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http://prism.ccbb.ku.edu.tr/prism/

It is a web-server that can be used to explore protein interfaces and predict protein-protein interactions. It is a website for protein interface analysis and prediction of putative protein-protein interactions. It is composed of a database holding protein interface structures derived from the Protein Data Bank (PDB). The server also includes summary information about related proteins and an interactive protein interface viewer. A list of putative protein-protein interactions obtained by running our prediction algorithm can also be accessed. These results are applied to a set of protein structures obtained from the PDB at the time of algorithm execution. Users can browse through the non-redundant dataset of representative interfaces on which the prediction algorithm depends, retrieve the list of similar structures to these interfaces or see the results of interaction predictions for a particular protein. Another service provided is interactive prediction. This is done by running the algorithm for user input structures.

Proper citation: Protein Interactions by Structural Matching (RRID:SCR_002116) Copy   

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http://hupi.ircm.qc.ca/hupi/

The Human Proteotheque Initiative is a multidisciplinary project aimed at building a repertoire of comprehensive maps of human protein interaction networks. The information contained in the Proteotheque is made publicly available through an interactive web site that can be consulted to visualize some of the fundamental molecular connections formed in human cells and to determine putative functions of previously uncharacterized proteins based on guilt by association. The process governing the evolution of HuPI towards becoming a repository of accurate and complete protein interaction maps is described.

Proper citation: Database of the Human Proteotheque Initiative (RRID:SCR_002076) Copy   

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http://www.jcvi.org/mpidb

Database that collects and provides all known physical microbial interactions. Currently, 24,295 experimentally determined interactions among proteins of 250 bacterial species/strains can be browsed and downloaded. These microbial interactions have been manually curated from the literature or imported from other databases (IntAct, DIP, BIND, MINT) and are linked to 26,578 experimental evidences (PubMed ID, PSI-MI methods). In contrast to these databases, interactions in MPIDB are further supported by 68,346 additional evidences based on interaction conservation, co-purification, and 3D domain contacts (iPfam, 3did). (spoke/matrix) binary interactions inferred from pull-down experiments are not included.

Proper citation: MPIDB (RRID:SCR_001898) Copy   

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http://www.bacteriome.org

Database integrating physical (protein-protein) and functional interactions within the context of an E. coli knowledgebase. Presently the resource offers access to two types of network: * A network of functional interactions derived through exploiting available functional genomic datasets within a Bayesian framework * Two networks of experimentally derived protein-protein interactions - a "core" network consisting of interactions deemed to be of "high quality"; and an "extended" network which extends the "core" network by including interactions for which experimental evidence is less strong.

Proper citation: Bacteriome.org (RRID:SCR_001934) Copy   

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http://kinase.bioinformatics.tw/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. A comprehensive human kinase interactome and phospho-protein database. PhosphoPOINT also annotates any amino acids near the phosphorylation sites where a cSNP may cause a phosphorylation site to be lost, and at the same time identifies how such alteration of the phosphorylation site may lead to human disease. PhosphoPOINT integrates 4,195 phospho-proteins, 518 serine/threonine/tyrosine kinases, and their corresponding PPI datasets with the goal of delineating the interactions among kinases, their potential substrates and their interacting (phospho)-proteins. PhosphoPOINT has integrated human protein kinases, phospho-proteins, and PPI datasets with the goal to delineate four kinds of links among kinases. These include their interacting proteins, substrates, and substrates as well as interacting phospho-proteins. Some of these interacting proteins for kinases are phospho-proteins, which might have the potential to serve as substrates for the interacting kinases.

Proper citation: PhosphoPOINT (RRID:SCR_002109) Copy   

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http://crbs.ucsd.edu/

CRBS is a UCSD organized research unit (ORU) that exists to provide human resources, high technology equipment, and administrative services to researchers engaged in fundamental research on cell structure and function relationships in central nervous system processes, cardiovascular networking, and muscular contraction through multiple scales and modalities. CRBS scientists investigate these processes through invention, refinement, and deployment of sophisticated technologies, especially: - High-powered electron microscopes that reveal three-dimensional cell structures - State-of-the-art X-ray crystallography and magnetic resonance analysis that provide detail on protein structures at high-resolution - Laser-scanning and confocal light microscopes that reveal molecules tagged with fluorescent markers as they traffic within cells and pass transfer signals within and between cells - High performance computing and grid-based integration of distributed data CRBS facilitates an interdisciplinary infrastructure in which people from biology, medicine, chemistry, and physics can work with those from computer science and information technologies in collaborative research. Researchers share interests in the study of complex biological systems at many scales, from the structures of enzymes, proteins, and the body's chemical communications network at atomic and molecular levels, to an organism's physiology, strength, and support at cellular and tissue levels. The CRBS infrastructure integrates resources for high-performance computing, visualization, and database technologies, and the grid-integration of large amounts of archival storage data. The California Institute for Telecommunications and Information Technology (Cal-IT2) and the San Diego Supercomputer Center (SDSC) are collaborators in simulating the activity of biological systems, analyzing the results, and organizing the growing storehouse of biological information. CRBS is an entity evolving as research evolves. It forges interactions with biotechnology and biocomputing companies for technology transfer. Interaction, collaboration, and multiscale research produce new perspectives, reveal fruitful research topics, lead to the development of new technologies and drugs, and train a new generation of researchers in biological systems. Sponsors: CRBS is supported by the University of California at San Diego.

