Characterizing common substructures of ligands for GPCR protein subfamilies.

Bekir Erguner, Masahiro Hattori, Susumu Goto, Minoru Kanehisa
Author Information
  1. Bekir Erguner: Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan. bekir@kuicr.kyoto-u.ac.jp.

Abstract

The G-protein coupled receptor (GPCR) superfamily is the largest class of proteins with therapeutic value. More than 40% of present prescription drugs are GPCR ligands. The high therapeutic value of GPCR proteins and recent advancements in virtual screening methods gave rise to many virtual screening studies for GPCR ligands. However, in spite of vast amounts of research studying their functions and characteristics, 3D structures of most GPCRs are still unknown. This makes target-based virtual screenings of GPCR ligands extremely difficult, and successful virtual screening techniques rely heavily on ligand information. These virtual screening methods focus on specific features of ligands on GPCR protein level, and common features of ligands on higher levels of GPCR classification are yet to be studied. Here we extracted common substructures of GPCR ligands of GPCR protein subfamilies. We used the SIMCOMP, a graph-based chemical structure comparison program, and hierarchical clustering to reveal common substructures. We applied our method to 850 GPCR ligands and we found 53 common substructures covering 439 ligands. These substructures contribute to deeper understanding of structural features of GPCR ligands which can be used in new drug discovery methods.

MeSH Term

Algorithms
Animals
Binding Sites
Cluster Analysis
Computational Biology
Databases, Protein
Drug Discovery
Humans
Ligands
Mice
Models, Molecular
Protein Binding
Protein Conformation
Rats
Receptors, G-Protein-Coupled

Chemicals

Ligands
Receptors, G-Protein-Coupled

Word Cloud

Created with Highcharts 10.0.0GPCRligandsvirtualcommonsubstructuresscreeningmethodsfeaturesproteinproteinstherapeuticvaluesubfamiliesusedG-proteincoupledreceptorsuperfamilylargestclass40%presentprescriptiondrugshighrecentadvancementsgaverisemanystudiesHoweverspitevastamountsresearchstudyingfunctionscharacteristics3DstructuresGPCRsstillunknownmakestarget-basedscreeningsextremelydifficultsuccessfultechniquesrelyheavilyligandinformationfocusspecificlevelhigherlevelsclassificationyetstudiedextractedSIMCOMPgraph-basedchemicalstructurecomparisonprogramhierarchicalclusteringrevealappliedmethod850found53covering439contributedeeperunderstandingstructuralcannewdrugdiscoveryCharacterizing

Similar Articles

Cited By