the contribution of each source of data to the prediction accuracy, we compared the performance of models trained on individual kernels. These results are presented in Table In the species-specific view, the PPI kernel yields the highest accuracy, and outperforms all sequence-based predictors in biological function and cellular component namespaces, including the full cross-species SVM from Table A manual analysis of incorrect predictions using literature features was performed to examine what information GOstruct used to make the prediction. Analysis of the top 25 false positives from the molecular function namespace can be found in Additional File For further analysis of performance we examined our classifiers in the context of individual GO terms. For each namespaces we wanted to see whether there are trends in performance as a function of the GO term depth, and whether there are certain categories that are particularly easy or difficult to predict. Overall, we observed a slight upward trend, with predictors achieving higher accuracy on terms deeper in the ontologies. This was most pronounced in the biological process namespace.Figure The results for the biological process namespace are presented in Figure Figure This paper presented a multi-view extension to the GOstruct structured output protein function prediction framework. We demonstrated the framework's capability to combine multiple heterogeneous sources of data--annotated proteins from multiple species, and species-specific data that includes PPIs, gene expression, and information mined from the biomedical literature--each providing an increase in performance. The empirical results suggest that sequence-based features are more informative of a protein's molecular function, while functional association features from PPI and text mining data provide a stronger contribution for the prediction of biological process and cellular component annotations. Gene expression provided only a marginal increase in performance and we speculate that more sophisticated kernels are needed to extract more meaningful features. Future work includes the design of these kernels as well as framework extensions to make it more scalable to a higher number of species and larger datasets.The authors declare that they have no competing interests.AS and KG collected protein sequence, gene expression and protein-protein interaction data. CF collected protein-GOterm co-mention data. AS implemented the GOstruct framework, performed the baseline set of experiments and wrote the first versions of the manuscript. AS and KG performed the experiments involving gene expression data. KV planned and directed the NLP experiments, performed data analysis, and wrote or integrated text for the sections pertaining to NLP experiments. KV and CF performed all the experiments and analysis of results pertaining to the NLP portion of the manuscript. ABH supervised all aspects of the work. All authors read and approved the final manuscript.Analysis of the top 25 false positive predictions made by GOstruct. We present the best supporting sentence for the function of each protein, the document source, and the most recent known annotation along with the associated evidence code.Click here for file


Schizosaccharomyces pombe) is a valuable model organism for which no systematic protein-interaction data are available. We exploited gene and protein properties, global genome regulation datasets, and conservation of interactions between budding and fission yeast to predict fission yeast protein interactions in silico. We have extensively tested our method in three ways: first, by predicting with 70–80% accuracy a selected high-confidence test set; second, by recapitulating interactions between members of the well-characterized SAGA co-activator complex; and third, by verifying predicted interactions of the Cbf11 transcription factor using mass spectrometry of TAP-purified protein complexes. Given the importance of the pathway in cell physiology and human disease, we explore the predicted sub-networks centered on the Tor1/2 kinases. Moreover, we predict the histidine kinases Mak1/2/3 to be vital hubs in the fission yeast stress response network, and we suggest interactors of argonaute 1, the principal component of the siRNA-mediated gene silencing pathway, lost in budding yeast but preserved in S. pombe. Of the new high-quality interactions that were discovered after we started this work, 73% were found in our predictions. Even though any predicted interactome is imperfect, the protein network presented here can provide a valuable basis to explore biological processes and to guide wet-lab experiments in fission yeast and beyond. Our predicted protein interactions are freely available through PInt, an online resource on our website (www.bahlerlab.info/PInt).A systems-level understanding of biological processes and information flow requires the mapping of cellular component interactions, among which protein–protein interactions are particularly important. Fission yeast ( Progress in experimental techniques has uncovered highly interconnected protein networks. Protein interactions not only hold together protein complexes, the operational units within cells, but also mediate cellular information flow along signaling pathways and networks. Acquiring a global knowledge of protein interconnections is important for understanding biological processes and their disruption in disease.Saccharomyces cerevisiae is the model organism in which the largest portion of protein interactions has been studied experimentally, covering ∼56,363 unique interactions among 5786 proteins  .Assaying all possible protein interactions experimentally in several species, in multiple repeats and under different conditions remains an enormously expensive and time-consuming challenge. Hence, there is a need for developing approaches that can predict physical protein interactions based on some pre-existing experimental data. The machine learning approach suggested requires positive and negative training sets of interactions as well as a vector of features for each pair of proteins. The feature vector was assembled by concatenating the features of the first and second protein as well as the corresponding pair features. However, to ensure consistency in defining symmetric interactions , we ordered the features such that the smaller value always came first. In a previous version, the two symmetric interactions were both included, but this slowed down the training procedure and did not lead to better results.We then used these features to classify any two proteins into either interacting or not interacting pairs. The starting point was a positive training set (list of known interacting pairs) and a negative training set (list of pairs believed not to interact).In recent years, numerous interaction databases have attempted to summarize the knowledge on protein interactions in different species. We used data from BioGRID . A previi.e. the number of connections for each protein. We also removed from the pool of possible negative pairs any interactions that have been experimentally shown at least once with any method in either of the two yeasts. The negative sets were generated using BRS-nonint  and a random forest (RF). The features for all protein pairs were concatenated in feature vectors for the positive and negative training sets, and only pairs for which none of the features were missing were used for the classification. Training and testing of the SVM was performed using a radial basis function kernel with the e1071 package for R. Optimal parameters were chosen using the tune function in a cross-validation framework. The random forests were built using the package randomForest, and the cforest function from the party package was used for estimating the feature importance  as the number of predicted interactions which are true interactions; true negatives (TN) as the number of false interactions correctly not predicted; false positives (FP) as the number of false interactions incorrectly predicted; and false negatives (FN) as the number of true interactions not predicted. Using these measures, we can define the following: specificity = TN/(TN+FP); sensitivity = TP/(TP+FN); accuracy = (TP+TN)/(TP+TN+FP+FN); and false-positive rate (FPR) = FP/N = FP/(FP+TN). Moreover, we used Receiver Operator Characteristic curves (ROC), Areas Under the Curve (AUC), areas under the partial curve (AUC50), and precision-recall curves as a threshold-independent measure of the performance, using the ROCR package for R. The main purpose of providing these measures for our classification was to be able to compare the RF and SVM methods.We constructed a positive test set by collecting high-confidence protein interactions in fission yeast. We found 204 pairs of proteins that were shown to interact both by Y2H and a different capture experiment in fission yeast (BioGRID). Any interactions involving proteins that appear in this high-confidence fission yeast interactome were eliminated from our training sets. We then proceeded to build negative test sets.The first negative test set was constructed making random pairs of the proteins from the positive set, eliminating all interactions documented at least once in budding or fission yeast and preserving the degree of each protein. This ensures that the positive and negative test sets contain the same proteins (none of them present in the training set) and have the same topological features.In a more realistic scenario, there should be a strong imbalance between the number of positive and negative interactions, so we proceeded to also generate a negative set to reproduce this bias. To construct this negative test set, we made random pairs of the proteins known to interact in budding or fission yeast paired at random, maintaining the same degree for each protein. This degree-balanced negative set comprises 32,482 pairs among ∼4500 genes, which, combined with the 204 positives, leads to a ratio between positives and total pairs of ∼0.006, the same as what we expect for the real interactome [12.5 million possible pairs; an upper estimate of between 60,000 and 80,000 true interactions (The Spt-Ada-Gcn5 acetyltransferase (SAGA) complex is involved in regulating RNA polymerase II transcription through chromatin remodeling and histone modification. It is one of the largest and