The success of genome-scale models (GEMs) can be attributed to the high-quality, bottom-up reconstructions of metabolic, protein synthesis, and transcriptional regulatory networks on an organism-specific basis. Such reconstructions are biochemically, genetically, and genomically structured knowledge bases that can be converted into a mathematical format to enable a myriad of computational biological studies. In recent years, genome-scale reconstructions have been extended to include protein structural information, which has opened up new vistas in systems biology research and empowered applications in structural systems biology and systems pharmacology.Escherichia coli and Thermotoga maritima. We show the utility of integrating molecular scale analyses with systems biology approaches by discussing several comparative analyses on the temperature dependence of growth, the distribution of protein fold families, substrate specificity, and characteristic features of whole cell proteomes. Finally, to aid in the grand challenge of big data to knowledge, we provide several explicit tutorials of how protein-related information can be linked to genome-scale models in a public GitHub repository (https://github.com/SBRG/GEMPro/tree/master/GEMPro_recon/).Here, we present the generation, application, and dissemination of genome-scale models with protein structures (GEM-PRO) for Translating genome-scale, protein-related information to structured data in the format of a GEM provides a direct mapping of gene to gene-product to protein structure to biochemical reaction to network states to phenotypic function. Integration of molecular-level details of individual proteins, such as their physical, chemical, and structural properties, further expands the description of biochemical network-level properties, and can ultimately influence how to model and predict whole cell phenotypes as well as perform comparative systems biology approaches to study differences between organisms. GEM-PRO offers insight into the physical embodiment of an organism’s genotype, and its use in this comparative framework enables exploration of adaptive strategies for these organisms, opening the door to many new lines of research. With these provided tools, tutorials, and background, the reader will be in a position to run GEM-PRO for their own purposes.The online version of this article (doi:10.1186/s12918-016-0271-6) contains supplementary material, which is available to authorized users. The success of genome-scale modeling can be attributed to high-quality, bottom-up reconstructions of metabolic, protein synthesis, and transcriptional regulatory networks on an organism-specific basis –4. Such The complementarity of molecular-level and systems-level data types has led to the integration of protein structurally-derived data into genome-scale models. Using genome-scale models of metabolism (GEMs), we link metabolic enzyme activities to characteristics of observed phenotypes, whereas using structural biology, we link molecular interaction details  to the activities of enzymes. The genome-scale models with protein structures (GEM-PRO) framework, therefore, gives a direct mapping of gene to transcript, to protein structure, to biochemical reaction, to network states, and finally to phenotype Fig. . Understin silico tools that increase data accessibility and training.In recent years, the number of publicly available biological macromolecule structures has grown to more than 110,000 entries, and continues to increase yearly by roughly 10 % . The incT. maritima and E. coli. For the generation and updating of GEM-PRO, we present a novel pipeline that systematically maps genes in a metabolic model to their respective high-quality structural data. We present four novel applications areas which demonstrate the utility of modeling at the intersection of systems and structural biology: (i) metabolic protein specificity; (ii) the relationship between protein complex stoichiometry and in vivo protein abundance; (iii) the diversity of bacterial proteomes; (iv) protein properties of growth rate-limiting reactions at high temperatures. Finally, for dissemination and training purposes, we distribute the GEM-PRO knowledgebase together with tutorials, which explicitly describe how GEM-PRO can address the following questions: (i) How are protein fold families distributed over metabolism? (ii) How does temperature, and hence protein instability, determine growth rate?Here, we address each of the above implications and demonstrate how linking protein structural data to GEMs enables the generation, dissemination, and application of GEM-PRO for studying two contemporary organisms, E. coli has increased substantially  and the iJO1366 . The worT. maritima and E. coli is between 30–45 %, which is 6–10 % higher compared to the original GEM-PRO reconstructions  and E. coli (iBM1366-GP) include 336 and 3425 PDB structures, respectively, an additional 5–10 % of newly deposited protein structures compared to the original versions  protein structures (PDB) for genes in ons Fig. . The updE. coli model to available I-TASSER homology models [T. maritima, we have performed homology modeling using the I-TASSER protocol to generate models for a total of 333 genes lacking experimental protein structure information. We find that the updated GEM-PRO models make use of over 100 recently deposited  experimental structures compared to the previous models . Small variations in sequence can have large-scale effects on the structure and function of proteins. Thus, we perform a rigorous assessment of the quality of all structural data for each model organism. To determine which experimental structures require further modeling  and sequence identical structures of proteins. To further assess the quality of protein structures in the updated GEM-PRO, we have evaluated all structures using PROCHECK [In the previous PROCHECK , which aPROCHECK . While tT. maritima and E. coli, respectively, require minimal modifications to revert the PDB sequence to the wild-type sequence. See Table 1 for details on average sequence identity and completeness.The final step in the workflow Fig.  carries in silico findings can then be tested with experiments to provide input into the next round of this iterative workflow .The overall coverage and quality of the selected experimental and homology-based structures for each organism is detailed in Table Once a GEM-PRO database has been constructed, it can be queried and used in conjunction with experimental data and genome-scale modeling approaches to understand the nature of the underlying biology. Here, we present four novel case studies which demonstrate how properties derived from the structures of proteins determine systems-level behavior.X) [Evaluating protein structural properties together with their binding capacities provides insight into structure-function relationships of isozymes and proteins that catalyze similar reactions. We are interested in using GEM-PRO to formulate hypotheses about which proteins are most likely to act promiscuously on substrates other than their native one . Assessing the degree of substrate ambiguity with EC numbers has been explored through evaluation of fourth digit of the enzyme commission number  . Here, wE. coli and T. maritima, we find that both organisms have a subset of multi-functionality genes ; in E. coli, 4.4 % (60) of metabolic genes are involved in multiple enzymatic complexes and in T. maritima, over 19 % (90) are multi-functional. Although T. maritima has a higher degree of multifunctional peptides, the number of reactions with isozymes is consistent with that of E. coli (~30 %).Many enzymes in the transaminase family are known to be capable of dual substrate recognition , 40. QueE. coli GEM-PRO model are representative holoenzymes (26 % of T. maritima genes). Surprisingly, we observe a large amount of metabolite binding versatility in E. coli, as 50 % of holoenzymes are experimentally shown to bind more than six different ligands , whereas lyases (EC 4.-.-.-.), are bound to the most structurally diverse set of substrates  also provide a wealth of information on different protein-ligand interactions, as they can be directly compared to existing enzyme-substrate interactions in the metabolic network. We analyzed proteins bound to a representative set of compounds present in metabolism . To filter the large majority of these cases from the dataset, we classified the types of ligands bound to protein structures, which clusters ligands using a fast heuristic graph-matching algorithm , 42. TheDoes protein complex stoichiometry determine in vivo enzyme abundance? Previous work using ribosome profiling techniques revealed that multi-protein complexes have proportional synthesis rates . This isE. coli [Information about the stoichiometry (or ratio) of genes in the respective enzyme complex  is found in organismal , 46 or pE. coli . The covE. coli . FurtherE. coli. As depicted in Fig. Coupling information from genome-scale reconstructions, known enzyme complex stoichiometry, and ribosomal profiling data, we can predict in vivo protein abundance in T. maritima and other Eubacteria [T. maritima is the most Archaea-like organism compared to other eubacterial species [functionally relevant sets of genes: homologous genes, genes that catalyze more than one reaction , genes that catalyze similar reactions  and genes with high sequence or structural similarity. Here, we apply GEM-PRO to address the question, “How different are bacterial proteomes and what are the main properties that distinguish them?”To date, there has been a great deal of attention placed on understanding the genetic differences between bacteria –63. Whol species –63, with species . Less at species , 66, andE. coli and T. maritima, is the spread of molecular motifs across metabolic proteins, which greatly distinguishes the two proteomes from one another. We used the Flexible structure AlignmenT by Chaining AFPs (Aligned Fragment Pairs) with Twists (FATCAT) [E. coli and T. maritima GEM-PRO models, we found a total of 874 and 197 unique domains (according to SCOP or PDB-based annotations), respectively, which span the whole of metabolism . We find that 36 domains are shared between T. maritima and E. coli .The first notable difference, when comparing GEM-PROs of (FATCAT)  algorith(FATCAT) . The obsα-helical and β-extended strand, solvent-accessible surface area and percentage of charged residues  of this cluster of proteins are membrane-bound proteins, which are known to have distinguishing exterior domains [α-helices and a neutral surface charge, compared to those proteins in other clusters.To understand whether the properties of entire proteomes are distinguishable between organisms, we carried out PCA on 29 computed secondary structural properties . Comparing the unique aspects of proteins within each of the clusters, we find that certain characteristic features distinguish proteins based on their metabolic roles as well as based on which organism they belong to. For most clusters, proteins belong to a single (or a select few) subsystem(s), which suggests that these features may play a