Modeling the 3D structure of GPCRs from sequence

Sharon Shacham, Maya Topf, Noa Avisar, Fabian Glaser, Yael Marantz, Shay Bar-Haim, Silvia Noiman, Zvi Naor, Oren M. Becker

Research output: Contribution to journalArticlepeer-review

Abstract

G-protein-coupled receptors (GPCRs) are a large and functionally diverse protein superfamily, which form a seven transmembrane (TM) helices bundle with alternating extracellular and intracellular loops. GPCRs are considered to be one of the most important groups of drug targets because they are involved in a broad range of body functions and processes and are related to major diseases. In this paper we present a new technology, named PREDICT, for modeling the 3D structure of any GPCR from its amino acid sequence. This approach takes into account both internal protein properties (i.e., the amino acid sequence) and the properties of the membrane environment. Unlike competing approaches, the new technology does not rely on the single known structure of rhodopsin, and is thus capable of predicting novel GPCR conformations. We demonstrate the capabilities of PREDICT in reproducing the known experimental structure of rhodopsin. In principle, PREDICT-generated models offer new opportunities for structure-based drug discovery towards GPCR targets.

Original languageEnglish
Pages (from-to)472-483
Number of pages12
JournalMedicinal Research Reviews
Volume21
Issue number5
DOIs
StatePublished - 2001

Keywords

  • GPCR
  • Modeling
  • Structure based drug discovery

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