Mycobacterium tuberculosis is an infectious bacterium posing serious threats to human health. Due to the difficulty in performing molecular biology experiments to detect protein interactions, reconstruction of a protein interaction map of M. tuberculosis by computational methods will provide crucial information to understand the biological processes in the pathogenic microorganism, as well as provide the framework upon which new therapeutic approaches can be developed.M. tuberculosis protein interaction network by machine learning and ortholog-based methods. Firstly, we built a support vector machine (SVM) method to infer the protein interactions of M. tuberculosis H37Rv by gene sequence information. We tested our predictors in Escherichia coli and mapped the genetic codon features underlying its protein interactions to M. tuberculosis. Moreover, the documented interactions of 14 other species were mapped to the interactome of M. tuberculosis by the interolog method. The ensemble protein interactions were validated by various functional relationships, i.e., gene coexpression, evolutionary relationship and functional similarity, extracted from heterogeneous data sources. The accuracy and validation demonstrate the effectiveness and efficiency of our framework.In this paper, we constructed an integrated M. tuberculosis is inferred from genetic codons and interologs. The prediction accuracy and numerically experimental validation demonstrate the effectiveness and efficiency of our method. Furthermore, our methods can be straightforwardly extended to infer the protein interactions of other bacterial species.A protein interaction map of  M. tuberculosis which causes tuberculosis affecting lungs and other organs is the second largest cause of death from infectious diseases [M. tuberculosis can lead to more comprehensive screens of cellular operations. In this context, development of approaches to infer its interactome will contribute to identifying infectious mechanisms, detecting important drug target proteins and promoting potential therapy innovations. To date, genome-wide experimental and computational systems for studying PPIs in M. tuberculosis are unavailable [M. tuberculosis. E. coli is one of the best model systems to study bacterial physiology [diseases . An extevailable . It is nysiology , with reysiology . It is bysiology . The intysiology ,7, and aysiology .H. sapiens, S. cerevisiae and E. coli [M. tuberculosis. Thus, a novel integration method is necessary to be developed. In this work, we provided cross-species PPI predictions in M. tuberculosis by integrating different types of protein interaction information of other species. Genetic information in the form of codons, i.e. tri-nucleotide sequences, are translated into proteins [Compared with other methods, sequence-based prediction methods are superior for their simple requirement on the data, which could be implemented when the species have completely sequenced genomes. There were some studies that are based on sequence information have been successfully performed on PPI prediction of some model organisms such as  E. coli ,9-12. Hoproteins . It is wproteins . The codM. tuberculosis. The codon features of interacting protein pairs are detected and used to train an SVM classifier. Then the interactome of M. tuberculosis is predicted by the codon-based method. Moreover, the interactions from 14 other species are mapped to M. tuberculosis by the interolog method. The available data from multiple levels including gene coexpression, evolutionary relationship and functional similarity are implemented to assess these predicted interactions by confidence significance. The evidence from various sources validates the effectiveness of our method. The network properties of the constructed protein-interaction map are also identified. The predicted protein interaction network as well as the proposed method provide a framework for the functional specificities study of M. tuberculosis.In this work, we developed a systematic method combining heterogeneous data sources to infer a comprehensive protein interaction map for E. coli is one of the best characterized organisms [M. tuberculosis. The positive and negative sets of protein interactions in E. coli were designed to test the performance of our codon-based prediction methods. The genome and proteome of E. coli were downloaded and prepared for the interacting sets as well as all known opening reading frames (ORFs) [c is defined asrganisms ,4 and wes (ORFs) . The disif(c) and jf(c) are relative frequencies of codon c in ORF i and ORF j. By codon definition, ∑k if(kc) = 1 and ∑k jf(kc) = 1 for k = 1, 2,..., 64 in all codons. There are 14058 pairs of interactions and 27882 pairs of non-interactions in 4227 proteins of E. coli. A five-fold cross validation process is implemented in these pairs, i.e., we train the SVM classifier based on the related codons in the 80% interacting pairs forming the training part and test the prediction in the rest part. Figure E. coli. These results provide pieces of evidence for the effectiveness and efficiency of predicting protein interactions from the genetic codons by machine learning method.where M. tuberculosis, we used the formerly trained SVM classifier to infer the interactions of M. tuberculosis by the codon message of ORFs in gene sequence level. Based on the genetic codons of the laboratory strain H37Rv of M. tuberculosis, we predicted 12,899 interactions in 3,266 proteins. Furthermore, the known protein interactions of other species were mapped to the proteome of M. tuberculosis by interolog method. We collected the documented interactions of 14 species from PPI databases, IntAct and DIP, and the sequence features of interacting proteins were transferred into the M. tuberculosis proteome by ortholog detection. Table M. tuberculosis from various databases, such as BIND [M. tuberculosis. The inferred protein interaction map of M. tuberculosis is shown in Figure To explore protein interactions in  as BIND  and Reac as BIND . CombiniM. tuberculosis species, we got these available heterogeneous data sources to annotate every predicted interacting pairs.Interacting protein pairs have been identified with close relationship of gene coexpression , coevoluP-value = 4.69 × 10-3, Mann-Whitney U test). Secondly, we identified the evolutionary relationship of the interacting proteins by the clusters of orthologous group (COG) information. The interacting proteins were detected in their own COG individually. Figure P-value = 0.53) is not significant, while every predicted interaction gets a confidence value of evolutionary relationship. Thirdly, we calculated the functional similarities underlying these predicted interactions. We detected the semantic similarity between the gene ontology (GO) term pairs of interacting proteins. We have considered the hierarchical structure of GO directed acyclic graph and the specificity of GO terms in the identification. We identified the functional relationships between predicted interacting proteins by three ontology categories, i.e. cellular component ('CC'), molecular function ('MF') and biological process ('BP'), respectively. The boxplots of the three values of GO similarities in the random pairwise proteins and that in predicted pairs are shown in Figure P-values are 6.63 ×10-4, < 2.2 ×10-16 and < 2.2 ×10-16, respectively), which further provides evidence for the effectiveness of our methods. After annotating from various information, we provide the evaluation confidence to every predicted interactions.Firstly, we annotated the predicted interacting protein pairs by their corresponding Pearson's correlation coefficient (PCC) of gene coexpression. For comparison, we calculated the corresponding correlation values of these same-size random selected protein pairs. Every prediction was then annotated by a coexpression value in gene expression profiling. Figure M. tuberculosis. The hub proteins as well as interested proteins can be selected to analyze for particular dysfunctions of M. tuberculosis. From the validations of gene coexpression, evolutionary relationship in COGs and functional similarity, we can check and filter out those pairs consistently included in various level information by evaluating the reliability of interactions. We then calculated the features in the filtered network by omitting the pairs with lower confidence values, while we kept the predictions when there are no available evaluations for them. We also identified the distribution of node degree and found that the constructed protein interaction network satisfied the topology features of complex networks [γ value is asymptotically in the range 1 <γ < 2 in the power-law distribution fitting. There are 477 hub proteins in the protein interaction map when the degree threshold is 50. The hub proteins from different thresholds can be found in Additional file For global views of the protein-interaction map, we identified the topological features of the integrated protein interaction map and the features of particular interactions. Firstly, we detected the original features in the primary constructed network by machine learning and interologs combined with the known interacting protein pairs. The measures of degree distributions, clustering coefficients, characteristic shortest path and network diameter are identified individually. Table networks . The proM. tuberculosis by machine learning and interologs. We obtained the interaction features of genetic codon underlying interacting proteins in relatively well-established interactome of E. coli. The features of genetic codons of interacting proteins of E. coli were mapped to the proteome of M. tuberculosis by training an SVM classifier. The cross validation showed the effectiveness and efficiency of our predictor. We also implemented the interolog method to map the documented protein interactions of other organisms into M. tuberculosis. Moreover, the available functional genomic information about M. tuberculosis has been used to evaluate the predicted interactions. These heterogeneous data were combined in a novel framework to infer the interactions in M. tuberculosis. The predicted pairs were checked and can be filtered with these information for potential applications. The constructed protein interaction network of M. tuberculosis provides more information for the infectious bacterium threatening human health.In this work, we proposed a method to build the protein-interaction map in M. tuberculosis, we integrated the heterogeneous information in an alternative framework for assessing the predictions rather than predicting the interactions. Filtering interactions by different confidence values result in different networks of different size and reliability. This will provide valuable resources for biological information in tuberculosis research, which implies the promising applications based on our constructed protein interaction map, which are our future research topics.We used multiple sources of available functional genomic data to provide evaluation of these predicted interactions. Gene coexpression, evolutionary relationship and functional similarity are implemented to check the reliability in the targeted pairs. The information could be directly used to build the functional relationship of protein pairs -25. Due M. tuberculosis. For completeness, we collected some known curated interactions. We also tested the predictions in these known interactions in M. tuberculosis by training the codon features to check our predictions. Figure In our framework, we proposed a cross-species prediction by mapping the documented interactions of other species into M. tuberculosis, i.e., the SVM-based machine learning method and the interolog mapping method. The two methods are essentially close-related. The gene sequence information of interacting pair of proteins has been learned by the predictor and that of these known interactions is mapped to the protein pairs of M. tuberculosis. In the same manner, the interolog method identifies the interaction between a pair of proteins which have interacting homologs in another organism. The protein sequence information of known interaction is mapped by the cross-species sequence similarity