role in self assembly and cellular localization. For example, comparing the second and third clusters (1 and 2), many of the members (over 70 %) function as transport proteins versus alternative carbon metabolism and cofactor biosynthesis (33 %). For differences between proteomes, we find that the first cluster (0) consists of only E. coli proteins . The novelty of this approach is that we can hypothesize which “hotspot” proteins are under selective pressure (on the basis of how important their function is to the entire metabolic network) and require adaptation to function at higher temperatures. High temperatures impose a heavy burden on organisms with respect to the functioning of cellular metabolism. Understanding the molecular basis for stability is necessary to grasp the the fundamental nature of protein structure as well as to engineer high-temperature industrial processes . In geneT. maritima homologs that set them apart from their E. coli counterparts, potentially allowing for functional proteins at higher temperatures. To begin, we focused on hotspot proteins in E coli, which are known to be growth-rate limiting at high temperatures. To identify  T. maritima homologs within the subset of hotspot proteins , we took advantage of the extensive database of both GEMs to effectively map between E. coli and T. maritima genes that have a similar sequence and metabolic function  based on the root-mean-squared-deviation (RMSD) of the protein backbones  and an alignment coverage of greater than 70 % of the total length of the protein. Surprisingly, we find that, out of 219 homologs, only 10 % (19) of E. coli genes share a structurally similar domain with their T. maritima homologs . Of the 10 % that are structurally similar, we linked their respective metabolic functions to amino acid biosynthesis, cofactor biosynthesis, or cell envelope biosynthesis. A few cases related to tRNA and methionine metabolism also show a high degree of structural similarity, despite low nucleotide sequence identity .Here, we are interested in the characterization of molecular properties of b subunit of atp synthase . Comparing the extremely stable thermophilic pgk with its less stable, mesophilic homolog reveals that this peptide correlates to proteins in cluster 2, whereas the thermophilic pgk correlates to proteins in cluster 1. The crystallographic structure of the thermophilic pgk shows increased rigidity from the many intramolecular contacts, alpha helices, and loop regions [T. maritima pgk is three times that of its E. coli counterpart (280 kDa versus 43 kDa), as it is a tetrameric fusion protein (pgkfus) of two enzymes, namely pgk and triosephosphate isomerase , illustrated in Fig. E. coli, confirming the authenticity of the two separable proteins and enzyme activities resulting from this gene in the mesophilic host [Particularly interesting cases pulled out from this analysis are those of 3-phosphoglycerate kinase  and the  regions  consistelic host . In thisT. maritima protein has a higher degree of buried, nonpolar residues that, on average, are less solvent exposed . In contrast, the E. coli peptide is much more solvent exposed and its residues are, on average, more polar or positively charged. A previous study, which characterized the chimeric soluble β polypeptides in vitro showed that the T. maritima protein melted cooperatively with a midpoint more than 20 °C higher than that of the E. coli sequence [E. coli peptide, showing which parts of the peptide tolerated the most change without a loss of function and which changes led to an increased thermostability. The structural differences brought out by this pairwise comparison are consistent with the fact that the average relative contact order (which correlates to solvent accessibility) of T. maritima proteins is significantly different than their close mesophilic homologs [A structural comparison of the β subunit of ATP synthase polypeptides indicates that the sequence . The stuhomologs .E. coli and T. maritima.Equally important to providing higher quality models is providing the community with complete knowledge bases, tools, and training examples for the continuous development of genome-scale modeling approaches. Historically, advances in genome-scale modeling have been accelerated by the wide dissemination of network reconstructions, modeling methods, and their continual curation and updating to incorporate new information. Furthermore, as GEM-PRO enables modeling of cellular processes that span a wide range of biological, chemical, and structural detail, input from different scientific disciplines could vastly enhance the capabilities of current methods and approaches used in systems biology. To make GEM-PRO accessible to a wide-range of scientific backgrounds, we present GEM-PRO workflows for these two contemporary organisms, As Additional file Protein structures and their molecular assemblies offer a wide range of possibilities to further enhance the predictive scope of genome-scale modeling by providing information on the sequence of molecular events in a pathway, how to interfere with a pathway to treat a pathology, or the evolutionary history of contemporary organisms. The further integration of protein-related data into metabolic network reconstructions will rely on clear mapping protocols and the development of bioinformatics tools that will aid in this process. This contribution, the bioinformatics tools, and the accompanying tutorials, which are based on constraint-based modeling methods through COBRApy , describin silico tools capable of identifying isozyme activity on a genome-scale. Recently, a number of studies have emerged [The dissemination of the GEM-PRO modeling framework is likely to broadly impact work in a wide array of disciplines, including structural biology, computational chemistry, systems biology, and biotechnology. The ability to characterize the structural, chemical, and binding characteristics of metabolic proteins in different organisms also enables the further development of  emerged , 81, 82  emerged , 84 for  emerged , 85, 86. emerged –89, wherFinally, GEM-PRO models offer insight into the physical embodiment of an organism’s genotype and provides a new way to compare genomes by linking genes to their encoded gene product, to the protein’s structure, and finally, to the reaction catalyzed by that protein (or its molecular assembly). The use of GEM-PRO models as a comparative systems biology approach demonstrates that important aspects of the functional differences between organisms  are not only derived from differences in their genetic components but also from the physical interactions of their molecular components. Together with previous applications on the phylogenomic analysis of protein structure , global Incorporating protein-related information into a GEM involves four stages of semi-automated curation: (i) map the genes of the organism to available experimental protein structures, found in publicly available databases, such as the Protein Data Bank (PDB); (ii) determine genes with and without available protein structures and perform homology modeling using the I-TASSER suite of programs  to fill E. coli at different temperatures; (iii) predicting the effects of antibacterial drugs in E. coli; (iv) the discovery of patterns in fold families distributed across the metabolic network in E. coli and (v) the discovery of ligand similarity and potential for promiscuity in the metabolism E. coli. The tutorials provided in Supporting Information are designed in such a way that aids the user to properly access information in the GEM-PRO database, easily reproduce previously reported findings and organize information into meaningful representations. The main objective of the designed framework is to assist in (i) mapping between useful and unique identifiers; (ii) locate and query various data sources and (iii) identify fruitful and meaningful associations between the disparate datasets. We provide tutorial-like IPython notebooks as a means to organize the output of the database into easily manageable and understandable modules. Such a framework is the first of its kind for constraint-based modeling and provides full details that can be reproduced and updated as new data becomes available. For more details, see the Additional file In the Supporting Information, we provide discrete examples of how to use the expanded metabolic network reconstructions with protein information to predict cellular phenotypes, which include (i) the discovery of multimeric properties of metabolic enzymes; (ii) the predicted growth of The I-TASSER protocol is described by the following steps: (i) for each protein of interest, homologous templates are identified and used to assemble the queried protein; (ii) modified Monte Carlo based replica exchange simulations are performed to cluster the lowest-free energy states of the assembled structure; (iii) the fragment-based assembly simulation is performed a second time to further refine the model and remove steric clashes; (iv) the function of the query protein is inferred by structurally matching the predicted 3D models against the proteins of known structure and function in the PDB. In order to assess the quality of the predicted structure, the accuracy is predicted from a confidence score (C-score or TM-score), which is defined based on the quality of the threading alignments and the convergence of the assembly refinement simulation used in steps ii and iii. I-TASSER is capable of generating multiple model predictions with a rank-ordered C-score. For more details about I-TASSER, please refer to the published literature .http://pfam.sanger.ac.uk/ and http://pfam.xfam.org/). This information allows for the classification of proteins via amino acid sequence into distinct protein families who share domain architecture through the HMMER suite of programs [The database currently maintains 14,831 manually curated entries in the current release and is accessible via web servers  were determined using both experimental and predicted values for the melting temperatures of proteins. The two main sources of this experimental data were taken from ProTherm [iJO1366 model of E. coli, we find low coverage of temperature related data . Thus, the experimentally determined TM values were supplemented with predicted TM using a previously published method [M which derived from the amino acid sequence.Temperature-related properties of proteins , ovality (SASA/Nres 2/3), residue depth (distance of the C atom from the protein surface), percent of the total structure that is nonpolar, polar, positively charged, or negatively charged, and percentage of the surface/buried residues that are nonpolar, polar, positively charged, or negatively charged. SASA was calculated according to the algorithm of Lee and Richards [2 are assigned as surface residues. The residue depth has been calculated for all atoms in the entire protein based on Michel Sanner's Molecular Surface (MSMS) method [We calculate 29 physical properties of the protein to construct a multidimensional data matrix, including solvent-accessible surface area (SASA), number of total contacts, disulfide