MINT focuses on experimentally verified protein-protein interactions mined from the scientific literature by expert curators

The MIPS mammalian protein-protein interaction database (MPPI) is a new resource of high-quality experimental protein interaction data in mammals. The content is based on published experimental evidence that has been processed by human expert curators. It is a collection of manually curated high-quality PPI data collected from the scientific literature by expert curators. We took great care to include only data from individually performed experiments since they usually provide the most reliable evidence for physical interactions. To suit different users needs we provide a variety of interfaces to search the database: -Expert interface Simple but powerful boolean query language. -PPI search form Easy to use PPI search -Protein search Just find proteins of interest in the database Sponsors: This work is funded by a grant from the German Federal Ministry of Education and Research.

It has been acknowledged that source databases recording experimentally supported human protein-protein interactions (PPIs) exhibit limited overlap. Thus, the reconstruction of a comprehensive PPI network requires appropriate integration of multiple heterogeneous primary datasets, presenting the PPIs at various genetic reference levels. Existing PPI meta-databases perform integration via normalization; namely, PPIs are merged after converted to a certain target level. Hence, the node set of the integrated network depends each time on the number and type of the combined datasets. Moreover, the irreversible a priori normalization process hinders the identification of normalization artifacts in the integrated network, which originate from the nonlinearity characterizing the genetic information flow. PICKLE (Protein InteraCtion KnowLedgebasE) 2.0 implements a new architecture for this recently introduced human PPI meta-database. Its main novel feature over the existing meta-databases is its approach to primary PPI dataset integration via genetic information ontology. Building upon the PICKLE principles of using the reviewed human complete proteome (RHCP) of UniProtKB/Swiss-Prot as the reference protein interactor set, and filtering out protein interactions with low probability of being direct based on the available evidence, PICKLE 2.0 first assembles the RHCP genetic information ontology network by connecting the corresponding genes, nucleotide sequences (mRNAs) and proteins (UniProt entries) and then integrates PPI datasets by superimposing them on the ontology network without any a priori transformations. Importantly, this process allows the resulting heterogeneous integrated network to be reversibly normalized to any level of genetic reference without loss of the original information, the latter being used for identification of normalization biases, and enables the appraisal of potential false positive interactions through PPI source database cross-checking. The PICKLE web-based interface (www.pickle.gr) allows for the simultaneous query of multiple entities and provides integrated human PPI networks at either the protein (UniProt) or the gene level, at three PPI filtering modes.

a database of modulators of protein-protein interactions. It contains exclusively small molecules and therefore no peptides. The data are retrieved from the literature either peer reviewed scientific articles or world patents. A large variety of data is stored within IPPI-DB: structural, pharmacological, binding and activity profile, pharmacokinetic and cytotoxicity when available, as well as some data about the PPI targets themselves.

Database to catalog experimentally determined interactions between proteins combining information from a variety of sources to create a single, consistent set of protein-protein interactions that can be downloaded in a variety of formats. The data were curated, both, manually and also automatically using computational approaches that utilize the the knowledge about the protein-protein interaction networks extracted from the most reliable, core subset of the DIP data. Because the reliability of experimental evidence varies widely, methods of quality assessment have been developed and utilized to identify the most reliable subset of the interactions. This CORE set can be used as a reference when evaluating the reliability of high-throughput protein-protein interaction data sets, for development of prediction methods, as well as in the studies of the properties of protein interaction networks. Tools are available to analyze, visualize and integrate user's own experimental data with the information about protein-protein interactions available in the DIP database. The DIP database lists protein pairs that are known to interact with each other. By interact they mean that two amino acid chains were experimentally identified to bind to each other. The database lists such pairs to aid those studying a particular protein-protein interaction but also those investigating entire regulatory and signaling pathways as well as those studying the organization and complexity of the protein interaction network at the cellular level. Registration is required to gain access to most of the DIP features. Registration is free to the members of the academic community. Trial accounts for the commercial users are also available.

The DIP database catalogs experimentally determined interactions between proteins. It combines information from a variety of sources to create a single, consistent set of protein-protein interactions. The data stored within the DIP database are curated both manually by expert curators and also automatically using computational approaches that utilize the the knowledge about the protein-protein interaction networks extracted from the most reliable, core subset of the DIP data.

