通过分子动力学和机器学习预测植物微生物界面中的受体-连接体配对偏好
Erica T Prates1, Omar Demerdash1, Manesh Shah2
1Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA.
Computational and structural biotechnology journal
|July 18, 2025
概括
一种新的混合分子动力学/机器学习方法准确地预测了对大型植物信号分子的结合亲和力,例如脂-胆醇糖 (LCOs). 这有助于我们更好地理解植物微生物相互作用和共生.
科学领域:
- 植物科学 植物科学
- 微生物学 微生物学
- 计算生物学是一种计算生物学.
背景情况:
- 微生物组组装影响植物健康.
- 微生物信号分子调解植物共生.
- 脂基酸 (LCOs) 被植物的lysin动因受体类似激酶 (LysM-RLKs) 识别出来.
研究的目的:
- 开发一种用于大,灵活的配体和植物受体之间的分子相互作用的预测方法.
- 克服LCOs的结构性特征和约束性亲和预测方面的挑战.
- 为了更好地理解内共生开始的过程.
主要方法:
- 开发了一种混合分子动力学/机器学习 (MD/ML) 方法.
- 使用粗的初始结构模型.
- 采用基于MD的符合性选择协议.
主要成果:
- 在预测大,灵活的带的结合亲和度排名方面取得了高准确性.
- 与豆类LysM-RLKs结合LCOs的实验趋势有很强的一致性.
- 提供了对基质特异性和结合机制的结构性见解.
结论:
- MD/ML 工作流程是选具有挑战性的联体受体对的一种强有力的方法.
- 这种方法促进了对植物微生物殖民的分子基础的理解.
- 能够预测对植物健康和共生至关重要的分子相互作用.
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