best-annotated protein complexes in budding and fission yeast  enrichment analysis was performed using GO TermFinder . The purified proteins were then TCA-precipitated and digested with trypsin (Promega). The resultant peptides were subjected to two-dimensional LC-MS/MS analysis on a Thermo LTQ as previously detailed  was searched with the MyriMatch algorithm  to the complete S. pombe database and reversed and concatenated all sequences to allow estimation of FDRs . MyriMatch parameters were as follows: strict tryptic cleavage; modification of methionine , S/T/Y , and cysteine  was allowed; precursor ions were required to be within m/z 0.6 of the peptide monoisotopic mass; fragment ions were required to fall within m/z 0.5 of the expected monoisotopic mass. IDPicker (e.g. keratin) were removed from the final protein list.Fission yeast strain (JB795) expressing a C-terminally TAP-tagged version of Cbf11 from its endogenous chromosomal locus was grown to mid-exponential phase in 4× concentrated YES medium (Formedium), washed twice with water, and snap-frozen in liquid nitrogen. Cells were broken in the RM 200 mortar (Retch), and lysates were clarified by ultracentrifugation in the Sorvall Discovery 90SE ultracentrifuge  at 35,000 rpm for 90 min. Due to low abundance of the bait protein, only the first purification step of the standard TAP protocol  and a corresponding set of supposedly non-interacting pairs (negative training set), as well as a list of features. We constructed both a support vector machine  and a rae.g. sequence-based features) or their interactions (e.g. co-expression). An overview of all features is presented in File S1. These features include physical position on the chromosome, physical properties of the protein , functional category information (through GO Superslim classification), as well as protein pair information, such as expression correlation over hundreds of different conditions, co-localization, distance along the chromosome, and the presence of genetic interactions .As a basis for the machine learning approaches, we compiled two types of features, describing either the fission yeast proteins themselves (File S2). Considering predictions with a score above 0.5, the specificity (0.80 for SVM and 0.74 for RF) was better than the sensitivity (0.71 for both SVM and RF), suggesting that the false negatives were higher than the false positives at this threshold. We can see from the ROC curves . The results for RF in this cross-validation test were marginally worse  and higher AUC50 (0.291) compared with RF . The specificities ranged from 0.66 to 0.81, and the sensitivities ranged from 0.63 to 0.73. Generally, RF tended to produce fewer false positives, with the consequence of missing more of the true interactions compared with SVM.Protein interaction networks are generally sparse; therefore, we expect a small fraction of real interactions among all possible protein pairs in fission yeast. To account for the excess of negative interactions, we also tested the performance of our method using a negative test set much larger than the positive test set. The results showed that, even with a large imbalance between the number of true and false interactions in the test sets, our methods can successfully limit the false-positive rate while correctly identifying the true positives. Although both the RF and SVM methods showed comparable performance , RF achiFile S3). To quantify the predictive power of each feature alone, we conducted t-tests and Fisher’s tests estimating the significance of the differences between feature values in the positive and negative training sets. This analysis generally confirms our initial results (File S3).We estimated the importance of each feature using a method that was proven to correctly take into account redundancy between the features and a mixture of feature types . SimilarUbp8 and Sgf11 in a discrete functional module within SAGA . At a score threshold of 0.9, SVM predicted 92 interactions, 86 of which were part of the 171 total known SAGA interactions, whereas no interactions were predicted with RF.Next we focused on the prediction of interactors of the SAGA complex. At a score threshold of 0.5, we only failed to predict two interactions with either SVM or RF: one between Ubp8 and Sus1 (P < 10−16). To further check the specificity of our predictions, we calculated precision and recall curves for identifying the SAGA interactions from the total of the possible interactions of SAGA units with other proteins .RF scores generally tended to be lower than SVM scores so that choosing a specific score threshold led to a different number of predictions in the two methods. However, a correlation between the ranking of interactions according to SVM and RF was evident . For example, many of the highest scoring interactions were between known SAGA proteins and Phf1, a histone H3-K9 demethylase. We therefore hypothesize that some of these predicted interactions could in fact be true and thus point to potential new SAGA components or to proteins transiently interacting with the SAGA complex. For example, we have found some evidence for Phf1, Cdc17, and Pku80 to be interacting with SAGA in other organisms (File S9). In conclusion, our classification approaches succeeded in recapitulating most of the SAGA complex, and they also suggested potential additional proteins that plausibly could associate with SAGA.We predicted 132 and 126 additional interactions with SVM and RF, respectively, for which at least one of the proteins was in the control set. Notably, 105 of these interactions involving non-SAGA proteins were predicted both by SVM and RF, 28 of them with SVM scores > 0.8. The control proteins predicted to interact with SAGA components were characterized by involvement in chromatin and DNA damage repair (CBF1/RBP-Jκ/Suppressor of Hairless/Lag-1 (CSL) family of transcription factors. In metazoans, the CSL proteins serve as effectors of the Notch signaling pathway, which is critical for development. This protein family has been lost in budding yeast, and it seems to exert pleiotropic functions in fission yeast (Cbf11 is a fission yeast member of the P = 0.007), suggesting that our confidence scores could also reflect the strength of the experimental evidence. The scores obtained with RF, however, did not correlate with the peptide count (P > 0.5).A purification experiment identified ∼300 potential components of the Cbf11 complex. The performance of the prediction compared with experimental results is shown in SVM and RF contributed 15 and 10 unique predictions, respectively, that were confirmed experimentally. Thus, the two methods were somewhat complementary and, if used together, may provide better coverage of true predictions. On the other hand, using the overlap of predictions from both SVM and RF provides a more conservative and, hence, more reliable list of protein interactions that could be used as a starting point for further investigations.P = 10−14), RF (Fisher’s P = 10−28), and in the overlap of the two methods (Fisher’s P = 10−28). So far, there are no known genetic interactions for Cbf11 and no functional interactions due to the protein not being conserved in budding yeast. For this reason, our method for predicting its physical association partners can only be compared with selecting proteins at random from the whole genome. The results of this test are shown in The Cbf11 interactors predicted were significantly enriched for the experimentally determined targets, both in the case of SVM . Both these lists were significantly enriched for transcriptional control, RNA processing, and chromatin remodeling; these enrichments were particularly significant among the 97 proteins that were both predicted by the two techniques and experimentally confirmed as Cbf11 interactors. The terms that we found in the enrichment are of finer level compared with the ones used as features in the training, so this enrichment is not trivial.We performed a GO term enrichment analysis on the predicted Cbf11 interactors as well as on the experimentally identified interactors . Notably, there were also 398 interactions predicted with both SVM and RF, but not confirmed experimentally, which showed strong enrichment for the same GO terms that characterize the experimentally verified interactors (File S4). These genes showed significantly lower mRNA levels  and shorter mRNA half-lives (P = 10−5), raising the possibility that they escaped the purification steps or were below the detection sensitivity of the affinity capture-MS approach. We conclude that our list of predicted targets contains approximately one third of the experimentally verified Cbf11 interactors and is not biased by protein levels that could affect the affinity capture-MS data.The proteins that are not predicted to interact but are experimentally found to co-purify with Cbf11 were only weakly enriched for organelle and chromosome organization and for metabolic proteins, suggesting that some of these interactions could be false positives of the affinity capture-MS method or background interactions. Consistent with the latter hypothesis, more than 20% of these interactions were with proteins that could be considered non-specific background (File S4). The general theme of chromatin remodeling among Cbf11 interactors, with an emphasis on histone modification and silencing, suggests a role for this transcription factor in epigenetic processes, which could explain the wide range of phenotypes that have been associated with it.Moreover, our list of predictions contains whole complexes that could interact through some of their units with Cbf11. Figure S2). Importantly, the assumption that there are no interactions between units of these different complexes and that all complex units interact directly is very likely wrong, showing that these estimates are only very rough approximations of the real performance.Using GO annotations, a curated list of fission yeast complexes can be compiled (GO complex interactions). Assuming that all members of a complex interact with each other and not with members of other complexes, we constructed a set of 28,771 interactions and 1,374,879 non-interactions. Using a threshold of 0.5 for both SVM and RF, we confirmed 14,720 of the interactions and 1,191,506 of the non-interactions, with a precision of 0.07 and an accuracy of ∼0.86 .Looking at