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DIP / Database of Interacting Proteins
Catalogs experimentally determined interactions between proteins. DIP combines information from a variety of sources to create a single, consistent set of protein-protein interactions. The data stored within the DIP database were curated, both, manually by expert curators and also automatically using computational approaches that utilize the knowledge about the protein-protein interaction networks extracted from the most reliable, core subset of the DIP data.
MINT / Molecular INTeraction Database
Stores, in a structured format, information about molecular interactions by extracting experimental details from work published in peer-reviewed journals. The new version of MINT is based on a completely remodeled database structure, which offers more efficient data exploration and analysis, and is characterized by entries with a richer annotation. MINT includes, as an integrated addition, HomoMINT, a database of interactions between human proteins inferred from experiments with ortholog proteins in model organisms.
APID Interactomes
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Identifies and handles all the proteins and maps them into the reference proteomes of each species. APID is a bioinformatics database developed to provide protein interactomes at different quality levels and allowing their analysis and visualization as networks. This method is focused solely on the generation and delivery of unified compendiums of known and experimentally proven protein–protein physical interactions (PPIs).
Reflects cellular localization, biological process, and molecular function, enabling functional characterization of thousands of proteins. BioPlex structure also reveals associations among thousands of protein domains, suggesting a basis for examining structurally-related proteins. This tool can be used to reveal interactions of biological or clinical significance. The BioPlex (biophysical interactions of ORFeome-based complexes) network is the result of creating thousands of cell lines with each expressing a tagged version of a protein from the ORFeome collection.
BioGRID / Biological General Repository for Interaction Datasets
Assists in the capture of biological interaction data from the primary biomedical literature. BioGRID builds collection and creates annotations of genetic and protein interaction data for all major model organism species and humans. It allows users to investigate the function of individual genes and pathways, as well as to analyze the properties of large biological networks. This database is curated with an automated random re-curation procedure.
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Contains comprehensive information about the twelve neurodegenerative diseases under one portal. NeuroDNet offers to user a bouquet of tools and features to analyze the information by creating protein-protein interaction (PPI) networks, signal-gene protein interaction pathways and Boolean networks. Higher degree neighbourhood networks can be visualized to determine critical hubs and crosstalk associations between interacting partners. This resource may also be used to design directed experiments that provide better insight and reveal potential druggable targets.
CPDB / ConsensusPathDB
Allows searching, visualizing and retrieving of integrated interaction data. CPDB is an integrative interaction database that gathers molecular interaction data integrated from 32 different public repositories and provides a set of computational methods and visualization tools to explore these data. Its applications comprise over-representation analysis to characterize diverse sets of molecules, gene set enrichment analysis (GSEA) and identification of upstream regulators spanning various biological context. It is also used as a database by other tools, for instance by Cytoscape and Chipster.
Defines and spatially clusters protein binding sites for knowledge-based protein docking. KBDOCK is a 3D database system that combines the PFAM domain classification with coordinate data from the Protein Data Bank (PDB) to analyse the spatial arrangements of domain-domain interactions (DDIs) and domain-peptide interactions (DPIs) by Pfam family, and to propose structural templates for protein docking. It can also find DDIs involving structurally similar Pfam domains to the query domains using pre-calculated Pfam neighbour lists.
S/MARtDB / scaffold/matrix attached regions transaction database
Covers scaffold/matrix attached regions (S/MARs) and nuclear matrix proteins that are implicated in the chromosomal attachment to the nuclear scaffold. S/MARtDB was constructed by collecting data in original publications and, wherever necessary, data were re-confirmed by contacting the authors. It is organized in three hyperlinked flat files. The database offers a search engine that enables the user to make research through all fields of the flat files.
PINA / Protein Interaction Network Analysis
Provides an integrated platform for protein interaction network construction, filtering, analysis, visualization and management. PINA includes a quarterly updated, nonredundant database based on integration of data from six public protein-protein integration (PPI) databases: IntAct, MINT, BioGRID, DIP, HPRD and MIPS MPact. This application provides capabilities allowing to construct PPIs networks, such as queries for single proteins, a list of proteins, a list of protein pairs or two lists of proteins.
AtPID / Arabidopsis thaliana Protein Interactome Database
Depicts and integrates the information pertaining to protein-protein interaction networks, domain architecture, ortholog information and GO annotation in the Arabidopsis thaliana proteome. AtPID predicts the Protein-protein interaction pairs by integrating several methods with the Naive Baysian Classifier. All other related information curated in the AtPID is manually extracted from published literatures and other resources from some expert biologists. AtPID collects 5564 mutants with significant morphological alterations which were manually curated to 167 plant ontology (PO) morphology categories and predicts 4457 high confidence gene-PO pairs with 1369 genes as the complement. These single/multiple-gene mutants are indexed and linked to 3919 genes.
A consolidated resource of experimentally identified, physical protein-protein interactions with confidence scores to indicate their reliability. HitPredict provides a manually curated dataset of 398 696 physical associations between 70 808 proteins from 105 species. Manual confirmation was used to resolve all issues encountered during data integration. For improved reliability assessment, this version combines a new score derived from the experimental information of the interactions with the original score based on the features of the interacting proteins. The combined interaction score performs better than either of the individual scores in HitPredict as well as the reliability score of another similar database. HitPredict provides a web interface to search proteins and visualize their interactions, and the data can be downloaded for offline analysis. Data usability has been enhanced by mapping protein identifiers across multiple reference databases. Thus, the latest version of HitPredict provides a significantly larger, more reliable and usable dataset of protein-protein interactions from several species for the study of gene groups.
