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Journal of Computer-Aided Molecular Design

, Volume 32, Issue 3, pp 415–433 | Cite as

Enabling the hypothesis-driven prioritization of ligand candidates in big databases: Screenlamp and its application to GPCR inhibitor discovery for invasive species control

  • Sebastian Raschka
  • Anne M. Scott
  • Nan Liu
  • Santosh Gunturu
  • Mar Huertas
  • Weiming Li
  • Leslie A. Kuhn
Article

Abstract

While the advantage of screening vast databases of molecules to cover greater molecular diversity is often mentioned, in reality, only a few studies have been published demonstrating inhibitor discovery by screening more than a million compounds for features that mimic a known three-dimensional (3D) ligand. Two factors contribute: the general difficulty of discovering potent inhibitors, and the lack of free, user-friendly software to incorporate project-specific knowledge and user hypotheses into 3D ligand-based screening. The Screenlamp modular toolkit presented here was developed with these needs in mind. We show Screenlamp’s ability to screen more than 12 million commercially available molecules and identify potent in vivo inhibitors of a G protein-coupled bile acid receptor within the first year of a discovery project. This pheromone receptor governs sea lamprey reproductive behavior, and to our knowledge, this project is the first to establish the efficacy of computational screening in discovering lead compounds for aquatic invasive species control. Significant enhancement in activity came from selecting compounds based on one of the hypotheses: that matching two distal oxygen groups in the 3D structure of the pheromone is crucial for activity. Six of the 15 most active compounds met these criteria. A second hypothesis—that presence of an alkyl sulfate side chain results in high activity—identified another 6 compounds in the top 10, demonstrating the significant benefits of hypothesis-driven screening.

Keywords

Virtual screening Structure based drug discovery G protein-coupled receptor Chemoinformatics Computer-aided molecular design Structure–activity relationships 

Abbreviations

2D

Two-dimensional

3D

Three-dimensional

3kPZS

3-keto petromyzonol sulfate

3sPZS

Trisulfated petromyzonol sulfate

GLL

GPCR Ligand Library

GPCR

G protein-coupled receptor

Mgβ1AR

β1-Adrenergic receptor from Meleagris gallopavo

PAIN

Pan-assay interference compound

PZS

Petromyzonol sulfate

SQL

Structured Query Language

SLOR1

Sea lamprey olfactory receptor 1

TFM

Trifluoromethyl nitrophenol

EOG

Electro-olfactogram

Notes

Acknowledgements

We thank Qinghua Yuan for her contributions to the homology modeling of SLOR1 and Stacey Kneeshaw for evaluating protein–ligand energy minimization protocols for SLOR1-3kPZS docking and analyzing charge distributions for matching functional groups. This research was supported by funding from the Great Lakes Fishery Commission from 2012-present (Project ID: 2015_KUH_54031). We gratefully acknowledge OpenEye Scientific Software (Santa Fe, NM) for providing academic licenses for the use of their ROCS, OMEGA, QUACPAC (molcharge), and OEChem toolkit software.

Supplementary material

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Biochemistry and Molecular BiologyMichigan State UniversityEast LansingUSA
  2. 2.Department of Fisheries and WildlifeMichigan State UniversityEast LansingUSA
  3. 3.Department of ChemistryMichigan State UniversityEast LansingUSA
  4. 4.Department of Computer Science and EngineeringMichigan State UniversityEast LansingUSA
  5. 5.Department of BiologyTexas State UniversitySan MarcosUSA
  6. 6.Protein Structural Analysis and Design Lab, Department of Biochemistry and Molecular BiologyMichigan State UniversityEast LansingUSA

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