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 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.

The microbial protein interaction database (MPIDB) provides physical microbial interaction data. The interactions are manually curated from the literature or imported from other databases, and are linked to supporting experimental evidence, as well as evidences based on interaction conservation, protein complex membership, and 3D domain contacts.

This database is a collection of protein-protein homo- and hetero-complexes as well as domain-domain structures. This issue of the database contains 17.024 entries (as of October 2007) of which 1350 are two-chain protein hetero-complexes, 7773 homodimers and 1589 are one-chain proteins parsed into two domains (domain structures).

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.

A web-based database of protein interaction sites. PiSITE provides not only information of interaction sites of a protein from single PDB entry, but also information of interaction sites of a protein from multiple PDB entries including similar proteins. PiSite also provides a list of sociable proteins, proteins with multiple binding states and multiple binding partners.In PiSITE, the identification of the binding sites of protein chains is performed by searching the same proteins with different binding states in PDB at first, and then mapping those binding sites onto the query proteins. The database PiSITE provides real interaction sites of proteins using the complex structures in PDB. According to the progress of several structural genomic projects, we have a large amount of structural data in PDB. Consequently, we can observe different binding states of proteins in atomic resolutions, and can analyze actual interaction sites of proteins. It will lead better understandings of protein interaction sites in near future. Usual practice to identify the interaction site has been done using a representative complex in PDB. However, for the proteins with multiple partners, non-interaction sites identified by using a single complex structure is not enough, because some part of the non-binding sites may be involved in the interaction sites with another proteins. Therefore, the real interaction sites should be obtained by using all of the binding states in PDB. For the purpose, the identifications of the binding site in PiSITE are done by searching the same proteins with different binding sites in PDB at first, and then mapping the binding sites onto the query proteins. PiSITE also provides the lists of transient hub proteins, which we call sociable proteins to clarify the different of so-called hub proteins. The sociable proteins are identified as the proteins with multiple binding states and multiple binding partners. On the other hand, so-called hub proteins have been identified as the proteins at the hub position in protein-protein interaction networks obtained by large-scale experiments, but the definition of the hub proteins cannot differentiate transient hub proteins from stable ones, although the differentiation is critically important for the better understanding of protein interaction networks. In addition, the usual definition of hub proteins can contain supermolecules as hub proteins. The supermolecules can be identified as the proteins with a single binding state and multiple binding partners, which we call stable hub proteins.

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.

The Division of Acquired Immunodeficiency Syndrome (DAIDS) of the National Institute of Allergy and Infectious Diseases (NIAID) has initiated a project, in collaboration with Southern Research Institute and the National Center for Biotechnology Information (NCBI), designed to compile a comprehensive database of the described interactions between HIV-1 and cellular proteins. The goal of this project is to provide scientists in the field of HIV/AIDS research 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. This database has been designed to track the following information for each protein-protein interaction identified in the literature: * 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. * National Library of Medicine (NLM) PubMed identification numbers (PMIDs) for all journal articles describing the interaction.

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.

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.

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 prediction of protein complexes from protein-protein interactions (PPIs) is a well-studied problem in bioinformatics. However, the currently available PPI data is not enough to describe all known protein complexes. In this paper, we express the problem of determining the minimum number of (additional) required protein-protein interactions as a graph theoretic problem under the constraint that each complex constitutes a connected component in a PPI network. For this problem, we develop two computational methods: one is based on integer linear programming (ILPMinPPI) and the other one is based on an existing greedy-type approximation algorithm (GreedyMinPPI) originally developed in the context of communication and social networks. Since the former method is only applicable to datasets of small size, we apply the latter method to a combination of the CYC2008 protein complex dataset and each of eight PPI datasets (STRING, MINT, BioGRID, IntAct, DIP, BIND, WI-PHI, iRefIndex). The results...

Analyze the landscape of protein complex nomenclature resources, species-agnostic.

