Protein-protein Interaction Pairs Research Articles

Retraction note: Analysis of protein-protein interaction network and functional modules on primary osteoporosis.

Primary osteoporosis is an age-related disease, and the main cause of this disease is the failure of bone homeostasis. Previous studies have shown that primary osteoporosis is associated with gene mutations. To explore the functional modules of the PPI (protein-protein interaction) network of differentially expressed genes (DEGs), and the related pathways participating in primary osteoporosis. The gene expression profile of primary osteoporosis GSE35956 was downloaded from the GEO (Gene Expression Omnibus) database and included five MSC (mesenchymal stem cell) specimens of normal osseous tissue and five MSC specimens of osteoporosis. The DEGs between the two types of MSC specimens were identified by the samr package in R language. In addition, the functions and pathways of DEGs were enriched. Then the DEGs were mapped to String to acquire PPI pairs and the PPI network was constructed with by these PPI pairs. Topological properties of the network were calculated by Network Analyzer, and modules in the network were screened by Cluster ONE software. Subsequently, the fronting five modules whose P-value was less than 1.0e-05 were identified and function analysis was conducted. A total of 797 genes were filtered as DEGs from these ten specimens of GSE35956 with 660 up-regulated genes and 137 down-regulated genes. Meanwhile, up-regulated DEGs were mainly enriched in functions and pathways related to cell cycle and DNA replication. Furthermore, there were 4,135 PPI pairs and 377 nodes in the PPI network. Four modules were enriched in different pathways, including cell cycle and DNA replication pathway in module 2. In this paper, we explored the genes and pathways involved in primary osteoporosis based on gene expression profiles, and the present findings have the potential to be used clinically for the future treatment of primary osteoporosis.

마이크로어레이 데이터와 PPI 데이터를 이용한 에스트로겐 수용체 음성 유방암 환자의 예후 특이 네트워크 식별 및 예후 예측

This study proposes an algorithm for predicting breast cancer prognosis based on genetic network. We identify prognosis-specific network using gene expression data and PPI(protein-protein interaction) data. To acquire the network, we calculate Pearson's correlation coefficient(PCC) between genes in all PPI pairs ∙제1저자 : 황유현 ∙제2저자 : 오민 ∙교신저자 : 윤영미 ∙투고일 : 2014. 11. 12, 심사일 : 2014. 12. 31, 게재확정일 : 2015. 1. 29. * 가천대학교 컴퓨터공학과(Dept. of Computer Engineering, Gachon University) **가천대학교 컴퓨터공학과 교수(교신저자)(Dept. of Computer Engineering, Gachon University) ※이 논문은 정부(교육과학기술부)의 재원으로 한국연구재단의 기초연구사업 지원을 받아 수행된 것임(NRF-2010-0008639). 138 Journal of The Korea Society of Computer and Information February 2015 using gene expression data. We develop a prediction model for breast cancer patients with estrogen-receptor-negative using the network as a classifier. We compare classification performance of our algorithm with existing algorithms on independent data and shows our algorithm is improved. In addition, we make an functionality analysis on the genes in the prognosis-specific network using GO(Gene Ontology) enrichment validation. ▸

HPIminer: A text mining system for building and visualizing human protein interaction networks and pathways

The knowledge on protein–protein interactions (PPI) and their related pathways are equally important to understand the biological functions of the living cell. Such information on human proteins is highly desirable to understand the mechanism of several diseases such as cancer, diabetes, and Alzheimer’s disease. Because much of that information is buried in biomedical literature, an automated text mining system for visualizing human PPI and pathways is highly desirable. In this paper, we present HPIminer, a text mining system for visualizing human protein interactions and pathways from biomedical literature. HPIminer extracts human PPI information and PPI pairs from biomedical literature, and visualize their associated interactions, networks and pathways using two curated databases HPRD and KEGG. To our knowledge, HPIminer is the first system to build interaction networks from literature as well as curated databases. Further, the new interactions mined only from literature and not reported earlier in databases are highlighted as new. A comparative study with other similar tools shows that the resultant network is more informative and provides additional information on interacting proteins and their associated networks.

PPCM: Combing Multiple Classifiers to Improve Protein-Protein Interaction Prediction.

