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Provides a platform for identifying odorant receptors (ORs) for small molecules and for browsing existing OR-ligand pairs. ODORactor enables the prediction of ORs from the molecular structures of arbitrary chemicals. It permits to verify odorant and to recognize OR. The tool is capable to identify novel odorants and their receptors. It may provide an effective way to decipher olfactory coding and could be a useful server tool for both basic olfaction research in academia and for odorant discovery in industry.

SSFA-GPHR / Sequence-Structure-Function-Analysis of Glycoprotein Hormone Receptors

Provides information on glycoprotein hormone receptor (GPHR)-related topics. SSFA-GPHR is a database that contains protein sequences, multiple sequence alignments (MSA), protein structures, homology models and data from mutagenesis experiments of glycoprotein hormone receptors GPHRs. A web-application facilitates interactive linkage between unified functional and 3D structural information. It can be useful for exploring and recognizing spatial interrelationships between side-chains and causally-induced malfunctions of proteins.

CLICs / CLuster In Conservation

A general tool for genome-wide definition of genomic gene clusters conserved in multiple species. Syntenic orthologs, defined as gene pairs showing conservation of both genomic location and coding sequence, were subjected to a graph theory algorithm for discovering CLICs (clusters in conservation). When applied to ORs in five mammals, including the marsupial opossum, more than 90% of the OR genes were found within a framework of 48 multi-species CLICs, invoking a general conservation of gene order and composition.

ORDB / Olfactory Receptor DataBase

A central repository of olfactory receptor (OR) and olfactory receptor-like gene and protein sequences. ORDB began as a database of vertebrate OR genes and proteins and continues to support sequencing and analysis of these receptors by providing a comprehensive archive with search tools for this expanding family. Because of the associated growth of interest in other CRs, the database has grown over the years to include a broad range of chemosensory genes and proteins, that includes in addition to ORs the taste papilla receptors (TPRs), vomeronasal organ receptors (VNRs), insect olfactory receptors (IORs), Caenorhabditis elegans chemosensory receptors (CeCRs), fungal pheromone receptors (FPRs).


A database for experimental restaints of GPCRs. GPCRRD is designed to systematically collect all experimental restraints (including residue orientation, contact and distance maps) available from the literature and primary GPCR resources using an automated text mining algorithm combined with manual validation, with the purpose of assisting GPCR 3D structure modeling and function annotation. The current dataset contains thousands of spatial restraints from mutagenesis, disulfide mapping distances, electron cryo-microscopy and Fourier-transform infrared spectroscopy experiments.

GPCR NaVa database

Integrates data on natural variants in human GPCRs from online databases, the scientific literature, and patents. Where available, variants contain information on their location in the DNA (and protein sequence), the involved nucleotides (and amino acids), the average frequency of each allele, reported disease associations, and references to public databases and the scientific literature. The GPCR NaVa database aims to facilitate studies into pharmacogenetics, genotype-phenotype, and structure-function relationships of GPCRs.

CORP / Classifier for Olfactory Receptor Pseudogenes

A probabilistic method for annotation of olfactory receptor (OR) pseudogenes. The algorithm assesses the probability of an OR gene with intact open reading frame to encode a non-functional protein (i.e. pseudogene) by examining the deviation of its protein sequences from the OR functionally crucial consensus. The CORP algorithm was tested on a large dataset of OR genes and demonstrated excellent distinction between functional and non-functional ORs.


Contains 3D structural models of 1,026 putative G protein-coupled receptors (GPCRs) in the human genome generated by the GPCR-I-TASSER pipeline. In GPCR-I-TASSER, the GPCR sequences are first threaded through the GPCR template library to identify muliple structure templates by the LOMETS programs. When close homolgous templates are identified, full-length models will be constructed by the I-TASSER based fragment assembly simulations, assisted by a GPCR and membrane specific force field and spatial restraints collected from mutagenesis experiments in GPCR-RD.


Facilitates access to relevant data on G-protein coupled receptors (GPCRs) mutants. tGRAP is a database that provides all papers describing experiments (such as radioligand binding or functional testing) on mutant GPCRs, including splice variants and polymorphisms, have been incorporated into the database. Apart from these data, the database provides detailed information on the amino acids being altered, qualitative information on experiments and experimental conditions, and literature references.

GPCR-CA / GPCR-cellular automaton

Predicts G protein-coupled receptors (GPCRs) using cellular automaton (CA) data. GPCR-CA utilizes CA to reveal the pattern features hidden in piles of long and complicated protein sequences. Meanwhile, the gray-level co-occurrence matrix factors extracted from the CA images are used to represent the samples of proteins through their pseudo amino acid composition. GPCR-CA is a two-layer predictor: the first layer prediction engine is for identifying a query protein as GPCR on non-GPCR; if it is a GPCR protein, the process will automatically continue with the second-layer prediction engine to further identify its type among the following six functional classes: (a) rhodopsinlike, (b) secretin-like, (c) metabotrophic/glutamate/pheromone; (d) fungal pheromone, (e) cAMP receptor, and (f) frizzled/smoothened family.


Predicts G protein-coupled receptors (GPCRs) at five levels. GPCR-MPredictor first determines whether a protein sequence is a GPCR or a non-GPCR. If the predicted sequence is a GPCR, then it is further classified into family, subfamily, sub-subfamily, and subtype levels. Features are extracted using amino acid composition, pseudo amino acid composition, and dipeptide composition of protein sequences. The proposed hierarchical Genetic algorithm-based ensemble classifier exploits the prediction results of support vector machine (SVM), k-nearest neighbor (KNN), probabilistic neural networks (PNN), and J48 at each level.