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http://pir.georgetown.edu/iprolink/biothesaurus

BioThesaurus is a web-based system designed to map a comprehensive collection of protein and gene names to UniProt Knowledgebase protein entries. It covers all UniProtKB protein entries, and consists of several millions of names extracted from multiple resources based on database cross-references in iProClass. The web site allows the retrieval of synonymous names of given protein entries and the identification of ambiguous names shared by multiple proteins. Searches can be done on protein/gene name, organism, or unique identifier.

Proper citation: BioThesaurus (RRID:SCR_007566) Copy   

•   Source: SciCrunch Registry

http://cmckb.cellmigration.org

It is a database of keys facts about proteins, families, and complexes involved in cell migration. This ongoing project provides a large amount of automated and curated data, collected from numerous online resources that are updated monthly. These data include names, synonyms, sequence information, summaries, CMC research data, reagents, structures, as well as protein family and complex details. CMKB''s ultimate goal is to create a database that will enable the cell migration community to conveniently access significant information about molecules of interest. This will also serve as a stepping stone to pathway analysis and demonstrate how these molecules coordinate with one another during cell adhesion and movement. Sponsors: This resource is supported by the Cell Migration Consortium.

Proper citation: CMKB (RRID:SCR_007229) Copy   

•   Source: SciCrunch Registry

http://posa.sanfordburnham.org/fatcat-cgi/cgi/FSN/fsn.pl

Flexible Structural Neighborhood is a database of structural neighbors of proteins as seen by FATCAT - a flexible protein structure alignment program. The server accepts either a protein (PDB ID) or a domain (SCOP ID) as a query. For the former case, the server first displays the information of chains and domains of a given protein. Afterwards, users can retrieve similar structures for a domain (if domain information is available, i.e., the protein is collected by SCOP), or for a chain otherwise. The protein structure database we collected for similar structure search includes a representative set at 90% sequence identity of SCOP domains, and of up-to-date PDB entries that are not included in the latest release of SCOP.

Proper citation: FATCAT Flexible Structural Neighborhood (RRID:SCR_007665) Copy   

•   Source: SciCrunch Registry

http://spock.genes.nig.ac.jp/~genome/gtop.html

GTOP is a database consists of data analyses of proteins identified by various genome projects. This database mainly uses sequence homology analyses and features extensive utilization of information on three-dimensional structures. GTOP is built by the Laboratory of Gene-Product Informatics at the National Institute of Genetics. This research is supported by the Japan Science and Technology Corporation and Grants-in-Aid for Scientific Research (Genomes in category C) from the Ministry of Education, Science, Sports and Culture of Japan. We use the following methods: Prediction of 3D structure Sequence homology search of PDB, using REVERSE PSI-BLAST. Functional predictions (family classifications) Sequence homology search of Swiss-Prot, a well-annotated sequence database, with the use of BLAST. Other analytical methods We are also carrying out the following analyses: Motif Analysis(PROSITE) Family classification(Pfam) Prediction of transmembrane helix domains(SOSUI) Prediction of coiled-coil regions(Multicoil) Repetitive sequence analysis(RepAlign)

Proper citation: GTOP - Genomes To Protein structures (RRID:SCR_007698) Copy   

•   Source: SciCrunch Registry

http://nectarmutation.org/main

A database and web application to annotate disease-related and functionally important amino acids in human proteins., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: NECTAR (RRID:SCR_007757) Copy   

•   Source: SciCrunch Registry

http://merops.sanger.ac.uk/

An information resource for peptidases (also termed proteases, proteinases and proteolytic enzymes) and the proteins that inhibit them. The MEROPS database uses an hierarchical, structure-based classification of the peptidases. In this, each peptidase is assigned to a Family on the basis of statistically significant similarities in amino acid sequence, and families that are thought to be homologous are grouped together in a Clan. There is a Summary page for each family and clan, and these have indexes. Each of the Summary pages offers links to supplementary pages. About 3000 individual peptidases and inhibitors are included in the database, and there is a Summary page describing each one. You can navigate to this by any of several routes. There are indexes of Name, MEROPS Identifier and source Organism on the menu bar. Each Summary page describes the classification and nomenclature of the peptidase or inhibitor, and provides links to supplementary pages showing sequence identifiers, the structure if known, literature references and more.

