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http://www.sanger.ac.uk/cgi-bin/teams/team30/arnie

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 1,2023. Database that integrates the extracellular protein interaction network generated in our lab using AVEXIS technology with spatiotemporal expression patterns for all genes in the network. The tool allows users to browse the network by clicking on individual proteins, or by specifying the spatiotemporal parameters. Clicking on connector lines will allow users to compare stage-matched expression patterns for genes encoding interacting proteins. Additionally, users can rapidly search for their genes in the network using the BLAST server provided.

Proper citation: ARNIE (RRID:SCR_000514) Copy   

•   Source: SciCrunch Registry

http://stdgen.northwestern.edu/stdgen/bacteria/hhv1/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 26, 2016. The scope of the project includes molecular information pertaining to oral pathogens, bacterial and viral. The website contains a table of protein-protein interactions for human herpesvirus 1. It is operated for the U.S. Department of Energy's National Nuclear Security Administration.

Proper citation: Protein-Protein Interactions Table for Human herpesvirus 1 (RRID:SCR_000397) Copy   

•   Source: SciCrunch Registry

http://gpcr.biocomp.unibo.it/esldb

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 22,2022. database of protein subcellular localization annotation for eukaryotic organisms. It contains experimental annotations derived from primary protein databases, homology based annotations and computational predictions.

Proper citation: eSLDB - eukaryotic Subcellular Localization database (RRID:SCR_000052) Copy   

•   Source: SciCrunch Registry

http://interolog.gersteinlab.org/

Interolog/Regulog quantitatively assess the degree to which interologs can be reliably transferred between species as a function of the sequence similarity of the corresponding interacting proteins.

Proper citation: Interolog/Regulog Database (RRID:SCR_000755) Copy   

•   Source: SciCrunch Registry

http://cddb.nhlbi.nih.gov/cddb/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 16, 2013. This database is intended to serve as a learning tool to obtain curated information for the design of microarray targets to scan collecting duct tissues (human, rat, mouse). The database focuses on regulatory and transporter proteins expressed in the collecting duct, but when collecting duct proteins are a member of a larger family of proteins, common additional members of the family are included even if they have not been demonstrated to be expressed in the collecting duct. An Internet-accessible database has been devised for major collecting duct proteins involved in transport and regulation of cellular processes. The individual proteins included in this database are those culled from literature searches and from previously published studies involving cDNA arrays and serial analysis of gene expression (SAGE). Design of microarray targets for the study of kidney collecting duct tissues is facilitated by the database, which includes links to curated base pair and amino acid sequence data, relevant literature, and related databases. Use of the database is illustrated by a search for water channel proteins, aquaporins, and by a subsequent search for vasopressin receptors. Links are shown to the literature and to sequence data for human, rat, and mouse, as well as to relevant web-based resources. Extension of the database is dynamic and is done through a maintenance interface. This permits creation of new categories, updating of existing entries, and addition of new ones. CDDB is a database that organizes lists of genes found in collecting duct tissues from three mammalian species: human, rat, and mouse. Proteins are divided into categories by family relationships and functional classification, and each category is assigned a section in the database. Each section includes links to the literature and to sequence information for genes, proteins, expressed sequence tags, and related information. The user can peruse a section or use a search engine at the bottom of the web page to search the database for a name or abbreviation or for a link to a sequence. Each entry in the database includes links to relevant papers in the kidney and collecting duct literature. It uses links to PubMed to generate MEDLINE searches for retrieval of references. In addition, each entry includes links to curated sequence data available in LocusLink. Individual links are made to sequence and protein data for human, rat, and mouse. Links are then added as curated sequences become available for proteins identified in the renal collecting duct and for proteins identified in kidney and similar in function or homologous to proteins identified in the collecting duct.

Proper citation: Collecting Duct Database (RRID:SCR_000759) Copy   

•   Source: SciCrunch Registry

http://www.proglycprot.org/

Manually curated, comprehensive repository of experimentally characterized bacterial glycoproteins and archaeal glycoproteins, generated from an exhaustive literature search. This is the focused effort to provide concise relevant information derived from rapidly expanding literature on prokaryotic glycoproteins, their glycosylating enzyme(s), glycosylation linked genes, and genomic context thereof, in a cross-referenced manner. The database is arranged into two sections namely, ProCGP and ProUGP. ProCGP is the main section containing characterized prokaryotic glycoproteins, defined as entries with at least one experimentally known glycosylated residue (glycosite). Whereas, ProUGP is the supplementary section, presenting uncharacterized prokaryotic glycoproteins, defined as entries with experimentally identified glycosylation but unidentified glycosites. The ProGlycProt has been developed with to aid and advance the emerging scientific interests in understanding the mechanisms, implications, and novelties of protein glycosylation in prokaryotes that include many pathogenic as well as economically important bacterial species. The website supports a dedicated structure gallery of homology models and crystal structures of characterized glycoproteins in addition to two new tools developed in view of emerging information about prokaryotic sequons (conserved sequences of amino acids around glycosites) that are never or rarely seen in eukaryotic glycoproteins. ProGlycProt provides an extensive compilation of experimentally identified glycosites (334) and glycoproteins (340) of prokaryotes that could serve as an information resource for research and technology applications in glycobiology. A general data update policy is once in three months. Existing entries are updated in real-time.