interactions from the GO complex list described above, which are also verified in a study for interaction of chromatin-associated proteins (P = 10−5), suggesting that they are relatively more abundant. However, this is not the case in either of the other networks. It is hard to determine whether this trend is biologically relevant. The correlation of the degree with the protein level is to be expected in experiments, where capture techniques are more sensitive to abundant proteins , but it was not found in our predictions or in a high-quality dataset. This finding is, therefore, consistent with a bias in the experimental data. Interestingly, highly connected proteins tended to be longer, both in the known network  and in the predicted network  (File S6), which could reflect the presence of more interaction domains on longer proteins.We then checked whether particular types of proteins were predicted to have many interactors. To answer this question, we examined the tendency of proteins with specific feature values to have many interacting partners in three different networks: (1) a network of protein interactions in fission yeast from databases (including the ones mapped from budding yeast); (2) the filtered subset of this known network used as our positive training set (two independent lines of evidence); and (3) the network of predicted interactions with scores higher than 0.9. In the protein interaction network built from databases, we find that proteins with more connections have a higher relative polymerase II occupancy . This result indicated the presence of redundant information in the different features used.Having assessed the accuracy of our prediction method, we calculated probability scores for all possible fission yeast protein interactions. The almost 5000 proteins can potentially interact in over 12.46 million possible pairs. We performed the predictions with both SVM and RF and used the overlap as a conservative estimate of the fission yeast protein interactome. We chose a score threshold (0.87) that resulted in a number of interactions comparable to the ∼60,000 interactions known for budding yeast . This fiAlthough budding yeast interactions were used in the training sets, the predictions were based entirely on fission yeast features. The resulting predicted interactome, therefore, offers a species-specific picture. The light green GO:transcription cluster appears to be split into two sub-clusters . A GO teThe interaction score provided by the SVM can be exploited using two complementary approaches. First, we can use a large number of predicted interactions to explore the context of a biological pathway, aware of the presence of false positives. Alternatively, we can select a few high-confidence predictions and verify them experimentally. It should be mentioned that most of our false positives are likely to be functionally related proteins. Filtering the positive predictions for a protein of interest based on the presence of compatible interaction domains on the proteins would help to distinguish more confidently the true direct physical interactions. In the following, we used the predicted interactome to investigate the stress response sub-network.The cellular stress response is a universal mechanism of pivotal significance for both cellular physiology and pathology. It represents a defense reaction of cells to maintain homeostasis and survive environmental challenges. The response of fission yeast to environmental stress has been the subject of multiple studies that provide a framework to explore the predicted protein interaction network . We there.g. the one between Prr1 and Mpr1 . As examples for how our predictions can be used for the generation of new biological hypotheses, we highlight below a few of the predicted interactions and discuss them within their biological context.We compiled a list of 32 proteins known or suspected to be involved in stress response from the literature. Most of these proteins are highly conserved, with some of them having multiple paralogs in budding and fission yeast. S. pombe Tor kinases are involved in cell shape determination; cortical patch localization and cell polarity; septum formation; vacuole targeting and sorting; endocytosis; and autophagy  are central for the regulation of spatial cytoarchitecture (polarity and growth) via regulation of cytoskeletal elements . These predictions thus provide an intriguing list of candidates to follow up with targeted wet-lab experiments. Given the strong conservation of the Tor pathway, our predicted interactions should also be fruitful to pursue in other organisms.The evolutionary conserved target of rapamycin (Tor) pathway mediates fundamental cellular activities, such as translation, spatial and temporal growth, and cell-cycle progression in response to nutrient signals. Fission yeast Tor1 and Tor2 proteins are serine/threonine kinases of the phosphatidylinositol kinase–related family . Althougutophagy . A largeelements , autophaS. pombe Tor1 kinase are implicated in disease and have Online Mendelian Inheritance in Man (OMIM) entries related to various cancers, neuronal diseases or syndromes, myopathies, and porphyria . Histidine kinases are reported to have important roles in bacteria, other fungal species, and plants in regulation of osmotic and oxidative stress response . Within these 127 proteins, we found 41 genetic interactions and 37 physical interactions inferred from budding yeast (as documented in BioGRID). As many as 18 transcription factors were predicted to be interacting with the Mak proteins, 6 of them specifically with Mak2 . We conclude that the predicted stress response sub-network is a useful framework to generate specific hypotheses for this important biological process.Histidine kinases mediate a histidyl-aspartyl phosphorelay, also known as a two-component system (TCS). We predicted the response , 2004. FS. pombe has the machinery for siRNA production and processing that S. cerevisiae lacks and has, thus, emerged as a useful model for RNA interference: it has single-copy genes for components of the RNAi pathway, such as argonaute, dicer, and RNA-dependent RNA polymerase (RdRP) . For example, the accuracy for the RF method is 0.81, 0.71, and 0.75 for thresholds of 0.5, 0.7, and 0.9, respectively, whereas the SVM method records accuracies of 0.77, 0.92, and 0.97 respectively. The large difference between SVM accuracies at 0.5 and 0.7 thresholds suggests that 0.5 is probably too low a cut-off. For the RF case, on the other hand, a threshold of 0.5 effectively limits the number of false positives, as shown by a high accuracy. Notably, the score assigned by SVM to Cbf11 interactors correlates significantly with the strength of the experimental evidence for these interactions. The classification underlying the SVM provides a score which should reflect the confidence of the predictions, whereas RF results do not guarantee this. We suggest that users of our tool consider the ranking of the predictions and determine an appropriate threshold based on the purpose of the prediction. Using the data provided in File S2 should further guide the interpretation of the predictions.An important issue in establishing a correct use of these predictions is to understand how changing the threshold affects their accuracy. To this end, we calculated our performance measures on the imbalanced sets for two additional thresholds: 0.7 and 0.9  purification followed by mass spectroscopy. In this way, a role in chromatin-related processes is suggested for this uncharacterized transcription factor. Many of our predicted targets that are not found in the experimental hit lists were enriched for similar specific GO processes and are likely to be at least functionally related to Cbf11. Moreover, we showed how some low-abundance proteins could be false negatives of the experiment and stressed the fact that high-abundance proteins can bias specific experimental approaches.File S8).There are no other predicted physical interaction networks for fission yeast, with the only available functional interactions available from STRING . The low precision recorded  can be attributed to this lack of knowledge and is characteristic of predicted networks, which can still be of use to guide hypothesis generation and experiments. The experimental validations presented in this article offer a practical example of the validity of the method and show possible uses of our predictions.Estimating the real size of the interactome is not trivial and by necessity renders the accurate estimation of our method’s performance subject to large error margins .We believe that this tool will be of particular use to the yeast community to generate hypotheses and to investigate the role of yet uncharacterized proteins in fission yeast, like Cbf11, for which no physical interaction information is available and for which even functional interactions are unknown (