Proper citation: MEROPS (RRID:SCR_007777) Copy   

•   Source: SciCrunch Registry

http://www.mapuproteome.com

Database containing several body fluid proteomes, including plasma, urine, and cerebrospinal fluid. Cell lines have been mapped to a depth of several thousand proteins and the red blood cell proteome has also been analyzed in depth. The liver proteome is represented with 3200 proteins. By employing high resolution MS and stringent validation criteria, false positive identification rates in MAPU are lower than 1:1000. Thus MAPU datasets can serve as reference proteomes in biomarker discovery. MAPU contains the peptides identifying each protein, measured masses, scores and intensities using a clickable interface of cell or body parts. Proteome data can be queried across proteomes by protein name, accession number, sequence similarity, peptide sequence and annotation information. More than 4500 mouse and 2500 human proteins have already been identified in at least one proteome. Basic annotation information and links to other public databases are provided in MAPU and we plan to add further analysis tools.

Proper citation: Max Planck Unified Proteome Database (RRID:SCR_007771) Copy   

•   Source: SciCrunch Registry

https://leger2.helmholtz-hzi.de/cgi-bin/expLeger.pl

Knowledge database and visualization tool for comparative genomics of pathogenic and non-pathogenic Listeria species.Provides information on gene functions (as annotated or supposed by literature from homologous organisms) , protein expression levels under defined experimental conditions ,subcellular localization of proteins (expected and/or experimentally validated) , biological meaning of genes and proteins based on KEGG, InterPro and Gene Ontology.

Proper citation: LEGER: the post-genome Database for Listeria Research (RRID:SCR_007760) Copy   

•   Source: SciCrunch Registry

http://cbm.bio.uniroma2.it/surface

A database containing the results of a large-scale protein annotation and local structural comparison project. A non-redundant set of protein chains is used to build a database of protein surface patches, defined as putative surface functional sites. Each patch is annotated with sequence and structure-derived information about function or interaction abilities. Users can search the annotations and the results of the surface patches comparisons stored in the DB based on PDB code, PROSITE pattern, or ligand. A new procedure for structure comparison is used to exert an all-versus-all patches comparison. Selection of the results obtained with stringent parameters offers a similarity score that can be used to associate different patches and may allow reliable annotation by similarity. protein, protein structure, structural comparison, protein ligand, protein surface, protein morphology

Proper citation: SURFACE: Surface Residues and Functions Annotated, Compared and Evaluated (RRID:SCR_007953) Copy   

•   Source: SciCrunch Registry

http://www.geneatlas.org/gene/main.jsp

This website allows visitors to search for genes of interest based on their spatial expression patterns in the Postnatal Day 7 mouse brain. Geneatlas provides two searching tools: A graphical interface for customized spatial queries; A textual interface for querying annotated structures. Geneatlas is the product of a collaboration between researchers at Baylor College of Medicine, Rice University, and University of Houston.

Proper citation: Gene Atlas (RRID:SCR_008089) Copy   

•   Source: SciCrunch Registry

http://compbio.mcs.anl.gov/sentra/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 17, 2013. A database of signal transduction proteins encoded in completely sequenced prokaryotic genomes. Sentra consists of two principal components, a manually curated list of signal transduction proteins in 202 completely sequenced prokaryotic genomes and an automatically generated listing of predicted signaling proteins in 235 sequenced genomes that are awaiting manual curation. In addition to two-component histidine kinases and response regulators, the database now lists manually curated Ser/Thr/Tyr protein kinases and protein phosphatases, as well as adenylate and diguanylate cyclases and c-di-GMP phosphodiesterases, as defined in several recent reviews. All entries in Sentra are extensively annotated with relevant information from public databases (e.g. UniProt, KEGG, PDB and NCBI). Sentra's infrastructure was redesigned to support interactive cross-genome comparisons of signal transduction capabilities of prokaryotic organisms from a taxonomic and phenotypic perspective and in the framework of signal transduction pathways from KEGG. Sentra leverages the PUMA2 system to support interactive analysis and annotation of signal transduction proteins by the users.