Mammalian protein-protein interaction database focusing on synaptic proteins. The Protein-Protein Interaction Database was originally a single-person's attempt to integrate a gamut of biological/bibliographical/molecular data and build a framework which might help understanding how cells orchestrate their protein content in order to become what they are: machines with a purpose. This is based on the simple paradigm that functionality like signal cascades are held together in a close space, thereby allowing specific events to occur without the necessity of passive diffusion and random events. The PPID database arose from the need to interpret Proteomic datasets, which were generated analysing the NMDA-receptor complex (see H. Husi, M. A. Ward, J. S. Choudhary, W. P. Blackstock and S. G. Grant (2000). Proteomic analysis of NMDA receptor-adhesion protein signaling complexes. Nat Neurosci 3, 661-669.). To study these clusters of proteins requires unavoidably the handling of large datasets, which PPID is generally aimed and tailored for. This database is unifying molecular entries across three species, namely human, rat and mouse and is is footed on sequence databases such as SwissProt, EMBL, TrEMBL (translated EMBL sequences) and Unigene and the literature database PubMed. A typical entry in PPID holds up to three general entries for the three species, all protein and gene accession numbers associated with them (assembled from Blast2 searches of the databases) and the OMIM entry as maintained by Johns Hopkins University. Furthermore protein sequence information is also included, together with known and novel splice-variants of each molecule as found by ClustalW sequence alignments. Entry points also include protein-binding information together with the literature reference. The whole database is curated manually to insure accuracy and quality. Querying the database will be possible by online browsing and batch-submission for large datasets holding accession number information, as can be generated using software like Mascot for mass-spectrometry. Cluster-analysis of the submitted datasets in the form of a graphical output will be developed as well as an easy-to-use web-interface. An interface is currently being built in collaboration with the Department of Informatics (T. Theodosiou and D. Armstrong) and will be deployed soon The current team of people collating and deploying the database are H. Husi (database mining and information gathering) and T. Theodosiou (web-interface and deployment). Please note that this database is not funded financially, and cannot survive without sponsorship.

A database of interactions between HIV-1 and human proteins published in the peer-reviewed literature. The goal is to provide a concise, yet detailed, summary of all known interactions of HIV-1 proteins with host cell proteins, other HIV-1 proteins, or proteins from disease organisms associated with HIV/AIDS. For each HIV-1 human protein interaction the following information is provided: * NCBI Reference Sequence (RefSeq) protein accession numbers. * NCBI Entrez Gene ID numbers. * Amino acids from each protein that are known to be involved in the interaction. * Brief description of the protein-protein interaction. * Keywords to support searching for interactions. * PubMed identification numbers (PMIDs) for all journal articles describing the interaction. In addition, all protein-protein interactions documented in the database are integrated into Entrez Gene records and listed in the ''HIV-1 protein interactions'' section of Entrez Gene reports. The database is also tightly linked to other databases through Entrez Gene, enabling users to search for an abundance of information related to HIV pathogenesis and replication.

Knowledge of virus-host interactomes has advanced exponentially in the last decade by the use of high-throughput screening technologies to obtain a more comprehensive landscape of virus-host protein–protein interactions. In this article, we present a systematic review of the available virus-host protein–protein interaction database resources. The resources covered in this review are both generic virus-host protein–protein interaction databases and databases of protein–protein interactions for a specific virus or for those viruses that infect a particular host. The databases are reviewed on the basis of the specificity for a particular virus or host, the number of virus-host protein–protein interactions included, and the functionality in terms of browse, search, visualization, and download. Further, we also analyze the overlap of the databases, that is, the number of virus-host protein–protein interactions shared by the various databases, as well as the structure of the virus-host protein–protein interaction network, across viruses and hosts.