We define an interface-interaction network (IIN) to capture the specificity and competition between protein-protein interactions (PPI). This new type of network represents interactions between individual interfaces used in functional protein binding and thereby contains the detail necessary to describe the competition and cooperation between any pair of binding partners. Here we establish a general framework for the construction of IINs that merges computational structure-based interface assignment with careful curation of available literature. To complement limited structural data, the inclusion of biochemical data is critical for achieving the accuracy and completeness necessary to analyze the specificity and competition between the protein interactions. Firstly, this procedure provides a means to clarify the information content of existing data on purported protein interactions and to remove indirect and spurious interactions. Secondly, the IIN we have constructed here for proteins involved in clathrin-mediated endocytosis (CME) exhibits distinctive topological properties. In contrast to PPI networks with their global and relatively dense connectivity, the fragmentation of the IIN into distinctive network modules suggests that different functional pressures act on the evolution of its topology. Large modules in the IIN are formed by interfaces sharing specificity for certain domain types, such as SH3 domains distributed across different proteins. The shared and distinct specificity of an interface is necessary for effective negative and positive design of highly selective binding targets. Lastly, the organization of detailed structural data in a network format allows one to identify pathways of specific binding interactions and thereby predict effects of mutations at specific surfaces on a protein and of specific binding inhibitors, as we explore in several examples. Overall, the endocytosis IIN is remarkably complex and rich in features masked in the coarser PPI, and collects relevant detail of protein association in a readily interpretable format. Much of the work inside the cell is carried out by proteins interacting with other proteins. Each edge in a protein-protein interaction network reflects these functional interactions and each node a separate protein, creating a complex structure that nevertheless follows well-established global and local patterns related to robust protein function. However, this network is not detailed enough to assess whether a particular protein can bind multiple interaction partners simultaneously through distinct interfaces, or whether the partners targeting a specific interface share similar structural or chemical properties. By breaking each protein node into its constituent interface nodes, we generate and assess such a detailed new network. To sample protein binding interactions broadly and accurately beyond those seen in crystal structures, our method combines computational interface assignment with data from biochemical studies. Using this approach we are able to assign interfaces to the majority of known interactions between proteins involved in the clathrin-mediated endocytosis pathway in yeast. Analysis of this interface-interaction network provides novel insights into the functional specificity of protein interactions, and highlights elements of cooperativity and competition among the proteins. By identifying diverse multi-protein complexes, interface-interaction networks also provide a map for targeted drug development. Protein-protein interaction (PPI) networks aim to capture the interactions between proteins that mediate many of their molecular functions To refine the coarse protein-protein interaction network and to capture these important structural and chemical aspects of interactions Although the importance of structural details in protein interactions has led to increasing efforts to identify protein-binding interfaces in a systematic way In order to analyze the specificity, cooperativity, and topological properties of an IIN one requires both an accurate assessment of shared and distinct binding interfaces and a dense collection of protein-protein interactions. Therefore, we here combine both structure-based computational approaches with literature-curated biochemical data to build an IIN for the proteins involved in clathrin-mediated endocytosis (CME) in yeast In addition to collecting detailed data on protein structures, a particular advantage of our curated approach is to eliminate false positives from the PPI by creating a coherent and consistent picture of the protein interactions. We identify the specific mechanism mediating an observed protein-protein interaction and determine whether the interaction is direct or indirect. Of particular concern are indirect interactions, mediated through intervening proteins, because they are not always distinguishable from direct interactions in high-throughput affinity purification/mass spectrometry (AP/MS) The CME network constructed and characterized in this way reveals significant complexity with permanent and dynamic assemblies of few or many proteins, a mixture of binding modes with both shared and distinct binding, and both large and small binding interfaces. This detailed information is necessary for building models of protein-protein interactions where both competitive and cooperative binding reactions contribute to function. The accuracy and coverage of the protein IIN we have generated allows us to draw generalizable insights about the structure of the IIN, the overlap of binding interfaces, the identification of indirect interactions, and the implications towards the biological functions with the parent PPI. Compiling this information for more parts of PPI networks will help prune indirect and spurious interactions, highlight areas of poorly resolved structural and biochemical characterization, and facilitate investigation of the physical and evolutionary origins of the IIN topology and in turn of protein binding.The aims of the present work are (1) to develop a general framework for the construction of IINs from a combination of structural and biochemical data that measure the support of proposed protein interactions; (2) to characterize the general network properties of the resulting endocytosis IIN as compared to PPI networks and randomized networks, and (3) to demonstrate the applications of the IIN, including as a resource for predicting response to mutation and to specific binding inhibitors. At the network level, we examine whether the IIN retains the complex characteristics of the PPI network, including a high connectivity, hub structures, local clustering, and a scale-free character manifested in power-law distributions of the number of binding partners. We also quantify the fragmentation of the fully connected parent PPI network into separate interface modules at the IIN level. We provide several examples for the use of the IIN in selecting possible drug targets and in predicting the effects of mutations by identifying specific pathways of communication between proteins via their interfaces. For the CME proteins we discuss the central role of SH3 domains and multi-interface proteins. We emphasize that the process of assigning protein interfaces has generated not only a useful map of interactions among these highly-studied proteins but has highlighted the difficulties associated with trying to make automated assignments, including overlapping residues and inconsistencies between sources. Therefore, we discuss the insights derived from our interface procurement process that are relevant for high-throughput methods of interface determination.As a first step, we constructed a curated PPI network of 56 proteins involved in CME in yeast The differing support for the CME protein interactions is represented visually in In We determined the degree distributions of both the original and the curated PPI networks, as a statistical measure of the number of interaction partners of each node. Upon going from the full combined-database PPI network  to the curated PPI network, the decrease in total edges results in a less dense network in which the average number of partners per protein dropped significantly from 13.5 to 6.4. Although large PPI networks typically have degree distributions characterized as power law or truncated power laws The majority of proteins in this endocytosis network have multiple interfaces, with an average number of 3.5 distinct binding interfaces per protein. The correlation between protein size and the number of interfaces is quite weak (R = 0.26). One reason for this weak correlation is that several of these proteins have additional binding partners outside the CME network module considered here. Another reason is the size of the interfaces varies broadly, from just a few residues  as either competing or not, since many proteins have multiple shared interfaces. The interface assignments also highlight redundancy in the network, where the recruitment of a particular protein during the endocytic pathway could happen via multiple mechanisms, as many of the proteins are chimeras of the most frequently represented domains The designation of distinct domains on each protein allows one to contrast the specific structural elements present in these CME proteins versus CME proteins in other organisms. Much of the CME pathway between yeast and mammals is conserved. However, a major distinction is that CME in yeast requires the actin network to initiate the membrane invagination Distinguishing interface domains in each protein also enables direct visual identification of multi-interface proteins that act to bring together multiple proteins with different functions, which again is not possible if one only marks edges in the PPI as competing. Both PAN1 and SLA1 have many interfaces that can connect simultaneously to both the scaffolding proteins of the clathrin pit formation  or to the EH domains  would generate the same phenotype. The extent to which they do not match would first indicate possible missing nodes from the network. Alternatively, the result could indicate that one of these domains acts cooperatively with another domain to affect the global behavior of the protein, not just this specific interaction.In terms of the anticipated biological response to mutation of either the EH domain or the NPF motifs (assuming independence), this interaction helps stabilize a scaffold of proteins at the membrane that recruits the clathrin trimer. From the IIN combined with the PPI, cutting these edges out of the network would not prevent any of the proteins from connecting to the membrane or the early coat module, as PAN1 could still connect via SLA2 and EDE1 via SYP1. Clathrin and actin would still be recruited normally. What this mutation should affect is crosslinking between these proteins and therefore clustering of these proteins in one place on the membrane. If crosslinking and clustering of proteins is necessary for efficient coat formation then eliminating these interactions could decrease or slow down clathrin-pit formation.As one of the main challenges in IIN construction, there is more than one way to define whether a binding interface on a protein is shared between multiple binding partners or is completely distinct. The two main criteria we use to characterize shared and distinct interfaces are (1) if the same residues are present in both interfaces, (2) if the binding of one protein partner would interfere with the binding of another partner due to structural overlap or allosteric effects. Both criteria are important to the function of the proteins in the cell. Concerning the first criterion, the sequence makeup of the interface is central to achieving binding specificity, as even proteins with the same domain structures do not necessarily share the same partners The