bond distance (SS-bond), percent of the protein that is buried, percent of the protein that is on the surface, secondary structure composition  method  and is eE. coli. Excel file containing GEM-PRO related information for E. coli (Additional file Table 01: GEM-PRO master dataframe. All reactions, genes, sequence and structure ID mappings.Table 02: Enzyme complex information for the associated reaction.Table 02a: Updates to the previous complex information available in 2013.Table 03: 3D structural properties of all representative structures per gene.Table 03a: 3D structural properties of all homology models.Table 04: PFAM retrieved and computed properties.Table 05: Structural quality of PDB structures, including PSQS and PROCHECK scores.Table 06: Structural quality of homology, including TM-scores, C-scores, PSQS, and PROCHECK scores.Database S1: T. maritima. Excel file containing GEM-PRO related information for T. maritima (Additional file Table 01: GEM-PRO master dataframe. All reactions, genes, sequence and structure ID mappings.Table 02: Enzyme complex information for the associated reaction.Table 03: 3D structural properties of all representative structures per gene.Table 04: PFAM retrieved and computed properties.Table 05: Structural quality of PDB structures, including PSQS and PROCHECK scores.Table 06: Structural quality of homology, including TM-scores, C-scores, PSQS, and PROCHECK scores.Database S2: https://github.com/SBRG/GEMPro/tree/master/GEMPro_recon/.Dataframes, mapping files, analysis scripts, tutorials and other documentation have been uploaded to a public Github repository and are available at: I.https://github.com/SBRG/GEMPro/blob/master/GEMPro_recon/Ecoli/tutorials/Protein_Fold_Familes.ipynb, https://github.com/SBRG/GEMPro/blob/master/GEMPro_recon/Tmaritima/tutorials/Protein_Fold_Familes.ipynb.Understanding evolutionary relationships of fold families in metabolism: II.https://github.com/SBRG/GEMPro/blob/master/GEMPro_recon/Ecoli/tutorials/Temperature_Dependent_Growth_Prediction.ipynb.Predicting growth rate at various temperatures: III.https://github.com/SBRG/GEMPro/blob/master/GEMPro_recon/Ecoli/tutorials/Classify_PDB_Ligands.ipynb, https://github.com/SBRG/GEMPro/blob/master/GEMPro_recon/Tmaritima/tutorials/Classify_PDB_Mols.ipynb. Classify and characterize the co-crystallized ligands in GEM-PRO: IV.https://github.com/SBRG/GEMPro/blob/master/GEMPro_recon/Ecoli/tutorials/Complex_Stoichiometry.ipynb, https://github.com/SBRG/GEMPro/blob/master/GEMPro_recon/Tmaritima/tutorials/Complex_Stoichiometry.ipynb. Protein complex stoichiometry for M-Model enzymes: Four iPython tutorial notebooks are hosted in the same Git repository and are available for viewing:


Phenotype-based high-throughput screening is a useful technique for identifying drug candidate compounds that have a desired phenotype. However, the molecular mechanisms of the hit compounds remain unknown, and substantial effort is required to identify the target proteins associated with the phenotype.In this study, we propose a new method to predict target proteins of drug candidate compounds based on drug-induced gene expression data in Connectivity Map and a machine learning classification technique, which we call the “transcriptomic approach.”Unlike existing methods such as the chemogenomic approach, the transcriptomic approach enabled the prediction of target proteins without dependence on prior knowledge of compound chemical structures. The prediction accuracy of the chemogenomic approach was highly depended on compounds structure similarities in data sets. In contrast, the prediction accuracy of the transcriptomic approach was maintained at a sufficient level, even for benchmark data consisting of structurally diverse compounds.The transcriptomic approach reported here is expected to be a useful tool for structure-independent prediction of target proteins for drug candidate compounds. Phenotype-based high-throughput screening (PHTS) is a useful technique for identifying drug candidate compounds that have a desired phenotype; thus, it is a prominent part of the drug development process . Howeverin silico methods for compound target prediction have been proposed in the context of chemogenomics, where target prediction is based on compound structures and protein sequences as well as pre-existing knowledge from databases about known compound–protein interactions [Recently, a variety of ractions –7. Chemoractions –10. AlthRecent advances in transcriptome technologies  have enabled us to measure the expression profiles of all human genes at low cost, and several databases containing gene expression data have been constructed worldwide –13. ConnIn this study, we propose a new method to predict target proteins of drug candidate compounds, termed the “transcriptomic approach,” which is based on drug-induced gene expression data in CMap with a machine learning classification technique. We compare the performance of the transcriptomic approach with that of the chemogenomic approach, which is based on chemical structures and protein sequences, and we show that the transcriptomic approach can predict target proteins independent of data on compound chemical structures. The prediction accuracy of the transcriptomic approach was maintained at a sufficient level, even for benchmark data consisting of structurally diverse compounds. Therefore, the transcriptomic approach is expected to be useful for predicting target proteins of drug candidate compounds in a chemical structure-independent manner.CMap (build 02) is a collection of 6,100 gene expression profiles for 13,469 human genes from four cell lines  treated with 1,309 bioactive small compounds. The CEL files of CMap were downloaded from the database website . The CMaThe gene expression similarities of compounds and of proteins  were evaluated by using Pearson’s correlation coefficients on the row and column profiles of the gene expression matrix, respectively. The expression profile of each compound is a real-valued feature vector, so we used Pearson’s correlation coefficient for "compound expression similarities", and the expression profile of each protein is a real-valued feature vector, so we used Pearson’s correlation coefficient for "protein expression similarities". In fact, Pearson’s correlation coefficient is a standard similarity measure in transcriptomic data analysis.The compound structures used in CMap were obtained from ChemBank , 24. Theμ M in IC50), as well as independent information about compound–target interactions stored in KEGG [The information about compound–target interactions was obtained from DrugBank , 31 and  in KEGG , Matador in KEGG , and PDS in KEGG .The most straightforward method for compound target prediction involves directly using the fold change values of the associated compound–protein pairs in the gene expression matrix, X. The rationale for using this method is that drug-affected genes are highly variable in the observed gene expression. Thus, high scoring compound–protein pairs are predicted to be candidates for interacting compound–protein pairs. In this study, this method is referred to as the “direct method.”We tested three possible scores for compound–protein pairs: i) X, ii) –X, and iii) abs (X). For the X score, the original fold change values of X are used, and higher scores correspond to up-regulated genes. For the –X score, the sign-inversed values of X are used, and higher scores correspond to down-regulated genes. For the abs (X) score, the absolute values of X are used, and higher scores correspond to up- or down-regulated genes. Compound-protein pairs with high scores in i) X, ii) –X, and iii) abs (X) are predicted to be candidates of interacting compound-protein pairs.Another method for compound target prediction involves comparing the gene expression profiles of query compounds with those of other compounds of known target proteins. For this method, the rationale is that drugs with similar gene expression patterns are likely to share common target proteins.We attempted to solve the problem of compound target prediction by supervised classification, which consists of the following two steps. First, a classifier for discriminating interacting compound–protein pairs from the other pairs is learned based on partially known compound–protein interactions. Second, the learned classifier is applied to new compound–protein pairs, and high scoring compound–protein pairs are predicted to be candidates for interacting pairs. In this study, this method is referred to as the “classification method.” The related classification algorithms or their variants have also been used in the context of chemogenomics in several previous studies –7.nx × ny compound–protein pairs  , f should be estimated to predict whether a compound x’ interacts with a protein y’. Among the pair classification algorithms, we used pairwise kernel regression (PKR) [kcompound for compounds and a similarity function kprotein for proteins; thus, a statistical model of PKR for a given compound–protein pair  is defined as follows:Given a training set of on (PKR)  because βij is a weight parameter to be optimized based on the training set. In practice, high-scoring compound–protein pairs are predicted as the interacting pairs. The inputs of the PKR model are similarity scores for both compound pairs and protein pairs. Therefore, the performance depends heavily on the similarities of compounds and proteins.In the transcriptomic approach, we used compound expression similarity (Pearson’s correlation) for “compound similarity” and protein expression similarity (Pearson’s correlation) for “protein similarity”. In the chemogenomic approach, we used chemical structure similarity  for “compound similarity” and protein sequence similarity  for “protein similarity”. In the integrative approach, we used the average of compound expression similarity and compound structure similarity for “compound similarity” and the average of protein expression similarity and protein sequence similarity for “protein similarity”.We evaluated the predictive performance by using the receiver operating characteristic (ROC) curve and Precision-Recall (PR) curve, which are plots of true positive rates against false positive rates based on various thresholds (ROC) and precision  against recall (sensitivity) based on various thresholds for the prediction score (PR), respectively. We computed the area under the ROC curve , where 1 is returned for a perfect inference and 0.5 is returned for a random inference, and the area under the PR curve , where 1 is returned for a perfect inference and the ratio of positive examples against all samples in the gold standard data is returned for a random inference. We used the ROCR package in the R language. In the program, many threshold values  were prepared for the predictions scores, the true positive rates against the false positive rates at each threshold value were plotted, and the ROC curve was drawn by connecting the dots. The AUC and AUPR scores were computed for each compound, and the average scores were used to summarize the results.We also evaluated the performance of top ranked predictions