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http://source.rcsb.org/jfatcatserver/ceHome.jsp
CE is a databases of alignments for all polypeptide chains. A representative set of proteins is available and kept current with the PDB, a method for calculating pairwise structure alignments. CE aligns two polypeptide chains using characteristics of their local geometry as defined by vectors between C alpha positions. Matches are termed aligned fragment pairs (AFPs). Heuristics are used in defining a set of optimal paths joining AFPs with gaps as needed. The path with the best RMSD is subject to dynamic programming to achieve an optimal alignment. For specific families of proteins additional characteristics are used to weight the alignment. Complete details are described in the paper (PDF format). Databases of alignments for all polypeptide chains and a representative set of proteins is available and kept current with the PDB
Proper citation: Combinatorial Extension (CE) (RRID:SCR_007585) Copy
http://caps.ncbs.res.in/gendis/home.html
Genomic Distribution of structural Superfamilies identifies and classifies evolutionary related proteins at the superfamily level in whole genome databases. GenDiS has been curated in direct correspondence with SCOP and represents 4001 highly resolved domains in 1194 structural superfamilies across protein sequence databases. Sequences showing reliable homology to entries in SCOP and PASS2 databases have been obtained from the non-redundant protein sequence database and aligned. Similar alignments of the superfamily members are provided in the genome level. GenDiS provides a platform for cross genome comparison at the superfamily level. GenDis relates proteins sequence information across all strata of taxonomy. One may navigate through the database to obtain structural homologues across different levels in taxonomic classification. The nomenclature of the various genomes and their hierarchy is in direct correspondence with the taxonomy database maintained at the NCBI. Sequence homologues for the various structural members are obtained from the non-redundant protein sequence database employing sensitive sequence search methods. Multiple approaches such as PSI-BLAST, HMMsearch of the HMMer suite and an interacting motif constrained PHI-BLAST have been employed to identify homologues in the sequence databases.
Proper citation: Genomic Distribution of structural Superfamilies (RRID:SCR_007670) Copy
http://mendel.gene.cwru.edu/adamslab/cgi-bin/paml/pbrowser.py
THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 16, 2013. It provides access to the results of tests for positive selection in 14,000 human genes. Multiple alignments of protein-coding regions of genes from human and other mammals were extracted from whole-genome alignments available from UC-Santa Cruz. Each gene was analyzed using the maximum likelihood tests of selection using PAML. Branch, site, and branch+site tests were performed, each with at least one matching null model.
Proper citation: Human PAML Browser (RRID:SCR_007715) Copy
dbPTM is a database that compiles information on protein post-translational modifications (PTM) such as the modified sites, solvent accessibility of surrounding amino acids, protein secondary and tertiary structures, protein domains, and protein variations. The version 2.0 of dbPTM integrates the experimentally validated PTM sites with referable literatures from Swiss-Prot, Phospho.ELM, O-GLYCBASE, and UbiProt. In all of the collected PTM information, about 25 types of PTM with enough experimentally validated sites are trained the profile hidden Markov models (HMMs) to detect the potential PTM sites with 100% specificity against Swiss-Prot proteins. To help users investigating more detail in each type of PTM, the substrate peptide specificity such as positional amino acid frequency, solvent accessibility and secondary structure surrounding the modified sites are also provided. Moreover, the information of orthologous protein clusters is provided to users for analyzing whether the PTM sites located in the evolutionary conserved regions or not., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.
Proper citation: dbPTM: An informational repository of proteins and post-translational modifications (RRID:SCR_007619) Copy
http://firedb.bioinfo.cnio.es/
A database of Protein Data Bank structures, ligands and annotated functional site residues. The database can be accessed by PDB codes or UniProt accession numbers as well as keywords. FireDB contains information on every chemical compound in the PDB, including their descriptions, the PDB structures in which the compounds are found and the amino acids that are in contact with the ligand.
Proper citation: FireDB (RRID:SCR_007655) Copy
FCP is a publicly accessible web tool dedicated to analyzing the current state and trends of available proteome structures along the classification schemes of enzymes and nuclear receptors. It offers both graphical and quantitative data on the degree of functional coverage in that portion of the proteome by existing structures and on the bias observed in the distribution of those structures among proteins. Users can choose to search the website based on structures or ligands, and can also sort by enzyme or receptor. Users can also view data based on structural and population (species) filters.
