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利用计算机辅助药物发现(CADD)进行战略先导化合物设计与开发,以解决麦田中抗除草剂的小粒雀麦问题。

Strategic lead compound design and development utilizing computer-aided drug discovery (CADD) to address herbicide-resistant Phalaris minor in wheat fields.

作者信息

Rani Priyanka, Rajak Bikash Kumar, Mahato Gopal Kumar, Rathore Ravindranath Singh, Chandra Girish, Singh Durg Vijay

机构信息

Molecular Modelling and Computer-Aided Drug Discovery Laboratory Department of Bioinformatics, School of Earth, Biological and Environmental Sciences, Central University of South Bihar, Gaya, India.

Department of Chemistry, School of Physical and Chemical Sciences, Central University of South Bihar, Gaya, India.

出版信息

Pest Manag Sci. 2025 May;81(5):2469-2479. doi: 10.1002/ps.8455. Epub 2024 Oct 8.

DOI:10.1002/ps.8455
PMID:39377567
Abstract

Wheat (Triticum aestivum) is a vital cereal crop and a staple food source worldwide. However, wheat grain productivity has significantly declined as a consequence of infestations by Phalaris minor. Traditional weed control methods have proven inadequate owing to the physiological similarities between P. minor and wheat during early growth stages. Consequently, farmers have turned to herbicides, targeting acetyl-CoA carboxylase (ACCase), acetolactate synthase (ALS) and photosystem II (PSII). Isoproturon targeting PSII was introduced in mid-1970s, to manage P. minor infestations. Despite their effectiveness, the repetitive use of these herbicides has led to the development of herbicide-resistant P. minor biotypes, posing a significant challenge to wheat productivity. To address this issue, there is a pressing need for innovative weed management strategies and the discovery of novel herbicide molecules. The integration of computer-aided drug discovery (CADD) techniques has emerged as a promising approach in herbicide research, that facilitates the identification of herbicide targets and enables the screening of large chemical libraries for potential herbicide-like molecules. By employing techniques such as homology modelling, molecular docking, molecular dynamics simulation and pharmacophore modelling, CADD has become a rapid and cost-effective medium to accelerate the herbicide discovery process significantly. This approach not only reduces the dependency on traditional experimental methods, but also enhances the precision and efficacy of herbicide development. This article underscores the critical role of bioinformatics and CADD in developing next-generation herbicides, offering new hope for sustainable weed management and improved wheat cultivation practices. © 2024 Society of Chemical Industry.

摘要

小麦(普通小麦)是一种重要的谷类作物,也是全球主要的食物来源。然而,由于受到小粒雀麦的侵害,小麦籽粒产量显著下降。由于小粒雀麦与小麦在生长早期存在生理相似性,传统的杂草控制方法已被证明是不够的。因此,农民们转向了针对乙酰辅酶A羧化酶(ACCase)、乙酰乳酸合成酶(ALS)和光系统II(PSII)的除草剂。针对PSII的异丙隆于20世纪70年代中期被引入,用于控制小粒雀麦的侵害。尽管这些除草剂有效,但它们的重复使用导致了抗除草剂的小粒雀麦生物型的出现,对小麦生产力构成了重大挑战。为了解决这个问题,迫切需要创新的杂草管理策略和发现新型除草剂分子。计算机辅助药物发现(CADD)技术的整合已成为除草剂研究中一种有前途的方法,它有助于识别除草剂靶点,并能够筛选大型化学文库以寻找潜在的类除草剂分子。通过采用同源建模、分子对接、分子动力学模拟和药效团建模等技术,CADD已成为一种快速且经济高效的手段,可显著加速除草剂的发现过程。这种方法不仅减少了对传统实验方法的依赖,还提高了除草剂开发的精度和效果。本文强调了生物信息学和CADD在开发下一代除草剂中的关键作用,为可持续杂草管理和改进小麦种植实践带来了新的希望。© 2024化学工业协会。

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