Suppr超能文献

iORandLigandDB:一个用于昆虫气味受体三维结构预测及与气味剂对接的网站。

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

作者信息

Jin Shuo, Qian Kun, He Lin, Zhang Zan

机构信息

College of Plant Protection, Southwest University, Chongqing 400716, China.

出版信息

Insects. 2023 Jun 15;14(6):560. doi: 10.3390/insects14060560.

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.

摘要

使用昆虫特异性气味剂来控制昆虫行为一直是昆虫“绿色”防控策略研究的热点。然而,用传统的反向化学生态学方法探索昆虫特异性气味剂通常既耗时又费力。在此,开发了一个昆虫气味受体(OR)和配体数据库网站(iORandLigandDB),用于通过深度学习算法专门探索昆虫特异性气味剂。该网站在分子生物学实验之前提供一系列特异性气味剂以及近缘昆虫中OR的特性。目前,可以从数据库中检索昆虫中OR的现有三维结构以及与相关气味剂的对接数据,并进行进一步分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e71/10299237/d2e6fd3b3d7b/insects-14-00560-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验