Proper citation: Center for Research in Biological Systems (RRID:SCR_002666) Copy   

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http://www.novusbio.com/

Commercial antibody vendor which supplies antibodies and other products to life science researchers.

Proper citation: Novus Biologicals (RRID:SCR_004286) Copy   

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http://cudasw.sourceforge.net/

CUDASW++ is a bioinformatics software for Smith-Waterman protein database searches that takes advantage of the massively parallel CUDA architecture of NVIDIA Tesla GPUs to perform sequence searches 10x-50x faster than NCBI BLAST. In this algorithm, we deeply explore the SIMT (Single Instruction, Multiple Thread) and virtualized SIMD (Single Instruction, Multiple Data) abstractions to achieve fast speed. This algorithm has been fully tested on Tesla C1060, Tesla C2050, GeForce GTX 280 and GTX 295 graphics cards, and has been incorporated to NVIDIA Tesla Bio Workbench. * Operating System: Linux * Programming language: CUDA and C * Other requirements: CUDA SDK and Toolkits 2.0 or higher

Proper citation: CUDASW++ (RRID:SCR_008862) Copy   

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http://expasy.org/

Portal which provides access to scientific databases and software tools (i.e., resources) in different areas of life sciences including proteomics, genomics, phylogeny, systems biology, population genetics, transcriptomics etc. It contains resources from many different SIB groups as well as external institutions.

Proper citation: ExPASy Bioinformatics Resource Portal (RRID:SCR_012880) Copy   

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https://www.jax.org/research-and-faculty/resources/optogenetics-resource

Mouse lines expressing proteins that activate, inhibit or detect neuronal activity are available from The Jackson Laboratory Repository. Many of these strains have been generated by HHMI Janelia Farm GENIE Project or by Allen Institute for Brain Science.

Proper citation: Optogenetics Resource at JAX (RRID:SCR_017508) Copy   

•   Source: SciCrunch Registry

http://www.sanger.ac.uk/resources/software/vagrent/

Software tool set for calculating the biological consequences of genomic variations. The suite of perl modules compares genomic variations with reference genome annotations and generates the possible effects each variant may have on the transcripts it overlaps. It evaluates each variation/transcript combination and describes the effects in the mRNA, CDS and protein sequence contexts. It provides details of the sequence and position of the change within the transcript / protein as well as Sequence Ontology terms to classify its consequences.

Proper citation: VAGrENT (RRID:SCR_005180) Copy   

•   Source: SciCrunch Registry

http://www.i3crb.fr/

Project to improve data and sample exchanges and to facilitate large scale analysis of data by improving interoperability of French Biological Resources Centres (BRC or biobanks) IT systems and biological databases. The work done in this project will be linked to other national (IBiSA, ANR, R��seau des Biobanques, Club 3C-R), European (BBMRI, ELIXIR) or international project (P3G). In the preliminary phase (2009-2010) I3-CRB has developed a directory of French Biological Resource Centres / Biobanks where one may register their French BRC or perform a search across all of them. Detail by overall data or kingdom is provided as well as many filtering options. Access to biological samples is provided by the participating BRC''''s. Biological Resources Centres (BRC or biobanks) collect annotated biological samples from various sources (human, animal, plant, bacteria...). The type of samples depends on the collection and the associated thematic (DNA, proteins, cells, tissues, blood, serum, organisms...). The aims of these centers are to collect, to store, to transform and to distribute the biological samples. They constitute a vital infrastructure for life science and health research. Goals of the French Biobanks/Biological Resource Centres: * List French biobanks and their biological collections * Improve sample exchanges * Improve the international visibility of the French biological collections MeSH terms have been integrated: Domains, diseases, and location of the disease (Anatomy). Collections/species are based on NCBI Taxonomy.

