利用人工智能探索药物结合后翻译性修改的结构背景
Kirill E Medvedev1, R Dustin Schaeffer1, Nick V Grishin1,2
1Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
bioRxiv : the preprint server for biology
|April 1, 2025
概括
翻译后修改 (PTMs) 影响药物结合. 像AlphaFold3这样的AI工具可以模拟PTM对蛋白质结构和药物相互作用的影响,帮助药物发现和了解疾病.
科学领域:
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
- 结构生物信息学 结构生物信息学
背景情况:
- 翻译后修饰 (PTMs) 对蛋白质功能和细胞调节至关重要.
- PTM缺陷与各种疾病有关,包括癌症和神经退行性疾病.
- PTMs显著影响药物相互作用和结合亲和力,使它们成为药物发现的关键目标.
研究的目的:
- 为了识别影响人类蛋白质中药物结合的小分子结合相关PTMs.
- 利用人工智能驱动的方法进行PTM及其对药物相互作用的影响的大规模建模.
- 为影响小分子结合的PTM提供结构上下文.
主要方法:
- 开发了DrugDomain数据库,以识别与小分子结合相关的PTMs.
- 通过使用 ECOD 数据库将 6,131 个已识别的 PTM 映射到结构域.
- 利用基于人工智能的蛋白质结构预测工具 (AlphaFold3,RoseTTAFold All-Atom,Chai-1) 来生成14,178个具有对接联体的PTM修饰蛋白质模型.
主要成果:
- 人工智能方法可以预测PTM对小分子结合的影响,尽管对于精确的准确性评估需要更大的基准测试.
- 发现NADPH-细胞染色体P450降解酶的酸化会导致结合口袋的显著结构破坏,可能会损害功能.
- 所有生成的数据和模型都通过DrugDomain数据库和GitHub公开提供.
结论:
- 人工智能驱动的方法可以进行大规模的PTM结构分析及其对药物结合的影响.
- 这项研究为了解药物发现中PTMs的结构上下文提供了一个新的资源.
- 这些发现突出了针对PTM治疗干预的潜力.
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