Proper citation: SENTRA: a database of prokaryotic signal transduction proteins (RRID:SCR_007922) Copy   

•   Source: SciCrunch Registry

http://www.metnetdb.org/MetNet_db.htm

MetNet database contains information on networks of metabolic and regulatory and interactions in Arabidopsis. This information is based on input from biologists in their area of expertise. Types of interactions in MetNetDB include transcription, translation, protein modification, assembly, allosteric regulation, translocation from one subcellular compartment to another. Other fields describing the interactions are subcellular location, confidence, directionality, references, evidence, and synonyms. Data on entities (DNA, RNA, polypeptides, protein complexes, metabolites) are derived from web databases (gene related databases: TAIR, GO, MapMan/GabiPD; protein related databases: PPDB, AMPDB, AtNoPDB, AraPerox, PLprot, BRENDA; metabolite related databases: ChEBI, PubChem, KEGG, NCI compound library, NIST MS library), in some cases with additional annotation by experts. Network information from MetNetDB can be converted to an XML file by XML Builder. From this XML file, it can be transferred to exploRase, which uses the network in conjunction with statistical analysis of expression data; to Cytoscape/FCM, which finds cycles and pathways in the network, and visualizes and models it in combination with expression data; and to MetNetVR, where the network can be visualized in 3D.

Proper citation: Metabolic Network Exchange (RRID:SCR_008124) Copy   

•   Source: SciCrunch Registry

http://kinasedb.ontology.ims.u-tokyo.ac.jp

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. KinasePathwayDatabase 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 47,000 and orthologous tables.

Proper citation: Kinase Pathway Database (RRID:SCR_008199) Copy   

•   Source: SciCrunch Registry

http://knots.mit.edu/

The knot server allows the user to check PDB entries or uploaded structures for knots and to visualize them. The size of a knot is determined by deleting amino acids from both ends. This procedure is, however, not perfect and the resulting size should only be treated as a guideline. Mathematically, knots are only well defined in closed (circular) loops. However, both the N- and C-termini of open proteins are typically located close to the surface of the protein and can be connected unambiguously: We reduce the protein to its backbone and draw two lines outward starting at the termini in the direction of the connection line between the center of mass of the backbone and the respective ends. The two lines are joined by a big loop, and the structure is topologically classified by the determination of its Alexander polynomial. To determine an estimate for the size of the knotted core, we successively delete amino acids from the N-terminus until the protein becomes unknotted. The procedure is repeated at the C-terminus starting with the last N-terminal deletion structure that contained the original knot. For each deletion, the outward-pointing line through the new termini is parallel to the respective lines computed for the full structure. Unfortunately, the size of a knot is not always precisely determined by this procedure, so reported sizes should therefore only be treated as approximate. Sponsors: Knots is funded by MIT.

Proper citation: Protein Knots (RRID:SCR_008353) Copy   

•   Source: SciCrunch Registry

http://www.peroxisomedb.org/

The aim of the PEROXISOME database (PeroxisomeDB) is to gather, organize and integrate curated information on peroxisomal genes, their encoded proteins, their molecular function and metabolic pathway they belong to, and their related disorders. PeroxisomeDB contains the complete peroxisomal proteome of Homo sapiens (encoded by 85 genes) and Saccharomyces cerevisiae (encoded by 61 genes). Now, we have included 34 new organism genomes with the acquisition of 2426 new peroxisomal homolog proteins. PeroxisomeDB 2.0 integrates the peroxisomal metabolome of whole microbody family by the new incorporation of the glycosome proteomes of trypanosomatids and the glyoxysome proteome of Arabidopsis thaliana. The site also provides a Peroxisome Metabolome of peroxisomal genes and proteins, their molecular interactions and metabolic pathways, tools for comparative genomics, predictive tools. Sponsors: Preoxisome Database is funded by Institut de Gntique et deBiologie Molculaire et Cellulaire.

