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.

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.

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.

Database for molecular interaction information integrated with various other bio-entity information, including pathways, diseases, gene ontology (GO) terms, species and molecular types. The information is obtained from several manually curated databases and automatic extraction from literature. There are protein-protein interaction, gene/protein regulation and protein-small molecule interaction information stored in the database. The interaction information is linked with relevant GO terms, pathway, disease and species names. Interactions are also linked to the PubMed IDs of the corresponding abstracts the interactions were obtained from. Manually curated molecular interaction information was obtained from BioGRID, IntAct, NCBI Gene, and STITCH database. Pathway related information was obtained from KEGG database, Pathway Interaction database and Reactome. Disease information was obtained from PharmGKB and KEGG database. Gene ontology terms and related information was obtained from Gene Ontology database and GOA database.

The PPI view displays H-InvDB human protein-protein interaction (PPI) information. It is constructed by assigning interaction data to H-InvDB proteins which were originally predicted from transcriptional products generated by the H-Invitational project. The PPI view is now providing 32,198 human PPIs comprised of 9,268 H-InvDB proteins. H-Invitational Database (H-InvDB) is an integrated database of human genes and transcripts. By extensive analyses of all human transcripts, we provide curated annotations of human genes and transcripts that include gene structures, alternative splicing isoforms, non-coding functional RNAs, protein functions, functional domains, sub-cellular localizations, metabolic pathways, protein 3D structure, genetic polymorphisms (SNPs, indels and microsatellite repeats) , relation with diseases, gene expression profiling, molecular evolutionary features, protein-protein interactions (PPIs) and gene families/groups. Sponsors: This research is financially supported by the Ministry of Economy, Trade and Industry of Japan (METI), the Ministry of Education, Culture, Sports, Science and Technology of Japan (MEXT) and the Japan Biological Informatics Consortium (JBIC). Also, this work is partly supported by the Research Grant for the RIKEN Genome Exploration Research Project from MEXT to Y.H. and the Grant for the RIKEN Frontier Research System, Functional RNA research program.

Database of human tissue protein-protein interactions (PPIs) that associates each interaction with human tissues that express both pair mates. This was achieved by integrating current data of experimentally detected PPIs with extensive data of gene and protein expression across 16 main human tissues. Users can query TissueNet using a protein and retrieve its PPI partners per tissue, or using a PPI and retrieve the tissues expressing both pair mates. The graphical representation of the output highlights tissue-specific and tissue-wide PPIs. Thus, TissueNet provides a unique platform for assessing the roles of human proteins and their interactions across tissues.

Database of known and predicted protein interactions. The interactions include direct (physical) and indirect (functional) associations and are derived from four sources: Genomic Context, High-throughput experiments, (Conserved) Coexpression, and previous knowledge. STRING quantitatively integrates interaction data from these sources for a large number of organisms, and transfers information between these organisms where applicable. The database currently covers 5''214''234 proteins from 1133 organisms. (2013)

A database that focuses on experimentally verified protein-protein interactions mined from the scientific literature by expert curators. The curated data can be analyzed in the context of the high throughput data and viewed graphically with the MINT Viewer. This collection of molecular interaction databases can be used to search for, analyze and graphically display molecular interaction networks and pathways from a wide variety of species. MINT is comprised of separate database components. HomoMINT, is an inferred human protein interatction database. Domino, is database of domain peptide interactions. VirusMINT explores the interactions of viral proteins with human proteins. The MINT connect viewer allows you to enter a list of proteins (e.g. proteins in a pathway) to retrieve, display and download a network with all the interactions connecting them.

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.

The compartmentalized protein-protein interaction database (ComPPI), provides qualitative information on the interactions, proteins and their localizations integrated from multiple databases for protein-protein interaction network analysis.

Gold Standard annotations of the DrugProt corpus (training and development sets)

Introduction

The aim of the DrugProt track (similar to the previous CHEMPROT task of BioCreative VI) is to promote the development and evaluation of systems that are able to automatically detect in relations between chemical compounds/drug and genes/proteins. We have therefore generated a manually annotated corpus, the DrugProt corpus, where domain experts have exhaustively labeled:(a) all chemical and gene mentions, and (b) all binary relationships between them corresponding to a specific set of biologically relevant relation types (DrugProt relation classes). There is also an increasing interested in the integration of chemical and biomedical data understood as curation of relationships between biological and chemical entities from text and storing such information in form of structured annotation databases. Such databases are of key relevance not only for biological but also for pharmacological and clinical research. A range of different types chemical-protein/gene interactions are of key relevance for biology, including metabolic relations (e.g. substrates, products) inhibition, binding or induction associations.

The DrugProt track aims to address these needs and to promote the development of systems able to extract chemical-protein interactions that might be of relevance for precision medicine as well as for drug discovery and basic biomedical research.

The DrugProt track in BioCreative VII (BC VII) will explore recognition of chemical-protein entity relations from abstracts.