detection. It is an interesting research topic to identify the quantitative relationship between the prediction results of the two methods. The various mapping schemes of the sequence information have been integrated in our predictions. The gene sequence information as well as the protein sequence information is exploited to infer the protein-interaction map of M. tuberculosis. The other research direction is to implement other schemes to encode the sequence information in the machine learning method, such as the autocorrelation encoding scheme [Basically, we implemented two pipelines of building the protein-interaction map of g scheme  and tripg scheme . We combM. tuberculosis. We predicted the protein interactions in M. tuberculosis by an SVM based classifier by genetic codons. And the documented protein interactions from various species were also mapped to the proteome of M. tuberculosis by interolog method. The information from gene expression, evolutionary and functional relationship provided reliability measures of evaluating our predictions. The validations provided clear evidence for the effectiveness of our method. Our framework can easily be extended to infer the large-scale protein interaction map in other species. These predicted interactions provide a valuable reference of interactome for M. tuberculosis research. The PPIs build a frame to further study the functional implications underlying the interactome of M. tuberculosis. They are listed in Additional file http://www.aporc.org/doc/wiki/MTBPPI.In conclusion, we provided a novel framework to integrate genomic data to infer a protein interaction map of M. tuberculosis. The protein interactions were predicted by two main pipelines. Firstly, we built the protein interaction network of M. tuberculosis from codon features of interacting proteins in E. coli by a machine learning approach. The integrated interaction map and gene sequences of E. coli were downloaded from EcID, which collects comprehensive PPIs in E. coli by combining various knowledge [E. coli to train an SVM classifier to get the genetic codon features underlying these interacting pairs. The interactions in M. tuberculosis were then predicted by the trained SVM predictor with the genetic codons of ORFs in gene sequences of M. tuberculosis. We chose the laboratory strain of H37Rv as our model organism [M. tuberculosis by interolog method from the documented protein interactions in 14 other species. We collected these interactions from IntAct [M. tuberculosis by BLAST [E. coli and in the known interactions of M. tuberculosis. Three pieces of available information of M. tuberculosis, i.e., gene expression profiling, evolutionary relationship from ortholog database and functional similarity, were used to evaluate the confidence of prediction results. The known protein interactions were of course included in our constructed interactome of M. tuberculosis. Finally, we inferred an integrated protein interaction map of M. tuberculosis.Figure nowledge . We usedorganism . The prom IntAct  and DIP m IntAct . The intby BLAST  individuM. tuberculosis.We used the SVM method  as the cThe prediction performance was evaluated by various parameters, such as sensitivity (SN), specificity (SP), accuracy (ACC) and precision (PRE). The evaluation is usually displayed in a ROC graph with measure of area under curve (AUC). Mathematically, these measures are defined aswhere TP, TN, FP and FN refer to number of true positive, number of true negative, number of false positive, and number of false negative predictions, respectively.M. tuberculosis by genetic codons and ortholog mapping. We also deposited known interactions in databases from experimental results about M. tuberculosis in literatures. Integrated with these known protein interactions, we built a comprehensive PPI map of M. tuberculosis. We collected multiple available resources to access the constructed protein interaction map in M. tuberculosis. The confidence of interactions was evaluated by three extra data sources, namely, gene expression, evolutionary relationship and functional similarity.We constructed the protein interaction map of M. tuberculosis H37Rv from NCBI GEO (ID: GSE9776) [Firstly, we identified the PCCs of gene coexpression of pairwise proteins in the predicted network. We downloaded the gene expression profiling data of GSE9776) . Correlai xand jx, i σand j σare the standard deviations of them. Secondly, we presented the evaluation of evolutionary relationship between the predicted interacting proteins. COGs were delineated by comparing protein sequences encoded in complete genomes, representing major phylogenetic lineages [M. tuberculosis H37Rv from GOA [where lineages . Figure lineages . The maxlineages  similarifrom GOA . In GO hfrom GOA . The freanno(c) is the number of proteins annotated with this terms in our database. The set of child nodes of term c is the children(c). The probability of a term t is then defined as p(c) = freq(c)/freq(root), where freq(root) is the frequency of the root term [where oot term ,38. We uoot term -38 to evPPI: protein-protein interaction; SVM: support vector machine; ROC: receiver operating characteristic; AUC: area under curve; ACC: accuracy; SP: specificity; SN: sensitivity; PRE: precision; TP: true positive; TN: true negative; FP: false positive; FN: false negative; BIND: biomolecular interaction network database; BioGrid: biological general repository for interaction datasets; IntAct: molecular interaction database; ORF: opening reading frame; COG: cluster of orthologous group; GO: gene ontology; CC: cellular component; MF: molecular function; BP: biological process; NCBI: national center for biotechnology information; GEO: gene expression omnibus.The authors declare that they have no competing interests.ZPL, JW and LC conceived the research. ZPL and JW designed and performed the study. YQQ, RKKL, XSZ and SKWT gave valuable suggestions and improvements. LC supervised the project. ZPL wrote the paper with contributions from others. All authors read and approved the manuscript.Hub proteins in the protein interaction map of M. tuberculosis.Click here for fileThe protein interaction map of M. tuberculosis.Click here for file


ProteinHistorian to make tools for performing analyses of protein origins widely available. Given a list of proteins of interest, ProteinHistorian estimates the phylogenetic age of each protein, quantifies enrichment for proteins of specific ages, and compares variation in protein age with other protein attributes. ProteinHistorian allows flexibility in the definition of protein age by including several algorithms for estimating ages from different databases of evolutionary relationships. We illustrate the use of ProteinHistorian with three example analyses. First, we demonstrate that proteins with high expression in human, compared to chimpanzee and rhesus macaque, are significantly younger than those with human-specific low expression. Next, we show that human proteins with annotated regulatory functions are significantly younger than proteins with catalytic functions. Finally, we compare protein length and age in many eukaryotic species and, as expected from previous studies, find a positive, though often weak, correlation between protein age and length. ProteinHistorian is available through a web server with an intuitive interface and as a set of command line tools; this allows biologists and bioinformaticians alike to integrate these approaches into their analysis pipelines. ProteinHistorian's modular, extensible design facilitates the integration of new datasets and algorithms. The ProteinHistorian web server, source code, and pre-computed ages for 32 eukaryotic genomes are freely available under the GNU public license at http://lighthouse.ucsf.edu/ProteinHistorian/.The evolutionary history of a protein reflects the functional history of its ancestors. Recent phylogenetic studies identified distinct evolutionary signatures that characterize proteins involved in cancer, Mendelian disease, and different ontogenic stages. Despite the potential to yield insight into the cellular functions and interactions of proteins, such comparative phylogenetic analyses are rarely performed, because they require custom algorithms. We developed  PLoS Computational Biology Software Article.”“This is a The proteins present in a species arose at a range of evolutionary times, and the context of a protein's origin can provide information about its cellular functions and interactions ProteinHistorian tool was used in a recent investigation of the evolutionary origins of the sirtuins, a protein family that contains several histone deacetylases. By computing ages for all seven human sirtuins and several of their substrates, we successfully predicted a novel substrate for a mitochondrial sirtuin By assigning a phylogenetic “age” to each protein in a species based on the distribution of evolutionarily related sequences across other species, several recent studies identified enrichment for proteins of specific ages in biologically relevant conditions. For example, the proteins expressed during developmental stages that exhibit morphological similarity across phyla were found to be older than those expressed during stages that exhibit species-specific morphologies ProteinHistorian —an integrated web server, database, and set of command line tools for carrying out eukaryotic protein age analyses in a simple, intuitive pipeline. Our approach is similar to that commonly used for Gene Ontology annotation enrichment analysis ProteinHistorian offers several strategies for estimating ages from phylogenetic patterns that make use of different ancestral family reconstruction algorithms ProteinHistorian currently estimates ages for eukaryotic proteins only, because of the potential confounding effects of horizontal gene transfer on inferring evolutionary trees in prokaryotes. In addition, it does not produce explicit ancestral genome reconstructions.In this article, we present ProteinHistorian, we describe the computation of ages for all proteins in 32 eukaryotic species using two ancestral family reconstruction algorithms and several different evolutionary databases. We then contrast the age distributions of several protein sets of interest. In the first analysis, we demonstrate that proteins with high expression in human—compared to chimpanzee and rhesus macaque expression patterns—are significantly younger than those with human-specific low expression. Next, we show how external annotation databases can be used to test hypotheses about the origins of protein functions. Taking functional annotations from the Gene Ontology ProteinHistorian analysis, we compare protein age and length in 24 metazoa and fungi. These tests confirm previous results and reveal a modest, though consistent and significant, positive correlation between protein age and length in nearly all species.To illustrate the use of ProteinHistorian web server and command line tools can perform a variety of protein age enrichment analyses , ProteinHistorian provides resources for extending the pre-computed databases. Users can build new protein age databases from their own species trees and protein families; they can also select subsets of species from existing data sets for analysis. The flexibility provided by our open source command line programs, which are described in more detail in the Implementation section, enables advanced users to develop their own analysis pipelines.We provide for download estimated ages for all proteins from the 32 eukaryotic species listed in ProteinHistorian is the only tool available for protein age enrichment analysis and visualization. However, there are other web servers that perform complementary phylogenetic analyses based on existing protein family databases. For example, the PhyloPat To our knowledge, ProteinHistorian rely on the assignment of a phylogenetic age to each protein in a species. Given the diverse contexts in which phylogenetic analysis can be applied, there is not a single consistent definition of protein age or origin. In this section, we describe the different ways ProteinHistorian computes ages to help users select the best parameters for their analyses and interpret