Determining protein-protein interaction (PPI) in biological systems is of considerable importance, and prediction of PPI has become a popular research area. Although different classifiers have been developed for PPI prediction, no single classifier seems to be able to predict PPI with high confidence. We postulated that by combining individual classifiers the accuracy of PPI prediction could be improved. We developed a method called protein-protein interaction prediction classifiers merger (PPCM), and this method combines output from two PPI prediction tools, GO2PPI and Phyloprof, using Random Forests algorithm. The performance of PPCM was tested by area under the curve (AUC) using an assembled Gold Standard database that contains both positive and negative PPI pairs. Our AUC test showed that PPCM significantly improved the PPI prediction accuracy over the corresponding individual classifiers. We found that additional classifiers incorporated into PPCM could lead to further improvement in the PPI prediction accuracy. Furthermore, cross species PPCM could achieve competitive and even better prediction accuracy compared to the single species PPCM. This study established a robust pipeline for PPI prediction by integrating multiple classifiers using Random Forests algorithm. This pipeline will be useful for predicting PPI in nonmodel species.

Discovering novel protein-protein interactions by measuring the protein semantic similarity from the biomedical literature.

Protein-protein interactions (PPIs) are involved in the majority of biological processes. Identification of PPIs is therefore one of the key aims of biological research. Although there are many databases of PPIs, many other unidentified PPIs could be buried in the biomedical literature. Therefore, automated identification of PPIs from biomedical literature repositories could be used to discover otherwise hidden interactions. Search engines, such as Google, have been successfully applied to measure the relatedness among words. Inspired by such approaches, we propose a novel method to identify PPIs through semantic similarity measures among protein mentions. We define six semantic similarity measures as features based on the page counts retrieved from the MEDLINE database. A machine learning classifier, Random Forest, is trained using the above features. The proposed approach achieve an averaged micro-F of 71.28% and an averaged macro-F of 64.03% over five PPI corpora, an improvement over the results of using only the conventional co-occurrence feature (averaged micro-F of 68.79% and an averaged macro-F of 60.49%). A relation-word reinforcement further improves the averaged micro-F to 71.3% and averaged macro-F to 65.12%. Comparing the results of the current work with other studies on the AIMed corpus (ranging from 77.58% to 85.1% in micro-F, 62.18% to 76.27% in macro-F), we show that the proposed approach achieves micro-F of 81.88% and macro-F of 64.01% without the use of sophisticated feature extraction. Finally, we manually examine the newly discovered PPI pairs based on a literature review, and the results suggest that our approach could extract novel protein-protein interactions.

Protein–protein interaction network of the marine microalga Tetraselmis subcordiformis: prediction and application for starch metabolism analysis

Under stressful conditions, the non-model marine microalga Tetraselmis subcordiformis can accumulate a substantial amount of starch, making it a potential feedstock for the production of fuel ethanol. Investigating the interactions of the enzymes and the regulatory factors involved in starch metabolism will provide potential genetic manipulation targets for optimising the starch productivity of T. subcordiformis. For this reason, the proteome of T. subcordiformis was utilised to predict the first protein-protein interaction (PPI) network for this marine alga based on orthologous interactions, mainly from the general PPI repositories. Different methods were introduced to evaluate the credibility of the predicted interactome, including the confidence value of each PPI pair and Pfam-based and subcellular location-based enrichment analysis. Functional subnetworks analysis suggested that the two enzymes involved in starch metabolism, starch phosphorylase and trehalose-phosphate synthase may be the potential ideal genetic engineering targets.

Identification of biological targets of therapeutic intervention for diabetic nephropathy with bioinformatics approach.

We aimed to discover the potential microRNA (miRNA) targets for diabetic nephropathy (DN) treatment. The microarray data of GSE1009 was downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between DN patients and normal individuals were analyzed using limma package. The significant miRNAs targeting DEGs were collected based on Web-based Gene Set Enrichment Analysis Toolkit (WebGestalt) system. Then we predicted the protein-protein interaction (PPI) pairs regulated by significant miRNAs and constructed the PPI pairs-miRNA network using Cytoscape software. Besides, the significant function modules were explored using Molecular Complex Detection (MCODE) and Biological Networks Gene Ontology (BiNGO) plugin. Total 752 DEGs were obtained, including 318 down-regulated ones and 434 up-regulated ones. There were 10 significant miRNAs, among which miRNA-25 was the most significant. The PPI pairs-miRNA network was established with 103 PPI pairs and 10 miRNAs. Three function modules were obtained, including module A involved with miRNA-29, module B with miRNA-106 and module C with miRNA-124A and miRNA-21B. MiRNA-25, 29, 124 and 21 play key roles in DN progression and these miRNAs may be potential targets for DN treatment.

