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

SIMPLE SUMMARY: Smell is an important sense for insects. The olfactory sense of insects is involved in their feeding, mating, egg-laying, predation avoidance, and communication behaviors. The use of insect-specific odorants to control insect behavior is an important control method. Here, we built iO...

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Autores principales: Jin, Shuo, Qian, Kun, He, Lin, Zhang, Zan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10299237/
https://www.ncbi.nlm.nih.gov/pubmed/37367376
http://dx.doi.org/10.3390/insects14060560
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author Jin, Shuo
Qian, Kun
He, Lin
Zhang, Zan
author_facet Jin, Shuo
Qian, Kun
He, Lin
Zhang, Zan
author_sort Jin, Shuo
collection PubMed
description SIMPLE SUMMARY: Smell is an important sense for insects. The olfactory sense of insects is involved in their feeding, mating, egg-laying, predation avoidance, and communication behaviors. The use of insect-specific odorants to control insect behavior is an important control method. Here, we built iORandLigandDB, a platform for the batch prediction of insect-specific odorants based on artificial intelligence technology. The 3D structure of existing ORs in insects and the docking data with relevant odorants can be retrieved from the database. ABSTRACT: 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.
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spelling pubmed-102992372023-06-28 iORandLigandDB: A Website for Three-Dimensional Structure Prediction of Insect Odorant Receptors and Docking with Odorants Jin, Shuo Qian, Kun He, Lin Zhang, Zan Insects Article SIMPLE SUMMARY: Smell is an important sense for insects. The olfactory sense of insects is involved in their feeding, mating, egg-laying, predation avoidance, and communication behaviors. The use of insect-specific odorants to control insect behavior is an important control method. Here, we built iORandLigandDB, a platform for the batch prediction of insect-specific odorants based on artificial intelligence technology. The 3D structure of existing ORs in insects and the docking data with relevant odorants can be retrieved from the database. ABSTRACT: 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. MDPI 2023-06-15 /pmc/articles/PMC10299237/ /pubmed/37367376 http://dx.doi.org/10.3390/insects14060560 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Jin, Shuo
Qian, Kun
He, Lin
Zhang, Zan
iORandLigandDB: A Website for Three-Dimensional Structure Prediction of Insect Odorant Receptors and Docking with Odorants
title iORandLigandDB: A Website for Three-Dimensional Structure Prediction of Insect Odorant Receptors and Docking with Odorants
title_full iORandLigandDB: A Website for Three-Dimensional Structure Prediction of Insect Odorant Receptors and Docking with Odorants
title_fullStr iORandLigandDB: A Website for Three-Dimensional Structure Prediction of Insect Odorant Receptors and Docking with Odorants
title_full_unstemmed iORandLigandDB: A Website for Three-Dimensional Structure Prediction of Insect Odorant Receptors and Docking with Odorants
title_short iORandLigandDB: A Website for Three-Dimensional Structure Prediction of Insect Odorant Receptors and Docking with Odorants
title_sort iorandliganddb: a website for three-dimensional structure prediction of insect odorant receptors and docking with odorants
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10299237/
https://www.ncbi.nlm.nih.gov/pubmed/37367376
http://dx.doi.org/10.3390/insects14060560
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