Proper citation: Peroxisome Database (RRID:SCR_008352) Copy   

•   Source: SciCrunch Registry

http://www.roselab.jhu.edu/coil/

The Protein Coil Library is a library of protein structure fragments derived from the Protein Data Bank (PDB). The fragments in this library are those fragments in the PDB that cannot be classified as either alpha-helix or beta-strand. Three-dimensional structures as well as side-chain and backbone torsion angles are stored in the database. The Protein Coil Library allows rapid and comprehensive access to non-alpha-helix and non-beta-strand fragments contained in the Protein Data Bank (PDB). The library contains both sequence and structure information together with calculated torsion angles for both the backbone and side chains. Several search options are implemented, including a query function that uses output from popular PDB-culling servers directly. Additionally, several popular searches are stored and updated for immediate access. The library is a useful tool for exploring conformational propensities, turn motifs, and a recent model of the unfolded state. The library stores the complete torsion angle descriptions for the fragments as well as the three dimensional structures of the fragments themselves. The goal of extracting and pre-calculating this data is to allow for more straightforward investigation of peptide structure without the background of secondary structure elements. In addition to searching by PDB ID, it is possible to download a particular size class, perform a batch search of PDB/chain ID''s, or download precompiled lists of PDB ID''s of interest (PDB Select, etc.). For users interested in browsing the entire database at once or maintaining their own locally-updated copy of the library, FTP access instructions are also provided. The files stored in the coil library FTP site or returned after a batch search are organized heirarchically by PDB ID. This is done to reduce filesystem access times and fascilitate searches using the UNIX find utility. At the lowest directory level in the heirarchy, files are further sorted by fragment length. As a result, the number of files in a particular directory is generally less then 50, yielding relatively fast access on UNIX/Linux filesystems. The heirarchical organization is based on the middle two letters of the PDB ID. For example, hen egg lysozyme, which has a PDB ID of 1HEL, will be located in the directory h/he/. At the final level, fragments of varying sizes are stored in directories that correspond to their fragment length. Again, using lysozyme as an example, any seven-residue fragments, if they exist, will reside in the directory h/he/7/. Similarly, seven-residue fragments from 2HEX and 1HE0 will also be in this location. Sponsors: The Protein Coil Library is funded by Johns Hopkins University.

Proper citation: The Protein Coil Library (RRID:SCR_008233) Copy   

•   Source: SciCrunch Registry

http://cssb.biology.gatech.edu/skolnick/files/gpcr/gpcr.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 19,2019.Database of tertiary structural modeling results of threading assembly refinement (TASSER) method for all 907 G protein-coupled receptors (GPCRs) in human genome. All sequences were collected from GPCR database http://www.gpcr.org/7tm/ and http://www.expasy.org/cgi-bin/lists?7tmrlist.txt. Unlike traditional homology modeling approaches, TASSER modeling does not require solved homologous template structures; moreover, it often refines the structures closer to native. G protein-coupled receptors (GPCRs), encoded by about 5% of human genes, comprise the largest family of integral membrane proteins and act as cell surface receptors responsible for the transduction of endogenous signal into a cellular response. Although tertiary structural information is crucial for function annotation and drug design, there are few experimentally determined GPCR structures. To address this issue, we employ the recently developed threading assembly refinement (TASSER) method to generate structure predictions for all 907 putative GPCRs in the human genome. Unlike traditional homology modeling approaches, TASSER modeling does not require solved homologous template structures; moreover, it often refines the structures closer to native. These features are essential for the comprehensive modeling of all human GPCRs when close homologous templates are absent. Based on a benchmarked confidence score, approximately 820 predicted models should have the correct folds. The majority of GPCR models share the characteristic seven-transmembrane helix topology, but 45 ORFs are predicted to have different structures. This is due to GPCR fragments that are predominantly from extracellular or intracellular domains as well as database annotation errors. Our preliminary validation includes the automated modeling of bovine rhodopsin, the only solved GPCR in the Protein Data Bank. With homologous templates excluded, the final model built by TASSER has a global C(alpha) root-mean-squared deviation from native of 4.6 angstroms, with a root-mean-squared deviation in the transmembrane helix region of 2.1 angstroms. Models of several representative GPCRs are compared with mutagenesis and affinity labeling data, and consistent agreement is demonstrated. Structure clustering of the predicted models shows that GPCRs with similar structures tend to belong to a similar functional class even when their sequences are diverse. These results demonstrate the usefulness and robustness of the in silico models for GPCR functional analysis. Sponsors: GPCR is funded by the University at Buffalo, Buffalo, New York.