Unraveling human protein interaction networks underlying co-occurrences of diseases and pathological conditions.

BackgroundHuman diseases frequently cause complications such as obesity-induced diabetes and share numbers of pathological conditions, such as inflammation, by dysfunctions of common functional modules, such as protein–protein interactions (PPIs).MethodsOur developed pipeline, ICod (Interaction analysis for disease Comorbidity), grades similarities between pairs of disease-related PPIs including comorbid diseases and pathological conditions. ICod displayed a disease similarity network consisting of nodes of disease PPIs and edges of similarity value. As a proof of concept, eight complex diseases and pathological conditions, such as type 2 diabetes, obesity, inflammation, and cancers, were examined to discover whether PPIs shared between diseases were associated with comorbidities.ResultsBy comparing Medicare reports of disease co-occurrences from 31 million patients, the disease similarity network shows that PPIs of pathological conditions, including insulin resistance, and inflammation, overlap significantly with PPIs of various comorbid diseases, including diabetes, obesity, and cancers (p < 0.05). Interestingly, maintaining connectivity between essential genes was more drastically perturbed by removing a node of a disease-related gene rather than a pathological condition-related gene, such as one related to inflammations.ConclusionThus, PPIs of pathological symptoms are underlying functional modules across diseases accompanying comorbidity phenomena, whereas they contribute only marginally to maintaining interactions between essential genes.

TAKES: Two-step Approach for Knowledge Extraction in Biomedical Digital Libraries

This paper proposes a novel knowledge extraction system, TAKES (Two-step Approach for Knowledge Extraction System), which integrates advanced techniques from Information Retrieval (IR), Information Extraction (IE), and Natural Language Processing (NLP). In particular, TAKES adopts a novel keyphrase extraction-based query expansion technique to collect promising documents. It also uses a Conditional Random Field-based machine learning technique to extract important biological entities and relations. TAKES is applied to biological knowledge extraction, particularly retrieving promising documents that contain Protein-Protein Interaction (PPI) and extracting PPI pairs. TAKES consists of two major components: DocSpotter, which is used to query and retrieve promising documents for extraction, and a Conditional Random Field (CRF)-based entity extraction component known as FCRF. The present paper investigated research problems addressing the issues with a knowledge extraction system and conducted a series of experiments to test our hypotheses. The findings from the experiments are as follows: First, the author verified, using three different test collections to measure the performance of our query expansion technique, that DocSpotter is robust and highly accurate when compared to Okapi BM25 and SLIPPER. Second, the author verified that our relation extraction algorithm, FCRF, is highly accurate in terms of F-Measure compared to four other competitive extraction algorithms: Support Vector Machine, Maximum Entropy, Single POS HMM, and Rapier.

RETRACTED ARTICLE: Analysis of protein-protein interaction network and functional modules on primary osteoporosis

BackgroundPrimary osteoporosis is an age-related disease, and the main cause of this disease is the failure of bone homeostasis. Previous studies have shown that primary osteoporosis is associated with gene mutations.To explore the functional modules of the PPI (protein-protein interaction) network of differentially expressed genes (DEGs), and the related pathways participating in primary osteoporosis.MethodsThe gene expression profile of primary osteoporosis GSE35956 was downloaded from the GEO (Gene Expression Omnibus) database and included five MSC (mesenchymal stem cell) specimens of normal osseous tissue and five MSC specimens of osteoporosis. The DEGs between the two types of MSC specimens were identified by the samr package in R language. In addition, the functions and pathways of DEGs were enriched. Then the DEGs were mapped to String to acquire PPI pairs and the PPI network was constructed with by these PPI pairs. Topological properties of the network were calculated by Network Analyzer, and modules in the network were screened by Cluster ONE software. Subsequently, the fronting five modules whose P-value was less than 1.0e-05 were identified and function analysis was conducted.ResultsA total of 797 genes were filtered as DEGs from these ten specimens of GSE35956 with 660 up-regulated genes and 137 down-regulated genes. Meanwhile, up-regulated DEGs were mainly enriched in functions and pathways related to cell cycle and DNA replication. Furthermore, there were 4,135 PPI pairs and 377 nodes in the PPI network. Four modules were enriched in different pathways, including cell cycle and DNA replication pathway in module 2.ConclusionsIn this paper, we explored the genes and pathways involved in primary osteoporosis based on gene expression profiles, and the present findings have the potential to be used clinically for the future treatment of primary osteoporosis.

