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iORandLigandDB: A Website for Three-Dimensional Structure Prediction of Insect Odorant Receptors and Docking with Odorants.

Shuo JinZheng WeiLin HeZan Zhang
Published in: Insects (2023)
The use of insect-specific odorants to control the behavior of insects has always been a hot spot in research on "green" control strategies of insects. However, it is generally time-consuming and laborious to explore insect-specific odorants with traditional reverse chemical ecology methods. Here, an insect odorant receptor (OR) and ligand database website (iORandLigandDB) was developed for the specific exploration of insect-specific odorants by using deep learning algorithms. The website provides a range of specific odorants before molecular biology experiments as well as the properties of ORs in closely related insects. At present, the existing three-dimensional structures of ORs in insects and the docking data with related odorants can be retrieved from the database and further analyzed.
Keyphrases
  • deep learning
  • aedes aegypti
  • machine learning
  • molecular dynamics
  • emergency department
  • zika virus
  • small molecule
  • mass spectrometry
  • big data
  • convolutional neural network
  • single molecule