procedure of manually assigning interfaces has also highlighted some important issues for consideration in computerized interface assignment. For one, residue overlap does not necessarily mean that proteins compete for binding to the protein, as demonstrated by multi-subunit complex formation . None ofFor attempts at homology modeling or docking, it would first be useful to assess how reliable a purported interaction might be. Particularly in the case of interactions involving subunits of multi-protein complexes, many of the interactions are actually indirect. Arp2, for instance, has relatively high homology to actin and shares several binding partners; however, Arp2 acts as part of a multi-subunit complex and binds to these shared partners (such as LAS17) in distinct ways. Also, higher thresholds for sequence similarity could be warranted in particular cases, such as SH3 domains, where small variations in sequence distinguish specific from nonspecific partners One of the major distinctions between the procedure used here and current automated methods is the inclusion of detailed information on binding interfaces between proteins from biochemical studies, not just from high-resolution protein structures. This information preempts or complements the use of homology or docking models of protein interactions. Unfortunately, the domain or interface details from these studies is not collected in a convenient database, whether it is the specific residues that comprise the interface or more general information on inhibition or competition between binding partners. There are also ambiguities and inconsistencies in existing data that are difficult to resolve without combining multiple literature resources in a coherent analysis. Nevertheless, mining these data would provide a valuable resource for generating more complete networks of interface-interface interactions.Our protein list is composed of 56 proteins that were selected because they all participate in the yeast clathrin-mediated endocytosis pathway and have been identified as central components Given a PPI network, the first step in assigning the binding interfaces was collecting information on the particular proteins from the SGD Crystal structures of complexes were available for a few of the protein interactions, including the ARP2/3 complex and several actin binding interactions  and PRDs to which SH3 domains bind . We took advantage of several large-scale studies The endocytic protein subset contains three kinases  and similar to the SH3 domains, the specificity of kinases for their phosphorylation targets has also been studied at large scale In some cases the data implicating two proteins as interacting only came from high-throughput studies and these interactions were generally unassigned. Others came from a literature source that did not isolate binding interfaces, with no additional evidence available from homologs or functionally related proteins. Edges that were identified between the ARP2/3 complex subunits and other proteins were considered indirect if PDB structures or biochemical evidence implicated a specific subunit in the direct interaction. For a few interactions, evidence from the literature suggested that such proteins did not bind directly to one another upon further investigation, and as a result these edges were removed. We note these in the interaction table. For example, we were unable to find any evidence for the protein RVS161 forming direct physical interactions with any proteins other than RVS167. Furthermore, there was some biochemical evidence suggesting that proposed edge interactions were mediated via RVS167 rather than directly through RVS161 We added 9 new edges to the network to account for literature studies providing evidence for the binding interactions. These were largely actin related interactions that lacked references in the PPI databases but have been well established as functionally important binding partners of actin. One was an SH3-PRD interactions defined in two separate publications that were missing from the database.Several of these proteins have domains known to bind at the membrane that are important to their function in endocytosis. Therefore we pointed these out on the protein-interface interaction network in As the first criterion to assign an interface, we used the residues involved in the binding, if available. Specific residues were available from PDB structures and for several peptide motifs like PRDs When the specific residues of the folded protein interfaces were not available, the next description of the interface was the domain structure represented by sequential sequence residues . These domains were generally identified in biochemical studies and the sizes of the domains varied from a few residues  to hundreds of residues . In some cases the assigned interfaces may not represent a known domain but they are designated as unique interfaces because they do not overlap with any of the protein's other binding partners. Lastly, if residue level detail is not available, then the fact that two binding partners are competing with one another is used as justification for listing the interface as shared.To summarize, we did not use a strict cutoff of overlapping residue numbers for defining shared versus distinct interfaces. All subunits of a multi-subunit complex were assigned distinct interfaces for these inter-subunit contacts because they could clearly bind simultaneously. This is despite the fact that pairs of proteins could have as many as 10 overlapping residues if a long disordered region of a protein sat at the seam of an interaction between two other proteins. For most biochemical studies, stretches of residues were identified and shared interfaces were assigned when proteins bound to overlapping stretches of residues and there was no evidence that they could bind simultaneously. For the distinct surfaces in actin, there was in some cases overlap between residues, but there was also evidence that the proteins could bind simultaneously. For example, several actin binding proteins bind to the actin filaments, and therefore they can bind simultaneously with the actin-actin binding interactions, despite overlapping with some residues.k binding partners, there are k(k−1)/2 possible pairs of partners sharing an interface. To keep track of the interface assignments, each protein had its own file with a k-by-k matrix indicating the overlap between the k binding partners  counts how many distinct pairs of the k(i) partners of interface i have an edge between them to form closed triangles with node i. Self-loops were ignored in this calculation. We also use a global clustering coefficient globalC as the number of distinct closed triangles triangleN in the network divided by the total number of distinct triplets,openN the number of open triplets.We evaluated clustering coefficients of our networks using the expression p(k), where k counts the number of partners per node (either protein or interface), and p(k) is the probability for finding a node in the network with that degree. For the degree distribution, we note that we treated self-loops as a single partner, rather than the standard method of treating a self-loop as counting as degree of 2, so that the degree would reflect the physical number of binding partners per protein (or interface).We computed degree distributions, We enumerated the 4-node motifs present in our networks by identifying all distinct sets of 4-node subgraphs that are connected by at least one path (each node in the subgraph can be reached by the others). There are six distinct 4-node subgraph architectures To generate networks that shared the same number of interfaces, edges, and the same degree distribution as the IIN in −γ/ζ(γ) normalized over the range x≥xminWe fit our degree distributions to power laws using the maximum likelihood method, where the discrete data is fit to a power law distribution xTable S1Five sub-tables list (1) the justifications and literature sources used for the interface assignments, including network edges left unassigned, and details about the (2) SH3 domain, (3) PRD (4) kinase interactions and (5) membrane binding domains.(XLSX)Click here for additional data file.


Protein-Protein Interaction (PPI) extraction is an important task in the biomedical information extraction. Presently, many machine learning methods for PPI extraction have achieved promising results. However, the performance is still not satisfactory. One reason is that the semantic resources were basically ignored. In this paper, we propose a multiple-kernel learning-based approach to extract PPIs, combining the feature-based kernel, tree kernel and semantic kernel. Particularly, we extend the shortest path-enclosed tree kernel (SPT) by a dynamic extended strategy to retrieve the richer syntactic information. Our semantic kernel calculates the protein-protein pair similarity and the context similarity based on two semantic resources: WordNet and Medical Subject Heading (MeSH). We evaluate our method with Support Vector Machine (SVM) and achieve an F-score of 69.40% and an AUC of 92.00%, which show that our method outperforms most of the state-of-the-art systems by integrating semantic information. Extracting biomedical information from the literature is an important research topic in the field of biomedical natural language processing (BioNLP). With the rapidly growing number of research papers, it is becoming increasingly difficult for biomedical experts to detect the protein information manually. Thus automated protein-protein interactions extraction (PPIE) from biomedical literature has attracted substantial attention. PPIE is of great value on the application and practical significance, particularly establishing the network of protein knowledge, predicting the protein-protein relations and developing new drugs and so on.At present, many methods have been used to extract protein-protein interaction relations. Most of these methods for PPI extraction task are pattern-based methods The Pattern-based methods employ pre-defined patterns and rules to match the labeled sequence. Fundel et al. The statistical machine learning methods can effectively avoid the disadvantages of the above methods. The current machine learning methods for PPI extraction task involve feature vectors based and kernel-based methods. Liu et al. However, few