Proper citation: Functional Coverage of the Proteome (RRID:SCR_007654) Copy
A database ofhuman disease-related mutated proteins identified by mass-spectrometry (MS). For achieving this goal, we collected human mutated sequences known to be related to diseases till now. After surveying mutated sequence sources: PMD, OMIM, SwissProt polymorphism, HGMD, etc, we found that currently HGMD contains the largest human gene mutation information. However, because, for academic users, HGMD does not provide with whole data download service, we decided to systematically extract and curate mutation information from PMD, OMIM, SwissProt, MSIPI database to form SysPIMP and provide it free for academic users.
Proper citation: Systematic Platform for Identifying Mutated Proteins (SysPIMP) (RRID:SCR_007954) Copy
SYSTERS is a database of protein sequences grouped into homologous families and superfamilies. The SYSTERS project aims to provide a meaningful partitioning of the whole protein sequence space by a fully automatic procedure. A refined two-step algorithm assigns each protein to a family and a superfamily. The sequence data underlying SYSTERS release 4 now comprise several protein sequence databases derived from completely sequenced genomes (ENSEMBL, TAIR, SGD and GeneDB), in addition to the comprehensive Swiss-Prot/TrEMBL databases. To augment the automatically derived results, information from external databases like Pfam and Gene Ontology are added to the web server. Furthermore, users can retrieve pre-processed analyses of families like multiple alignments and phylogenetic trees. New query options comprise a batch retrieval tool for functional inference about families based on automatic keyword extraction from sequence annotations. A new access point, PhyloMatrix, allows the retrieval of phylogenetic profiles of SYSTERS families across organisms with completely sequenced genomes. Gene, Human, Vertebrate, Genome, Human ORFs
Proper citation: SYSTERS (RRID:SCR_007955) Copy
http://supfam.org/SUPERFAMILY/
SUPERFAMILY is a database of structural and functional protein annotations for all completely sequenced organisms. The SUPERFAMILY annotation is based on a collection of hidden Markov models, which represent structural protein domains at the SCOP superfamily level. A superfamily groups together domains which have an evolutionary relationship. The annotation is produced by scanning protein sequences from over 1,700 completely sequenced genomes against the hidden Markov models.
Proper citation: SUPERFAMILY (RRID:SCR_007952) Copy
Database to explore known and predicted interactions of chemicals and proteins. It integrates information about interactions from metabolic pathways, crystal structures, binding experiments and drug-target relationships. Inferred information from phenotypic effects, text mining and chemical structure similarity is used to predict relations between chemicals. STITCH further allows exploring the network of chemical relations, also in the context of associated binding proteins. Each proposed interaction can be traced back to the original data sources. The database contains interaction information for over 68,000 different chemicals, including 2200 drugs, and connects them to 1.5 million genes across 373 genomes and their interactions contained in the STRING database.
Proper citation: Search Tool for Interactions of Chemicals (RRID:SCR_007947) Copy
Collection of transmembrane protein datasets containing experimentally derived topology information from the literature and from public databases. Web interface of TOPDB includes tools for searching, relational querying and data browsing, visualisation tools for topology data.
Proper citation: Topology Data Bank of Transmembrane Proteins (RRID:SCR_007964) Copy
It provides a database based on a pre-computed similarity matrix covering the similarity space formed by >4 million amino acid sequences from public databases and completely sequenced genomes. The database is capable of handling very large datasets and is updated incrementally. For sequence similarity searches and pairwise alignments, we implemented a grid-enabled software system, which is based on FASTA heuristics and the Smith Waterman algorithm. SimpleSIMAP and AdvancedSIMAP retrieve homologs for given protein sequences that need to be contained in the SIMAP database. While SimpleSIMAP provides only selected parameters and preconfigured search spaces, the AdvancedSIMAP allows the user to specify search space, filtering and sorting parameters in a flexible manner. Both types of queries result in lists of homologs that are linked in turn to their homologs. So the web interfaces allow users to explore quickly and interactively the protein world by homology. Sponsors: SIMAP is supported by the Department of Genome Oriented Bioinformatics of the Technische Universitt Mnchen and the Institute for Bioinformatics of the GSF-National Research Center for Environment and Health.
Proper citation: SIMAP (RRID:SCR_007927) Copy
http://www.grt.kyushu-u.ac.jp/spad/
It is divided to four categories based on extracellular signal molecules (Growth factor, Cytokine, and Hormone) and stress, that initiate the intracellular signaling pathway. SPAD is compiled in order to describe information on interaction between protein and protein, protein and DNA as well as information on sequences of DNA and proteins. There are multiple signal transduction pathways: cascade of information from plasma membrane to nucleus in response to an extracellular stimulus in living organisms. Extracellular signal molecule binds specific intracellular receptor, and initiates the signaling pathway. Now, there is a large amount of information about the signaling pathway which controls the gene expression and cellular proliferation. We have developed an integrated database SPAD to understand the overview of signaling transduction.