their results. We provide several pre-computed protein age sets for each species on the ProteinHistorian web server in the hope that users will rarely have to compute their own ages.All analyses performed by ProteinHistorian are based on three inputs: a species tree, a protein family database, and an ancestral family reconstruction algorithm. Ages are defined with respect to the species tree; each protein is assigned to the branch in the tree on which its family is estimated to have appeared. This calculation is based on the ancestral history produced by running a family reconstruction algorithm on the protein family. Thus, the notion of “evolutionary relatedness” encapsulated in the family database has a major effect on the meaning of the resulting age. We now describe each of the inputs to the age estimation pipeline.The protein ages computed by ProteinHistorian allows users to input their own trees.The species tree used in all analyses presented here  is basedProteinHistorian uses several sets of protein family predictions from the Princeton Protein Orthology Database (PPOD) Each protein family database provides a partition of all proteins in all species represented in the tree into evolutionarily related families. The particular database selected defines the meaning of “relatedness” between two proteins in the resulting set of ages. ProteinHistorian, we analyzed the phylogenetic distribution of all Pfam domains Several recent analyses assigned ages to proteins based on the phylogenetic distribution of the functional subdomains that they contain ProteinHistorian also includes several species-specific sets of protein ages that have been used in previous studies. For baker's yeast, Saccharomyces cerevisiae, age estimates used in a recent study of novel genes are available ProteinHistorian users.ProteinHistorian uses a relative gain penalty of 1. Since we focus on eukaryotic species in which horizontal gene transfer is rare, this largely serves to prevent false positives in the protein family databases from biasing age distributions.Two reconstruction algorithms, Dollo parismony and Asymmetric Wagner parsimony are currently available to infer the series of gains and losses that best explains the observed phylogenetic distribution of proteins in a family. Dollo parsimony is based on the assumption that gaining a complex structure is much more rare than losing one. Thus, it assumes that there was a single gain event for each family, potentially followed by many losses in specific lineages. In other words, under Dollo parsimony, a family's origin is the most recent common ancestor (MRCA) of all species in which it is observed. In contrast, asymmetric Wagner parsimony allows multiple gain and loss events as well as the ability to set weights on the relative likelihood of these events. By default, Given these inputs, each family's evolutionary history is reconstructed. Then, each protein is assigned as its age the branch in the species tree on the path between its species (the leaf) and the root in which its family first appeared according to the reconstruction.ProteinHistorian can be interpreted as estimates of the time at which the ancestors of proteins with recognizable homology first evolved; however, the exact nature of that similarity will vary from database to database. For example, relationships in the PPOD databases are based on varying levels of detectable sequence similarity over the entire protein, while the Pfam databases indicate the existence of similarity over individual functional domains. Sequence similarity across proteins from different species suggests a similar common function, but it does not necessarily imply that the ancestral proteins' functions were the same as those observed in current species.In general, the protein ages calculated by ProteinHistorian are based on different assumptions, analyzing the same protein set based on different family partitions may yield different results. For example, the use of Wagner parsimony produces a younger protein distribution than Dollo parsimony, since it allows multiple gain events to explain families with patchy existence profiles  format 2.0 Regulatory proteins are significantly younger than catalytic proteins Click here for additional data file.Figure S1Dollo parsimony produces older protein age estimates than Wagner parsimony. Each set of age estimates is based on a species tree, ancestral family reconstruction algorithm, and a protein family database. Different choices for each of these inputs will produce age distributions with different properties. For example, the Dollo parsimony ancestral reconstruction algorithm produces older ages for human proteins on average (average age: 1154.5 mya) than Wagner parsimony Click here for additional data file.Figure S2Jaccard Clustering produces older protein age estimates than OrthoMCL. The PPOD protein family database based on Jaccard clustering produces older ages for human proteins on average (average age: 1289.1 mya) than the OrthoMCL-based database Click here for additional data file.Figure S3Comparison of the age distributions resulting from the oldest Pfam domain and youngest Pfam domain age estimation strategies. As expected, assigning human proteins the age of their oldest Pfam domain produces older ages on average (average age: 2433.7 mya) than assigning them the age of their youngest domain Click here for additional data file.Figure S4Example comparisons of age distributions for protein sets of interest. The comparisons of the age distributions of different protein sets of interest presented in the text (A) Proteins with high expression on the human lineage (compared to non-human primates) have an average origin of 1215.4 mya and are significantly younger than proteins with human-specific low expression  Proteins with annotated regulatory functions are significantly younger (average age: 1150.7 mya) than proteins with catalytic functions Click here for additional data file.Figure S5Correlation between human protein age and length. The length of a human protein is significantly positively correlated with its age (Spearman (PDF)Click here for additional data file.Figure S6Comparison of Pfam catalytic and regulatory domain age distributions. Pfam domains with catalytic activities are significantly older on average (average age: 3135.5 mya) than regulatory domains Click here for additional data file.Table S1Correlation of protein age and length across 24 fungi and metazoa. Using Dollo parsimony on the PPOD-PANTHER OrthoMCL database, the correlations between age and length are very similar to those reported in the main text  and mouse show the strongest correlations overall.ain text . Nearly (PDF)Click here for additional data file.


A significant part of our biological knowledge is centered on relationships between biological entities (bio-entities) such as proteins, genes, small molecules, pathways, gene ontology (GO) terms and diseases. Accumulated at an increasing speed, the information on bio-entity relationships is archived in different forms at scattered places. Most of such information is buried in scientific literature as unstructured text. Organizing heterogeneous information in a structured form not only facilitates study of biological systems using integrative approaches, but also allows discovery of new knowledge in an automatic and systematic way. In this study, we performed a large scale integration of bio-entity relationship information from both databases containing manually annotated, structured information and automatic information extraction of unstructured text in scientific literature. The relationship information we integrated in this study includes protein–protein interactions, protein/gene regulations, protein–small molecule interactions, protein–GO relationships, protein–pathway relationships, and pathway–disease relationships. The relationship information is organized in a graph data structure, named integrated bio-entity network (IBN), where the vertices are the bio-entities and edges represent their relationships. Under this framework, graph theoretic algorithms can be designed to perform various knowledge discovery tasks. We designed breadth-first search with pruning (BFSP) and most probable path (MPP) algorithms to automatically generate hypotheses—the indirect relationships with high probabilities in the network. We show that IBN can be used to generate plausible hypotheses, which not only help to better understand the complex interactions in biological systems, but also provide guidance for experimental designs. Relationships among bio-entities, such as proteins, genes, diseases, biological pathways and gene ontology (GO) terms, constitute a significant part of our biological knowledge. Protein-protein interactions, for example, play central roles in almost all biological processes and are indispensable for our understanding of the mechanisms of biological processes and for development of drugs1) using two relationships A and B (R2), and B and C (R3), the probability of R1 can be more accurately inferred if R2 and R3 are true. Secondly, those previous knowledge discovery methods use co-occurrence of terms in abstracts to infer their relationships. Such approach is not effective for inferring molecular interaction information since it has been shown that even using co-occurrence in the same sentence, the false positive rate can be very highinhibit, activate, and phosphorylate express different types of interaction information. Without incorporating the information on the type of interactions, a knowledge discovery system tends to return a large number of false positives .Integration of information from different sources/domains makes it possible to discovery new knowledge through automatic hypothesis generation. Knowledge discovery has been an active topicinformation flow from one bio-entity to another. For example, in protein-protein interaction databases, how proteins interact with other proteins, such as inhibit, regulate, phosphorylate etc, is usually not well documented. Other information such as the directionality of the interactionSeveral difficulties need to be overcome to make automatic knowledge discovery systems effective tools in biomedical research. Firstly, information from different sources/domains needs to be integrated in a structured way. This is highly nontrivial due to the difference in data organizations and discrepancy in information from different databases caused by inevitable annotation errors or inherent ambiguity/uncertainty of certain information. Secondly, relationship information needs to be well annotated to allow for effective In this study, we collect several important types of bio-entity relationship information from manually annotated databases and literature, including protein–protein interactions, protein/gene regulations, protein–small molecule interactions, protein–GO term relationships, protein–pathway relationships, pathway–disease relationships and protein–species relationships. We further integrate the relationship information in a graph data structure, called integrated bio-entity network (IBN), where the vertices are bio-entities and edges are their relationships. Edges in IBN contain information on the types of the relationships, the directionalities of the relationships and the probabilities of the relationships. The rich information in the edges makes IBN a very effective system for knowledge discovery. To generate hypotheses automatically, we design graph-theoretic algorithms to extract high probability indirect relationships between bio-entities in the network. We show with examples that IBN can be used to generate plausible hypotheses for a given query, which can help researchers to better understand biological systems and design experiments.We first collect molecular interaction information from manually curated databases. For protein–protein interactions, BioGRID In addition to molecular interaction information, we also collected other types of bio-entity relationships including protein–GO terms, protein–pathway, pathway–disease, and protein–species relationships. There have been some previous studies aiming to extract some of the above relationships automatically from literatureWe performed large