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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   

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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   

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http://www.imtech.res.in/raghava/bhairpred/

Bhairpred server is based on machine learning technique SVM using single sequence information, evolutionary profile, predicted and observed secondary structure (as obtained using Psipred and DSSP), predicted and observed accessibility values (as obtainned from Netasa and DSSP). The methods were trained and tested on dataset of 2880 proteins and their performance was evaluated on dataset of 534 proteins used by Thornton (PNAS, 2002). Best prediction results were obtained with hybrid approach that combined prediction results from evolutionary profile, predicted secondary structure and accessibility.

Proper citation: SVM based method for predicting beta hairpin structures in proteins (RRID:SCR_008349) Copy   

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http://mitointeractome.kobic.kr/

Database that gathers data on interactions in the mitochondrial proteome that has been used to construct a network for the aging process in humans and to identify interactions that influence this process, since mitochondria is a major source of cellular reactive oxygen species that accumulate during aging. It will: # aid in increasing our understanding of the molecular functions and interaction networks of mitochondrial proteins, # help in identifying new target proteins for experimental research using predicted protein-protein interaction information, and # help in identifying biomarkers for diagnosis and new molecular targets for drug development related to mitochondria. How is MitoInteractome different? * Provides protein-protein interaction information with graphical display. * Applies newly added new mitochondrial protein information by using BLAST incorporated in Mitointeractome * Shows correlation of mutation with their impact * Provides specific pathway information to aid study of their impact * Contains SNP Information

Proper citation: MitoInteractome (RRID:SCR_010225) Copy   

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http://www.grenoble.prabi.fr/obiwarehouse/unipathway

A manually curated database of enzyme-catalyzed and spontaneous chemical reactions. It provides a hierarchical representation of metabolic pathways and a controlled vocabulary for pathway annotation in UniProtKB. UniPathway data are cross-linked to existing metabolic resources such as ChEBI/Rhea, KEGG and MetaCyc. Users may do a quick search, browse pathway, browse compound, or browse organism.

Proper citation: UniPathway (RRID:SCR_010513) Copy   

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http://bleoberis.bioc.cam.ac.uk/mcsm

Data analysis service to the study of missense mutations which relies on graph-based signatures.

Proper citation: mCSM (RRID:SCR_010776) Copy   

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http://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=tblastn&PAGE_TYPE=BlastSearch&LINK_LOC=blasthome

Tool to search translated nucleotide databases using a protein query.

Proper citation: TBLASTN (RRID:SCR_011822) Copy   

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http://www.imtech.res.in/raghava/hslpred/

A support vector machine (SVM)-based method for the prediction of 4 major subcellular localization (cytoplasm, mitochondrial, nuclear and plasma membrane) of human proteins using various features such as i) amino acid composition, ii) dipeptide composition and iii) evolutionary information of proteins.

Proper citation: HSLPred (RRID:SCR_011972) Copy   

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http://jci-bioinfo.cn/iLoc-Plant

Data analysis service for predicting subcellular localization of plant proteins with single and multiple sites.

Proper citation: iLoc-Plant (RRID:SCR_011973) Copy   

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http://bio-cluster.iis.sinica.edu.tw/kbloc/index.html

A knowledge-based data analysis service to predict the localization site(s) of both single-localized and multi-localized proteins.

Proper citation: KnowPredsite (RRID:SCR_011974) Copy   

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http://www.informatics.jax.org/

Community model organism database for laboratory mouse and authoritative source for phenotype and functional annotations of mouse genes. MGD includes complete catalog of mouse genes and genome features with integrated access to genetic, genomic and phenotypic information, all serving to further the use of the mouse as a model system for studying human biology and disease. MGD is a major component of the Mouse Genome Informatics.Contains standardized descriptions of mouse phenotypes, associations between mouse models and human genetic diseases, extensive integration of DNA and protein sequence data, normalized representation of genome and genome variant information. Data are obtained and integrated via manual curation of the biomedical literature, direct contributions from individual investigators and downloads from major informatics resource centers. MGD collaborates with the bioinformatics community on the development and use of biomedical ontologies such as the Gene Ontology (GO) and the Mammalian Phenotype (MP) Ontology.

Proper citation: Mouse Genome Database (RRID:SCR_012953) Copy   

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

A predictor for the subcellular localization of proteins in eukaryotes that is based on a decision tree of several support vector machines (SVMs). It classifies up to four localizations for Fungi and Metazoan proteins and five localizations for Plant ones. BaCelLo's predictions are balanced among different classes and all the localizations are considered as equiprobable.

Proper citation: BaCelLo (RRID:SCR_011965) Copy   

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http://www.csbio.sjtu.edu.cn/bioinf/Cell-PLoc/

A package of web-servers for predicting subcellular localization of proteins in different organisms.

Proper citation: Cell-PLoc (RRID:SCR_011966) Copy   

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http://cello.life.nctu.edu.tw/

A subCELlular LOcalization predictor based on a multi-class support vector machine (SVM) classification system. CELLO uses 4 types of sequence coding schemes: the amino acid composition, the di-peptide composition, the partitioned amino acid composition and the sequence composition based on the physico-chemical properties of amino acids. They combine votes from these classifiers and use the jury votes to determine the final assignment.