Protein-Protein, Genetic, and Chemical Interactions for Brandao MM (2010):AtPIN: Arabidopsis thaliana protein interaction network. curated by BioGRID (https://thebiogrid.org); ABSTRACT: BACKGROUND: Protein-protein interactions (PPIs) constitute one of the most crucial conditions to sustain life in living organisms. To study PPI in Arabidopsis thaliana we have developed AtPIN, a database and web interface for searching and building interaction networks based on publicly available protein-protein interaction datasets. DESCRIPTION: All interactions were divided into experimentally demonstrated or predicted. The PPIs in the AtPIN database present a cellular compartment classification (C3) which divides the PPI into 4 classes according to its interaction evidence and subcellular localization. It has been shown in the literature that a pair of genuine interacting proteins are generally expected to have a common cellular role and proteins that have common interaction partners have a high chance of sharing a common function. In AtPIN, due to its integrative profile, the reliability index for a reported PPI can be postulated in terms of the proportion of interaction partners that two proteins have in common. For this, we implement the Functional Similarity Weight (FSW) calculation for all first level interactions present in AtPIN database. In order to identify target proteins of cytosolic glutamyl-tRNA synthetase (Cyt-gluRS) (AT5G26710) we combined two approaches, AtPIN search and yeast two-hybrid screening. Interestingly, the proteins glutamine synthetase (AT5G35630), a disease resistance protein (AT3G50950) and a zinc finger protein (AT5G24930), which has been predicted as target proteins for Cyt-gluRS by AtPIN, were also detected in the experimental screening. CONCLUSIONS: AtPIN is a friendly and easy-to-use tool that aggregates information on Arabidopsis thaliana PPIs, ontology, and sub-cellular localization, and might be a useful and reliable strategy to map protein-protein interactions in Arabidopsis. AtPIN can be accessed at http://bioinfo.esalq.usp.br/atpin.

This dataset includes all protein-protein interactions as well as associated annotation data obtained from the Biological General Repository for Interaction databases (BIOGRID) for major model organisms species, including involved experimental systems used to disclose the interaction. The data is a curation of thousands of publications of research experiments that found a link (interaction) between two proteins.

Protein-protein interactions (PPIs) play an ubiquitous and fundamental role in all biological processes. Information on PPIs described in the literature is annotated and made available by several protein-interaction databases. Because most databases have their own curation rules and priorities, they often annotate overlapping sets of publications, which leads to redundancies. We developed a semantic-based approach which enables to accurately detect redundancies within PPI datasets from multiple databases. We applied this approach to assemble a "reproducible interactome", with PPIs supported by at least two methods or publications.

ataset representing a Protein-Protein Interaction (PPI) network of human proteins. Data generated and scored using the comprehensive STRING database resource. Focuses on analyzing functional and physical associations between proteins. Includes confidence scores (e.g., text-mining, experimental) for each interaction. A foundational resource for systems biology and identifying molecular hubs in disease pathways.

The datasets contains information about protein-protein interactions (PPI) and protein-protein complex interactions (PCI) in human. It was received by querying the IntAct database based on the criteria that the organism is human and the confidence level of the interaction is based on MI score ≥ 0.45 The confidence level of each interaction is characterised by IntAct MI score. The result was downloaded from IntAct molecular interaction database version 4.2.6 https://www.ebi.ac.uk/intact/.

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 26, 2016. PRIME is a developed version of Kinase Pathway Database which is an integrated database concerning completed sequenced major eukaryotes, which contains the classification of protein kinases and their functional conservation and orthologous tables among species, protein-protein interaction data, domain information, structural information, and automatic pathway graph image interface. The protein-protein interactions are extracted by natural language processing (NLP) from abstracts using basic word pattern and protein name dictionary GENA: developed by our group. In this system, pathways are easily compared among species using protein interactions data more than 1,510,000 and orthologous tables. Further, using other organisms interaction data, interaction prediction is also possible.

Table S3. Third co-fractionation dataset. (CSV 782 kb)

An index of protein interactions available in a number of primary interaction databases including BIND, BioGRID, CORUM, DIP, HPRD, IntAct, MINT, MPact, MPPI and OPHID. This index includes multiple interaction types including physical and genetic (mapped to their corresponding protein products) as determined by a multitude of methods. This index allows the user to search for a protein and retrieve a non-redundant list of interactors for that protein. iRefIndex uses the Sequence Global Unique Identifier (SEGUID) to group proteins and interactions into redundant groups. This method allows users to integrate their own data with the iRefIndex in a way that ensures proteins with the exact same sequence will be represented only once. iRefIndex project has three long term objectives: # to facilitate exchange of interaction data between interaction databases. # to consolidate interaction data from multiple sources. # to provide feedback to source interaction databases. iRefIndex is made available in a number of formats: MITAB tab-delimited text files, iRefWeb interface, iRefScape plugin for Cytoscape, PSICQUIC Web services, and an interface for the R programming language environment.