studies employed the semantic knowledge obtained from ontologies such as MeSH The remainder of the paper is organized as follows: The detailed implementation of our method is described in Section “Methods”. Section “Results” presents our experimental results and the comparisons with other systems. The discussion and error analysis are illustrated in Section “Discussions”, followed by Section “Conclusions and Future Work”.Our method is based on SVM and three distinctive types of kernels are combined, i.e., feature-based, tree and semantic kernel.The following features are used in our feature-based kernel.The word features are the most basic and important feature. There are four sets of words features in our method.Words from protein names: all the words in two protein names are included.Words between two protein names: these features include all words that are located between two protein names. If no word appears between two protein names, the feature will be “NULL”.n words of the first protein name and the right n words of the second protein name. n is set to be five in our experiments. If there are no words surrounding two protein names, “NULL” will be used.Words surrounding two protein names: these features include the left Interaction term: the interaction word  often implies the existence of PPI. If only one keyword appears between or among the surrounding words of two protein names, the keyword is added into the interaction term feature. If there is more than one keyword, the first one will be used. If no keyword appears, the feature will be set to “NULL”.From the corpus, we find that the shorter distance (the number of words) between the two proteins is, the more likely the protein pair has interaction relation. The distance feature can be divided into two classes.The number of the non-proteins between two proteins (Word-Num)If Word-Num≤3, the feature value will be set to “1”; if 3<Word-Num≤6, the value will be set to “2”; if 6<Word-Num≤9, it will be set to “3”; else, it will be set to “4”.2. The number of the proteins between two proteinsIf no other proteins appear between the two proteins, the feature value will be set to “0”; otherwise, it is the number of other proteins.Our tree kernel adopts the convolution tree kernel proposed by Collins et al. Despite the better performance of SPT, in some cases, the information contained in SPT is not sufficient to determine the relation between two proteins. For example, the word “interact” is critical to determine the relation between PROTEIN_1 and PROTEIN_2 in the sentence “PROTEIN_1 and PROTEIN_2 interact with each other”. However, it is not contained in the SPT  (CC and) (NNP PROTEIN_2))” to “(S (NP (NNP PROTEIN_1) (CC and) (NNP PROTEIN_2)) (VP (VBP interact) (PP (IN with) (NP (DT each) (JJ other)))))”, which includes the keyword “interact” and richer context information. We call it Dynamic Extended Tree (DET).Thus the extended SPT (solid circle in ppsim) and Context Semantic Similarity (consim). They are mainly based on the following two assumptions:Our semantic kernel consists of two parts: Protein Pair Similarity , where WordNet is a well-known upper ontology, storing rich semantic information. Unlike MeSH, entries in WordNet are more common. We take advantage of WordNet to measure the context semantic similarity. All words are treated as a context except protein names and stop words in a sentence.According to previous assumptions, the similar contexts indicate the same relations. We exploit the Kuhn-Munkres Algorithm where The semantic kernel (where Each kernel can capture some aspects of the available information in a sentence while losing other aspects. Combining different kernels produce a new useful similarity measure to reduce the danger of missing important information. We define the liner ensemble kernel as formula (5):where We evaluate our method on the AIMED corpus There are many assessing methods for PPI extraction. Von Mering et al. In the following experiments, our results are obtained with 10-fold cross-validation.In this section, we firstly discuss the effectiveness of SPT and its dynamic extended tree (DET). Then the results of different kernels on AIMED corpus are presented.We achieve an F-score of 51.95% and an AUC of 85.50% with the original SPT. With the dynamic extended tree, the F-score and AUC are improved to 53.27% and 87.00% respectively (1.32% increase in F-score and 1.50% increase in AUC). Yang et al. The performance of different kernels is shown in The experimental results show that, better performance is achieved if two or more individual kernels are combined. When the tree kernel is combined with the feature-based kernel, the performance is improved by 14.01% in F-score and 2.10% in AUC. When further combined with context semantic similarity, the F-score is improved by 4.44% while AUC slightly reduces by 0.10%. Added the protein pair similarity, the F-score and AUC are improved to 69.38% and 92.00% respectively (5.68% increases in F-score and 1.20% in AUC). Finally, the addition of From the above experimental results, we can see: (1) the context semantic similarity does not contribute more than the protein pair similarity (4.44% vs. 5.68% in F-score); (2) when the context semantic similarity is combined with the feature-based, DET and protein pair similarity, the F-score only increases 0.08% and AUC does not change. One reason is that all words in the sentence are treated as the context except for protein names and stop words, which might introduce random noise features to degrade the performance when combined with protein pair similarity; the other is that the sparseness of the training set affects the performance. No common or similar words exist in the corresponding contexts, resulting in the lower context semantic similarity. The same or similar contexts indicate the same relation, thus the sparseness of the training set produces certain effect on the performance. However, the kernels with context semantic similarity always perform better than the kernels without integrating any semantic information.This section presents the comparisons between our method and some state-of-the-art works on the AIMED corpus.We combine the dynamic extended tree kernel and semantic kernel with feature-based kernel to extract PPI. Experimental results show that our PPIE method outperforms most of the state-of-the-art systems. The effectiveness analysis is as follows.Effective Individual KernelFirstly, a novel dynamic extended method is proposed. The extended SPT contains the information most relevant to PPI extraction. For example, the protein pair “HFE” and “TfR” in the instance S1 will be replaced by PROETIN_1 and PROTEIN_2 in our tree kernel. And the corresponding SPT and DET are also given as follows.S1: “The HFE-TfR complex suggests a binding site for transferrin on TfR and sheds light upon the function of HFE in regulating iron homeostasis.”SPT: (NP (NN PROTEIN_1)(NN PROTEIN_2)DET: (NP (DT The)(NN PROT_1)(NN PROT_2)(NN complex))The protein pair “HFE” and “TfR”, classified as a false negative case by SPT, can be classified correctly as a true positive one by DET, because DET includes the interaction word “complex” to get richer context information.Secondly, the semantic kernel takes advantage of the semantic knowledge derived from MeSH and WordNet, to calculate the semantic similarity between two instances. Results in S2: “JAK2, a member of the Janus kinase superfamily was found to interact functionally with Raf-1, a central component of the ras/mitogen-activated protein kinase signal transduction pathway.”2. Combination of KernelsWe propose an ensemble kernel including the feature-based kernel, tree kernel and semantic kernel. The feature-based kernel is simple and efficient, but can not capture the syntactic and semantic information. The tree kernel can calculate the similarity between two shortest paths by utilizing the syntactic structure information. The semantic kernel considers the deep semantic information. Given their individual characteristics, different kernels calculate the similarity between two instances from different aspects. Thus, combining different kernels produce a new useful similarity measure to reduce the danger of missing important information.From the above analysis, it can be seen that our PPI extraction algorithm based on integrated kernels is statistically effective.Our method improves the performance of PPI extraction on AIMED, but there are still some disadvantages. A few error examples are listed here. In these instances, the protein pairs focused are marked with bold slash.Raf-1 residues 143–145 impairs binding of 14-3-3, but not Ras, to the Raf-CRD”. Our method classifies it as a negative instance while it is a positive one in fact.The feature-based kernel considers the negative words as an indicative feature. When a negative word exists in an instance, it tends to be classified as a negative one. However, it may lead to a false negative instance. For example, it can be observed that “not” exists in the instance “Moreover, mutation of 80K-H, pp66” will be classified as a false negative instance. The instances have no adequate lexical features, syntactic features and rich context information, except for the protein names.Some instances have no sufficient information. For example, the protein pair in the instance “Sos and Some errors in the corpus’s annotation. This case will lead to some incorrect classifications.Confined by the complexity of the PPI expression as well as the quantity and quality of the corpus, some inevitable false classifications will be generated which are not itemized here.In this paper, a multiple-kernel learning-based approach is presented for the protein-protein interaction extraction. The approach contains the following three individual kernels: feature-based kernel, tree kernel and semantic kernel. Our method aims to improve the performance of PPIE by embedding the semantic information into kernel calculation. Experiments and comparisons demonstrate that our PPIE method works better than the state-of-art systems on the AIMED corpus, with an F-score of 69.46% and an AUC of 92.00%.There are still spaces for improvement. For example, the DET kernel only contains the structure of parse tree and neglects the dependency path tree. When the dependency tree kernel is combined with SPT tree kernel, the performance is expected to be improved