by determining whether known target proteins appeared in the top 10 or top 50 high-scoring predictions. The accuracy of top ranked predictions is important in practice because experimental validation will be preferentially conducted on high-confidence predictions rather than low-confidence predictions. We computed the ratio of the number of correctly predicted compounds with at least one known target protein in the top 10 or top 50 against the number of all compounds of known targets in the gold standard data. We denote the ratio scores for top 10 and top 50 as “Top10 ratio” and “Top50 ratio,” respectively. AUC/AUPR represents the global accuracy of all compounds in each benchmark dataset, while Top10/Top50 represents the local accuracy of top compounds in each benchmark dataset. Note that the prediction accuracy scores were evaluated from different viewpoints.The classification method requires supervised learning with pre-existing knowledge about known compound–protein interactions. To evaluate the predictive performance of the classification method, we performed a cross-validation experiment using the gold standard compound–protein interaction data. This experiment was designed to simulate the practical situation in which researchers are required to predict the potential target proteins of new drug candidate compounds.We performed the following 5-fold cross-validation. (i) We randomly split compounds into five compound subsets. (ii) We took each compound subset as test compounds, and constructed a test set of compound–protein pairs. (iii) We took the remaining compound subsets as training compounds, and constructed a training set of compound–protein pairs. (iv) We trained a classifier using the training set, and applied it to the test set. (v) We computed the prediction scores for compound–protein pairs in the test set, and evaluated the prediction accuracy over the 5-fold process. It should be noted that only compounds were split into the training and test sets, and the list of proteins was common across both sets.The gold standard dataset contained many drugs that are structurally almost identical because some of them are derivatives of the same lead compound. If these identical drugs were divided into a training set and a test set, the prediction in the cross-validation experiment would be trivial. To avoid overestimation of the prediction accuracy, we therefore performed filtering of similar drugs based on their chemical structures. Hence, we used only diverse drugs that were structurally different to some extent.The process proceeded as follows. First, we calculated the generalized Jaccard coefficient  of chemical descriptors for all compounds. Second, we identified the compounds that shared high Jaccard coefficients and selected one representative compound based on a threshold. Third, we constructed a set of representative compounds that shared low Jaccard coefficients. Finally, we prepared seven sets of benchmark data consisting of representative drugs by gradually varying the chemical similarity threshold  on the dendrogram. When the threshold is <0.4, the number of drug clusters is very small; thus, it was not possible to test thresholds of 0.1–0.3 in the 5-fold cross-validation.As a preliminary analysis, we examined the correlation between drug-induced gene expressions and compound chemical structures or target protein sequences. We also analyzed the distributions of compound expression similarities, compound structure similarities, protein expression similarities, and protein sequence similarities. Figure We tested the direct method with three scoring schemes for drug-induced gene expression values, namely X, –X, and, abs(X), and evaluated their correspondences to known compound–protein interactions using the gold standard compound–protein interaction data. Based on the distributions of AUC scores for compounds when using X, –X, and, abs(X), the abs(X) score showed the superior performance, with X and –X resulting in almost random inference Fig. . NegativWe tested the classification method with compound expression similarities and protein expression similarities as the inputs of the PKR model, and we evaluated its performance in compound target prediction by performing 5-fold cross-validation experiments on the gold standard data. In addition, we compared the predictive performance of the classification method with that of the direct method. Based on a comparison of the AUC and AUPR scores, and Top10 and Top50 ratios, from the direct method with those obtained from the classification method Fig. , the claWe compared the performance of the transcriptomic approach with that of chemogenomic approach . Figure We also investigated the effects of integrating the transcriptomic approach with the chemogenomic approach, which we called the “integrative approach.” Note that the input in the integrative approach is the average of the compound expression similarity and compound structure similarity. As shown in Fig. Finally, we conducted a comprehensive prediction of unknown compound–protein interactions involving 1,294 compounds and 13,469 proteins-coding genes in CMap. We trained a predictive model using all known compound–protein interactions in the gold standard data, and predicted unknown compound–protein interactions. Consequently, by using the transcriptomic, chemogenomic, and integrative approaches, we predicted potential off-targets for 756 compounds of the known targets (compounds present in the gold standard data) and potential target profiles for 538 uncharacterized compounds of unknown targets (compounds absent from the gold standard data).We investigated the validity of the top 1,000 high-scoring predictions by using independent resources on compound–protein interactions and the latest information in KEGG, DrugBank, Matador, ChEMBL, and PDSP Ki. As a result, we confirmed the validity of 150 predictions from the transcriptomic approach and 186 predictions from the chemogenomic approach and 213 predictions from integrative approach. Figure Figure In contrast, more than 50 prediction pairs from the chemogenomic approach were proteins associated with metabolism of drugs. For example, the top ranked target proteins corresponded mainly to drug metabolizing enzymes , which are known to have low substrate specificity and to bind many compounds with ubiquitous properties such as large dipole, negative charge, oxygen-richness, and aromatic rings , 38. TheIn this study, we examined the performance of computational methods for CMap-based compound target prediction. We observed that the direct method worked poorly, and was only slightly better than random inference. This suggests that chemically perturbed genes do not always correspond to target proteins of a query compound and the direct method is therefore not useful in practice. In contrast, the classification method performed well, which indicates that comparing gene expression profiles among a query compound and other compounds of known targets is more useful than simply selecting the regulated genes in the gene expression profile of the query compound.The previously developed methods for predicting drug/compound targets can be mainly categorized into gene expression-based methods such as CMap and chemical structure-based methods such as the chemogenomic approach. However, to the best of our knowledge, the relationship between the two types of methods has not previously been investigated; therefore, this study was the first to systematically compare the usefulness of gene expression and chemical structure information in the context of compound target prediction . Our results showed little correlations between gene expression and chemical structure similarities, which suggests that the two resources are complementary.We also observed that the chemogenomic approach worked poorly when test compounds did not share sufficient similarity in chemical structure, whereas the transcriptomic approach maintained prediction accuracy even without this similarity. Thus, the performance of the transcriptomic approach is apparently independent of compound chemical structures, and this property has practical importance for predicting the targets of compounds that have novel chemical structures.We observed that the correctly predicted compound–protein interaction pairs with high confidence scores differed depending on the approach used. For example, the top predictions from the chemogenomic approach contained low substrate-specific proteins . This might be because a variety of compounds that interact with such enzymes were included in the training data, and such predictions therefore may have arisen from the dependency on various chemical structures of the enzyme ligands. This property might also have contributed to the observation that the predictions from the chemogenomic approach were more accurate than those of the transcriptomic approach in the cross-validation experiment with full benchmark data derived from a chemical similarity threshold of 1. In contrast to those of the chemogenomic approach, the top predictions from the transcriptomic approach did not contain low substrate-specific proteins, but instead contained many proteins associated with the mechanism of drug actions . This implies that the transcriptomic approach is more likely to predict target proteins that affect the expression of genes at downstream pathways rather than predict drug-metabolizing enzymes that have no effect on gene expression. Because the transcriptomic and chemogenomic approaches have different properties in terms of compound target prediction, care should be taken to choose the appropriate approach depending on the research objective.The majority methods for drug/compound target prediction are based on chemical structure, including the chemogenomic approach. One reason for its popularity is the huge number of compound structures that can be easily obtained at a low cost from the many molecular databases such as DrugBank and ChEMBL. Conversely, considerable experimental costs are required to obtain drug-induced gene expression profiles. The CMap project has changed this situation somewhat by providing a large number of drug-induced gene expression profiles, and has thereby enabled the comparison of compounds in terms of their gene expression . BecauseIn this paper, we proposed a new method to predict target proteins of drug candidate compounds based on drug-induced gene expression data from CMap and a machine learning classification technique. We compared the performance of this method, termed the transcriptomic approach, with that of the chemogenomic approach, and we found that it does not depend on compound chemical structures and is suitable for predicting target proteins that cannot be expected from analyzing such structures. To the best of our knowledge, this is the first study to report the relationship between a transcriptomic approach and a chemogenomic approach to target prediction. In practice, the novel transcriptomic approach described here is expected to be particularly useful for predicting target proteins for drug candidate compounds in a chemical structure-independent manner.