scale automatic extraction of protein-protein interaction information from literature including both physical interactions and regulatory information using a Bayesian network approach developed earlierdimerization, phosphorylation etc. Totally, we obtained 2,960,499 PSI triplets, and 505,060 are predicted as describing interactions using the BN model trained with protein-protein interactionsTo extract protein–small molecule interaction (PSI) information, we obtained the small molecule name dictionary from NCBI PubChem databaseWith the relationships information collected, we would like to organize them in a structured form so that the power of integrated data can be harnessed conveniently and efficiently. Since all the relationships naturally form a network, graph representation is a compelling choice. We name the network formed by the relationships among bio-entities as integrated bio-entity network (IBN). Vertices or nodes in IBN represent bio-entities and edges represent relationships between bio-entities. Edges can be information confirmed manually such as those obtained from databases or extracted automatically from literature. Indirect links between two bio-entities through more than one edge may be valuable information that has not been documented in previous literature. Searching such information in IBN allows scientists to discover valuable new information. In fact, such practice has been done routinely by scientists in their research through a combination of manually reading the literature and performing searches on multiple databases. IBN thus can serve as a platform to assist researchers to automatically generate new hypotheses, which can be further tested through targeted experiments or literature review. The overview of the system is shown in The generated hypotheses from IBN can be relationships between any two types of bio-entities in the network. For example, a researcher may want to find the effect of a small molecule to cells. Such effect can be a therapeutic effect of the small molecule to a disease or can be drug-side effect. To find such effects, the small molecule can be queried through IBN to retrieve all proteins, pathways, GO terms and diseases that related to this small molecule. Another typical question a researcher may ask is whether there is any relationship between two bio-entities he/she is interested in. In such case, the two bio-entities can be used as the input of a query that searches for all direct and indirect relationships with high probabilities between the two bio-entities. To perform the above searches, we designed graph theoretic algorithms, breadth-first search with pruning (BFSP) and most probable path (MPP)  starting with insulin pathway and retrieving only the bio-entities within two edges away from insulin pathway, the search returns more than two thousand interactions. The proteins directly related to insulin pathway are shown in cp = 1 resulted in 144 proteins, where cp is the probability cutoff to prune away low probability relationships in BFSP algorithm. These are proteins that are known to directly interact with aspirin. Reducing cp to 0.5, we obtained 155 pathways and 1 disease (GO terms are ignored since they are not linked to diseases in IBN). Since the goal here is to find diseases related to aspirin, proteins indirectly interact with aspirin are ignored because they will not lead to a disease given cp = 0.5 . The reason that only one disease was returned is because the only way we obtain disease information is through the aspirin-protein-pathway-disease route and pathway-disease relationship is poorly annotated in the current databases. The disease found is ALPS (Autoimmune Lymphoproliferative Syndrome). We did a literature search and did not find literature support for aspirin being a treatment for ALPS. In IBN, aspirin is connected to ALPS through pathway apoptosis and a few proteins associated with that pathway. The query result not only links aspirin to the disease ALPS, but also provides the edges that connect the two entities, which may shed light on the mechanism of the action of aspirin on the disease (if it is indeed effective). To find out more diseases related to aspirin, we did a text mining study using one of the proteins that is well known to interact with aspirin, Cox-2. We searched all PubMed abstracts for co-occurrences of pathway and disease names with Cox-2. We found that there are totally 444 diseases and 45 pathways. Some diseases co-occur with Cox-2 many more time than others, such as cancer (2585 co-occurrences) and pain (335 co-occurrences). Higher frequency of co-occurrence indicates higher likelihood of true association or stronger relationship. The diseases and pathways that are strongly associated with Cox-2, together with ALPS, are plotted in We use aspirin as an example to illustrate how one can use IBN to search for diseases related to a small molecule. A BFSP search with aspirin and its synonyms as the query keyword and cp = 0.5 returned thousands of proteins and interactions. This is not very surprising since many of proteins in PKC family and those regulating (or regulated by) them are hub proteins that are important in many biological processes. However, not all the reported proteins, pathways or GO terms are actually affected by PMA. The reason is that a significant part of the interaction information used by us is obtained from databases and there is no detailed interaction information available such as directions of the interactions. Proteins that are not affected by PMA directly or indirectly can also be returned, which is not desirable. Clearly, without the directionality information, many false positives are produced and the effect of the signal/query can be difficult to infer accurately.PMA  is a potent tumor promoter often employed in biomedical research to activate the signal transduction enzyme, protein kinase C (PKC)We built a smaller network for PMA by requiring the interactions to be either regulatory type or phosphorylation using interactions extracted from literature, which resulted in only 79 proteins and 166 interactions in total. We manually verified the interactions and kept only the correct ones. The resulting directed network is shown in http://www.genome.jp/dbget-bin/www_bget?uniprot:O95644. Of course, even there is no any reported evidence for a relationship it can still be true.In Another pathway we examined is muscle contraction, which is separated from PMA by four proteins. PubMed search using PMA and muscle contraction as the keywords returned 182 articles. The first articleIn this study, we performed a large scale integration of a diverse set of bio-entities and their relationship information from both databases and literature and built a network based system, integrated bio-entity network (IBN), for biological knowledge discovery. We aim to address the three challenges faced by the current knowledge discovery studies, namely, data integration, relationship annotation and hypothesis ranking. Although there is still a lot of room for further improvement in all three areas, the framework we set up in this study presents a clear path toward effective automatic biological knowledge discovery. With the network data structure, graph theoretic algorithms can be designed to search for high probability indirect relationships (hypotheses) in IBN. Those automatically generated hypotheses based on the current knowledge base can help researchers to better understand their experimental results and design future experiments. A goal of future research would be to implement a publicly accessible knowledge discovery system.Finally, we point out several directions that the current system can be further improved. Firstly, some relationship information is still poorly documented in the current databases such as protein-disease relationships and protein-pathway relationships. These relationships can be extracted automatically from literatureIn this study, we use a previously developed protein interaction extraction methodIn information extraction, we use dictionaries to tag molecular names or interaction words. The synonyms of molecular names are incorporated in the dictionaries. All the synonyms of a molecule are linked to one vertex in IBN.This dictionary contains totally 7,663,997 protein names. It was constructed by combining protein names from several sources including NCBI Gene databaseObtained from NCBI PubChem databaseIt contains words that describe interactions of molecules including regulatory relationships conftaining 192 words as used in the previous studyWe obtained GO terms from Gene Ontology databaseWe obtained pathway names from KEGG pathway databaseWe obtained 29,018 disease names from PharmGKBWe obtained species names from NCBI database. The protein–species relationships were obtained from UniProtKB/Swiss-ProtThe probabilities of the relationships between any two vertices that are not connected by an edge in IBN can be calculated using the probabilities of existing edges. Any edge, representing a relationship between two bio-entities, has a probability assigned to it. For relationships obtained from manually annotated databases, the probabilities are 1. For relationships extracted from literature, the probabilities are given by the extraction method. When multiple instances are extracted for one particular relationship  from the literature, the highest probability among the instances is assigned as the probability for the relationship. Below we describe two algorithms that can be used to search for high probability relationships in IBN.i in a graph G is given below. Here we are only interested in vertices whose relationships to i have probabilities greater than a threshold value, cp. The additional pruning step aims to only include those significant relationships in the search result, which is essential in large scale knowledge discovery.To search for all indirectly connected vertices from a given vertex we perform a modified breadth-first search (BFS) algorithmprocedure BFSPQcreate a queue i onto Qenqueue vertex imark vertex whileQ is not emptyv from Qdequeue a vertex for each unmarked neighbour W of Vifw is not markedipw,  =  ipv, × vpw, × dpi,wp is the probability for the relationship between node i and w, i,vp is the probability between node i and v, v,wp is the probability for node v and w, and dp is a parameter to model the uncertainty when inferring relationships through indirect edges *//* ifi,wd > cpcp is the threshold for selecting more relevant relationships *//* wmark w onto Qenqueue dp is used to model the uncertainty when inferring relationships through indirect edges. For example, even relationship between vertices A and B has probability 1 and that between vertices B and C also has probability 1, the relationship between A and C is not necessarily 1. In fact, in many cases such relationship can be false. For instance, even A interacts with B and B interacts with C, A may not interact with C. In principle, this probability can be learned from data and does not have to be a constant. In this study, we simply set dp to 0.8 and cp to 0.5, unless otherwise specified. This means all the indirect relationships involving more than three edges will not satisfy the cp threshold. In another word, the BFSP algorithm will not visit any vertices which are more than three edges apart from the query vertex.In the above procedure the probability This algorithm is used to find the path between two bio-entities in IBN with the highest probabilities among all paths connecting the two bio-entities, which is based on Djikstra's shortest path algorithmprocedure MPP G, where V is the set of vertices and E is the set of edges *//* initialize all the vertices in v in Vfor each vertex v,sp  =  infinityv]  =  nilpi is the predecessor set of vertex v to s. *//* S  =  {0} /* Make S empty */Q  =  V /* Put the vertices in Q */Q)while not Empty(s *//* extract the vertex in Q which has the highest probability relationship with u  =  ExtractMostProbable( Q );u  =  =  t)if; /* Add u to S */AddNode( v can be improved by/* checks whether the current best estimate of the MPP to u */going through v in Adjacent( u ) for each vertex v,sp < u,sp × u,vp × dp) thenif Click here for additional data file.


