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iBio-GATS-A Semi-Automated Workflow for Structural Modelling of Insect Odorant Receptors.

Vaanathi Chidambara Thanu1, Amara Jabeen1, Shoba Ranganathan1

  • 1Applied Biosciences, Macquarie University, Sydney 2109, Australia.

International Journal of Molecular Sciences
|March 13, 2024
PubMed
Summary
This summary is machine-generated.

A new computational method, iBio-GATS, models insect odorant receptors (iORs) for pest control. This approach aids in designing novel attractants by predicting 3D iOR structures, overcoming experimental limitations.

Keywords:
3D structural modelinsect odorant receptormembrane proteinsemi-automated workflowseven transmembrane helical proteintemplate-based modelling

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Area of Science:

  • Structural biology
  • Computational chemistry
  • Insect chemical ecology

Background:

  • Insects use seven transmembrane (7TM) odorant receptors (iORs) to detect environmental cues, crucial for pest control strategies.
  • Developing novel chemical attractants for invasive pests requires understanding iOR 3D structures, which are experimentally challenging to determine.
  • Existing template-based modeling relies on sequence identity, which is insufficient for the divergent iOR family.

Purpose of the Study:

  • To develop a novel, semi-automated workflow (iBio-GATS) for generating high-quality 3D models of insect odorant receptors (iORs).
  • To provide a method for designing novel odorants tailored to specific iOR structures for effective pest biocontrol.
  • To overcome limitations of experimental structure determination and sequence-identity-based modeling for iORs.

Main Methods:

  • Adaptation of the Bio-GATS template selection method, focusing on hydrophobicity correspondence, for iORs.
  • Development of a semi-automated workflow using Python and shell scripting for iOR structure modeling.
  • Validation of the iBio-GATS workflow using experimental iOR structures and comparison with AlphaFold2 models.

Main Results:

  • The iBio-GATS workflow successfully generated high-quality 3D models for iORs from validated templates.
  • Models generated for fruit fly iORs (OR59b and Orco) showed functional ligand binding predictions consistent with experimental mutagenesis data.
  • iBio-GATS models demonstrated better concordance with experimental findings than AlphaFold2 models for the tested fruit fly iORs.

Conclusions:

  • iBio-GATS offers a robust and accessible method for modeling insect odorant receptors, advancing the design of targeted pest control agents.
  • This computational approach facilitates the development of novel odorants by accurately predicting iOR structures.
  • The study highlights the utility of iBio-GATS in insect chemical ecology and pest management research.