Blocking specific protein interactions can lead to human diseases. Accordingly, protein interactions and the structural knowledge on interacting surfaces of proteins (interfaces) have an important role in predicting the genotype-phenotype relationship. We have built the phenotype specific sub-networks of protein-protein interactions (PPIs) involving the relevant genes responsible for lung and brain metastasis from primary tumor in breast cancer. First, we selected the PPIs most relevant to metastasis causing genes (seed genes), by using the “guilt-by-association” principle. Then, we modeled structures of the interactions whose complex forms are not available in Protein Databank (PDB). Finally, we mapped mutations to interface structures , in order to spot the interactions that might be manipulated by these mutations. Functional analyses performed on these sub-networks revealed the potential relationship between immune system-infectious diseases and lung metastasis progression, but this connection was not observed significantly in the brain metastasis. Besides, structural analyses showed that some PPI interfaces in both metastasis sub-networks are originating from microbial proteins, which in turn were mostly related with cell adhesion. Cell adhesion is a key mechanism in metastasis, therefore these PPIs may be involved in similar molecular pathways that are shared by infectious disease and metastasis. Finally, by mapping the mutations and amino acid variations on the interface regions of the proteins in the metastasis sub-networks we found evidence for some mutations to be involved in the mechanisms differentiating the type of the metastasis. Metastasis is the mechanism that causes the distant spread of cancer . As our In the recent years, numerous studies have been trying to shed light on molecular mechanisms of metastasis. Some of them are: oncogene activation with new experimental methods , identifGenes related with metastasis are usually biologically related with each other . For thiOn the other hand, structural data can be very useful for explaining the molecular mechanisms leading to disease when used in conjunction with information about the mutation responsible for the disease . For insIntegrating structure to PPI networks has recently been used to provide insights on the mechanism of interactions ,25. ThisIn order to understand the molecular mechanism of the brain/lung metastasis of breast cancer patients, we have generated lung and brain metastatic breast cancer sub-networks by finding the most relevant edges to the seed genes identified by Massagué and his co-workers. Then, we enriched these networks with structural information of 3D structural models of known protein-complexes and predicted its protein-protein interfaces. We have analyzed the protein-protein interfaces commonly employed in these sub-networks and observed that interactions of microbial origin played an important role. We also investigated the mutations happening on the most relevant proteins of the breast cancer metastasis sub-networks. Our results suggested that key protein-protein interfaces may be mediating the metastasis, in which a certain mutation could be selectively altering the interaction.Experimental data on protein interactions are spread among multiple databases. Even if the data in these databases partially overlap, the reliability of data differs because of the variations in the experimental techniques and the organisms used. In addition, information of the same protein can be stored with different designations in different databases. Therefore, all the available data should be queried properly and matches should be combined to form a comprehensive human PPI network. We made use of BIANA   but not the edges (PPIs) and gene-gene associations), therefore we needed to transfer the score of the nodes into the edges. Thus, we defined the score of the edge as the average of the scores of its nodes . We selected a common threshold cut-off on the score of the edges to set up the sub-networks of brain and lung metastasis with similar size.We used HPRD , UNIPROTWe used the ClueGo , a CytosWe used PRISM –47 serveS1 in An interface template consists of two chains of a PDB structures. The template is named with the combination of PDB ID and chain names. For example, in Figure In recent years, we applied PRISM algorithm on various signaling pathways and obtained reasonable structural models of the unknown interactions ,48,49. TPRISM uses template based prediction approach, and needs the 3D structure of the queried proteins. It cannot make estimation for a protein, which does not have a 3D structure deposited to PDB. Accordingly if an edge is not connecting two proteins whose 3D structures are available, PRISM will not be able to find results for that edge. Thus, via the PDB we searched for the 3D structural information of the proteins of lung metastasis sub-networks (LMSN) and brain metastasis sub-networks (BMSN). Brain metastasis network has 255 proteins and for 117 of them we found 1612 PDB structures. On the other hand, LMSN has 322 proteins and for 182 proteins we found 2712 PDB structures. In BMSN there are 58 interactions connecting proteins with known structure stored in the PDB (these interactions can be modeled with PRISM) and in LMSN there are 102 such interactions. This means that, we could only make models for these edges.We eliminated redundancy of similar structures corresponding to the same interface using TM-align. AccordiIn this experiment we have used 7922 interface templates (mined in 2006 from PDB) . We filtWe have used “uniprot_sprot.dat”  for detecting the source organisms of the PDB chains used for modeling the protein complexes in both metastasis networks.For significance testing, we have calculated the p-value of a hyper-geometric distribution using the R package. P-valueEvery protein-protein interface consists of two chains. The 7922 template interfaces used in the experiments, consist of 15844 template chains. Among them the source organism of 11255 were available in “uniprot_sprot.dat” and 4918 were coming from microorganisms (bacteria/virus).The protein interfaces that are available in PDB are clustered according to their structural similarity. These clusters are provided in PRINT database which can be accessed from the http://prism.ccbb.ku.edu.tr/interface/ address. We mentioned these structurally similar protein clusters as PRINT clusters all through the text.While detecting the source organisms of the template interfaces, we have taken into account all the interfaces, not only the representative interfaces (in each PRINT cluster). Besides, we have used the biological process and the molecular functions listed in UNIPROT database for our analyses.S1 in S2 in We have made use of UNIPROT and HPIDB databaseThe average node degree is 2.6 for BMSN and 2 for LMSN. Nodes with 12 or more edges are considered to be hubs.We obtained the available point mutations related with cancer from COSMIC  databaseWe used Naccess  for dete For the statistical calculations of location preferences of genetic variations we used fisher’s (exact) test and two-tailed P-value for statistical significance  as described in David et al.’s  article.Hot spots are the residues that contribute more to the binding free energy with respect to other residues in the protein-protein interface. We have used HotPoint  for hot We have used VMD  for visuWe have built a comprehensive human PPI network that consisted of 11,123 proteins and 149,931 interactions. We ranked each PPI in the network, according to its relevance to the seed nodes causing breast cancer metastasis, using GUILD (Genes Underlying Inheritance Linked Disorders) network-based prioritization tool .We defined a score threshold and discarded interactions below the threshold based on the following reasoning: 1) we need two comparable sets of nodes and edges for brain and lung metastasis, where the topology may be different but not the size; 2) predicting the interface structures of interacting proteins is a highly time-consuming step, therefore we needed to reduce the network to a limited sub-network of small but highly relevant edges (i.e. less than 500) for each metastasis under study.S2 in Figure S3 in  and Table 3). Accordingly, we selected 0.178 as the common GUILD cut-off score to generate both sub-networks. This cutoff yielded a BMSN with 255 nodes and 335 edges  in our analyses, we tracked down the evidence for the expressions of the genes that coded the proteins in both sub-networks in breast tissue. We found that 87% of the genes in the LMSN . Introducing structural knowledge to PPI networks adds an extra dimension of data to the representation. When we know how proteins are interacting structurally, we can detect multiple proteins trying to bind the same region on a protein surface. This extra knowledge may help us realize which interactions cannot happen concurrently. Besides, there may be protein pairs interacting via similar interface architectures. A drug targeting on any of these PPIs will have a high probability of targeting the others as well ,30, sincAmong the PPIs of the BMSN, only 4 of them had 3D structural data of the binary complex in PDB. Similarly, for LMSN, only 2 PPIs were found with the structure of the binary complex in PDB see 4. In orPRISM produces template-based predictions and it models the structure of an interaction based on the known 3D structure of two interacting proteins. The BMSN has 58 interactions with known 3D structures for both partners. LMSN has 102 such interactions. PRISM modeled 18 out of 58 interactions as a binary complex in the BMSN see 1a. For S7 in We should note that PRISM can model an interaction using structurally different interface templates or can use the same template interface to model different interacting protein pairs. Besides, a protein may be embodied with different chains (as identified in the PDB) or domains describing different portions or protein-states . Therefore, the interaction between two proteins can imply more than one interface region (i.e. produced by two or more pairs of domains) that may or may not occur at the same time. This would explain the causes for multiple interface predictions. On the other hand, template interfaces can be assigned to several interactions, some of them being common for different sub-networks or highly frequent in some sub-network. This arises a particular interest because it can explain a phenotype but also has implications on the putative use of drugs disrupting a particular set of interactions. As a consequence, for BMSN we obtained 32 predictions for 18 PPIs coming from 28 interface templates. Therefore, the average template interface frequency in BMSN is 1.14 (32/28). For LMSN, we obtained 99 predictions for 50 interactions and 75 out of 99 corresponded to different template interfaces. Thus, the average template interface frequency for LMSN is 1.32 (99/75). The numbers of occurrences of interfaces in both metastasis networks are shown in Table We studied the common template interfaces in the BMSN and LMSN. We observed top 3 high frequency template interfaces in the LMSN: 1) 2b8nAB 8 times, the interface extracted from the homodimer Glycerate kinase, putative. 