The importance of this objective originates from the fact that protein complexes are key players in most cellular processes. The more complexes we identify, the better we can understand normal as well as abnormal molecular events. Up till now, various computational methods were designed for this purpose. However, despite their notable performance, questions arise regarding potential ways to improve them, in addition to ameliorative guidelines to introduce novel approaches. A close interpretation leads to the assent that the way in which protein interaction networks are initially viewed should be adjusted. These networks are dynamic in reality and it is necessary to consider this fact to enhance the detection of protein complexes. In this paper, we present “DyCluster”, a framework to model the dynamic aspect of protein interaction networks by incorporating gene expression data, through biclustering techniques, prior to applying complex-detection algorithms. The experimental results show that DyCluster leads to higher numbers of correctly-detected complexes with better evaluation scores. The high accuracy achieved by DyCluster in detecting protein complexes is a valid argument in favor of the proposed method. DyCluster is also able to detect biologically meaningful protein groups. The code and datasets used in the study are downloadable from  G =  is conventionally used to represent proteins V and their interconnections E as nodes and edges, respectively. Such representation was, and still is, the basis of many computational methods seeking to accurately describe and to identify enclosed protein-complex structures. A large number of these methods are based on the assumption that protein complexes correspond to dense and highly-interconnected sub-graphs. Among those methods, we here point out: Markov Clustering (MCL) 47] and Ik-means . The parcAT tool  to visuaFor the step consisting of pruning the PPI data at the biclusters levels, we adopted the PE method  with defk-core parameter, here set to 2, filters out clusters that do not contain a maximally inter-connected sub-cluster of at least degree k; and the maximum depth parameter which limits the distance from the seed node within which the algorithm can search for cluster members from seed was set to 3. Added to that, the generated sets of detected complexes were examined and refined as follows: if two complexes have a number of overlapping members greater than 75% of the size of the smaller complex; and if the members of the first complex interact with at least 50% of the members of the second complex, then they are merged.Given a protein interaction network, CMC generates maximal cliques which may overlap. The highly-overlapping ones, i.e. with overlap greater than a specified threshold, are examined for possible merging if their degree of inter-connectivity exceeds a merging threshold. The overlap and merging thresholds were set to 0.75 and 0.5, respectively. For MCODE: the degree cutoff for a node to be scored was set to 2; the node score cutoff was set to 0.2, i.e. a node can be added to a cluster (complex) only if its score is no more than 20% less than the score of the seed node of the cluster; the m predicted complexes and n reference complexes, the corresponding formulas are shown in tij represents the number of proteins that are found in both predicted complex m and reference complex n.The quality scores, used to evaluate our approach, included: (a) the number of complexes in the reference catalogue that are matched with at least one of the predicted complexes with an overlap score, OS ≥ 0.2; (b) the clustering-wise sensitivity (Sn) and (c) the clustering-wise positive predictive value (PPV) used to calculate the matching quality, mainly in terms of the correctly-matched protein members among the detected complexes; (d) the geometric accuracy (Acc) which is the geometric mean of Sn and PPV; and (e) the maximum matching ratio (MMR) which measures the maximal one-to-one mapping between predicted and reference complexes by dividing the total weight of the maximum matching with the number of reference complexes. Given k-means clustering algorithm, one at a time. The PPIs corresponding to the proteins contained in each of the resulting biclusters were extracted from the specified yeast PPI dataset and were pruned using PE technique. The protein complex-detection methods, listed above, were applied on the generated biclusters. Finally, the detected sets of complexes were merged, filtered and matched against the CYC2008 reference catalogue.According to the presented framework, the gene expression dataset, GSE3431, was processed by the three biclustering algorithms, OPSM, CC and ISA, and by the In order to observe the advantage of our approach, As the experimental results show, the incorporation of gene expression data in the process of detecting protein complexes in dynamic PPI networks is indeed beneficial, in contrast with the outcomes of detecting complexes in static networks. On one hand, it could notably increase the number of matched complexes, as it is the case for ProRank, ProRank+ and ClusterONE. We note here that the total number of detected complexes increased. Nevertheless, the quality scores, which depend on this number and the number of matched complexes as well, were slightly better. The former underlines the effectiveness and the potential of our framework in terms of increasing the number of matches while also ameliorating the quality of the detected entities. Here, we recall the need to develop a more suitable approach for merging, filtering and refining the identified sets of complexes (the last step of the presented framework) which would potentially lead to enhanced evaluation scores. On the other hand, biclustering genes based on their expression patterns could significantly reduce the large number of complexes detected by some algorithms, such as CMC and CFinder, while not compromising the quality of the results.We also examine the statistical significance of the improvements in the evaluation metrics . To do that, we perform a paired t-test to compare the results of just applying each complex-detection method on the PPI data, i.e. scores in each row of k-means clustering algorithm accentuate the improvement which can be gained by incorporating gene expression information to model the dynamics of PPI interactions and to detect protein complexes in PPI networks accordingly.The conveyed study validates the enhancement of protein complex-detection approaches by integrating gene expression data, particularly through biclustering techniques. The framework models the dynamic aspect of PPI networks by grouping proteins according to the similarities of their expression patterns across subsets of conditions. Moreover, our method is not restricted by single threshold imposition on gene expression levels. As mentioned earlier, biclustering approaches are better than conventional clustering methods when it comes to expression data analysis , 46. Non2 Profiler PCR Array profiles the expression of 84 key genes (available at http://www.sabiosciences.com/rt_pcr_product/HTML/PARN-012Z.html) involved in programmed cell death. Apoptosis plays a critical role in normal biological processes requiring cell removal including differentiation, development, and homeostasis. Similarly, the Rat Inflammatory Cytokines and Receptors RT2 Profiler PCR Array profiles the expression of another 84 key genes (available at http://www.sabiosciences.com/rt_pcr_product/HTML/PARN-011Z.html) mediating the inflammatory response. Acute inflammation occurs in response to cell damage due to infection or injury. During this process, cellular and plasma derived factors encourage extravasation, the recruitment of circulating immune cells into the affected tissue. The two set of genes which are relevant to liver cancer are then combined and housekeeping genes and redundant genes are removed. Monitoring the expression of these genes helps to determine the mechanisms behind programmed cell death. The genes are then processed using String 9.1 [In order to further test the effectiveness of the presented framework in identifying biologically related group of genes/proteins, we selected 140 pathway-focused genes implicated in programmed cell death in Rat Apoptosis and inflammation. The Rat Apoptosis RTring 9.1  (Search In this experimental work, the gene expression dataset, of accession number GSE17384, was downloaded from the GEO  reposito2 Profiler PCR Array Rat Apoptosis, PARN-012A. The ProRank+ algorithm was employed to detect the corresponding protein entities/complexes. Then, we examined the generated results for potential matching with the reference sub-networks/groups generated using String. The PPI dataset was deduced from two sets of genes involved in apoptosis (RTk-means clustering algorithm accentuate the improvement which can be gained by incorporating gene expression information to model the dynamics of PPI interactions and to detect protein complexes in PPI networks accordingly. Finally, the produced results based on the case study shown DyCluster was tested using several biclustering techniques and various protein complex detection methods. As the experimental results show, the incorporation of gene expression data in the process of detecting protein complexes in dynamic PPI networks is indeed beneficial, in contrast with the detection of complexes in static networks. DyCluster is a framework for the detection of protein complexes in dynamic protein interaction networks modeled by incorporating gene expression data, through biclustering techniques. It responds to the important shift from interpreting PPI data as a single static network to modeling and exploring the dynamic nature of these networks. That is done by incorporating gene expression data, interpreted using biclustering techniques, in the interaction networks and detecting complexes accordingly. The experimental results greatly favor our approach which allows the correct identification of more protein complexes. Moreover, in cases where this is not attained, the overall number of detected complexes is decreased and this leads to better evaluation scores. Hypothetically, the more biological information is added to PPI networks, the better the interaction dynamics are reflected. Therefore, and based on our results, further extensions consist of refining the modeling of PPI dynamics using additional biological data types.