The database NPIDB (Nucleic acid Protein Interaction DataBase) contains information derived from structures of DNA-protein and RNA-protein complexes extracted from Protein Data Bank (PDB) (1932 complexes in the end of 2007). It is equipped with a web-interface and a set of tools for extracting biologically meaningful characteristics of complexes. They are committed to satisfy all potential database users in order to: 1. Provide an essential information on structural features of DNA-protein and RNA-protein interaction for the users who need to get acquainted with the problem. 2. Give an effective access to the reasonably structured information about all DNA-protein and RNA-protein complexes containing in PDB. 3. Allow all visitors a quick access to our own research.

Overview

This page describes the automated construction of a cell-cell interaction database by filtering existing curated protein-protein interaction (PPI) data. Cell-cell interactions are important for understanding tissue organization. We and others have built cell-cell interaction databases (1-5). The resource available from this website represents an automatically built set of protein-protein interactions that can mediate cell-cell communication that is expanded compared to previous databases we have built.

Receptors

Receptor genes were defined based on the union of the annotations from

1. the set of Gene Ontology (GO) terms (6,7):

   • GO:0043235 - receptor complex,

   • GO:0008305 - integrin complex,

   • GO:0072657 - protein localized to membrane

   • GO:0043113 - receptor clustering

   • GO:0004872 - receptor activity,

   • GO:0009897 - external side of plasma membrane)

2. UniProt annotations

   • search term -"Receptor [KW-0675]" go:0005886 organism:human.

This created a set of 4364 receptor genes (prior to manual curation)

Ligands

Ligand genes were defined based on the union of the below annotations

1. the GO terms (6,7):

   • GO:0005102 - receptor binding

2. the set of proteins labelled as secreted in the Secretome dataset (http://www.proteinatlas.org/humanproteome/secretome) (8).

This created a set of 3209 Ligand genes (prior to manual curation)

Extracellular Matrix

Extracellular Matrix (ECM) genes were defined based on the union of the annotations from

1. the GO terms (6,7):

   • GO:0031012 - extracellular matrix

   • GO:0005578 - proteinacious extracellular matrix

   • GO:0005201 - extracellular matrix structural constituent

   • GO:1990430 - extracellular matrix protein binding

   • GO:0035426 - extracellular matrix cell signalling

This created a set of 433 ECM genes (prior to manual curation)

Manual Curation

ECM, Receptor and ligand lists were manually curated

• genes that were neither receptors or ligands were removed
• misclassified genes were moved to the correct list (i.e. receptors found on the ligand list or vice versa)

After curation, the resulting ligand, receptor and ECM sets consisted of:

• Receptors - 1851 genes
• Ligands - 1593 genes
• ECM - 433 genes

In each of the above sets there are genes that are part of other sets (e.g. a gene can be ECM and ligand at the same time)

Interaction Data

The set of protein interactions were downloaded from:

1. iRefIndex (version 14) (9). - all BioGRID interactions were excluded from the iRefIndex set as we imported the original source.

2. Pathway Commons (version 8) (10).

3. BioGRID (version 3.4.147) (11).

The entire interaction set was filtered to only include interactions that contained receptor-ligand, receptor-receptor, ligand-ligand, receptor-ecm, ligand-ecm or ecm-ecm interactions where the receptor, ligands and ecm were defined by the above lists.

The resulting Receptor-Ligand network contained 2,593 unique proteins and 38,446 unique interactions (115,900 interaction total)

Data files

• ligands.txt - table of ligands. (contains HGNC symbol and classification (Ligand, Ligand/ECM, Ligand/Receptor, Ligand/ECM/Receptor)

• receptors.txt - table of receptors. (contains HGNC symbol and classification (Receptor, Receptor/ECM, Ligand/Receptor, Ligand/ECM/Receptor)

• ecm.txt - table of ECM. (contains HGNC symbol and classification (ECM, ECM/Receptor, ECM/Ligand, Ligand/ECM/Receptor)

