{"title":"Characterizing common substructures of ligands for GPCR protein subfamilies.","authors":"Bekir Erguner, M. Hattori, S. Goto, M. Kanehisa","doi":"10.1142/9781848166585_0003","DOIUrl":null,"url":null,"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.","PeriodicalId":73143,"journal":{"name":"Genome informatics. International Conference on Genome Informatics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1142/9781848166585_0003","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Genome informatics. International Conference on Genome Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/9781848166585_0003","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
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.