Proper citation: ProGlycProt (RRID:SCR_000622) Copy   

•   Source: SciCrunch Registry

http://biozon.org

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Biozon is a unified biological resource on DNA sequences, proteins, complexes and cellular pathways. It currently provides data on pairwise similarities between proteins, the domain structure of proteins, structural similarities, threading-based and profile-profile similarities between protein families. Additional information about 3D models, predicted protein-protein interactions, assignment of genes to pathways and expression data analysis, as well as local and global maps of the protein space will be gradually added to Biozon.

Proper citation: Biozon (RRID:SCR_000725) Copy   

•   Source: SciCrunch Registry

http://pstiing.icr.ac.uk/

A publicly accessible knowledgebase about protein-protein, protein-lipid, protein-small molecules, ligand-receptor interactions, receptor-cell type information, transcriptional regulatory and signal transduction modules relevant to inflammation, cell migration and tumourigenesis. It integrates in-house curated information from the literature, biochemical experiments, functional assays and in vivo studies, with publicly available information from multiple and diverse sources across human, rat, mouse, fly, worm and yeast. The knowledgebase allowing users to search and to dynamically generate visual representations of protein-protein interactions and transcriptional regulatory networks. Signalling and transcriptional modules can also be displayed singly or in combination. This allow users to identify important "cross-talks" between signalling modules via connections with key components or "hubs". The knowledgebase will facilitate a "systems-wide" understanding across many protein, signalling and transcriptional regulatory networks triggered by multiple environmental cues, and also serve as a platform for future efforts to computationally and mathematically model the system behavior of inflammatory processes and tumourigenesis.

Proper citation: pSTIING (RRID:SCR_002045) Copy   

•   Source: SciCrunch Registry

http://atgc.lbl.gov/atgc/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. ATGC stands for Alignable Tight Genomic Cluster, which is cluster of closely related prokaryotic genomes. ATGC is the principal notion of this web resource. The purpose of this web resource is to prepare ATGC-derived data sets for a variety of research projects in functional and evolutionary genomics. Unique features of ATGC include: * Reliable identification of orthologs (high degree of similarity between the genomes in the set allow an extensive use of synteny in ortholog identification); * Fine granularity of protein classification (in comparisons of more distant genomes, proteins belonging to families of paralogs are often lumped into a singlegroup; under the ATGC approach, comparison of genomic sequences from highly similar genomes allows one to track each set of orthologs separately); * Relative rarity of changes of any kind (in sequence, genome organization and gene content) allows the use of parsimony-related methods of analysis.

Proper citation: Alignable Tight Genomic Cluster (RRID:SCR_001894) Copy   

•   Source: SciCrunch Registry

http://mint.bio.uniroma2.it/

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.

Proper citation: MINT (RRID:SCR_001523) Copy   

•   Source: SciCrunch Registry

http://www.uniprot.org/

Collection of data of protein sequence and functional information. Resource for protein sequence and annotation data. Consortium for preservation of the UniProt databases: UniProt Knowledgebase (UniProtKB), UniProt Reference Clusters (UniRef), and UniProt Archive (UniParc), UniProt Proteomes. Collaboration between European Bioinformatics Institute (EMBL-EBI), SIB Swiss Institute of Bioinformatics and Protein Information Resource. Swiss-Prot is a curated subset of UniProtKB.

Proper citation: UniProt (RRID:SCR_002380) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/swissprot/hpi/hpi.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 03, 2011. IT HAS BEEN REPLACED BY A NEW UniProtKB/Swiss-Prot ANNOTATION PROGRAM CALLED UniProt Chordata protein annotation program. The Human Proteome Initiative (HPI) aims to annotate all known human protein sequences, as well as their orthologous sequences in other mammals, according to the quality standards of UniProtKB/Swiss-Prot. In addition to accurate sequences, we strive to provide, for each protein, a wealth of information that includes the description of its function, domain structure, subcellular location, similarities to other proteins, etc. Although as complete as currently possible, the human protein set they provide is still imperfect, it will have to be reviewed and updated with future research results. They will also create entries for newly discovered human proteins, increase the number of splice variants, explore the full range of post-translational modifications (PTMs) and continue to build a comprehensive view of protein variation in the human population. The availability of the human genome sequence has enabled the exploration and exploitation of the human genome and proteome to begin. Research has now focused on the annotation of the genome and in particular of the proteome. With expert annotation extracted from the literature by biologists as the foundation, it has been possible to expand into the areas of data mining and automatic annotation. With further development and integration of pattern recognition methods and the application of alignments clustering, proteome analysis can now be provided in a meaningful way. These various approaches have been integrated to attach, extract and combine as much relevant information as possible to the proteome. This resource should be valuable to users from both research and industry. We maintain a file containing all human UniProtKB/Swiss-Prot entries. This file is updated at every biweekly release of UniProt and can be downloaded by FTP download, HTTP download or by using a mirroring program which automatically retrieves the file at regular intervals.