Background: Computer-based modeling is part of a new approach to predictive toxicology.Objectives: We investigated the usefulness of an integrated computational systems biology approach in a case study involving the isomers and metabolites of the pesticide dichlorodiphenyltrichloroethane (DDT) to ascertain their possible links to relevant adverse effects.Methods: We extracted chemical–protein association networks for each DDT isomer and its metabolites using ChemProt, a disease chemical biology database that includes both binding and gene expression data, and we explored protein–protein interactions using a human interactome network. To identify associated dysfunctions and diseases, we integrated protein–disease annotations into the protein complexes using the Online Mendelian Inheritance in Man database and the Comparative Toxicogenomics Database.p,p´-DDT, and 187 to o,p´-DDT.Dichlorodiphenyldichloroethylene  was the metabolite with the highest number of links, with 52. We grouped proteins for each compound based on their disease annotations. Although the two data sources differed in linkage to diseases, integrated results predicted that most diseases were linked to the two DDT isomers. Asthma was uniquely linked with p,p´-DDT, and autism with o,p´-DDT. Several reproductive and neurobehavioral outcomes and cancer types were linked to all three compounds.Results: We found 175 human proteins linked to Conclusions: Computer-based modeling relies on available information. Although differences in linkages to proteins may be due to incomplete data, our results appear meaningful and suggest that the parent DDT compounds may be responsible for more disease connections than the metabolites. The findings illustrate the potential use of computational approaches to toxicology. Toxicity Testing in the 21st Century, the National Research Council called for the development of new approaches to human health risk assessment that would rely, in part, on computer-based models rather than animal testing and epidemiology  was marketed as an ideal insecticide with long-term protection; however, the environmental persistence of DDT and its metabolite dichlorodiphenyldichloroethylene (DDE) resulted in bioaccumulation and delayed or latent adverse effects. Experimental studies have explored mainly the effects of DDT dosages, whereas epidemiology research primarily has used the serum concentration of the DDE metabolite as an exposure biomarker. The DDE concentration may reflect, to some degree, past exposures to the parent DDT compound that had been metabolized later, but it may also originate directly from DDE residues in food. Thus, concentrations present at the time of blood sampling may represent neither the correct chemical species nor the active dose present at the time when a possible adverse effect was initiated. This conundrum is difficult to resolve from epidemiology data alone, and the experimental data do not cover all the potential outcomes for the relevant isomers and metabolites [Agency for Toxic Substances and Disease Registry (ATSDR) 2002]. We therefore chose to carry out computational chemistry analyses of the DDT compounds from a human systems biology perspective. Our dual purpose was to obtain new information that might link relevant outcomes to specific DDT compounds and to ascertain to what degree the currently available data sources would allow such analyses using a systems chemical biology approach. Although this study was not intended as a formal validation study, we consider a family of compounds for which extensive although somewhat equivocal epidemiological and experimental evidence exists, with the result that our analysis includes suspected causal agents and anticipated negative controls.Using data from the ToxProfile of the More specifically, first we extracted the human protein–chemical associations from the newly established disease chemical biology database, ChemProt , a compiSecond, we explored protein–protein interactions through an in-house human interactome network based on experimental data from humans and 21 model organisms , 2008. Up,p´-DDT, o,p´-DDT, and p,p´-DDE), we integrated protein-specific information related to their involvement in disease pathogenesis with the proteins identified. The Online Mendelian Inheritance in Man (OMIM) database  and the Comparative Toxicogenomics Database  contains 3,748 connections involving 2,728 unique diseases, while the CTD (version 2009) contains 252,056  protein–disease associations involving 2,580 unique diseases. We translated diseases having a Medical Subject Heading identification (MeSH ID) number in CTD into OMIM ID numbers to the extent possible, given that a single MeSH ID number may correspond to several OMIM ID numbers. Based on OMIM ID numbers, 1,934 diseases are shared between the two databases, 2,042 proteins are common to both, and they correspond to 2,528 shared protein–disease associations of 255,804 interactions listed. Because of the limited overlap, we used both resources to explore the predictions in terms of protein–disease relationships and biological confidence with regard to grouping proteins within a binary metric distance .p-values were calculated for each group of proteins, assuming a hypergeometric distribution. To take into account the large numbers of potential protein–disease connections in the CTD, we performed Bonferroni adjustments of all p-values for protein clusters based on the CTD. For this source of information, we chose a classical family-wise error rate of 0.05 as cutoff to adjust p-values. We mapped all human proteins identified to EntrezGene (NCBI 2011a) identifiers using the Clone/GeneID converter (Using R software (R Development Core Team 2010), onverter .p,p´-DDT, 83 for o,p´-DDT, and 18 for p,p´-DDE . We used the resulting three lists of proteins independently to create three human protein networks. For o,p´-DDE, only 11 proteins were identified, and all had already been identified earlier as targets for p,p´-DDE. Likewise, p,p´-DDD was connected with only 10 proteins, and o,p´-DDD with only three . No information was obtained on the methylsulfonyl metabolites. Our data analysis therefore concentrated on the three substances first mentioned.Using the ChemProt database, we extracted 38 relevant human proteins for p,p´-DDT analysis, 187 proteins for o,p´-DDT, and 52 proteins for p,p´-DDE  and 210 diseases (based on CTD data) appeared to be unique for p,p´-DDT. All OMIM-based predictions had higher p-values than CTD-based predictions, but these differences should be interpreted in light of the fewer known proteins in the OMIM database associated with the diseases predicted. For example, asthma was connected with p,p´-DDT via a single protein, tumor necrosis factor, in the OMIM database (p = 0.147), whereas CTD information predicted asthma via connections through 48 proteins (p = 0.002) . In contrast, only a few diseases appeared to be unique for p,p´-DDE: 1 based on CTD data , and 8 based on OMIM data.We generated a human protein network for each chemical by determining protein–protein interaction partners associated with each protein network. Proteins with a GeneEntrez ID were retained. We identified 175 proteins for the p,p´-DDE . Diseasep,p´-DDE . Some dihttp://dx.doi.org/10.1289/ehp.1103533) for details on the p-values (adjusted when based on CTD data), sources of protein–disease information (CTD or OMIM), and specific genes linked to the proteins.] Depending on the source of protein–disease annotations, the phenotypes may be more or less specific, but all annotations identified in the databases were kept to avoid any subjective judgment in data extraction. As expected, the predictions varied somewhat between the two databases, OMIM and CTD.To synthesize this information, we focused on the four major categories of disease phenotypes previously linked to DDT exposures: inflammatory, reproductive and endocrine, neurobehavioral, and carcinogenic .  suggested that the DDTs may also have other endocrine-disrupting effects in humans. Type 2 diabetes mellitus appeared to be connected with all three substances via 9 proteins for p,p´-DDE (nonsignificant p), 40 proteins for p,p´-DDT (p < 0.0001), and 25 proteins for o,p´-DDT (nonsignificant p).Several links to reproductive phenotypes were retrieved. Female infertility disorders representing diminished ability or inability of a woman to achieve conception, although not further specified in the resources used, were connected with the three chemicals. In parallel, male reproductive disorders, including hypospadias and cryptorchidism, were predicted for p-values via the nuclear receptor binding SET domain protein 1 based on OMIM data for all three substances. When using the CTD, additional disorders were identified, including learning disorders . Similar results were obtained for memory disorders . These results again reflect the diversity of information present in the two data sources used. Interestingly, o,p´-DDT was the only substance studied that predicted a linkage with autism, via the hepatocyte growth factor receptor.For neurobehavioral diseases, Soto and Weaver syndromes were predicted with low p,p´-DDT is via a single protein, the hyaluronan-mediated motility receptor, p = 0.208). Using the CTD, the link for p,p´-DDT involved 22 proteins (p < 0.001), and for p,p´-DDE, 24 proteins (p = 0.021), again reflecting the larger amount of information on these chemicals in the CTD. For information on other cancers, see Supplemental Material, http://dx.doi.org/10.1289/ehp.1103533).Various cancers appeared to be connected with all three substances. Breast cancer was predicted for all three DDTs using CTD data and for both parent DDTs using OMIM data ]. These potential targets deserve attention in future experimental and epidemiological studies to provide a more complete basis for risk assessment.Overall, our findings demonstrate that the systems chemical biology approach is feasible and may have a pivotal role in considering potential causal associations derived from toxicology and epidemiology studies. Although our approach is based on the current knowledge base and may therefore have overlooked some linkages, the results show that the DDT compounds examined, while chemically related, have tertiary structures, gene expression profiling, and binding properties that deviate sufficiently from one another to predict outcomes that differ substantially. The differences in predicted outcomes are not likely to be due to differences in the amount of information available. Thus, the major parent compound, in silico approaches and findings, and cumulated experience will help in interpreting such analyses in light of possible unknown interactions and absent dose–effect relationships. Thus, the results obtained with the DDT compounds serve as an illustration of the potential use of computational predictions in toxicology, epidemiology, and environmental health research. The visions expressed by the National Research Council committee on transforming toxicology therefore seem reasonable and realistic.The usefulness and validity of the computational approach is likely to improve as more information becomes available, including more chemical–protein data as well as data from “omics”and gene–environment interaction studies. Furthermore, the results of the disease–chemical association analysis will improve in the future as newer, more complete, and curated data become available to expand and fine-tune our understanding of protein–disease associations. In addition, studies like this one will contribute to the necessary validation of (229 KB) PDFClick here for additional data file.