VirHostNet / Virus-Host Network
Supplies a collection of integrated virus-virus, virus-host and host-host interaction networks coupled to their functional annotations. VirHostNet simplifies systems biology and gene-centered analysis of infectious diseases and assists users in the recognition of new molecular targets for antiviral drugs design. It aims to improve the knowledge on molecular mechanisms involved in the antiviral response mediated by the cell and in the viral strategies selected by viruses to hijack the host immune system.
Associates experimentally-identified protein-protein interactions (PPIs) with human tissues. TissueNet allows users to select a protein and a tissue, and to obtain a network view of the query protein and its tissue-associated PPIs. TissueNet v.2 is an updated version of the TissueNet database. It includes over 40 human tissues profiled via RNA-sequencing or protein-based assays. Users can select their preferred expression data source and interactively set the expression threshold for determining tissue-association. The output of TissueNet v.2 emphasizes qualitative and quantitative features of query proteins and their PPIs. The tissue-specificity view highlights tissue-specific and globally-expressed proteins, and the quantitative view highlights proteins that were differentially expressed in the selected tissue relative to all other tissues. Together, these views allow users to quickly assess the unique versus global functionality of query proteins. Thus, TissueNet v.2 offers an extensive, quantitative and user-friendly interface to study the roles of human proteins across tissues.
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Provides unbiased training and validation datasets for the development of algorithms to predict binding affinity changes due to missense mutations. The PROXiMATE database helps about the study of disease-causing mutations in the progression, diagnosis and treatment of various diseases. It gives possible drug targets and novel therapy options. More, it supplies experimental data for the identification of mutants which show increased affinity to their interacting partners.
Interolog/Regulog Database
Covers genome-wide interaction maps and regulatory networks for several organisms. Interolog/Regulog Database aims to allow the measurement of the transferability of interactions based on sequence similarity. It gathers about 90 pairs of interacting families in yeast and more than 9300 interactions among which about 160 pairs, that represent 2 per cent are true positives. The resulting database is composed of map regulatory relationships between transcription factors (TFs) and their targets across organisms.
A collection of inferences of functional linkages between proteins using 4 methods. These methods include the Phylogenetic Profile method which uses the presence and absence of proteins across multiple genomes to detect functional linkages; the Gene Cluster method, which uses genome proximity to predict functional linkage; Rosetta Stone, which uses a gene fusion event in a second organism to infer functional relatedness; and the Gene Neighbor method, which uses both gene proximity and phylogenetic distribution to infer linkage.
RKD / Rice Kinase Database
A phylogenomic database to facilitate functional analysis of this large gene family. Sequence and genomic data, including gene expression data and protein-protein interaction maps, can be displayed for each selected kinase in the context of a phylogenetic tree allowing for comparative analysis both within and between large kinase subfamilies. Interaction maps are easily accessed through links and displayed using Cytoscape, an open source software platform. Chromosomal distribution of all rice kinases can also be explored via an interactive interface.
Focuses on the annotation of individual proteolytic events, both actual and predicted. A CutDB entry is defined by a unique combination of these three attributes: protease, protein substrate and cleavage site. Currently, CutDB integrates 3070 proteolytic events for 470 different proteases captured from public archives (such as MEROPS and HPRD) and publications. CutDB supports various types of data searches and displays, including clickable network diagrams. Most importantly, CutDB is a community annotation resource based on a Wikipedia approach, providing a convenient user interface to input new data online.
A database that maintains and visualizes global gene/protein networks of functional coupling that have been constructed by Bayesian integration of diverse high-throughput data. FunCoup distinguishes between four classes of couplings: participation in the same signaling cascade, participation in the same metabolic process, co-membership in a protein complex and physical interaction. For each of these four classes, several types of experimental and statistical evidence are combined by Bayesian integration to predict genome-wide functional coupling networks. The FunCoup framework has been completely re-implemented to allow for more frequent future updates. It contains many improvements, such as a regularization procedure to automatically downweight redundant evidences and a novel method to incorporate phylogenetic profile similarity.
SIGNOR / SIGnaling Network Open Resource
Organizes and stores in a structured format signaling information published in the scientific literature. The captured information is stored as binary causative relationships between biological entities and can be represented graphically as activity flow. The entire network can be freely downloaded and used to support logic modeling or to interpret high content datasets. The core of this project is a collection of more than 11000 manually-annotated causal relationships between proteins that participate in signal transduction. Each relationship is linked to the literature reporting the experimental evidence. In addition each node is annotated with the chemical inhibitors that modulate its activity. The signaling information is mapped to the human proteome even if the experimental evidence is based on experiments on mammalian model organisms.
ProtCid / Protein Common interfaces database
A database that contains clusters of similar homodimeric and heterodimeric interfaces observed in multiple crystal forms (CFs). Such interfaces, especially of homologous but non-identical proteins, have been associated with biologically relevant interactions. In ProtCID, protein chains in the protein data bank (PDB) are grouped based on their PFAM domain architectures. For a single PFAM architecture, all the dimers present in each CF are constructed and compared with those in other CFs that contain the same domain architecture. Interfaces occurring in two or more CFs comprise an interface cluster in the database.
IID / Integrated Interactions Database
Provides tissue-specific protein-protein interactions (PPIs) for model organisms and human. IID covers six species and up to 30 tissues per species. Users query IID by providing a set of proteins or PPIs from any of these organisms, and specifying species and tissues where IID should search for interactions. If query proteins are not from the selected species, IID enables searches across species and tissues automatically by using their orthologs; for example, retrieving interactions in a given tissue, conserved in human and mouse. Interaction data in IID comprises three types of PPI networks: experimentally detected PPIs from major databases, orthologous PPIs and high-confidence computationally predicted PPIs. Interactions are assigned to tissues where their proteins pairs or encoding genes are expressed.
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