G-protein-coupled receptors-ligand docking software tools | Protein interaction data analysis
Here, we surveyed bioinformatics software tools for predicting GPCR–ligand complex structures. G-protein-coupled receptors (GPCRs) play important physiological roles related to signal transduction and form a major group of drug targets. Prediction of GPCR-ligand complex structures has therefore important implications to drug discovery.
A user friendly pipeline that puts together several state-of-the-art tools and allows experienced and inexperienced users to obtain GPCR’s homology models together with predictions of ligand binding poses. GOMoDo was developed to be flexible and adaptable to the user’s demands. For this reason, every step of the GOMoDo pipeline allows user intervention (if needed) to i) insert alignments, ii) use homology models generated by other methods, iii) predict binding cavities and iv) include experimental restraints for performing knowledge-based virtual docking experiments.
A user friendly web interface that attemps to narrow the gap between known structures and receptors of interest to the biologists. GPCR automodel primary focus is the high-throughput modelization of olfactory receptor proteins and their docking against odorant molecules. The current version of GPCR automodel can potentially be employed to model any GPCR and its interaction with a molecule of interest for the user.
Contains 25 non-redundant high-resolution G Protein-Coupled Receptor (GPCR) co-structures with an accompanying set of diverse ligands and computational decoy molecules for each target. GPCR-Bench uses a set color-coded for class and ligand type. It permits to test several docking and scoring schemes before deciding on a suitable protocol. The tool is a well curated and high-quality GPCR benchmark set, in the spirit of DUD and DUD-E.
A pipeline for GPCR–ligand complex structures prediction. When tested on a set of GPCR models built by different homology modeling methods, Galaxy7TM could predict GPCR–ligand complex structures with higher accuracy than AutoDock Vina and Rosetta MPrelax. Galaxy7TM was especially successful in predicting contacts between GPCR and ligand and may potentially be applicable to practical problems related to drug discovery.
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