Proper citation: Signaling Pathway Database (RRID:SCR_008243) Copy
http://escience.invitrogen.com/ipath/
THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 26, 2016. LINNEA Pathways is a user-friendly comprehensive online resource for gene- or protein-based scientific research. It is based on a total of 248 signaling and metabolic human biological pathway maps created for Invitrogen by GeneGo. The current version of iPath features 225 maps displaying human regulatory and metabolic pathways established in experimental literature produced by MetaCore from GeneGo, Inc. The map objects (proteins, genes, EC functions, and compounds) are connected via metabolic transformations and physical protein interactions, which were assembled by the GeneGo team of experienced annotators, geneticists, and biochemists. The pathways are organized in a vertical fashion following the general signaling path from signaling molecules and membrane receptors, via signal transduction cascades, to transcription factors and their gene targets. Following the natural organization of cellular machinery with highly interconnected pathways and modules, many maps are linked together via hyperlinked box symbols. Such linkage allows the reconstruction of a big picture view of human cell biology., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.
Proper citation: Invitrogen iPath (RRID:SCR_008120) Copy
ITFP is an integrated transcription factor (TF) platform, which included abundant TFs and targets message of mammalian. Support vector machine (SVM) algorithm combined with error-correcting output coding (ECOC) algorithm was utilized to identify and classify transcription factor from protein sequence of Human, Mouse and Rat. For transcription factor targets, a reverse engineering method named ARACNE was used to derive potential interaction pairs between transcription factor and downstream regulated gene from Human, Mouse and Rat gene expression profile data. Detailed information of gene expression profile data can be found in help page. Moreover, all data provided by the platform is free for non-commercial users and can be downloaded through links on help page.
Proper citation: Intergrated Transcription Factor Platform (RRID:SCR_008119) Copy
iRefWeb is an interface to a relational database containing the latest build of the interaction Reference Index (iRefIndex) which integrates protein interaction data from nine different interaction databases: BioGRID, BIND, CORUM, DIP, HPRD, INTACT, MINT, MPPI, MPACT and OPHID. Integration is achieved through a rigorously documented procedure for mapping protein IDs across databases, enabling systematic backtracking of the links used to establish the identity of the interaction partners. The iRefWeb interface groups interaction records from the different databases into a single non-redundant view. In particular iRefWeb facilitates comparing interaction records as seen by the various source databases relative to the PubMeds they were annotated from. iRefWeb is one of several views of the iRefIndex resource. Data are also available in a tab-delimited plain-text format (PSI-MITAB) as well as planned releases of a PSI-XML formatted version and a Cytoscape plugin. Further details about the iRefIndex project as well as data downloads are available from here . The method used to build iRefIndex is described in a recent publication.
Proper citation: Interaction Reference Index Web Interface (RRID:SCR_008118) Copy
A horizontally and vertically structured database that pulls scientific and medical information and describes it consistently using the Ingenuity Ontology. The Knowledge Base pulls information from journals, public molecular content databases, and textbooks. Data is curated and and integrated into the Knowledge Base .
Proper citation: Ingenuity Pathways Knowledge Base (RRID:SCR_008117) Copy
http://www.ebi.ac.uk/ipd/mhc/bola/
This website is intended to be the definitive source of information on the bovine major histocompatibility complex - its genes, proteins and polymorphism. Its purpose is to collate data on the Bovine Leucocyte Antigens (BoLA) and provide a forum for the analysis and nomenclature of polymorphisms in the genes and proteins of the bovine MHC. The BoLA nomenclature committee is a standing committee of the International Society for Animal Genetics. Its purpose is to collate data on the Bovine Leucocyte Antigens (BoLA) and provide a forum for the analysis and nomenclature of polymorphisms in the genes and proteins of the bovine MHC. The information gathered here is based on the BoLA workshop reports, which are published in Animal Genetics and the European Journal of Immunogenetics. The workshop report data are reproduced with the permission of the publishers Blackwell Science, and other text on the site is used with the permission of CRC Press.
Proper citation: BoLA Nomenclature: International Society for Animal Genetics (RRID:SCR_008142) Copy
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.
Proper citation: MIPS Mammalian Protein-Protein Interaction Database (RRID:SCR_008207) Copy
http://mips.helmholtz-muenchen.de/genre/proj/mpcdb/
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.
Proper citation: Mammalian Protein Complex Data Base (RRID:SCR_008209) Copy
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