GPCR-IFP: A Web Server for Analyzing G-Protein Coupled Receptor-Ligand Fingerprint Interaction from Experimental Structures
Xu, M.; Li, Y.; Dong, J.; Yuan, S.
Show abstract
G-protein coupled receptors (GPCRs) are interesting targets for a broad range of drugs. The comprehensions of the interactions mode between GPCRs and their ligands remains to be resolved. Here, we present GPCR-IFP, a web server which drives to gather experimental information supports a better understanding of ligand-receptor interaction and the chemical space. Despite the vast chemical library used for drug discovery, the structures of ligands for a particular receptor share some common molecular features. Thus, we foraged to collect all experimental structures of GPCRs and analyzed protein-ligand interactions which resulted in important unique patterns for both agonist and antagonist. All those results can be analyzed and visualized in GPCR-IFP, a friendly-used webserver.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Prediction of Essential Binding Domains for the Endocannabinoid N-Arachidonoylethanolamine (AEA) in the Brain Cannabinoid CB1 receptor 97%
- iBRAB: in silico based-designed Broad-spectrum Fab against H1N1 Influenza A Virus 96%
- Identification of Natural Antiviral Drug Candidates Against Tilapia Lake Virus: Computational Drug Design Approaches 96%
Similar papers in this journal
- Structural analysis and ensemble docking revealed the binding modes of selected progesterone receptor modulators 97%
- Optimized structure of monoubiquitinated FANCD2 (human) at Lys 561: a theoretical approach 96%
- A program to automate the discovery of drugs for West Nile and Dengue virus -- programmatic screening of over a billion compounds on PubChem, generation of drug leads and automated In Silico modelling 96%
Similar papers in this journal
- DSSP in Gromacs: tool for defining secondary structures of proteins in trajectories 95%
- Structural characterization of LsrK to target quorum sensing and comparison between X-ray and homology model 95%
- Identification of Family-Specific Features in Cas9 and Cas12 Proteins: A Machine Learning Approach Using Complete Protein Feature Spectrum 94%
Similar papers in this journal
- Mechanistic insights into the deleterious role of nasu-hakola disease associated TREM2 variants 96%
- In Silico Analysis Predicting Effects of Deleterious SNPs of Human RASSF5 Gene on its Structure and Functions 95%
- Machine learning prediction of antiviral-HPV protein interactions for anti-HPV pharmacotherapy 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.