Proper citation: CELLO (RRID:SCR_011968) Copy   

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http://www.antibodypedia.com/

Open-access database of antibodies against human proteins developed through collaboration between Antibodypedia AB and the Nature Publishing Group. It aims to provide the scientific community and antibody distributors alike with information on the effectiveness of specific antibodies in specific applications--to help scientists select the right antibody for the right application. Antibodypedia's mission is to promote the functional understanding of the human proteome and expedite analysis of potential biomarkers discovered through clinical efforts. To this end, they have developed an open-access, curated, searchable database containing annotated and scored affinity reagents to aid users in selecting antibodies tailored to specific biological and biomedical assays. They envisage Antibodypedia as a virtual repository of validated antibodies against all human, and ultimately most model-organism, proteins. Such a tool will be exploitable to identify affinity reagents to document protein expression patterns in normal and pathological states and to purify proteins alone and in complex for structural and functional analyses. They hope to promote characterization of the roles and interplay of proteins and complexes in human health and disease. They encourage commercial providers to submit information regarding their inventory of antibodies with links to quality control data. Independent users can submit their own application-specific experimental data using standard validation criteria (supportive or non-supportive) developed with the assistance of an international advisory board recruited from academic research institutions. Users can also comment on specific antibodies without submitting validation data.

Proper citation: Antibodypedia (RRID:SCR_012782) Copy   

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http://www.syfpeithi.de/

SYFPEITHI is a database comprising more than 7000 peptide sequences known to bind class I and class II MHC molecules. The entries are compiled from published reports only. It contains a collection of MHC class I and class II ligands and peptide motifs of humans and other species, such as apes, cattle, chicken, and mouse, for example, and is continuously updated. Searches for MHC alleles, MHC motifs, natural ligands, T-cell epitopes, source proteins/organisms and references are possible. Hyperlinks to the EMBL and PubMed databases are included. In addition, ligand predictions are available for a number of MHC allelic products. The database is based on previous publications on T-cell epitopes and MHC ligands. It contains information on: -Peptide sequences -anchor positions -MHC specificity -source proteins, source organisms -publication references Since the number of motifs continuously increases, it was necessary to set up a database which facilitates the search for peptides and allows the prediction of T-cell epitopes. The prediction is based on published motifs (pool sequencing, natural ligands) and takes into consideration the amino acids in the anchor and auxiliary anchor positions, as well as other frequent amino acids. The score is calculated according to the following rules: The amino acids of a certain peptide are given a specific value depending on whether they are anchor, auxiliary anchor or preferred residue. Ideal anchors will be given 10 points, unusual anchors 6-8 points, auxiliary anchors 4-6 and preferred residues 1-4 points. Amino acids that are regarded as having a negative effect on the binding ability are given values between -1 and -3. Sponsors: SYFPEITHI is supported by DFG-Sonderforschungsbereich 685 and theEuropean Union: EU BIOMED CT95-1627, BIOTECH CT95-0263, and EU QLQ-CT-1999-00713.

Proper citation: SYFPEITHI: A Database for MHC Ligands and Peptide Motifs (RRID:SCR_013182) Copy   

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http://epsf.bmad.bii.a-star.edu.sg/cube/db/html/home.html

Cube-DB is a database of pre-evaluated conservation and specialization scores for residues in paralogous proteins belonging to multi-member families of human proteins. Protein family classification follows (largely) the classification suggested by HUGO Gene Nomenclature Committee. Sets of orhtologous protein sequences were generated by mutual-best-hit strategy using full vertebrate genomes available in Ensembl. The scores, described on documentation page, are assigned to each individual residue in a protein, and presented in the form of a table (html or downloadable xls formats) and mapped, when appropriate, onto the related structure (Jmol, Pymol, Chimera).

Proper citation: Cube-DB (RRID:SCR_013233) Copy   

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http://www.alzforum.org/res/com/ant/

The Alzheimer Research Forum is the web''s most dynamic scientific community dedicated to understanding Alzheimer''s disease and related disorders. It also contains a database of providers of antibodies directed against several hundred molecules and proteins of relevant to research on Alzheimer and other neurodegenerative diseases. The web site reports on the latest scientific findings, from basic research to clinical trials; creates and maintains public databases of essential research data and reagents, and produces discussion forums to promote debate, speed the dissemination of new ideas, and break down barriers across the numerous disciplines that can contribute to the global effort to cure Alzheimer''s disease. The ARF team of professional science writers and editors, information technology experts, web developers and producers all work closely with our distinguished and diverse Advisory Board to ensure a high-quality of information and services. We very much welcome our readers'' participation in all aspects of the web site. Sponsors: The Alzheimer Research Forum is an independent nonprofit organization. It is supported by grants and individual donations.

Proper citation: Alzforum Antibody Directory for Neuroscience Research (RRID:SCR_013601) Copy   

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http://research.bioinformatics.udel.edu/iptmnet/

A protein database which connects multiple disparate bioinformatics tools and systems text mining, data mining, analysis and visualization tools, and databases and ontologies.

Proper citation: iPTMnet (RRID:SCR_014416) Copy   

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http://web.mit.edu/glycomics/gt/gtdb.shtml

A pathway-based graphical interface for navigating the glycoenzyme database. The goal of the project is to define the paradigms by which carbohydrate binding proteins function in cellular communication. These pages are divided into six categories: -Glycosphingolipid: Sub-categories are Isogloboseries, Globoseries, Neo-lactoseries, Lactoseries and Ganglioseries - N-linked: Sub-categories are High-mannose, Hybrid and Complex -Mucin -Terminal Core 1 -Other O-linked -Terminal All: Includes all potential terminal structures for each glycan category

Proper citation: Glycosylation Pathways Database (RRID:SCR_013486) Copy   

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