Teams participating in this track are provided with:

PubMed abstracts

Manually annotated chemical compound mentions

Manually annotated gene/protein mentions

Manually annotated chemical compound-protein relations

Zip structure:

Training set folder with

drugprot_training_abstracts.tsv: PubMed records

drugprot_training_entities.tsv: manually labeled mention annotations of chemical compounds and genes/proteins

drugprot_training_relations.tsv: chemical-protein relation annotations

Development set folder with

drugprot_development_abstracts.tsv

drugprot_development_entities.tsv

drugprot_development_relations.tsv

Data format description

The input text files for the DrugProt track will be plain-text, UTF8-encoded PubMed records in a tab-separated format with the following three columns:

Article identifier (PMID, PubMed identifier)

Title of the article

Abstract of the article

DrugProt entity mention annotation files contain manually labeled mention annotations of chemical compounds and genes/proteins. Such files consist of tab-separated fields containing the following six columns:

Article identifier (PMID)

Term number (for this record)

Type of entity mention (CHEMICAL, GENE-Y, GENE-N)

Start character offset of the entity mention

End character offset of the entity mention

Text string of the entity mention

Each line contains one entity, and each entity is uniquely identified by its PMID and the Term Number. Besides, each annotation contains an annotation type, the start-offset -the index of the first character of the annotated span in the text-, the end-offset -the index of the first character after the annotated span- and the text spanned by the annotation.

Example DrugProt training entity mention annotations:

11808879 T1 GENE-Y 1860 1866 KIR6.2 11808879 T2 GENE-N 1993 2016 glutamate dehydrogenase 11808879 T3 GENE-Y 2242 2253 glucokinase 23017395 T1 CHEMICAL 216 223 HMG-CoA 23017395 T2 CHEMICAL 258 261 EPA

Example DrugProt development entity mention annotations (no distinction between GENE-Y and GENE-N):

11808879 T1 GENE 1860 1866 KIR6.2 11808879 T2 GENE 1993 2016 glutamate dehydrogenase 11808879 T3 GENE 2242 2253 glucokinase 23017395 T1 CHEMICAL 216 223 HMG-CoA 23017395 T2 CHEMICAL 258 261 EPA

DrugProt relation annotations will be distributed as a file that contains the detailed chemical-protein relation annotations prepared for the DrugProt track. It consists of tab-separated columns containing:

Article identifier (PMID)

DrugProt relation

Interactor argument 1 (of type CHEMICAL)

Interactor argument 2 (of type GENE)

Each line contains one relation, and each relation is identified by the PMID, the relation type and the two related entities. In the below example, to find the entities involved in the first relation, you must find the entities with Term Identifier T1 and T52 within the PMID 12488248.

Example DrugProt relation annotations:

12488248 INHIBITOR Arg1:T1 Arg2:T52 12488248 INHIBITOR Arg1:T2 Arg2:T52 23220562 ACTIVATOR Arg1:T12 Arg2:T42 23220562 ACTIVATOR Arg1:T12 Arg2:T43 23220562 INDIRECT-DOWNREGULATOR Arg1:T1 Arg2:T14

Please, cite:

@inproceedings{krallinger2017overview, title={Overview of the BioCreative VI chemical-protein interaction Track}, author={Krallinger, Martin and Rabal, Obdulia and Akhondi, Saber A and P{\'e}rez, Mart{\i}n P{\'e}rez and Santamar{\'\i}a, Jes{\'u}s and Rodr{\'\i}guez, Gael P{\'e}rez and others}, booktitle={Proceedings of the sixth BioCreative challenge evaluation workshop}, volume={1}, pages={141--146}, year={2017}}

Summary statistics:

    Training set  Development set

Documents 3500 750 Tokens 1001168 199620 Annotated Entities 89529 18858 Annotated Relations 17288 3765

Annotated Entities:

      Training Entities  Development Entities

CHEMICAL 46274 9853 GENE-Y [Normalizable] 28421 - GENE-N [Non-Normalizable] 14834 - Gene Total (N+Y) 43255 9005 Total 89529 18858

Annotated Relations:

    Training Relations Development Relations

INDIRECT-DOWNREGULATOR 1330 332 INDIRECT-UPREGULATOR 1379 302 DIRECT-REGULATOR 2250 458 ACTIVATOR 1429 246 INHIBITOR 5392 1152 AGONIST 659 131 AGONIST-ACTIVATOR 29 10 AGONIST-INHIBITOR 13 2 ANTAGONIST 972 218 PRODUCT-OF 921 158 SUBSTRATE 2003 495 SUBSTRATE_PRODUCT-OF 25 3 PART-OF 886 258 Total 17288 3765

For further information, please visit https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-1/ or email us at krallinger.martin@gmail.com and antoniomiresc@gmail.com

Related resources:

Web

Evaluation library

Relation annotation guidelines

Gene and protein annotation guidelines

Chemicals and drugs annotation guidelines

FAQ

Additional file 1: TableS1. The PPI number distribution for the 13 plants in PlaPPISite. Table S2. The number of experimentally verified PPIs of the 13 plants. Table S3. The number of experimentally verified PPIs of six model organisms. Table S4. The GO annotation covrage for the 13 plants. Table S5. The subcellular co-localization proportion for the 13 plants. Table S6. The known mutated information associated with predicted interaction sites.