• protein_types.txt - table of unique set of receptor, ligand and ECM genes (all of the above tables: contains HGNC symbol as well as classification (Receptor, Ligand, ECM, ECM/Receptor, ECM/Ligand, Receptor/Ligand, Ligand/ECM/Receptor)

• receptor_ligand_interactions_mitab_v1.0_April2017.txt(.zip/.gz) - tab delimited file in mitab 2.5 format containing the following columns:

  • AliasA - main Alias for molecule A (often the recognized gene symbol)

  • AliasB- main Alias for molecule B (often the recognized gene symbol)

  • uidA - unique identifier for molecule A (depending on the source database this can be one of the following types uniprot, refseq, entrez gene id, ensembl)

  • uidB - unique identifier for molecule A (depending on the source database this can be one of the following types uniprot, refseq, entrez gene id, ensembl)

  • altA - list of alternate identifiers for molecule A.

  • altB - list of alternate identifiers for molecule B.

  • aliasA - list of alternate aliases for molecule A.

  • aliasB - list of alternate aliases for molecule B.

  • method - list of psi-mi terms indicating experimental methods used to discover interaction.

  • author - text listing authors

  • pmids - list of pmids associated with the interaction.

  • taxa - taxon id for molecule A.

  • taxb - taxon id for molecule B.

  • interactionType - list of psi-mi terms indicating the type of interactions it is.

  • sourcedb - source database.

  • interactionIdentifier - source database interaction identifier

  • confidence - confidence of interaction as supplied by database source

References

1. Qiao W, Wang W, Laurenti E, Turinsky AL, Wodak SJ, Bader GD, Dick JE, Zandstra PW Intercellular network structure and regulatory motifs in the human hematopoietic system
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2. Kirouac DC, Ito C, Csaszar E, Roch A, Yu M, Sykes EA, Bader GD, Zandstra PW. Dynamic interaction networks in a hierarchically organized tissue. Mol Syst Biol. 2010 Oct 5;6:417
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3. Yuzwa SA, Yang G, Borrett MJ, Clarke G, Cancino GI, Zahr SK, Zandstra PW, Kaplan DR, Miller FD. Proneurogenic Ligands Defined by Modeling Developing Cortex Growth Factor Communication Networks. Neuron. 2016 Sep 7;91(5):988-1004
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4. Ramilowski JA, Goldberg T, Harshbarger J, Kloppmann E, Lizio M, Satagopam VP, Itoh M, Kawaji H, Carninci P, Rost B, Forrest AR. A draft network of ligand-receptor-mediated multicellular signalling in human. Nat Commun. 2015 Jul 22;6:7866.
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5. Rieckmann JC, Geiger R, Hornburg D, Wolf T, Kveler K, Jarrossay D, Sallusto F, Shen-Orr SS, Lanzavecchia A, Mann M, Meissner F. Social network architecture of human immune cells unveiled by quantitative proteomics. Nat Immunol. 2017 May;18(5):583-593. PMID: 28263321.

6. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000 May;25(1):25-9
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7. The Gene Ontology Consortium. Expansion of the Gene Ontology knowledgebase and resources. Nucleic Acids Res. 2017 Jan 4;45(D1):D331-D338
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8. Uhlén M, Fagerberg L, Hallström BM, Lindskog C, Oksvold P, Mardinoglu A, Sivertsson Å, Kampf C, Sjöstedt E, Asplund A, Olsson I, Edlund K, Lundberg E, Navani S, Szigyarto CA, Odeberg J, Djureinovic D, Takanen JO, Hober S, Alm T, Edqvist PH, Berling H, Tegel H, Mulder J, Rockberg J, Nilsson P, Schwenk JM, Hamsten M, von Feilitzen K, Forsberg M, Persson L, Johansson F, Zwahlen M, von Heijne G, Nielsen J, Pontén F. Proteomics. Tissue-based map of the human proteome. Science. 2015 Jan 23;347(6220)
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9. Razick S, Magklaras G, Donaldson IM. iRefIndex: a consolidated protein interaction database with provenance. BMC Bioinformatics. 2008 Sep

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. An online database of two-hybrid protein interactions in B. Subtilis. Interactions stored in SPID are either characterized by experimental evidence or by bibliographic references. A graphical user interface is provided to explore interaction networks as well as to view the details of each piece of evidence. The database contains 112 interactions between 79 proteins.