English

The Domesticated silkworm, Bombyx mori, an economically important insect has been used as a lepidopteran molecular model next only to Drosophila. Compared to the genomic information in silkworm, the protein-protein interaction data are limited. Therefore experimentally identified PPI maps from five model organisms such as E.coli, C.elegans, D.melanogaster, H. sapiens, S. cerevisiae were used to infer the PPI network of silkworm using the well-recognized Interlog based method. Among the 14623 silkworm proteins, 7736 protein-protein interaction pairs were predicted which include 2700 unique proteins of the silkworms. Using the iPfam interaction domains and the gene expression data, these predictions were validated. In that 625 PPI pairs of predicted network were associated with the iPfam domain-domain interactions and the random network has average of 9. In the gene expression method, the average PCC value of the predicted network and random network was 0.29 and 0.23100±0.00042 respectively. It reveals that the predicted PPI networks of silkworm are highly significant and reliable. This is the first PPI network for the silkworm which will provide a framework for deciphering the cellular processes governing key metabolic pathways in the silkworm, Bombyx mori and available at SilkPPI (http://210.212.197.30/SilkPPI/).

Exploring the role of human miRNAs in virus–host interactions using systematic overlap analysis

Human miRNAs have recently been found to have important roles in viral replication. Understanding the patterns and details of human miRNA interactions during virus-host interactions may help uncover novel antiviral therapies. Based on the abundance of knowledge available regarding protein-protein interactions (PPI), virus-host protein interactions, experimentally validated human miRNA-target pairs and transcriptional regulation of human miRNAs, it is possible to explore the complex regulatory network that exists between viral proteins and human miRNAs at the system level. By integrating current data regarding the virus-human interactome and human miRNA-target pairs, the overlap between targets of viral proteins and human miRNAs was identified and found to represent topologically important proteins (e.g. hubs or bottlenecks) at the global center of the human PPI network. Viral proteins and human miRNAs were also found to significantly target human PPI pairs. Furthermore, an overlap analysis of virus targets and transcription factors (TFs) of human miRNAs revealed that viral proteins preferentially target human miRNA TFs, representing a new pattern of virus-host interactions. Potential feedback loops formed by viruses, human miRNAs and miRNA TFs were also identified, and these may be exploited by viruses resulting in greater virulence and more effective replication strategies.

An Integrative Bioinformatics Framework for Genome-scale Multiple Level Network Reconstruction of Rice

Summary Understanding how metabolic reactions translate the genome of an organism into its phenotype is a grand challenge in biology. Genome-wide association studies (GWAS) statistically connect genotypes to phenotypes, without any recourse to known molecular interactions, whereas a molecular mechanistic description ties gene function to phenotype through gene regulatory networks (GRNs), protein-protein interactions (PPIs) and molecular pathways. Integration of different regulatory information levels of an organism is expected to provide a good way for mapping genotypes to phenotypes. However, the lack of curated metabolic model of rice is blocking the exploration of genome-scale multi-level network reconstruction. Here, we have merged GRNs, PPIs and genome-scale metabolic networks (GSMNs) approaches into a single framework for rice via omics’ regulatory information reconstruction and integration. Firstly, we reconstructed a genome-scale metabolic model, containing 4,462 function genes, 2,986 metabolites involved in 3,316 reactions, and compartmentalized into ten subcellular locations. Furthermore, 90,358 pairs of protein-protein interactions, 662,936 pairs of gene regulations and 1,763 microRNA-target interactions were integrated into the metabolic model. Eventually, a database was developped for systematically storing and retrieving the genome-scale multi-level network of rice. This provides a reference for understanding genotype-phenotype relationship of rice, and for analysis of its molecular regulatory network.

An integrative bioinformatics framework for genome-scale multiple level network reconstruction of rice.