2) 1jogCD 5 times, the interface extracted from the homodimer Uncharacterized protein HI_0074. 3) 2a6aAB 4 times, the interface extracted from the homodimer Peptidase M22 glycoprotease. We observed 4 template interfaces with less frequency (only in 2 PPIs) in the BMSN: 1) 2b8nAB (as for LMSN), 2) 1nqlAB, taken from the interface between EGFR-EGF, 3) 1qjcAB the interface extracted from the homodimer phosphopantetheine adenylyltransferase and 1moxAC (the interface between EGFR-TGFA). Interestingly, the 2b8nAB template interface is the most frequent interface in both sub-networks  and 59% of the models are coming from microbes in LMSN . Again, the protein complexes in LMSN, utilize more interface templates with microbial origin than the ones in brain network.Then we investigated the interactions modeled with templates of protein interactions found in microbes. 53% of the models are coming from microbial origin in BMSN 5, leftTable S1 in Table S2 in There are 14 proteins in BMSN whose interactions are modeled via templates originating from microbes. Seven out of these 14 proteins (Table S8 in  – Table S9 in File S1). Besides, in BMSN, 25% of the proteins modeled with non-microbial interface predictions are related with cell adhesion. Finally, 21% of the proteins in the LMSN use non-microbial interface architecture  to interact. Cell adhesion molecules play a significant role in cancer metastasis [Likewise, except 1nqlAB and 1moxAC templates, all the common interfaces observed in both metastasis sub-networks are coming from bacteria. The human proteins in our networks, which are using these frequent templates, have mostly cell adhesion biological process. Moreover, 50 % of all the proteins modeled with microbial templates in our sub-networks are related with cell adhesion  and L2 (between residues 312-481) domains . The intS12 in S13 in S14 in S15 in There are a number of EGFR complexes, one ERBB4 complex and one HBEGF complex available in PDB, while there are no EREG complexes. When we compare our model’s interface residues with the binding sites of the available PDB complexes, we see that they are all overlapping (see Table Position 102 in the amino-acid sequence of EREG acquires a SNP (p.R102L) in some cancer patients (derived from COSMIC database). This amino acid is on the interface region of EREG-ERBB4 interaction. Plus, this residue lies in the C-terminal of EREG that is known to be essential for its interactions with ErbB receptors. Moreover, ERBB4 acquires 5 different mutations that coincide with its interfaces. These mutations are observed in cancer patients (derived from COSMIC). Genetic variations p. L39F, p.A90T, p. 409L and p.V468F mutations are coinciding with ERBB4-EREG interactions. Furthermore p. L39F and p.N352S mutations are coinciding with ERBB4-HBEGF interaction. Additionally, 4 mutations of EGFR derived from COSMIC database are coinciding with its interactions. While p. D46N, p.Q432H and p.V441I are affecting EGFR-EREG interaction, p. R377S mutation is affecting both EGFR-EREG and EGFR-HBEGF interactions.These mutations may be making the mentioned interactions stronger or weaker but they are most probably changing the functions of the EREG, HBEGF, EGFR and ERBB4 proteins 6. BesidELANE interacts with CSF3 in BMSN, while it is switching its interaction partner to VCAM1 in LMSN. CSF3 is a seed gene in BMSN , while VELANE has a variants that coincides with its interfaces ). The variances in the amino acid 101, which are polymorphisms, coincides with one of the hotspots of the interface region between ELANE and CSF3 and the variances in the amino acids 98 (polymorphism) and 126 (unclassified variation) are inside the interface region of VCAM1 on ELANE 7. TheseIn this study we combined PPI networks, protein-protein interface structure and genetic variations together at the systems level to explain genotype-phenotype relationships. We have built two networks of proteins playing roles in different breast cancer metastasis and tried to explain the mechanisms behind metastasis process.We built a comprehensive human PPI network, by combining the available PPI data from various databases. Then we ranked all the interactions of this network according to their relevance to genes that are known to be mediating breast cancer to brain and lung metastasis. Subsequently, we formed two distinct metastasis PPI sub-networks from high ranked interactions. Next, we introduced structural knowledge to metastasis PPI sub-networks. Only a small proportion of our protein complexes were available in PDB. We modeled the interface structures of PPIs by using PRISM tool. Knowing the interface structure between two proteins and the residue numbers on the interface surface, allowed us checking whether the mutations are located in the interfaces or not. We preformed functional analysis on metastasis sub-networks and observed that the proteins engaged in LMSN are enriched in “Infectious Diseases”, “Cancer” and “Immune System” KEGG classes. This correlation pinpoints a relationship between pathogens, immune system and lung metastasis. This may be due to the fact that, brain is a better-protected area than the lung, due to the blood-brain barrier and being less exposed to outside world compared to lung. Besides, the protein complexes in LMSN utilize more interface templates found in PPIs in microbes than BMSN. This finding reinforces our conclusion about the relationship between lung metastasis progression and pathogens. Furthermore, we saw that in both metastasis sub-networks the proteins using microbial interface architectures are mostly related with cell adhesion. Cell adhesion is a very important mechanism for metastasis and our findings suggest that there may be some mechanistic commonalities, such as cell adhesion, between pathogens and metastatic cancer cells employed during cell invasion. Actually, most of these proteins have interactions with proteins of pathogens themselves. We provided structural predictions for the architecture of interfaces of interactions between EGFR-EREG, EGFR-HBEGF, ERBB4-EREG, ERBB4-HBEGF, ELANE-CSF3 and ELANE-VCAM1. Conclusively, we built two different breast cancer metastasis PPI sub-networks, and made use of protein structures to explain the phenotype-genotype relationships. These network models may provide a foundation for future studies and may also be helpful for finding escape pathways of breast cancer metastasis. File S1Supporting figures and tables.Figure S1, Structural enrichment of PPI networks with protein-protein interface predictions. FN1 and LTBP1 are predicted to be interacting via 1ywkAC template. This template is the interaction between A and C chains PDB ID: 1ywk complex. Figure S2, The increase in the number of interactions, as the number of GUILD score gets smaller. Figure S3, The increase in the number of interactions, as the number of nodes gets bigger. Table S1, Host-pathogen knowledge on proteins that use pathogenic interface architectures in BMSN. Table S2, Host-pathogen knowledge on proteins that use pathogenic interface architectures in LMSN. Table S3, The evidence for the presence of the genes of LMSN in different databases. Table S4, The evidence for the presence of the genes of BMSN in different databases. Table S5, The KEGG pathways enriched (P<0.05) in BMSN with respect to ClueGO p-value. Table S6, The KEGG pathways enriched (P<0.05) in LMSN with respect to ClueGO p-value. Table S7, The frequency of interfaces in both metastasis networks. Table S8, Proteins in BMSN that have PRISM interface predictions. Table S9, Proteins in LMSN that have PRISM interface predictions. Table S10, Distribution of the residue numbers and the mutation numbers per protein. Table S11, The total residues numbers/genetic variations observed in different locations and the odds ratio, 95% confidence interval, and the P-value for a two tailed test that OR is different from 1.0. Table S12, Interface residues (Sequence IDs) of HBEGF-EGFR model. The binding site residues of HBEGF protein’s complexes available in PDB and the binding site residues of EGFR protein’s complexes available in PDB. The interface residues that are overlaping with available binding site residues are in italic, bold fonts. Table S13, Interface residues (Sequence IDs) of EREG-EGFR model. The binding site residues of EGFR protein’s complexes available in PDB. The interface residues that are overlaping with available binding site residues are in italic, bold fonts. Table S14, Interface residues (Sequence IDs) of HBEGF-ERBB4 model. The binding site residues of HBEGF protein’s complexes available in PDB and the binding site residues of ERBB4 protein’s complexes available in PDB. The interface residues that are overlaping with available binding site residues are in italic, bold fonts. Table S15, Interface residues (Sequence IDs) of EREG-ERBB4 model. the binding site residues of ERBB4 protein’s complexes available in PDB. The interface residues that are overlaping with available binding site residues are in italic, bold fonts. Table S16, The interfaces in the 1jogCD PRINT cluster. Table S17, The biological processes of the proteins utilizing the most frequent interfaces of LMSN. Table S18, The molecular functions of the proteins utilizing the most frequent interfaces of LMSN. Table S19, The biological processes of the proteins utilizing the most frequent interfaces of BMSN. Table S20, The molecular functions of the proteins utilizing the most frequent interfaces of BMSN.(DOC)Click here for additional data file.