Most signalling and regulatory proteins participate in transient protein-protein interactions during biological processes. They usually serve as key regulators of various cellular processes and are often stable in both protein-bound and unbound forms. Availability of high-resolution structures of their unbound and bound forms provides an opportunity to understand the molecular mechanisms involved. In this work, we have addressed the question “What is the nature, extent, location and functional significance of structural changes which are associated with formation of protein-protein complexes?”A database of 76 non-redundant sets of high resolution 3-D structures of protein-protein complexes, representing diverse functions, and corresponding unbound forms, has been used in this analysis. Structural changes associated with protein-protein complexation have been investigated using structural measures and Protein Blocks description. Our study highlights that significant structural rearrangement occurs on binding at the interface as well as at regions away from the interface to form a highly specific, stable and functional complex. Notably, predominantly unaltered interfaces interact mainly with interfaces undergoing substantial structural alterations, revealing the presence of at least one structural regulatory component in every complex.Interestingly, about one-half of the number of complexes, comprising largely of signalling proteins, show substantial localized structural change at surfaces away from the interface. Normal mode analysis and available information on functions on some of these complexes suggests that many of these changes are allosteric. This change is largely manifest in the proteins whose interfaces are altered upon binding, implicating structural change as the possible trigger of allosteric effect. Although large-scale studies of allostery induced by small-molecule effectors are available in literature, this is, to our knowledge, the first study indicating the prevalence of allostery induced by protein effectors.The enrichment of allosteric sites in signalling proteins, whose mutations commonly lead to diseases such as cancer, provides support for the usage of allosteric modulators in combating these diseases. Protein-protein interactions participate in myriad processes of the cell such as replication, transcription, translation, signal transduction, immune response, metabolism, membrane-associated processes and development e.g., -4). Prot. Prot4])Given their importance, several high-throughput interaction assays ,15, suchProtein-protein interactions can be classified into different kinds : homo-olEarlier studies of structures of protein-protein complexes using both the unbound and bound form of proteins reveal that proteins undergo changes in their structure upon binding. Betts and Sternberg  were theIn this work, we have used a curated and non-redundant dataset of 76 protein-protein complexes, solved using X-ray crystallography in high resolution in both unbound and bound form, to address questions about the nature, extent and location of structural changes upon binding. We noticed that, in addition to changes in the interface, possibly allosteric changes causing structural alteration occur in about half of the complexes, indicating a much higher prevalence of this phenomenon caused due to protein binding than appreciated before.β-Gγ subunits in heterotrimeric G-proteins). The proteins constitute a mixture of single-domain and multi-domain members. Although some of the structures in the PPC dataset are solved at a resolution poorer than 2.5 Å, the highest resolution of the Control-Rigid and Control-Monomer datasets, the magnitude of structural changes captured across the datasets can be compared since 50/76 complexes of the PPC dataset were solved with a resolution ≤2.5 Å. The conclusions of comparison of various parameters capturing structural change in the different datasets, discussed below, remained unaltered when using either the ‘50’ or ‘76’ set of complexes (data not shown). The conclusions described below are for the entire dataset of 76 complexes.Structural change observed in different forms of a protein could be due to experimental artifacts , intrinsThree parameters were used to analyze structural change occurring in the different types of residues (see Methods) in a protein: root mean square deviation (RMSD), %PB change (PBc), PB substitution score (PBSSc). Although RMSD captures the magnitude of structural change, it does not distinguish the type of structural change – i.e. rigid body movement (or) conformational change. The use of Protein Blocks (PBs) enables this distinction since small yet significant changes in local conformation of a protein can be captured using PBs. Protein Blocks consists of 16 standard conformational states of pentapeptides ,49. PBs In order to distinguish structural variations caused due to protein binding from those occurring due to crystallographic artifacts, the upper bound values corresponding to the Control-Rigid dataset were used as reference for the three parameters  are considered pre-made, while those with Cα RMSD of >1.5 Å are considered induced-fit. The interfaces showing structural changes between these two values are classified in the ‘other’ category. We identify 33 pre-made interfaces, fitting the lock-and-key hypothesis proposed to explain protein-ligand binding . Such a Usually, interfaces with an average Cα RMSD of ≥1.5 Å showed substantial changes at interface, exemplifying the concept of induced-fit hypothesis  for formComparison of the structural change in terms of Cα RMSD and normalized PB substitution score can help in distinguishing cases of rigid body movements from conformational changes. Induced-fit interface regions with 0% PB change at interface can be considered to have rigid-body movements  are observed. These complexes either move out Figures B or reliIn general, interacting residues undergo larger structural change than non-interacting surface residues (≥10% residue surface accessibility (RSA)). Comparison of the three parameters quantifying structural changes studied for individual proteins showed that this trend holds true even in these cases Figure .(i) Change in non-interacting regions even when there are almost no changes in interacting regions (n = 22) (Table ii) Change in non-interacting regions accompanying changes in interacting regions (n = 12)  Table . (ii) ChChanges occurring in the non-interfacial regions are classified as near the interface region or away from the interface. All non-interfacial residues in a protein which are within a distance of ≤6 Å Cα distance from any of the interacting residues were considered as ‘residues nearby interface’, since they occur in the vicinity of the interfacial residues and are important for the formation of the structural scaffold . Figure To ascertain any known or potential biological relevance for these changes, all the ‘non-interacting regions with PB change’ in the identified proteins were analyzed using following parameters: (i) Crystallographic temperature factor (B-factor). Regions with low flexibility and different conformations are likely to have adopted the particular conformation. Studies report that interacting sites have lower B-factors than rest of the protein surface on average even in the unbound form . (ii) KnWe observed that most of the proteins with changes are signalling proteins . Therefore, 25/40 ‘other’ complexes  from the PPC dataset show significant structural changes at distal sites, indicating prevalence of this phenomenon in signalling proteins. In contrast, only 7/25 and 3/11 complexes of the enzyme-inhibitor and antigen-antibody classes, respectively, show such changes.Detailed information about the residue positions and the nature of conformational change observed in the residues possibly forming the target site for all examples of Case 1 and Case 2 are listed in Additional file Although literature studies implicate allosteric communication to be the reason for the observed structural changes away from the interface in nearly half of the complexes, we did not get clues for the other cases. Since flexibility of a region is known to be good indicator of functional relevance , we usedApart from NMA, the extent of evolutionary conservation of these regions was also determined, using Jensen-Shannon divergence measure. For regions where normal mode analysis did not provide an indication of intrinsic motion, we analyzed whether crystal packing effects could provide an explanation for conformational changes. To determine this, symmetry-related molecules were generated for the bound and unbound molecule using PyMOL , and cheEscherichia coli and other ‘group A colicins’ to penetrate and kill cells [a) TolB – Peptidoglycan associated lipoprotein  complex. The proteins TolB and Pal constitute the complex used by ll cells . TolB prll cells . Furtherll cells , indicatb) Complement C3 and Epstein-Barr virus receptor C2 complex. Complement component C3d binds to antigenic molecules. This binding helps in further amplification of B cell responses as a consequence of the simultaneous binding of antigen-bound C3d with complement receptor type 2 and binding to B cell receptor via bound antigen . Complemc) Ran GTPase and Regulator of chromosome condensation (RCC1) complex. Ran GTPase is a key component of G-protein signaling. It serves as a molecular switch which cycles between GDP- and GTP- bound states. It requires regulators for enhancing its low intrinsic hydrolysis and nucleotide dissociation rates. Guanine nucleotide exchange factors form the latter group which bind to G-proteins and induce rapid dissociation of bound GTP and hence enable fast activation to GTP bound form. The structure under consideration is a complex of Ran GTPase with the guanine nucleotide exchange factor RCC1 .Ran GTPase, which adopts the P-loop containing nucleoside triphosphate hydrolases fold, contains two regions of interest, one near the interface (residues 41–44) and the other far away from the interface (residues 151–155) , we studied the structures of the proteins in the complex to understand if there are any common characteristics of the protein exhibiting these changes (target protein) versus the binding partner (effector protein).Since structural changes away from the interface appear to be common on protein binding . However, the disparity in the values could be partly caused due to the differences in lengths  since RMSD is dependent on the number of residues. For proteins of case 2 complexes, there is no significant variation in terms of their lengths . However, the global RMSDs of the target proteins (1.65 ± 0.64) are slightly yet significantly higher than those of the effector proteins (1.04 ± 0.67) . Therefore, it appears that binding of an effector protein causes changes at the interface of the target protein that are propagated towards the distant allosteric site, providing credence to the views implicating flexibility in allosteric modulation [When we analysed the type of interface  in the target protein, we observe that pre-made interfaces constitute only 7/34 complexes undergoing structural changes away from the interface. Induced-fit and moderately induced-fit (‘other’ interfaces) constitute the bulk, accounting for 15/34 and 13/34 complexes, respectively. Allostery appears to be the most plausible explanation to connect protein binding with structural changes away from the interface, as discussed in the previous section. Therefore, most of the proteins comprising of pre-made interfaces (26/33) do not appear to be the target proteins involved in allosteric communication. On the contrary, they seem to serve as the effector molecules for transmitting the allosteric signal to the partner protein. Comparison of the global RMSDs of case 1 complexes indicates that the target proteins (Cα RMSD = 1.52 ± 0.71 Å) are significantly more flexible than their effector proteins  as well as atomic positions (captured using RMSD) compared to unliganded proteins Figure . These cNon-obligatory complexes occupy a niche position as key regulators of cellular homeostasis. Their specific and timely association and dissociation are crucial for bringing about required biological function. Spatial and temporal regulation of the interacting proteins is one of the ways of avoiding unsuitable complexation . The othTransformation of binding site structure into the active form can serve as a switch to ensure correct binding at the appropriate time. Our analysis of structural alterations provides credence to this view. Surprisingly, pre-made interfaces, which are structurally invariant upon binding, shows distribution of %PB changes similar to that observed for induced-fit interfaces. This indicates that there is some extent of conformational change in all interfaces; only the nature and magnitude varies Figure B. AdditiWe also observe a substantial proportion of instances with significant conformational changes in non-interacting regions away from the interface Figure . Identif1. Flexible regions are dynamic and can take up several distinct conformations, which can have specific functional relevance . Several2. Studies show that thermodynamic entropy redistribution is a common outcome of protein-protein interaction, irrespective of the net change in entropy after complexation . Loss of3. The region may be functionally relevant, for e.g. a ligand/macromolecule binding site, whose conformation is regulated by an allosteric mechanism. Since binding sites are observed to be a combination of flexible and rigid sites , the sig4. Crystallization is known to induce substantially altered conformations . In our 5. Trivial factors, such as missing residues near the region of interest (or) the region being near termini, could contribute to such changes . Since wIn-depth analysis of several complexes using rigorous coarse-grained NMA and literature survey indicates that a fair proportion of structural changes upon protein-protein complexation are allosteric Figure . Such coThe classical view of allostery is as a mechanism of effector binding causing functionally relevant conformational changes at a distant site . The salSignalling proteins are key drug targets and the usage of allosteric modulators as drugs is gaining acceptance ,101. In Comparison of bound and unbound structures of protein-protein complexes enables us to address various questions regarding structural alterations occurring due to interaction. Non-obligatory complexes