Identification and characterization of these interactions are fundamental to elucidating the molecular mechanisms of signal transduction and metabolic pathways at both the cellular and systemic levels. Although a number of experimental and computational studies have been performed on model organisms, the studies exploring and investigating PPIs in tomatoes remain lacking. Here, we developed a Predicted Tomato Interactome Resource (PTIR), based on experimentally determined orthologous interactions in six model organisms. The reliability of individual PPIs was also evaluated by shared gene ontology (GO) terms, co-evolution, co-expression, co-localization and available domain-domain interactions (DDIs). Currently, the PTIR covers 357,946 non-redundant PPIs among 10,626 proteins, including 12,291 high-confidence, 226,553 medium-confidence, and 119,102 low-confidence interactions. These interactions are expected to cover 30.6% of the entire tomato proteome and possess a reasonable distribution. In addition, ten randomly selected PPIs were verified using yeast two-hybrid (Y2H) screening or a bimolecular fluorescence complementation (BiFC) assay. The PTIR was constructed and implemented as a dedicated database and is available at  The increasing number of complete genome sequences has revealed the entire structure and composition of proteins, based mainly on theoretical predictions utilizing their corresponding DNA sequences. Although proteins are essential parts of organisms and participate in virtually every process within a cell, this annotation is only mapped in one dimension. In fact, proteins, as vital macromolecules, rarely act alone. At both the cellular and systemic levels, almost all the molecular processes involve a large number of protein-protein interactions (PPIs). Consequently, PPIs form a two-dimensional network to perform complex cellular functions and relay information between the environment, the cell and the genomeArabidopsis thaliana4Caenorhabditis elegans6Drosophila melanogaster8Homo sapiens1011Saccharomyces cerevisiae131820Arabidopsis thaliana2324Oryza sativaBrassica rapaZea maysPopulus trichocarpaMany efforts have been made to chart PPIs. In several model organisms, including While interologs-based approaches for predicting protein interactions have been successfully developed and applied to many species232627303129332535Arabidopsis thaliana), nematode worm (Caenorhabditis elegans), fruit fly (Drosophila melanogaster), human (Homo sapiens), rice (Oryza sativa), and yeast (Saccharomyces cerevisiae). To date, we have obtained 357,946 non-redundant tomato PPIs  among 10,626 proteins. These interactions are expected to cover 30.6% of the entire tomato proteome and possess a high level of accuracy. To facilitate further research, we have developed and characterized a searchable database called the Predicted Tomato Interactome Resource (PTIR). The PTIR features a user-friendly interface that allows individuals to search the database, browse the information and visualize the data. This resource and the related documents are freely accessible at http://bdg.hfut.edu.cn/ptir/index.html.Considering the scale of experimental PPI data and the high risk of error propagation, we have constructed a predicted tomato interactome by identifying potential PPIs from interacting orthologs in Arabidopsis  the number of species  the interactions were predicted from, (2) the number of species  in which the two interacting proteins evolved together, (3) the number of gene ontology (GO) terms (out of three) shared by each pair of interacting proteins, (4) whether the domains of the proteins have the potential to interact, (5) whether the subcellular localization of the proteins are available for interaction, and (6) whether the protein pairs are co-expressed. Based on these assessments, 12,291 high-confidence interactions , 226,553 medium-confidence interactions , and 119,102 low-confidence interactions  were identified. The logarithmic distributions of the more elaborate statistical scores (between 1 and 12) are displayed in Next, interacting proteins of the predicted tomato interactome along with their connections were loaded into the network-building program CYTOSCAPE40Brassica rapa , pipes (two interactions), and hubs (multiple interactions) of different sizes  . As disp23ica rapa . These cAdditionally, the proteins in large hubs (including major and super hubs) and free ends were assigned to molecular function and biological process categories according to the GO annotation from the Gene Ontology Consortium  and S343The initial interaction datasets used for our prediction were downloaded from the IntAct database. Although these interactions were characterized experimentally, they were originally generated from a range of different approaches, such as various experimental detection methods, observed evidence, and interaction typesInteracting protein pairs often co-evolve because they need to perform a given function together or disappear from evolution because they cannot work individually47Arabidopsis thalianaBrassica rapaDomains are the main functional and structural units of proteinsSubcellular localization is a process by which proteins are targeted to a specific location within a cell, such as the nucleus, cytoplasm or cell membrane. During interactions, the interacting proteins are generally co-localized in the same subcellular locationProteins that exhibit interactions may display similar dynamic or static patterns of gene expression under various experimental conditionsr) to the expression data for each PPI of the predicted tomato interactome (see the Methods section). Each interaction was given a co-expression score (CS) for the possibility of protein interactions. A total of 349,794 PPIs in our predicted tomato interactome were identified using the CS value, and only 8,152 (approximately 2.3%) interactions had no expression information in all 96 Gene Expression Omnibus (GEO) samples  two-hybrid analysisNicotiana Benthamiana) plant leaves by transiently co-expressing the putatively interacted partners. BiFC has been widely applied to identify PPIs at the genome level via enhanced yellow fluorescent protein-based reconstructionYeast (http://bdg.hfut.edu.cn/ptir/search.html) . The general process of data identification, integration, annotation, statistics and database development is illustrated in the Methods section. Thereafter, users could find the PPIs of interest through simply entering a Sol ID or UniProt AC. Three search categories are provided: (1) Single Search; (2) Pair Search; and (3) Batch Search (ch.html) .The single search option provides an interface for querying the PTIR with accession numbers (Sol ID or UniProt AC) or the keywords of gene/protein names. The full name and abbreviation are both feasible, where they are automatically normalized with synonyms.The pair search options are accession number centric. Users can find the potential PPIs with their identifiers of Sol ID and UniProt AC. Two identifiers in the text boxes should be consistent. Only if these two proteins interact with each other, the records will be found.The batch search option allows users to input a list of protein identifiers (Sol ID or UniProt AC), or to upload a file containing accession numbers. Before submitting, the algorithms in the settings should be specified to show the interactions between the proteins involved or the interactions involving any one of the proteins.After searching, the results are shown in a tabular format, containing PTIR AC, Sol ID (protein A), UniProt AC (protein A), Protein name (protein A), Sol ID (protein B), UniProt AC (protein B), Protein name (protein B) . From thr) for biological processes, molecular functions and cellular components between these two protein datasets were 0.9985  in the PTIR were higher than those in the tomato proteome; conversely, the proportions of other terms (pigment and stress response) were lower than in the tomato proteome . These dr = 0.11, P-value < 0.01). By combining these approaches, we identified 12,291 high scores , 226,553 medium scores (between 2 and 7), and 119,102 low scores (less than 2). In the PTIR, there are four predicted interactions that overlap with the 16 experimentally reported interactions collected in the IntAct database. This proportion (25%) is in good agreement with the statistical value of 26.44% in PAIRAnimals far outnumber plants in the reference species. This imbalance not only reduced the number of predicted plant-specific complexes but also affected the final scores of the predicted PPIs. In our study, a complementary approach was applied to assess the reliability of each PPI based on computational methods, such as shared GO terms, co-evolution, co-expression, co-localization, and available DDIs. The comparison analysis of the construction method and assessment approach showed that they are totally independent events . This result confirms the usefulness of the PTIR for predicting protein function using the guilt-by-association strategy and the feasibility of predicting the functionality of unannotated proteins based on their interactions.To evaluate the accuracy of the protein function predictions in the PTIR, a total of 7,695 proteins annotated by GO terms in the biological process category were used as testing data. Among them, 695 proteins were randomly selected as the targets with their annotations manually removed and the remaining 7,000 proteins were used as known proteins for prediction. Subsequently, the interacting partners of these target proteins were identified. Using the guilt-by-association strategy70P values were detected using the hypergometric test in the GO Term FinderTherefore, this strategy was used to predict the most possible functional terms of proteins labeled “Unknown Protein” in the PTIR. The significantly enriched terms and their individual A case study of DNA damage-binding protein 1 , revealed 124 interacting partners . Most fa757677Another case study of protein function prediction is also proved to be successful by integrating information from existing studies. A previously reported FR databaseIn summary, the PTIR was constructed and implemented as an easy-to-use affordable Web-based tool for the analysis of the tomato PIN, based on the evolutionary conservation of interacting proteins and their interactions across species. Each identified PPI was assigned a confidence score according to the total value of the sequence information and biological function. This increases the validity and reliability of the interactome. Although the PTIR still has limited coverage of the tomato interactome, it is sufficient to provide comprehensive information on the highly conserved protein networks and shed light on the functions of protein interactions. Collectively, these protein interactions could be used by both theorists and experimentalists to reassemble protein complexes, expand existing pathways and enrich genome annotation, thereby improving the understanding of biological processes at the systems level.http://www.ebi.ac.uk/intact/)http://inparanoid.sbc.su.se/cgi-bin/index.cgi)82The interactome datasets of Arabidopsis , nematode worm , fruit fly , human , rice (699 pairs) and yeast  were downloaded from the IntAct database  were obtained from Tomato Genetics Resource Center . The tomato plants were germinated and grown in a greenhouse under artificial conditions . The harvested tomato tissues, including the young leaves, flowers and fruits at various developmental stages, were immediately frozen in liquid nitrogen and stored at −80 °C prior to nucleic acid isolation and gene cloning.The tomatoes , PIRSF, Pfam, SUPFAM, and Prosite annotation] and the interaction map . The general process is outlined in http://www.uniprot.org/). Only the entries with labels in the following evidence codes were reservedProtein subcellular localization data were first obtained from the UniProt databaseThe transcriptome datasets of the tomato gene transcription profiles were downloaded from the GEO repository and Sequence Read Archive (SRA) database. They were generated using high-throughput technologies, such as microarrays and RNA-seq, and were derived from various samples covering different tissues, developmental stages, stress treatments and mutants. In total, 96 samples were gathered and calculated according to their expression levels. Because not all the proteins harbored in our predicted tomato interactome were found in every sample, we separately graded each PPI of each sample:1 and X2 represent the two values of each member in an interacting protein pair, where γ) was assigned according to the value (φ):Here, Xwhere 1 indicates that the gene expression patterns of the two interacting proteins are consistent, and 0 means that they are inconsistent. The final CS for the confidence of the PPIs in our predicted interactome was the average N is the number of expression samples (96 here). The values of CS range from 0 to 1, and a high value indicates a high-confidence protein interaction, and a low value indicates a low-confidence protein interaction.where Current Protocols in Protein ScienceThe Y2H assays were performed using the MATCHMAKER GAL4 Two-Hybrid System III according to a procedure described in N. benthamiana) plant leaves by Agrobacterium-mediated infiltrationThe coding sequences of six target genes were amplified with gene-specific primer sets harboring multiple restriction sites . The PCRHow to cite this article: Yue, J. et al. PTIR: Predicted Tomato Interactome Resource. Sci. Rep.6, 25047; doi: 10.1038/srep25047 (2016).