Proper citation: Structure modeling of 907 G protein coupled receptors in the human genome (RRID:SCR_008351) Copy   

•   Source: SciCrunch Registry

http://bibiserv.techfak.uni-bielefeld.de/HyPa/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 15, 2013. It contains annotated structural elements characteristic for certain classes of structural and/or functional RNAs. These elements are described in a language specifically designed for this purpose. The language allows convenient specification of hybrid patterns, i.e. motifs consisting of sequence features and structural elements together with sequence similarity and thermodynamic constraints. A system that searches complex patterns (on nucleic-acid or protein level) in large biosequence-databases. As patterns, they allow hybrid patterns, which combine sequence similarity, structure similarity and arbitrary characteristics, like thermodynamic constraints. Applications are in the research of highly specific Protein/RNA-interactions or in the search of RNA-tertiary-structure-interactions. They developed a declarative pattern description language, which is implemented by known and new pattern-matching algorithms and an optimizing backtracking procedure. To achieve high efficiency when screening large data sets, the patterns are divided and queries are composed. The significance of patterns is estimated by a Monte-Carlo procedure. Complex results of queries are processed by a visualizing component. A library of biologically relevant patterns is developed and it is provided on the WWW together with the search-tool. The evaluation of the tool w.r.t. to the biosequence databases will in some cases mean to make laboratory-experiments, in order to check algorithmically developed functional hypothesis. Sponsors: This project is supported by a grant from the Deutsche Forschungsgemeinschaft. It is part of the special program on Computational Methods for the Analysis and Interpretation of large genomic data

Proper citation: Hybrid Pattern Library (RRID:SCR_008193) Copy   

•   Source: SciCrunch Registry

http://cmbi.bjmu.edu.cn/cmbidata/cgf/CGF_Database/cytokine.medic.kumamoto-u.ac.jp/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 26, 2016. A collection of cDNA, gene and protein records of cytokines deposited in public databases provides various information about the cytokine members of vertebrates in other databases including NCBI GenBank, Swiss-Prot, UniGene, TIGR (The Institute for Genomic Research) Gene Indices, Ensembl, Entrez Gene, Mouse Genome Informatics (MGI) and Rat Genome Database (RGD). It also provides orthologous relationship of cytokine members and includes novel members identified in the databases.

Proper citation: Cytokine Family Database (RRID:SCR_008134) Copy   

•   Source: SciCrunch Registry

http://pbil.univ-lyon1.fr/acuts/ACUTS.html

THIS RESOURCE IS NO LONGER IN SERVICE, Documented on August 12, 2014. Database that identifies new regulatory elements in untranslated regions of protein-coding genes (5 prime flanks, 5 prime UTRs, introns, 3 prime UTRs and 3 prime flanks). The analyses is focused on genes from metazoan species (essentially vertebrates, insects and nematodes). Information on highly conserved regions (sequences, alignments, annotations, bibliographic references) are compiled. Currently 176 out of 326 detected highly conserved regions (HCRs) have been analyzed and incorporated in the database. You can also access the list of annotated conserved elements and the list of conserved elements that remain to be processed. Their approach is based on comparative sequence analysis, for the identification of phylogenetic footprints.

Proper citation: Ancient conserved untranslated sequences (RRID:SCR_008130) Copy   

•   Source: SciCrunch Registry