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GPCR-SSFE / G protein-coupled receptors-Sequence-Structure-Feature-Extractor

Allows nonexpert users to access 3D structural data that might otherwise be unattainable. GPCR-SSFE is a web server dedicated to template selection and homology modeling of G protein-coupled receptors (GPCRs), which has been updated to include the latest structural data and extended with new components. It is a user-friendly tool to store and make a comprehensive and up-to-date set of pre-calculated homology models of human, mouse and rat GPCRs.

GPCR-ModSim / G-protein coupled receptors Modeling Simulation

A web service for computational modeling and simulation of G-protein coupled receptors. GPCR-ModSim provides a modeling protocol for receptors and a molecular dynamics (MD) workflow for atomistic simulations in a lipidic membrane. GPCR-ModSim includes three main features: (i) the target receptor can be modeled in any of the three conformational states structurally characterized (inactive, partially-active, fully active); (ii) a novel modeling protocol gives the user the freedom to choose how to compose chimeric templates; (iii) MD refinement allows the receptor structure to undergo a final relaxation phase where a GPCR-conserved network of interhelical contacts is maintained through a series of pair-distance restraints.

GPCRM / G-protein coupled receptors modeling

A web app for comparative modeling of G-protein coupled receptors (GPCR) begins with detection of close homologs with solved 3D structures. GPCRM integrates various approaches for template detection, alignment generation, model building, loop refinement and model filtering based on the Z-coordinate. GPCRM was designed to significantly decrease the time of structure generation and analysis needed in large scale biological projects. The platform can be used not only by computational biologists but also experimentalists to visualize their findings on theoretical models.

GRIFFIN / G-protein and Receptor Interaction Feature Finding INstrument

Predicts G-protein coupling specificity using the support vector machine (SVM) and the hidden Markov model (HMM) methods. GRIFFIN employs the hierarchical SVM classifier using the feature vectors, which is useful for Class A G-protein coupled receptor (GPCR). For the opsins and olfactory subfamilies of Class A and other minor families (Classes B, C, frizzled and smoothened), this method predicted the binding G-protein with high accuracy using the HMM.