Among other effects, post-translational modifications (PTMs) have been shown to exert their function via the modulation of protein-protein interactions. For twelve different main PTM-types and associated subtypes and across 9 diverse species, we investigated whether particular PTM-types are associated with proteins with specific and possibly “strategic” placements in the network of all protein interactions by determining informative network-theoretic properties. Proteins undergoing a PTM were observed to engage in more interactions and positioned in more central locations than non-PTM proteins. Among the twelve considered PTM-types, phosphorylated proteins were identified most consistently as being situated in central network locations and with the broadest interaction spectrum to proteins carrying other PTM-types, while glycosylated proteins are preferentially located at the network periphery. For the human interactome, proteins undergoing sumoylation or proteolytic cleavage were found with the most characteristic network properties. PTM-type-specific protein interaction network (PIN) properties can be rationalized with regard to the function of the respective PTM-carrying proteins. For example, glycosylation sites were found enriched in proteins with plasma membrane localizations and transporter or receptor activity, which generally have fewer interacting partners. The involvement in disease processes of human proteins undergoing PTMs was also found associated with characteristic PIN properties. By integrating global protein interaction networks and specific PTMs, our study offers a novel approach to unraveling the role of PTMs in cellular processes. The function of proteins is frequently modulated by chemical modifications introduced after translation from RNA. These post-translational modifications (PTMs) have been shown to also influence the interaction between proteins carrying them. We tested whether specific PTM-types characterized by attaching different chemical groups are associated with proteins with characteristic and possibly strategic positions within the network of all protein interactions in cellular systems. Based on network-theoretic analyses of PTMs in the context of protein interaction networks of nine selected species, we indeed observed distinctive properties of twelve PTM-types tested. Phosphorylation was found associated with proteins in central locations with the broadest interaction scope, while glycosylation was more prominent in proteins at the periphery of the web of all protein interactions. The involvement in disease processes of human proteins undergoing PTMs was also found associated with characteristic protein interaction network properties. Our study highlights common and specific roles of the various PTM types in the orchestration of molecular interactions in cells. While t UniProt . With te UniProt . Several UniProt , dbPTM [ UniProt , PTMCura UniProt , PTMcode UniProt  and many UniProt –14 offerMycoplasma pneumoniae [Many studies on PTMs have focused on specific types and their relevance for protein function with phosphorylation representing the most actively researched PTM-type –19. Moreeumoniae . Furthereumoniae . A more eumoniae . Carboxyeumoniae .In addition to PTMs, protein function is also regulated and mediated by non-covalent protein-protein interactions –33. As mArabidopsis thaliana [Therefore, it appears plausible that proteins carrying a particular PTM-type may possess specific interaction characteristics. Indeed, for the important and intensively investigated PTM-type phosphorylation, it was found that in yeast, phospho-proteins engage in many more protein-protein interaction than proteins without phosphorylation sites , found sthaliana . Thus, pAs the different PTMs modify proteins in specific ways, it appears furthermore likely that their consequences on protein-protein interactions may be different as well. This hypothesis formed the starting point for the present study. Specifically, we asked whether different PTM-types are associated with characteristic protein-protein network properties, such as interaction degree, clustering coefficient, and closeness centrality, for those proteins carrying them. We selected these three properties as they each reflect on potential functional role such as scope of impact (degree), diversity of responses (clustering coefficient), and placement within a possible signaling cascade . Furthermore, we investigated whether those characteristics are conserved across different species, and if the particular functions the different PTMs fulfil may become apparent when inspected from the viewpoint of protein-protein interactions.Homo sapiens, Mus musculus, Rattus norvegicus, Bos taurus), an invertebrate (Caenorhabditis elegans), the insect Drosophila melanogaster, fungi , and the plant Arabidopsis thaliana  or N-linked (arginine and asparagine residues) attachments of simple or complex carbohydrates  to proteins. Upon Hydroxylation, hydroxyl groups are attached to proline residues. Methylation refers to transfer of methyl groups to arginine or lysine residues. Nitrosylation leads to the incorporation of nitric oxide into the thiol group of cysteine residues. Protein phosphorylation is associated with attaching phosphate groups to serine, threonine, or tyrosine protein residues. Both sumoylation and ubiquitination are characterized by the attachment of small proteins to target proteins modifying their function or stability or, in the case of ubiquitination, tagging them for degradation. While the previous 10 PTM-types are characterized by the attachment of chemical moieties to proteins, disulfide bond and proteolytic cleavage lead to posttranslational modifications via changing the chemical bond structure within a given protein and in the process removing atoms from it, either by forming a covalent bond  or by breaking peptide bonds (proteolytic cleavage removing HOH). As the latter two met our established count-criteria and do indeed modify the protein, we retained them for the initial analyses. However, as data were available for human only, and to confine the analyses to moiety-addition-type PTMs, both PTM-types were left out in the subsequent analyses of network properties. For several PTM-types, several subtypes exist, e.g. Ser/Thr/Tyr phosphorylation. Generally, we considered PTM-types based on the added moiety, but also repeated selected analyzes with a further subsetting of the datasets based on the receiving group on the protein.To base our analyses on high-confidence PTM-instances, we restricted our analyses to PTM-sites that have been identified experimentally leaving out all annotated PTMs that are based on computational predictions alone. PTM-site information was collected from 11 different database resources and consolidated into a single set via sequence position information. Currently available datasets proved sufficiently large to conduct statistical analyses on the role of PTMs in the context of protein interaction networks for the following twelve PTM-types: acetylation, amidation, carboxylation, disulfide bond, glycosylation, hydroxylation, methylation, nitrosylation, phosphorylation, proteolytic cleavage, sumoylation, and ubiquitination associated with a set of nine diverse eukaryotic species covering several lineages and kingdoms: mammals  across the nine selected species were first investigated for PTM-type specific biological process involvement based on GO-term enrichment statistics as well as probed for significant co-occurrence patterns of different PTM-types on the same protein. Subsequently, we investigated whether protein sets associated with particular types of PTMs exhibit characteristic protein interaction network (PIN) properties. For the latter, we computed three basic and commonly used network properties, the degree, the clustering coefficient, and the closeness centrality, associated with proteins belonging to different PTM-specific protein sets when mapped onto the species-specific PIN. Finally, we tested whether pairs of interacting proteins exhibit preferences with regard to PTM-types they carry.Frequently, proteins are modified not only by a single PTM event, but by several and of different PTM-types. Thus, if we wish to understand the role of individual PTM-types in the context of protein-protein interactions, we first need to understand their co-occurrence on the same protein as well as their functional profile as it seems plausible that PTM-types associated with similar functional involvement will also exhibit similar characteristics with regard to their protein interactions.The functional significance of specific PTM-types and the respective proteins carrying them has been amply investigated , 36, 37.The co-occurrence patterns of different PTM-types on the same protein are critical confounding factors for the analysis of the individual PTM-types in the context of protein interactions. Evidently, frequent PTM-types will have a greater chance of co-occurring with other PTM-types on the same protein. Indeed, as judged by the Jaccard-distance, protein sets in human associated with phosphorylation, acetylation, and ubiquitination—the three PTM-types with the most observed instances —exhibit In conclusion, the overlap of different PTM-types on the same proteins is extensive and greater than expected by chance. Even though suggested by the separate clustering of PTM-types based on their functional and location annotations , with redegree quantifies the average number of connections a protein engages in. Thus, it reflects on how many interaction partners may be affected by a PTM of a given protein. The clustering coefficient allows estimating whether the proteins connected to a central reference protein are in turn connected amongst themselves. High clustering coefficients would indicate a closely knit network of local interactions, whereas low clustering coefficients would suggest that separate molecular processes with little communication between them are modulated, when a central protein undergoes a PTM. Finally, the closeness centrality allows assessing how centrally a particular protein resides relative to the overall network. Proteins with high closeness centrality are situated in central network positions such that they may serve central information relay functions. By contrast, low closeness centrality corresponds to peripheral locations as typical of initial receptor molecules.  