Proper citation: I3-CRB: Interoperable IT Infrastructure for Biological Resources Centres / Biobanks - France (RRID:SCR_006991) Copy   

•   Source: SciCrunch Registry

http://www.cbil.upenn.edu/ParaDBs/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on October 28,2025. These databases were constructed by extracting the organism specific ESTs from dbEST, removing polyA sequences from the ends and trimming 5' and 3' regions with greater than 25% N's in a 20 base pair window. These quality sequences were then aligned using the cap2 program and the consensus sequences thus generated put into a database that is available on the web. A number of parasitic organisms were chosen that have between 3000 and 15000 ESTs. The attempt here is to provide useful information and analyses to the scientific community without curating the results in any way. A total of 55192 ESTs, deposited into dbEST/GenBank, were included in the analyses. The resulting sequences have been clustered into nonredundant gene assemblies and deposited into a relational database that supports a variety of sequence and text searches. This database has been used to compare the gene assemblies using BLAST similarity comparisons to the public protein databases to identify putative genes. Of these new entries, approximately 15%-20% represent putative homologs with a conservative cutoff of p < 10(-9), thus identifying many conserved genes that are likely to share common functions with other well-studied organisms. Gene assemblies were also used to identify strain polymorphisms, examine stage-specific expression, and identify gene families. An interesting class of genes that are confined to members of this phylum and not shared by plants, animals, or fungi, was identified. These genes likely mediate the novel biological features of members of the Apicomplexa and hence offer great potential for biological investigation and as possible therapeutic targets.

Proper citation: Parasite Databases of Clustered ESTs (RRID:SCR_002262) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/compneur-srv/LGICdb/

Database providing access to information about transmembrane proteins that exist under different conformations, with three primary subfamilies: the cys-loop superfamily, the ATP gated channels superfamily, and the glutamate activated cationic channels superfamily. Due to the lack of evolutionary relationship, these three superfamilies are treated separately. It currently contains 554 entries of ligand-activated ion channel subunits. In this database one may find: the nucleic and proteic sequences of the subunits. Multiple sequence alignments can be generated, and some phylogenetic studies of the superfamilies are provided. Additionally, the atomic coordinates of subunits, or portion of subunits, are provided when available. Redundancy is kept to a minimum, i.e. one entry per gene. Each entry in the database has been manually constructed and checked by a researcher of the field in order to reduce the inaccuracies to a minimum. NOTE: This database is not actively maintained anymore. People should not consider it as an up-to-date trustable resource. For any new work, they should consider using alternative sources, such as UniProt, Ensembl, Protein Databank etc.

Proper citation: Ligand-Gated Ion Channel Database (RRID:SCR_002418) Copy   

•   Source: SciCrunch Registry

http://www.hiv.lanl.gov/content/immunology/index

An annotated, searchable collection of HIV-1 cytotoxic and helper T-cell epitopes and antibody binding sites, plus related tools and information. The goal of this database is to provide a comprehensive listing of defined HIV epitopes. These data are also printed in the HIV Molecular Immunology compendium, which is updated yearly and provided free of charge to scientific researchers, both by online download and as a printed copy. The data included in this database are extracted from the HIV immunology literature. HIV-specific B-cell and T-cell responses are summarized and annotated. Immunological responses are divided into three sections, CTL (CD8+), T helper (CD4+), and antibody. Within these sections, defined epitopes are organized by protein and binding sites within each protein, moving from left to right through the coding regions spanning the HIV genome. We include human responses to natural HIV infections, as well as vaccine studies in a range of animal models and human trials. Responses that are not specifically defined, such as responses to whole proteins or monoclonal antibody responses to discontinuous epitopes, are summarized at the end of each protein sub-section. Studies describing general HIV responses to the virus, but not to any specific protein, are included at the end of each section. The annotation includes information such as cross-reactivity, escape mutations, antibody sequence, TCR usage, functional domains that overlap with an epitope, immune response associations with rates of progression and therapy, and how specific epitopes were experimentally defined. Basic information such as HLA specificities for T-cell epitopes, isotypes of monoclonal antibodies, and epitope sequences are included whenever possible. All studies that we can find that incorporate the use of a specific monoclonal antibody are included in the entry for that antibody. A single T-cell epitope can have multiple entries, generally one entry per study. Finally, tables and maps of all defined linear epitopes relative to the HXB2 reference proteins are provided. Alignments of CTL, helper T-cell, and antibody epitopes are available through the search interfaces. Only responses to HIV-1 and HIV-2 are included in the database.

