循环德克斯特林:通过确定亲和度常数in silico来建立人工智能驱动的药物设计的构建块
Amelia Anderson1,2,3, Ángel Piñeiro3, Rebeca García-Fandiño2
1Cyclarity Therapeutics, 8001 Redwood Blvd, Novato, CA 94945, USA.
Computational and structural biotechnology journal
|March 21, 2024
概括
计算方法准确量化了用于药物输送的环氧素 (CD) 结合亲缘关系. 经过修改的CD显示了不同的绑定方向和替代影响,使预测CD设计成为可能.
科学领域:
- 超分子化学 超分子化学
- 计算化学的计算化学
- 药物输送系统 药物输送系统
背景情况:
- 循环素 (CD) 是多功能循环碳水化合物,在药物输送和工业应用方面具有显著的潜力.
- 准确量化CD和客分子之间的结合亲和关系至关重要,但具有挑战性.
- 计算方法提供高分辨率的结构和机制见解,可以补充实验分析.
研究的目的:
- 为了计算性地描述改性β-cyclodextrins (βCDs) 和醇分子之间的 1:1 纳入复合体.
- 调查CD替代对结合亲和力和导向的影响.
- 为了验证一种计算效率高的方法,用于绑定能量计算.
主要方法:
- 引导的元动力学模拟来计算潜在的平均力 (PMF).
- 包括包括四个修改的βCD和两个固醇分子 (胆固醇,7-基托胆固醇) 的包括综合体的表征.
- 通过重复模拟和各种后处理技术验证可重复性.
主要成果:
- 观察到CD腔内固醇分子的明显方向偏好,尾部从更宽的面部突出.
- 证明CD替代对结合亲缘关系有显著的影响.
- 发现,仅采用CD腔的更宽面样本就能产生与全腔样本相比较的结合能,但效率更高.
结论:
- 这项研究为CD-胆固醇纳入复合物的形成和稳定性提供了新的见解.
- 开发的计算方法是可重复的,灵敏的,并且具有成本效益,使预测CD设计成为可能.
- 这项研究促进了人工智能驱动的分子设计和自动化药物开发的大规模研究.
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