of the nine selected species . By compHomo sapiens. Overall, all PTM-types exhibit a tendency to have higher degrees, lower clustering coefficients, and higher closeness centralities than protein sets not carrying the respective PTM-type, which includes a set of human proteins  currently not known to undergo any of the 12 PTM-types considered here. The latter set (no PTM), was observed with lower degree, higher clustering coefficient, and lower closeness centrality than proteins undergoing a PTM  showing slightly increased values for STRING-based PINs and decreased values in the remaining species. However, clearly more table cells are colored blue signifying lowered values than for the other PTM types further supported by negative fold-change values for IntAct PINs. Thus, overall, glycosylation appears to generally be associated with no significantly changed, or slightly lowered closeness centrality relative to control protein sets.Despite these data limitations, a similar overall picture emerges as observed in human. All examined PTM-types appear to be associated with proteins with increased degree and closeness centrality, but decreased clustering coefficient relative to protein sets not carrying the respective PTM-type. Glycosylation is a notable exception and seems associated with slightly decreased, rather than increased, degree centrality, while exhibiting similarly decreased clustering coefficients across all species as the other PTM-types. With regard to closeness centrality, the results for glycosylation are mixed with a few species  and their combinatorial interplay has attracted considerable attention in recent years –27. In tGeneralized across all PTM-types and species investigated here, PTM-carrying proteins appear engage in more physical contacts, with a reduced clustering coefficient among those proteins they are interacting with, and elevated closeness centrality than their respective protein sets devoid of the particular PTM-type  or that,Focusing on human PIN and PTM data, sumoylation and proteolytic cleavage stand out as being associated with the largest relative increase of degree and closeness centrality relative to reference sets. Proteolytic cleavage has been associated with activation processes and protein targeting events  and constitute a “dramatic” modification as the relative change of molecular composition of a protein can be significant. Furthermore, transporting proteins to different compartments will inevitably influence the possible interaction scope. The significance of sumoylation in a range of regulatory processes has been increasingly recognized . Our resPhosphorylation, the PTM-type with the largest data support, was identified as the PTM-type with the consistently central and with the largest potential influence scope . PhosphoBy contrast, glycosylation was found associated with proteins of low degree, low clustering coefficient, and low closeness centrality . In partAs shown in By including nine species from different kingdoms and lineages, we aimed to extract both general and species/lineage-specific trends. However, currently available datasets proved comprehensive enough for a few species only . In the case of phosphorylation, sufficient data were available across all nine species and provided a consistent result of increased degree and closeness centrality and a decreased clustering coefficient .The increased likelihood of a functional association of proteins with high interaction degree and their involvement in human disease has been reported before , 53. In Evidently, this study hinges on the completeness and accuracy of the available PTM and PIN data as well. Any bias towards a specific detection of particular protein classes and their associated PTM may further skew our results. By imposing a high significance cutoff for the PIN-data (confidence score > 0.9), and furthermore exploiting two data sources (STRING and IntAct), we believe to have taken proper precautionary steps even though some discrepancies were detected . Howeverr = 0.164) was detected suggesting that more heavily phosphorylated proteins occupy more central positions in the network of protein-protein interactions.A possible selection bias may also come from preferentially profiling those proteins for PTMs that possess “interesting” properties such has high degree. However, as PTMs are increasingly identified in massive, “shotgun” style omics studies, such selection bias may not be that critical. Rather, abundance may be a concern then. However, for phosphorylation it was reported that protein abundance is not correlated with network properties , 35. FurIn conclusion, proteins carrying different types of PTMs differ from average non-PTM-proteins and differ between each other with regard to their protein interaction characteristics. Thus, their location within the web of physical protein-protein interactions is not only non-random, but very likely indicates their specific functional roles in the orchestration of molecular processes mediated by the physical interactions between proteins.Post-translational modifications. Post-translational modifications annotated as “experimentally verified” and the associated proteins were extracted from UniProt [average(number of PTM sites)+3*sd(number of PTM sites)) were discarded as well. Twelve PTM types met those criteria in at least one species including 10 PTM-types in which a chemical moiety is attached: acetylation, amidation, carboxylation, glycosylation, hydroxylation, methylation, nitrosylation, phosphorylation, sumoylation, ubiquitination, and two PTM-types that modify the protein via forming or breaking bonds within the protein: disulfide bond and proteolytic cleavage. Nine species met those criteria for at least one PTM-type including representatives from the animal , plant , and fungal  kingdom, respectively . Then, protein sets were filtered further and referred to as “one-PTM-type-only” . For staProtein-protein interaction networks. For the nine species selected based on available PTM information, high confidence (confidence score>=0.9) protein-protein interactions (PINs) were extracted from STRING (version 9.05) [on 9.05) . In ordeon 9.05) , which sNetwork properties. The PINs associated with the selected species correspond to undirected networks. The degree of a node n is the number of edges linked to n [clustering coefficient [n is defined as. Cn = 2en/(kn(kn - 1)), where kn is the number of neighbors of n and. en is the number of connected pairs between all neighbors of n. It reflects the connectivity of adjacent nodes. The closeness centrality Cc(n) of a node n is defined as the reciprocal of the sum of shortest path length originating from n to all other nodes m [n and m. It ranges between 0 and 1. It corresponds to the inverse of the number of steps needed to traverse from all other nodes in the network to a selected node. The R package “igraph” (http://cran.r-project.org/web/packages/igraph/index.html) was used to compute the above mentioned network properties.ked to n . The clufficient  of a nod nodes m : Cc(n)=1p-values were corrected for multiple testing considering as the total number of tests all tests across species, all PTM-types for each network property. In all cases of multiple testing correction, the FDR method was used [For each PTM-type and across the selected species, the three selected network properties  were computed for all proteins carrying the respective PTM-type and compared to those proteins not carrying this particular PTM-type. Significant differences between the respective two distributions were detected based on a Mann–Whitney test. The was used . SignifiP-value was set to 0.01.For all species selected in this study, the available genome gene ontology (GO) process, function and cellular compartment annotations were extracted from UniProtKB-GOA  as the rA,SB)|/|Union|, where the set of proteins associated with A and B are denoted as SA and SB. Fisher’s exact test was used to test the significance of co-existence.For each pair of PTMs (A and B) in one species, The Jaccard index for the co-existence of A and B is defined as |Intersection as of May, 2014. In order to test the overlap between human diseases proteins with proteins associated with high (top 25%) or low (bottom 25%) network property values, Fisher’s exact test was used for all PTM-types. The p-values were corrected for multiple testing (FDR) across all PTM-types and network properties.Human disease proteins were downloaded from OMIM  threshold indicating significance was set to 0.01.The top five GO-terms were included that were found significantly enriched for each PTM-type. Each element in the heat map  represents the grey-scale-encoded p-value, in which a particular combination of PTM-type and GO-term was found significantly enriched. To he combined whole UniProtKB-GOA for all the selected species was used as the background set, Fisher’s exact test with FDR correction was used for the enrichment analysis, and the (TIF)Click here for additional data file.S2 Figp-value 0.05, **: p-value 0.01) according to a Mann-Whitney test.The red (blue) asterisks on the top of violin plot represents the corresponding non-PTM group has a significantly higher (lower) median value compared to the non-PTM group (*: (TIF)Click here for additional data file.S3 Figp<0.05 (p<0.01) by Mann-Whitney test with FDR correction, the values in red or blue text represent significantly higher or lower network properties which are inconsistent with the background color.The species are ordered according to their phylogenetic relationships as shown on the left. For every PTM-type, the log-2 of fold difference value for the degree/clustering coefficient/closeness centrality value relative to the respective value associated with proteins not carrying this particular PTM-type are given for PINs based on STRING and IntAct, respectively. Color scale indicates increased (red) or decreased (blue) values in the PTM-set relative to the non-PTM-set with symmetric color intervals  Bold-font (underlined) fold-changes indicate significant fold-changes at (TIF)Click here for additional data file.S4 Figp<0.05 (p<0.01) by Mann-Whitney test with FDR correction, the values in red or blue text represent significantly higher or lower network properties, which are inconsistent with the background color based on mean (not median) values.Protein sets were selected to contain one PTM-type only (one-PTM-type-only dataset). The species are ordered according to their phylogenetic relationships as shown on the left. For every PTM-type, the log-2 of fold difference value for the degree/clustering coefficient/closeness centrality value relative to the respective value associated with proteins not carrying this particular PTM-type are given for PINs based on STRING and IntAct, respectively. Color scale indicates increased (red) or decreased (blue) values in the PTM-set relative to the non-PTM-set with symmetric color intervals  Bold-font (underlined) fold-changes indicate significant fold-changes at (TIF)Click here for additional data file.S5 FigIncreased frequencies of protein-protein interactions  carrying the respective PTM-types relative to expectation. Line width is proportional to the number of species, which exhibit significant interactions of PTM-types carried by interacting proteins. The contingency table for the Fisher exact test contained the respective counts for number of proteins associated with a particular PTM-pair versus all alternative pairings and whether they have been reported to interact or not with applied FDR-corrected p-value threshold of <0.01.(TIF)Click here for additional data file.S6 Figr,  were: degree r = 0.056 (1.46E-06), clustering coefficient: r = -0.068 (6.27E-08), closeness centrality: r = 0.164 (0.00).The red line connects the mean network property values of proteins associated with different numbers of PTMs. Associated Pearson linear correlation coefficients, (TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.