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Target based virtual screening by docking into automatically generated GPCR models.

Christofer S Tautermann1

  • 1Department of Lead Identification and Optimization Support, Boehringer Ingelheim Pharma GmbH & Co. KG, Ingelheim am Rhein, Germany. christofer.tautermann@boehringer-ingelheim.com

Methods in Molecular Biology (Clifton, N.J.)
|September 15, 2012
PubMed
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Automated workflows build G-protein coupled receptor (GPCR) models for virtual screening (VS) drug discovery. This approach identifies potential drug candidates with minimal effort, overcoming challenges posed by limited GPCR structural data.

Area of Science:

  • Computational chemistry and structural biology
  • Drug discovery and medicinal chemistry

Background:

  • Target-based virtual screening (VS) is crucial for identifying small molecule hits, especially for G-protein coupled receptors (GPCRs).
  • A significant challenge in GPCR drug discovery is the limited availability of 3D structural information, hindering traditional VS approaches.
  • Building accurate GPCR structural models for docking is often time-consuming and may not be justified solely for VS purposes.

Purpose of the Study:

  • To present a fully automated workflow for building multiple G-protein coupled receptor (GPCR) models.
  • To identify the optimal GPCR model for subsequent docking and virtual screening (VS) with minimal manual intervention.
  • To facilitate efficient drug discovery by streamlining the process of generating and selecting GPCR models for VS.

Main Methods:

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  • Development of a fully automated workflow for the construction of numerous GPCR structural models.
  • Selection of the best-performing GPCR model from the generated set for virtual screening (VS) and docking.
  • Inputs include GPCR sequence, a reference ligand with experimental data, and a small molecule database for docking.

Main Results:

  • The automated workflow successfully generates a large number of GPCR models.
  • The workflow enables the identification of suitable GPCR models for virtual screening (VS) with moderate enrichment.
  • The process requires minimal user effort, with manual intervention being optional rather than essential.

Conclusions:

  • A fully automated workflow significantly reduces the effort required for G-protein coupled receptor (GPCR) model building for virtual screening (VS).
  • This method provides a practical solution for drug discovery targeting GPCRs, despite the inherent challenges of limited structural data.
  • The presented workflow offers a low-effort, efficient approach to identify potential drug candidates by enabling rapid generation and selection of GPCR models.