Understanding how metabolic reactions translate the genome of an organism into its phenotype is a grand challenge in biology. Genome-wide association studies (GWAS) statistically connect genotypes to phenotypes, without any recourse to known molecular interactions, whereas a molecular mechanistic description ties gene function to phenotype through gene regulatory networks (GRNs), protein-protein interactions (PPIs) and molecular pathways. Integration of different regulatory information levels of an organism is expected to provide a good way for mapping genotypes to phenotypes. However, the lack of curated metabolic model of rice is blocking the exploration of genome-scale multi-level network reconstruction. Here, we have merged GRNs, PPIs and genome-scale metabolic networks (GSMNs) approaches into a single framework for rice via omics&rsquo; regulatory information reconstruction and integration. Firstly, we reconstructed a genome-scale metabolic model, containing 4,462 function genes, 2,986 metabolites involved in 3,316 reactions, and compartmentalized into ten subcellular locations. Furthermore, 90,358 pairs of protein-protein interactions, 662,936 pairs of gene regulations and 1,763 microRNA-target interactions were integrated into the metabolic model. Eventually, a database was developped for systematically storing and retrieving the genome-scale multi-level network of rice. This provides a reference for understanding genotype-phenotype relationship of rice, and for analysis of its molecular regulatory network.

PPInterFinder--a mining tool for extracting causal relations on human proteins from literature.

One of the most common and challenging problem in biomedical text mining is to mine protein–protein interactions (PPIs) from MEDLINE abstracts and full-text research articles because PPIs play a major role in understanding the various biological processes and the impact of proteins in diseases. We implemented, PPInterFinder—a web-based text mining tool to extract human PPIs from biomedical literature. PPInterFinder uses relation keyword co-occurrences with protein names to extract information on PPIs from MEDLINE abstracts and consists of three phases. First, it identifies the relation keyword using a parser with Tregex and a relation keyword dictionary. Next, it automatically identifies the candidate PPI pairs with a set of rules related to PPI recognition. Finally, it extracts the relations by matching the sentence with a set of 11 specific patterns based on the syntactic nature of PPI pair. We find that PPInterFinder is capable of predicting PPIs with the accuracy of 66.05% on AIMED corpus and outperforms most of the existing systems.Database URL: http://www.biomining-bu.in/ppinterfinder/

Fully automated protein complex prediction based on topological similarity and community structure.

To understand the function of protein complexes and their association with biological processes, a lot of studies have been done towards analyzing the protein-protein interaction (PPI) networks. However, the advancement in high-throughput technology has resulted in a humongous amount of data for analysis. Moreover, high level of noise, sparseness, and skewness in degree distribution of PPI networks limits the performance of many clustering algorithms and further analysis of their interactions.In addressing and solving these problems we present a novel random walk based algorithm that converts the incomplete and binary PPI network into a protein-protein topological similarity matrix (PP-TS matrix). We believe that if two proteins share some high-order topological similarities they are likely to be interacting with each other. Using the obtained PP-TS matrix, we constructed and used weighted networks to further study and analyze the interaction among proteins. Specifically, we applied a fully automated community structure finding algorithm (Auto-HQcut) on the obtained weighted network to cluster protein complexes. We then analyzed the protein complexes for significance in biological processes. To help visualize and analyze these protein complexes we also developed an interface that displays the resulting complexes as well as the characteristics associated with each complex.Applying our approach to a yeast protein-protein interaction network, we found that the predicted protein-protein interaction pairs with high topological similarities have more significant biological relevance than the original protein-protein interactions pairs. When we compared our PPI network reconstruction algorithm with other existing algorithms using gene ontology and gene co-expression, our algorithm produced the highest similarity scores. Also, our predicted protein complexes showed higher accuracy measure compared to the other protein complex predictions.

Improving Protein-Protein Interaction Pair Ranking with an Integrated Global Association Score

Protein-protein interaction (PPI) database curation requires text-mining systems that can recognize and normalize interactor genes and return a ranked list of PPI pairs for each article. The order of PPI pairs in this list is essential for ease of curation. Most of the current PPI pair ranking approaches rely on association analysis between the two genes in the pair. However, we propose that ranking an extracted PPI pair by considering both the association between the paired genes and each of those genes’ global associations with all other genes mentioned in the paper can provide a more reliable ranked list. In this work, we present a composite interaction score that considers not only the association score between two interactors (pair association score) but also their global association scores. We test three representative data fusion algorithms to estimate this global association score—two Borda-Fuse models and one linear combination model (LCM). The three estimation methods are evaluated using the data set of the BioCreative II.5 Interaction Pair Task (IPT) in terms of area under the interpolated precision/recall curve (AUC iP/R). Our experimental results indicate that using LCM to estimate the global association score can boost the AUC iP/R score from 0.0175 to 0.2396, outperforming the best BioCreative II.5 IPT system.

