PREDICT modeling and in-silico screening for G-protein coupled receptors

Sharon Shacham, Yael Marantz, Shay Bar-Haim, Ori Kalid, Dora Warshaviak, Noa Avisar, Boaz Inbal, Alexander Heifetz, Merav Fichman, Maya Topf, Zvi Naor, Silvia Noiman, Oren M. Becker*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

105 Scopus citations


G-protein coupled receptors (GPCRs) are a major group of drug targets for which only one x-ray structure is known (the nondrugable rhodopsin), limiting the application of structure-based drug discovery to GPCRs. In this paper we present the details of PREDICT, a new algorithmic approach for modeling the 3D structure of GPCRs without relying on homology to rhodopsin. PREDICT, which focuses on the transmembrane domain of GPCRs, starts from the primary sequence of the receptor, simultaneously optimizing multiple 'decoy' conformations of the protein in order to find its most stable structure, culminating in a virtual receptor-ligand complex. In this paper we present a comprehensive analysis of three PREDICT models for the dopamine D2, neurokinin NK1, and neuropeptide Y Y1 receptors. A shorter discussion of the CCR3 receptor model is also included. All models were found to be in good agreement with a large body of experimental data. The quality of the PREDICT models, at least for drug discovery purposes, was evaluated by their successful utilization in in-silico screening. Virtual screening using all three PREDICT models yielded enrichment factors 9-fold to 44-fold better than random screening. Namely, the PREDICT models can be used to identify active small-molecule ligands embedded in large compound libraries with an efficiency comparable to that obtained using crystal structures for non-GPCR targets.

Original languageAmerican English
Pages (from-to)51-86
Number of pages36
JournalProteins: Structure, Function and Genetics
Issue number1
StatePublished - 1 Oct 2004
Externally publishedYes


  • Docking
  • GPCR
  • In silico screening
  • Modeling
  • Structure based drug discovery


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