利用人工智能探索药物结合后翻译性修改的结构上下文
Kirill E Medvedev1, R Dustin Schaeffer2, Nick V Grishin2,3
1Department of Biophysics, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX, 75390, USA. Kirill.Medvedev@UTSouthwestern.edu.
Journal of cheminformatics
|May 4, 2025
概括
翻译后修改 (PTMs) 影响药物结合. 人工智能工具现在模拟了PTM对药物相互作用的影响,揭示了结构变化和癌症等疾病的潜在治疗见解.
科学领域:
- 生物化学和分子生物学
- 计算生物学和生物信息学
- 药物发现和开发 药物发现和开发
背景情况:
- 翻译后修改 (PTM) 对蛋白质功能和细胞调节至关重要,缺陷与癌症和糖尿病等疾病有关.
- PTMs显著影响药物相互作用,包括结合亲和力,使它们成为药物发现的重要目标.
- 研究PTMs对药物结合的结构影响在实验上具有挑战性,限制了大规模分析.
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蛋白质结构预测来在规模上建模PTM对药物相互作用的影响.
- 为影响药物标中小分子结合的PTM提供结构上下文.
主要方法:
- 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PTM修饰的人类蛋白质与对接联体的模型.
- 在DrugDomain数据库中识别了6131个与人类蛋白质之间的小分子结合相关的PTM.
- 使用蛋白质域进化分类 (ECOD) 数据库将已识别的PTM映射到结构域.
主要成果:
- 使用人工智能方法生成了 14,178 个 PTM 修改的人类蛋白质模型,其中包含了对接联体.
- 证明了人工智能方法能够预测PTM对小分子结合的影响的能力,尽管需要更大的基准来准确评估准确性.
- 由于癌症中的酸化,NADPH-细胞染色体P450降解酶的结合口袋中观察到显著的结构性破坏,可能会损害功能.
结论:
- 人工智能驱动的结构建模提供了一个可扩展的方法来研究PTM对药物结合的影响.
- 药物领域数据库为探索药物目标中PTM的结构上下文提供了宝贵的资源.
- 了解PTM介导的结构变化对于设计更有效的治疗方法和了解疾病机制至关重要.
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