Proper citation: Human Proteomics Initiative (RRID:SCR_002373) Copy   

•   Source: SciCrunch Registry

https://omictools.com/protein-interactions-and-molecular-information-database-tool

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.

Proper citation: Protein interaction and molecular information database (RRID:SCR_002096) Copy   

•   Source: SciCrunch Registry

http://bibiserv.techfak.uni-bielefeld.de/agt-sdp/

Database providing automatic test cases for protein-protein docking. A consensus-type approach is proposed processing the whole PDB and classifying protein structures into complexes and unbound proteins by combining information from three different approaches. Out of this classification test cases are generated automatically. All calculations were run on the database. The information stored is available via a web interface. The user can choose several criteria for generating his own subset out of the test cases, e.g. for testing docking algorithms. In unbound protein--protein docking, the complex of two proteins is predicted using the unbound conformations of the proteins (Halperin et al.,2002). For testing of docking algorithms, two unbound proteins which form a known complex have to be identified, so that the result of the docking algorithm can be compared to the known complex. For the identification of test cases, the structures taken from the PDB have to be classified as unbound proteins or complexes and unbound proteins with a 100% sequence identity to one complex part have to be searched. By now, most groups use handpicked test sets. The largest collection of test cases used so far is described by Chen et al. (Chen et al.,2003) and contains 31 test cases for unbound docking. Because of the exponential growth of available protein structures in the PDB, automatic generation of test cases will become more and more important in the future.

Proper citation: Automatic Generated Test-Sets Database for Protein-Protein Docking (RRID:SCR_002281) Copy   

•   Source: SciCrunch Registry

http://domine.utdallas.edu

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 13,2026. 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)

Proper citation: DOMINE: Database of Protein Interactions (RRID:SCR_002399) Copy   

•   Source: SciCrunch Registry

http://www.bindingmoad.org/

Database of protein-ligand crystal structures that is a subset of the Protein Data Bank (PDB), containing every high-quality example of ligand-protein binding. The resolved protein crystal structures with clearly identified biologically relevant ligands are annotated with experimentally determined binding data extracted from literature. A viewer is provided to examine the protein-ligand structures. Ligands have additional chemical data, allowing for cheminformatics mining. The binding-affinity data ranges 13 orders of magnitude. The issue of redundancy in the data has also been addressed. To create a nonredundant dataset, one protein from each of the 1780 protein families was chosen as a representative. Representatives were chosen by tightest binding, best resolution, etc. For the 1780 best complexes that comprise the nonredundant version of Binding MOAD, 475 (27%) have binding data. This collection of protein-ligand complexes will be useful in elucidating the biophysical patterns of molecular recognition and enzymatic regulation. The complexes with binding-affinity data will help in the development of improved scoring functions and structure-based drug discovery techniques.

Proper citation: Binding MOAD (RRID:SCR_002294) Copy   

•   Source: SciCrunch Registry

http://edas2.bioinf.fbb.msu.ru/

Databases of alternatively spliced genes with data on the alignment of proteins, mRNAs, and EST. It contains information on all exons and introns observed, as well as elementary alternatives formed from them. The database makes it possible to filter the output data by changing the cut-off threshold by the significance level. It contains splicing information on human, mouse, dog (not yet functional) and rat (not yet functional). For each database, users can search by keyword or by overall gene expression. They can also view genes based on chromosomal arrangement or other position in genome (exon, intron, acceptor site, donor site), functionality, position, conservation, and EST coverage. Also offered is an online Fisher test.

Proper citation: EDAS - EST-Derived Alternative Splicing Database (RRID:SCR_002449) Copy   

•   Source: SciCrunch Registry

http://genome.jouy.inra.fr/spid/

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.

Proper citation: Subtilis Protein interaction Database (RRID:SCR_002123) Copy   

•   Source: SciCrunch Registry

http://www.pubgene.org/

It helps users retrieve information on genes and proteins. The underlying structure of PubGene can be viewed as a gene-centric database. Gene and protein names are cross-referenced to each other and to terms that are relevant to understanding their biological function, importance in disease and relationship to chemical substances. The result is a literature network organizing information in a form that is easy to navigate.

Proper citation: PubGene (RRID:SCR_002119) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/cdd

Database of annotations of functional units in proteins including multiple sequence alignment models for ancient domains and full-length proteins. This collection of models includes 3D structures that display the sequence/structure/function relationships in proteins. It also includes alignments of the domains to known three-dimensional protein structures in the MMDB database. The source databases are Pfam, Smart, and COG. Users can identify amino acids in protein sequences with the resources available as well as view single sequences embedded within multiple sequence alignments.

Proper citation: Conserved Domain Database (RRID:SCR_002077) Copy   

•   Source: SciCrunch Registry