HYST: A Hybrid Set-Based Test for Genome-wide Association Studies, with Application to Protein-Protein Interaction-Based Association Analysis

The extended Simes' test (known as GATES) and scaled chi-square test were proposed to combine a set of dependent genome-wide association signals at multiple single-nucleotide polymorphisms (SNPs) for assessing the overall significance of association at the gene or pathway levels. The two tests use different strategies to combine association p values and can outperform each other when the number of and linkage disequilibrium between SNPs vary. In this paper, we introduce a hybrid set-based test (HYST) combining the two tests for genome-wide association studies (GWASs). We describe how HYST can be used to evaluate statistical significance for association at the protein-protein interaction (PPI) level in order to increase power for detecting disease-susceptibility genes of moderate effect size. Computer simulations demonstrated that HYST had a reasonable type 1 error rate and was generally more powerful than its parents and other alternative tests to detect a PPI pair where both genes are associated with the disease of interest. We applied the method to three complex disease GWAS data sets in the public domain; the method detected a number of highly connected significant PPI pairs involving multiple confirmed disease-susceptibility genes not found in the SNP- and gene-based association analyses. These results indicate that HYST can be effectively used to examine a collection of predefined SNP sets based on prior biological knowledge for revealing additional disease-predisposing genes of modest effects in GWASs.

Prediction and characterization of protein-protein interaction networks in swine

BackgroundStudying the large-scale protein-protein interaction (PPI) network is important in understanding biological processes. The current research presents the first PPI map of swine, which aims to give new insights into understanding their biological processes.ResultsWe used three methods, Interolog-based prediction of porcine PPI network, domain-motif interactions from structural topology-based prediction of porcine PPI network and motif-motif interactions from structural topology-based prediction of porcine PPI network, to predict porcine protein interactions among 25,767 porcine proteins. We predicted 20,213, 331,484, and 218,705 porcine PPIs respectively, merged the three results into 567,441 PPIs, constructed four PPI networks, and analyzed the topological properties of the porcine PPI networks. Our predictions were validated with Pfam domain annotations and GO annotations. Averages of 70, 10,495, and 863 interactions were related to the Pfam domain-interacting pairs in iPfam database. For comparison, randomized networks were generated, and averages of only 4.24, 66.79, and 44.26 interactions were associated with Pfam domain-interacting pairs in iPfam database. In GO annotations, we found 52.68%, 75.54%, 27.20% of the predicted PPIs sharing GO terms respectively. However, the number of PPI pairs sharing GO terms in the 10,000 randomized networks reached 52.68%, 75.54%, 27.20% is 0. Finally, we determined the accuracy and precision of the methods. The methods yielded accuracies of 0.92, 0.53, and 0.50 at precisions of about 0.93, 0.74, and 0.75, respectively.ConclusionThe results reveal that the predicted PPI networks are considerably reliable. The present research is an important pioneering work on protein function research. The porcine PPI data set, the confidence score of each interaction and a list of related data are available at (http://pppid.biositemap.com/).

A comprehensive manually curated protein–protein interaction database for the Death Domain superfamily

The Death Domain (DD) superfamily, which is one of the largest classes of protein interaction modules, plays a pivotal role in apoptosis, inflammation, necrosis and immune cell signaling pathways. Because aberrant or inappropriate DD superfamily-mediated signaling events are associated with various human diseases, such as cancers, neurodegenerative diseases and immunological disorders, the studies in these fields are of great biological and clinical importance. To facilitate the understanding of the molecular mechanisms by which the DD superfamily is associated with biological and disease processes, we have developed the DD database (http://www.deathdomain.org), a manually curated database that aims to offer comprehensive information on protein–protein interactions (PPIs) of the DD superfamily. The DD database was created by manually curating 295 peer-reviewed studies that were published in the literature; the current version documents 175 PPI pairs among the 99 DD superfamily proteins. The DD database provides a detailed summary of the DD superfamily proteins and their PPI data. Users can find in-depth information that is specified in the literature on relevant analytical methods, experimental resources and domain structures. Our database provides a definitive and valuable tool that assists researchers in understanding the signaling network that is mediated by the DD superfamily.