occupy a niche position as key regulators of cellular homeostasis with appropriate and timely association and dissociation which are crucial for eliciting the necessary biological function. Structural alterations in most of the interfaces of these non-obligatory complexes support the view that conformational features by themselves can serve as a good mechanism to implement the required tight regulation.The interface is the most altered region in the entire protein structure upon protein-protein binding, as expected. The modifications are largely conformational in nature. In the rare case of one the partners remaining unaltered, the other partner is usually observed to undergo significant structural modification, thereby supporting the ‘induced fit hypothesis’  more thaThe observation of a substantial proportion of instances with significant structural changes in non-interacting regions away from the interface implies that the binding is likely to result in downstream effects. In-depth analysis of several complexes using rigorous coarse-grained NMA and literature survey indicates that these changes have functional relevance, with most of them being allosteric. The observation of allostery-like structural changes in about half of the transient complexes suggests this phenomenon is much more prevalent in signalling complexes than appreciated before. It also appears that the reversible nature of protein-protein association and dissociation, characteristic of transient complexes, affords nature with an attractive means to bring about allostery which is generally a reversible process.Two kinds of control datasets are used.Rigid-proteins dataset (Control dataset 1): A dataset of 50 independently determined structures of two rigid proteins  Monomeric-proteins dataset (Control dataset 2): To get a general idea about the flexibility in atomic positions for a random dataset, the PDB was mined for crystal structures of proteins with the following criteria: a single chain is present in the asymmetric unit and biological unit; crystallographic resolution of the structure should be 2.5 Å or better and the structure should not contain DNA, RNA, DNA-RNA hybrid, or other ligands bound to the protein. These molecules were clustered at a sequence identity of 95% and length coverage of 100% using BLASTCLUST (http://www.csc.fi/english/research/sciences/bioscience/programs/blast/blastclust). Finally, the clusters were refined to contain only one entry for each PubmedID per cluster, which ensures that mutants are not considered, to arrive at a dataset containing 95 clusters  The set of curated non-obligatory protein-protein interaction complexes solved in both unbound and bound form is taken from Benchmark 3.0 dataset . The setAlthough our dataset is intended to contain entries of identical proteins or protein domains available in both protein-bound and free forms, practically there could be some differences in the length and region of known 3-D structures in the bound and free forms. However the overwhelming majority of the same protein available in bound and free forms have >90% sequence identity  which is calculated using NACCESS ,105. Thii ranging from residue 1 to n of the dataset and d is the distance between N pairs of equivalent atoms. Two measures of RMSD have been employed: Cα RMSD and all-atom RMSD, based on deviation between the Cα positions of the same residue in unbound and bound forms in the former and between all-atoms of the same residue in unbound and bound forms for the latter. Deviation in side chain positions are generally expected [Structural change is estimated for a given residue in unbound and bound forms. A sequence alignment of the unbound and bound forms performed using CLUSTALW  providesexpected  whereas expected . The thrI’ indicates the average Cα RMSD difference between bound and unbound form for interface, and ‘CαRMSDROS’ indicates the average Cα RMSD difference between bound and unbound form for the rest of the surface.An interface with ≥0.5 Å Cα RMSD difference between the bound and unbound forms is classified as ‘pre-made’ interface whereas an interface with ≥1.5 Å Cα RMSD difference between the bound and unbound forms is classified as an ‘induced-fit’ interface. However, there are some interfaces with lower difference in terms of magnitude but with substantial difference at the interface in comparison to the rest of the surface residues (≥10% RSA). This cut-off was chosen since 90% of the interface residues have an RSA equal to or greater than this value in the unbound form. A normalization-based metric was used to identify induced-fit interfaces exhibiting smaller structural changes as For example, a value of two indicates a doubled change in magnitude of the interface with respect to the rest of the surface. This value is used as a cut-off to identify substantial changes localized to the interface.To identify cases where the interface is largely invariant/moderately altered:Criterion 1: Average Cα RMSD of ‘non-interacting residues with PB change’ should be ≥1 Å than the average Cα RMSD of the interface residues. For this comparison, the individual segments under consideration, were superimposed using SUPER .Criterion 2: Normalized PB substitution score of ‘non-interacting residues with PB change’ should be ≤ −2 than the normalized PB substitution score of interacting residues.To identify cases where there are large changes at interface:Criterion 1: Average Cα RMSD of both ‘non-interacting residues with PB change’ and interacting region should be ≥2 Å.Criterion 2: Average Cα RMSD of both ‘non-interacting residues with PB change’ and interacting region should be > > global Cα RMSD.The B-factor (temperature factor/atomic displacement factor) of an atom reflects the degree of isotropic smearing of electron density around its center . A low BBi,N) was computed as Bi is the B-factor of residue i, <Bi> is the mean B-factor of the protein surface residues and σ Bi is the s.d. for the same.To ascertain the flexibility/rigidity of a particular residue in a structure, its normalized backbone B-factor was considered . NormaliResidues with backbone B-factors ≥3, ≥2, and <1 standard deviations from the mean backbone B-factors for surface residues can be considered to have ‘very high’, ‘high’ and ‘low’ flexibility, respectively.GNM-based NMA of the unbound form of a protein was undertaken to identify intrinsic collective motions of the molecule. In this model, the biomolecule is modelled as a harmonic oscillator with every residue represented as a single site, connected by springs to nearby residues . The oGNxBased on the assumption that evolutionary conservation of a site in a protein family is an indicator of its functional relevance and/or structural integrity, the degree of conservation of all sites in a protein family was calculated using the Jensen-Shannon divergence measure. This metric operates on the premise that most sites in a protein family are not under any evolutionary pressure and hence have a distribution similar to background amino acid distribution. Sites under evolutionary pressure, such as functional or stabilizing sites, show amino acid distribution significantly different from the background distribution.Homologous sequences for every protein in our PPC dataset were identified by a search employing PSI-BLAST  against Symmetry-related molecules were generated for the bound and unbound molecule using PyMOL . The cryThe authors declare that they have no competing interests.NS and AdB conceived and designed the experiments. LSS and SM performed the experiments. LSS, SM, AdB and NS analyzed the data and wrote the paper. All authors read and approved the final manuscript.Table S1. List of PDB codes of structures of control datasets used in this analysis. Click here for fileTable S2. Details of various features of PPC dataset. Click here for fileFigure S1. Distribution of parameters capturing structural change for Control and Test datasets. Click here for fileFigure S2. Distribution of parameters capturing structural change with respect to ‘interface area’ and ‘length of protein’.Click here for fileFigure S3. Distribution of all-atom RMSD values for PPC dataset.Click here for fileFigure S4. Different kinds of interfaces.Click here for fileFigure S5. Parameters for identifying rigid-body movements. Click here for fileFigure S6. Different types of structural changes seen at interfaces. Click here for fileFigure S7. Distribution of parameters for interface vs. non-interacting surface regions per protein.Click here for fileTable S3. Details of non-interacting surface residues with PB change in Case 1 complexes. Click here for fileTable S4. Details of non-interacting surface residues with PB change in Case 2 complexes. Click here for file


Because it can introduce some biases due to scientists’ subjective perspectives, more objective viewpoints considering a wide range of interactions are required.Interpretation of binding modes of protein–small ligand complexes from 3D structure data is essential for understanding selective ligand recognition by proteins. It is often performed by visual inspection and sometimes largely depends on In this paper, we present a web server for analyzing protein–small ligand interactions on the basis of patterns of atomic contacts, or “interaction patterns” obtained from the statistical analyses of 3D structures of protein–ligand complexes in our previous study. This server can guide visual inspection by providing information about interaction patterns for each atomic contact in 3D structures. Users can visually investigate what atomic contacts in user-specified 3D structures of protein–small ligand complexes are statistically overrepresented. This server consists of two main components: “Complex Analyzer”, and “Pattern Viewer”. The former provides a 3D structure viewer with annotations of interacting amino acid residues, ligand atoms, and interacting pairs of these. In the annotations of interacting pairs, assignment to an interaction pattern of each contact and statistical preferences of the patterns are presented. The “Pattern Viewer” provides details of each interaction pattern. Users can see visual representations of probability density functions of interactions, and a list of protein–ligand complexes showing similar interactions.a priori knowledge of the users, by using our new web server named GIANT that is freely available at http://giant.hgc.jp/.Users can interactively analyze protein–small ligand binding modes with statistically determined interaction patterns rather than relying on  Elucidating molecular mechanisms in the selective recognition of small molecules (or ligands) by proteins is a central issue in biology. Structure data of protein–ligand complexes deposited in Protein databank (PDB)  are a veToward more objective analyses, a promising strategy is taking advantages of statistics of molecular interactions on PDB. Due to recent rapid increase in 3D structure data, this strategy has been become more attractive, and the statistics of protein–ligand interactions have been extensively studied . Many sePreviously, we reported a comprehensive classification of spatial arrangements of ligand atoms around molecular fragments of proteins that were defined as three covalently linked atoms . We analOn the basis of the classification of interactions, we present a new web server for analyzing molecular interactions in the 3D structure of protein–ligand complexes, named “GIANT”, which stands for “Gaussian mixture model-based Interaction ANalyzer focusing on Three-atom fragments”. GIANT provides a web browser-based user interface for visual inspection of protein–ligand interactions in 3D structures. Users can investigate how statistically overrepresented each atomic contacts in the PDB, and what protein–ligand complex uses similar interactions, for any kind of atomic contacts rather than well-known predefined types of interactions.th, 2010 using following criteria: (1) structures had been determined by X-ray crystallography with resolution ≤2.5 Å, (2) receptor proteins had ≥30 amino acid residues, and (3) bound ligands have the molecular weight between 80 and 800 Da, contained >5 atoms, and had < 0.6 relative accessible surface area. For each protein in the dataset, amino acids residues were decomposed into fragments consisting of three covalently linked atoms  and methotrexate (MTX) complex [PDB:3dfr]  was descWe show a tutorial-style example for analyzing interactions of a DHFR–methotrexate complex. In this example, it is assumed that users want to know the molecular mechanisms of methotrexate recognition by DHFR. We here aim to study what kinds of statistically preferred interactions are working on the recognition of the pteridine ring and what other complexes apply similar interactions, by using GIANT.http://jmol.org/) applet  of the row with Interaction-ID = 101 (the first column) in the table of interactions. The “Pattern Viewer” page will be opened and will show the spatial distribution of each interaction pattern as 3D meshes . The information about environment should be an important factor in the ligand recognition, we will take some statistics of co-occurrences of the interaction patterns in a future work.a priori knowledge and intuition, GIANT supports it with statistical, objective information and assists in realizing the concept of so-called rational drug-design.The web-server GIANT shows the statistical preferences of each atomic contact in user specified 3D structures of protein–small ligand complexes. This function provides an objective perspective for visual inspections of binding modes on the basis of results of the survey of interactions reported in the previous paper, and provides many implications for structural biologists and medicinal chemists. For example, when medicinal chemists perform lead optimization with 3D structure data of protein–compound complexes, GIANT suggests parts of compounds where chemical moieties without statistically overrepresented interaction patterns should be replaced to gain binding affinities. Although this process has usually been performed by experts using their http://giant.hgc.jp/.GIANT is freely available at the following URL: DHFR: Dihydrofolate reductase; MTX: Methotrexate; CDK: Cyclin-dependent kinase.The authors declare that they have no competing interests.K Kasahara performed researches and implemented the web-server. K Kasahara and K Kinoshita designed this work, and wrote the manuscript. Both authors read and approved the final manuscript.