Proper citation: HIV Molecular Immunology Database (RRID:SCR_002893) Copy   

•   Source: SciCrunch Registry

http://karg.cbi.pku.edu.cn

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 14,2026. Database of data and knowledge linking genes and chromosome regions to addiction that were extracted from reviewing more than 1,000 peer-reviewed publications from between 1976 and 2006. This list of publications included review papers on addiction selected from results of PUBMED query "(addiction OR drug abuse) AND review" as well as research papers selected from PUBMED query "(addiction OR drug abuse) AND (gene OR microarray OR proteomics OR QTL OR population association OR genetic linkage)". The data spanned multiple technology platforms including classical hypothesis-testing of single genes, identification of significantly differentially expressed genes in microarray experiments, identification of significantly differentially expressed proteins in proteomics assays, identification of addiction-vulnerable chromosome regions in animal QTL studies, genetic linkage studies, population association studies, and OMIM annotations. From each publication they collected the genes, proteins, or chromosome regions linked to addiction, as well as metadata such as species, nature of the addictive substance, studied brain regions, technology platforms, and experimental parameters. In total, they collected 2,343 items of evidence linking 1,500 human genes to addiction. Among them 396 genes were supported by two or more items of evidence. The interface supports browsing of the genes by chromosome or pathways, advanced text search by gene ID, organism, type of addictive substance, technology platform, protein domain, and/or PUBMED ID, and sequence search by BLAST similarity. All data, database schema, and MySQL commands are freely available for download.

Proper citation: Knowledgebase for Addiction Related Genes (RRID:SCR_002687) Copy   

•   Source: SciCrunch Registry

http://ecogene.org/

Database that contains updated information about the Escherichia coli K-12 genome and proteome sequences, including extensive gene bibliographies. Users are able to download customized tables, perform Boolean query comparisons, generate sets of paired DNA sequences, and download any E. coli K-12 genomic DNA sub-sequence. BLAST functions, microarray data, an alphabetical index of genes, and gene overlap queries are also available. The Database Table Downloads Page provides a full list of EG numbers cross-referenced to the new cross-database ECK numbers and other common accession numbers, as well as gene names and synonyms. Monthly release archival downloads are available, but the live, daily updated version of EcoGene is the default mysql database for download queries.

Proper citation: EcoGene (RRID:SCR_002437) Copy   

•   Source: SciCrunch Registry

http://www.ifti.org/ootfd/

ooTFD (object-oriented Transcription Factors Database) is a successor to TFD, the original Transcription Factors Database. This database is aimed at capturing information regarding the polypeptide interactions which comprise and define the properties of transcription factors. ooTFD contains information about transcription factor binding sites, as well as composite relationships within transcription factors, which frequently occur as multisubunit proteins that form a complex interface to cellular processes outside the transcription machinery through protein-protein interactions. ooTFD contains information represented in TFD but also allows the representation of containment, composite, and interaction relationships between transcription factor polypeptides. It is designed to represent information about all transcription factors, both eukaryotic and prokaryotic, basal as well as regulatory factors, and multiprotein complexes as well as monomers.

Proper citation: object-oriented Transcription Factors Database (RRID:SCR_002435) Copy   

•   Source: SciCrunch Registry

http://cubic.bioc.columbia.edu/db/LOC3d/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 16, 2013. LOC3d is a database of predicted subcellular localization for eukaryotic proteins of known 3-D structure taken from the Protein Databank. Subcellular localization is currently predicted using four different methods: predictNLS (nuclear localization signal), LOChom (using homology), LOCkey (using keywords) and LOC3d (neural network based prediction). The reported localization is based on the method which predicts localization of a given protein with the highest confidence. LOCtree is a novel system of support vector machines (SVMs) that predict the subcellular localization of proteins, and DNA-binding propensity for nuclear proteins, by incorporating a hierarchical ontology of localization classes modeled onto biological processing pathways. Biological similarities are incorporated from the description of cellular components provided by the gene ontology consortium (GO). GO definitions have been simplified and tailored to the problem of protein sorting. Technically the ontology has been implemented using a decision tree with SVMs as the nodes. LOCtree, was extremely successful at learning evolutionary similarities among subcellular localization classes and was significantly more accurate than other traditional networks at predicting subcellular localization. Whenever available, LOCtree also reports predictions based on the following: 1) Nuclear localization signals found by PredictNLS, 2) Localization inferred using Prosite motifs and Pfam domains found in the protein, and 3) SWISS-PROT keywords associated with a protein. Localization is inferred in the last two cases using the entropy-based LOCkey algorithm. Additional information can be found in the LOCtree manuscript and associated PredictNLS and LOCkey publications.

Proper citation: Database oDatabase of Predicted Subcellular Localization for Eukaryotic PDB Chainsf Predicted Subcellular Localization for Eukaryotic PDB Chains (RRID:SCR_002831) Copy   

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http://eyesite.cryst.bbk.ac.uk/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 14,2026. An information and modeling database for families of proteins that function in the eye. Homologues are collected from all species and clustered according to tissue type, function and sequence similarity. A principal feature of the site is structural annotations, which range from experimentally solved structures to close structural neighbors to distant structure predictions. Many pre-generated homology models are provided. Other features include domain architecture analysis and pre-generated sequence alignments, and the site is extensively linked to other bioinformatic resources on the web.

Proper citation: EyeSite (RRID:SCR_002669) Copy   

•   Source: SciCrunch Registry