Peptides are important biomolecules and their binding interactions with proteins make them useful in a variety of sensing and therapeutic applications. The development of computational methods to design peptides can benefit from having high-quality structures of peptide-protein complexes matched with experimental measurements of their thermodynamic properties. The Predicted and Experimental Peptide Binding Information (PEPBI) database contains 329 predicted peptide-protein complexes, with each complex based on an experimentally determined structure, and corresponding experimental measurements of changes in Gibbs free energy (DG), enthalpy (DH), and entropy (DS). In addition, PEPBI includes 40 predicted properties for each complex that were computationally calculated using the Rosetta Interface Analyzer. PEPBI is expected to be of use in the development of computational methods for designing peptides that bind to protein targets., This database has an associated publication and the methods of its generation are described in comprehensive detail there. Briefly, PEPBI was curated using a five-step process. The first step was the definition of the criteria for complexes to be included. In the next step, a literature review was conducted to identify the peptide-protein complexes that should be part of PEPBI. Once they were known, complexes that exactly match the proteins and peptides used in the ITC experiments were computationally predicted. This was followed by calculating properties of those complexes using the Rosetta Interface Analyzer. Ultimately, this yielded the PEPBI database of 329 peptide-protein complexes with matched thermodynamic data., , # Predicted and Experimental Peptide Binding Information (PEPBI) Database

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This dataset is the publicly available data associated with A Paired Database of Predicted and Experimental Protein Peptide Binding Information, submitted to Scientific Data and hereafter referred to as the PEPBI manuscript. The Predicted and Experimental Peptide Binding Information (PEPBI) dataset provides curated thermodynamic and structural data for protein–peptide complexes. It includes:

• Experimental Data: Isothermal titration calorimetry (ITC) data collected through an extensive literature review, compiled in the PEPBI.xlsx Excel spreadsheet.
• Computational Data: Computationally predicted protein–peptide structures in .pdb format, each derived from a shared reference structure (template). These templates are experimentally determined structures from the Protein Data Bank (PDB) and are used as the foundation for ...,

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.

A database of manually annotated mammalian protein complexes. To obtain a high-quality dataset, information was extracted from individual experiments described in the scientific literature. Data from high-throughput experiments was not included.

IPLID integrates protein-ligand interaction data from multiple well-known resources, including BindingDB, ChEMBL, DrugBank, GPCRDB, PubChem, LINCS-HMS KinomeScan, and four published kinome assay results. Our database can facilitate projects in machine learning or deep learning-based drug development and other applications by providing integrated data sets appropriate for many research interests. Our database can be utilized for small-scale (e.g. kinases or GPCRs only) and large-scale (e.g. proteome-wide), qualitative or quantitative projects. With its ease of use and straightforward data format, IPLID offers a great educational resource for computer science and data science trainees who lack familiarity with chemistry and biology.

Data statistics

Target (data type) Activities | Unique chemicals | Unique proteins | File name

All (binary) 96318 | 18107 | 3107 | integrated_binary_activity.tsv

All (numerical) 2798365 | 683009 | 5876 | integrated_continuous_activity.tsv

CYP450 (binary) 67552 | 17273 | 47 | integrated_cyp450_binary.tsv

CRT (binary) 4152 | 1219 | 412 | integrated_cancer_related_targets_binary.tsv

CDT (binary) 519 | 349 | 88 | integrated_cardio_targets_binary.tsv

DRT (binary) 4433 | 1325 | 852 | integrated_disease_related_targets_binary.tsv

FDA (binary) 6217 | 1521 | 592 | integrated_fda_approved_targets_binary.tsv

GPCR (binary) 1958 | 545 | 129 | integrated_gpcr_binary.tsv

NR (binary) 1335 | 657 | 264 | integrated_nr_binary.tsv

PDT (binary) 1469 | 674 | 404 | integrated_potential_drug_targets_binary.tsv

TF (binary) 1966 | 998 | 304 | integrated_tf_binary.tsv

*Abbreviations: CYP450 (Cytochrome P450), CRT (Cancer-Related Target), CDT (Cardiovascular Disease candidate Target), DRT (Disease-Related Target), FDA (FDA-approved target), GPCR (G-Protein Coupled Receptor), NR (Nuclear Receptor), PDT (Potential Drug Target), TF (Transcription Factor)

*These protein classifications are from UniProt database and the Human Protein Atlas (https://www.proteinatlas.org/)

IPLID data statistics

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.