The proper biological functioning of proteins often relies on the occurrence of coordinated fluctuations around their native structure, or on their ability to perform wider and sometimes highly elaborated motions. Hence, there is considerable interest in the definition of accurate coarse-grained descriptions of protein dynamics, as an alternative to more computationally expensive approaches. In particular, the elastic network model, in which residue motions are subjected to pairwise harmonic potentials, is known to capture essential aspects of conformational dynamics in proteins, but has so far remained mostly phenomenological, and unable to account for the chemical specificities of amino acids. We propose, for the first time, a method to derive residue- and distance-specific effective harmonic potentials from the statistical analysis of an extensive dataset of NMR conformational ensembles. These potentials constitute dynamical counterparts to the mean-force statistical potentials commonly used for static analyses of protein structures. In the context of the elastic network model, they yield a strongly improved description of the cooperative aspects of residue motions, and give the opportunity to systematically explore the influence of sequence details on protein dynamics. Decades of experimental evidence have underlined the fact that protein structures can hardly be considered as static objects. To understand how a protein achieves its biological purpose, it is therefore quite often necessary to unravel the complexity of its dynamical behavior. However, the definition of accurate and computationally tractable descriptions of protein dynamics remains a highly challenging task. Indeed, even though proteins are all built from a limited set of amino acids and local conformational arrangements, the specific nature of biologically relevant motions may vary widely from one protein to another, which constitutes a serious obstacle to the identification of common rules and properties. Here, instead of focusing on the study of a single protein, we adopt a more general perspective by condensing the information contained in a multitude of NMR conformational ensembles. This approach allows us to characterize the dynamical behavior of residues and residue pairs in a mean protein environment, independently of each protein's specific architecture. We describe how this analysis can be exploited to assess the performances of coarse-grained models of protein dynamics, to take advantage of existing experimental data for a more rational and efficient parametrization of these models and, ultimately, to improve our understanding of the intrinsic dynamical properties of amino acid chains. Deciphering the motions that underlie many aspects of protein function is a major current challenge in molecular biology, with the potential to generate numerous applications in biomedical research and biotechnology. Although molecular dynamics (MD) hold a prominent position among computational approaches, considerable efforts have been devoted to the development of coarse-grained models of protein dynamics The elegant simplicity of the elastic network models (ENM) certainly contributed to their popularity, and they have been successfully exploited in a wide range of applications Despite their many achievements, purely structural ENM also come with severe limitations. Notably, modeling the possible effects of mutations within this framework usually requires random local perturbations of the spring constants A major obstacle to the definition of accurate coarse-grained descriptions of protein dynamics lies in the highly cooperative nature of protein motions, which makes it difficult to identify the properties of the individual building blocks independently of the overall architecture of each fold. By condensing the information contained in a multitude of NMR ensembles, we build here a mean protein environment, in which the behavior of residue pairs can be tracked independently of each protein's specific structure. This methodology brings an efficient way of assessing coarse-grained models of protein dynamics and of deriving effective energy functions adapted to these models. In the context of the ENM, we identify a set of spring constants that depend on both the interresidue distances and the chemical nature of amino acids, and that markedly improve the performances of the model.The mean-square fluctuations of individual residues (MSRF) have been extensively relied on to characterize protein flexibility and to evaluate coarse-grained models of protein dynamics and  see .As illustrated in eins see .The influence of the distance separating two residues on the cooperativity of their motions can be investigated by considering amino acid types indistinctively in out −2.5 . Finer dThe comparison of pact on  . This diizations .The apparent stiffness en  and  : the corThe computation of the apparent stiffness of residue pairs in a mean protein environment provides an interesting tool to probe the dynamical properties of proteins. It also generates a very straightforward approach to assess the ability of coarse-grained models to reproduce accurately this general behavior.We focus here on four common variants of the residue-based ENM aset see , and to Strikingly, all ENM variants systematically predict  20–30 Å . These mThe ENM is often considered as an entropic model, not detailed enough to include sequence information in a relevant way  . Howeverfor the  . There iMean-force statistical potentials are commonly used to perform energetic evaluations of static protein structures For that purpose, let us define Since the behavior of a given residue pair is highly dependent on its environment, we can make the assumption that Indeed, if the spring stiffness of a residue pair is underestimated We used this approach to derive, from the NMR data, four novel ENM variants: the distance-dependent dENM ; the sequence-dependent dENM see .Interestingly, the   values . HoweverThe distance dependence of The the dENM . This isThe sdENM yields a much more accurate reproduction of the dynamical behavior of residue pairs in a mean protein environment than the common ENM variants, as demonstrated by the good agreement between experimental and predicted values of  , S5, and  proteins with controlled dynamical properties. On the other hand, the importance of orientational effects in protein dynamics has been underlined by both experimental and computational studies We retrieved, from the Protein Data Bank The network is built by considering each residue as a single bead, placed at the position of the corresponding For each pair of residues in a given protein ximation .To quantify the impact of the individual motions of residues on their relative positions, we use 6. For the dENM, The values of the spring constants of the new ENM variants were derived from the dataset of 1500 NMR ensembles using eq A correction for sparse data was devised to ensure that The To assess the robustness of the derivation scheme, and the sensitivity of the The ability of coarse-grained models to accurately describe protein dynamics is commonly evaluated by computing the Pearson correlation coefficient between predicted and experimental MSRF, Although it has been widely used in previous studies, rativity , Table 1Therefore, we introduce a new measure that exploits the information contained in the correlation matrix rimental 9 and prrimental 10 valueted ones 14. The Both Dataset S1Spring constants of the , , ,  (plain text).(BZ2)Click here for additional data file.Figure S1Comparison of the apparent stiffness  extracted from different protein datasets. The different lines correspond to the full dataset of 1500 proteins (bold line), a subset of 646 small (i.e. less than 100 residues) proteins (green), a subset of 225 larger (i.e. more than 150 residues) proteins (magenta), a subset of 253 all-(PDF)Click here for additional data file.Figure S2Comparison of the experimental and predicted apparent stiffness  on two individual proteins. (A) Schematic representation of the structural ensemble of ubiquitin, obtained by combining NMR information with molecular dynamics simulations (PDB: 1xqq) (PDF)Click here for additional data file.Figure S3Comparison of the experimental and predicted apparent stiffness  on the dataset of 1500 NMR ensembles. (A–E) For each amino acid, the median value of (PDF)Click here for additional data file.Figure S4Correlation between spring constants and contact potentials. The energy values of the static contact potentials previously derived by Miyazawa and Jernigan (PDF)Click here for additional data file.Figure S5Comparison of the experimental and predicted apparent stiffness  on the dataset of 1500 NMR ensembles. (A–E) For each amino acid, the median value of (PDF)Click here for additional data file.Figure S6Comparison of the apparent stiffness  computed with or without the linear approximation. The black bold line corresponds to the apparent stiffness observed in the test set of 349 proteins. The red and blue lines correspond to the apparent stiffness predicted by the (PDF)Click here for additional data file.Table S1Performances of the new ENM during the iterative procedure.(PDF)Click here for additional data file.Table S2Spring constants of the .(PDF)Click here for additional data file.Table S3Spring constants of the .(PDF)Click here for additional data file.Table S4Spring constants of the .(PDF)Click here for additional data file.Table S5Spring constants of the .(PDF)Click here for additional data file.Text S1Derivation of effective harmonic potentials.(PDF)Click here for additional data file.