Additional file 2: S2. = supplementary file 1: ‘interaction detection method conversion’.

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.

Database of known and predicted protein domain (domain-domain) interactions containing interactions inferred from PDB entries, and those that are predicted by 8 different computational approaches using Pfam domain definitions. DOMINE contains a total of 26,219 domain-domain interactions (among 5,410 domains) out of which 6,634 are inferred from PDB entries, and 21,620 are predicted by at least one computational approach. Of the 21,620 computational predictions, 2,989 interactions are high-confidence predictions (HCPs), 2,537 interactions are medium-confidence predictions (MCPs), and the remaining 16,094 are low-confidence predictions (LCPs). (May 2014)

Database that provides users with information on protein-protein interactions, as well as experimental and inferring interactions, for the organism Helicobacter pylori. Searching the database provides users with the ORF, locus, similarity comparisons, and description for the object queried.

Project - Huntingtin structure-function open lab notebook.

Rationale - To identify different huntingtin interaction partners.

Overview - Different online databases which detail protein interaction partners were searched for huntingtin protein interaction partners. Data detailing huntingtin interaction partners from 9 different databases was extracted and simplified – worksheets 1-15. The information from each database was collated – worksheet 16. Huntingtin protein interaction partners were ranked according to the number of databases they were found in as well as the number of different experiments detailing the interaction with huntingtin – worksheet 17.

Table S4. Subset of CORUM core complexes that consistently co-fractionate (Feb 2017 CORUM release). Complexes were chosen if they were significantly enriched for pairwise interactions in three published co-fractionation interactomes (Wan et al. 2015, Havugimana et al. 2012, and Kirkwood et al. 2013). Enrichment was calculated with a hypergeometric test, and significance was evaluated at four thresholds: p < 1, p < 1e-2, p < 1e-6, and p < 1e-10. All data aside from columns “p < 1”, “p < 1e-2”, “p < 1e-6”, and “p < 1e-10” are taken from the CORUM core data file. (XLSX 202 kb)

The dataset is a subset of the expert curated PPI dataset based on the proteins with an association to Alzheimer’s disease available from IntAct molecular interaction database https://www.ebi.ac.uk/intact/. The confidence level of each interaction is characterised by IntAct MI score.Dataset was downloaded from IntAct database version 4.2.6.

Protein-Protein, Genetic, and Chemical Interactions for Kathiresan T (2009):A protein interaction network for the large conductance Ca(2+)-activated K(+) channel in the mouse cochlea. curated by BioGRID (https://thebiogrid.org); ABSTRACT: The large conductance Ca(2+)-activated K(+) or BK channel has a role in sensory/neuronal excitation, intracellular signaling, and metabolism. In the non-mammalian cochlea, the onset of BK during development correlates with increased hearing sensitivity and underlies frequency tuning in non-mammals, whereas its role is less clear in mammalian hearing. To gain insights into BK function in mammals, coimmunoprecipitation and two-dimensional PAGE, combined with mass spectrometry, were used to reveal 174 putative BKAPs from cytoplasmic and membrane/cytoskeletal fractions of mouse cochlea. Eleven BKAPs were verified using reciprocal coimmunoprecipitation, including annexin, apolipoprotein, calmodulin, hippocalcin, and myelin P0, among others. These proteins were immunocolocalized with BK in sensory and neuronal cells. A bioinformatics approach was used to mine databases to reveal binary partners and the resultant protein network, as well as to determine previous ion channel affiliations, subcellular localization, and cellular processes. The search for binary partners using the IntAct molecular interaction database produced a putative global network of 160 nodes connected with 188 edges that contained 12 major hubs. Additional mining of databases revealed that more than 50% of primary BKAPs had prior affiliations with K(+) and Ca(2+) channels. Although a majority of BKAPs are found in either the cytoplasm or membrane and contribute to cellular processes that primarily involve metabolism (30.5%) and trafficking/scaffolding (23.6%), at least 20% are mitochondrial-related. Among the BKAPs are chaperonins such as calreticulin, GRP78, and HSP60 that, when reduced with siRNAs, alter BKalpha expression in CHO cells. Studies of BKalpha in mitochondria revealed compartmentalization in sensory cells, whereas heterologous expression of a BK-DEC splice variant cloned from cochlea revealed a BK mitochondrial candidate. The studies described herein provide insights into BK-related functions that include not only cell excitation, but also cell signaling and apoptosis, and involve proteins concerned with Ca(2+) regulation, structure, and hearing loss.