基于结构的模拟的前性评估揭示了它们预测激酶突变对抑制剂结合的影响的能力
Sukrit Singh1, Vytautas Gapsys2, Matteo Aldeghi3
1Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States.
The journal of physical chemistry. B
|March 7, 2025
概括
基于结构的计算方法可以准确预测影响癌症药物反应的激酶突变. 这项研究对这些方法进行了基准测试,为个性化医疗提供了一个工具,并预测癌症治疗中的耐药性.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 药理学 药理学是指药理学的学科.
背景情况:
- 小分子激酶抑制剂是癌症治疗的重要药物,但由于向突变的耐药性限制了疗效.
- 精确瘤学利用瘤突变概况进行向治疗,但对抑制剂结合的突变影响的分类仍然是一个挑战.
- 基于结构的计算方法在预测激酶突变如何影响抑制剂结合方面表现有前途,但需要严格的潜在验证.
研究的目的:
- 为了对基于结构的计算方法进行前性基准测试,以预测酶突变对抑制剂结合亲和力的影响.
- 为了比较基于物理的模拟,Rosetta和机器学习模型的准确性,使用一个盲目的数据集.
- 为准确瘤学预测模型的未来发展提供可靠的实验和计算基准.
主要方法:
- 利用NanoBRET记者测定来测量细胞内激酶抑制剂对Abl激酶突变的亲和力.
- 将实验结果与基于物理的模拟,Rosetta和之前的机器学习模型的预测进行了比较.
- 分析了突变对抑制剂结合亲和力的影响,量化为自由能量变化 (ΔΔG).
主要成果:
- 基于结构的方法准确地将激酶突变分类为耐抑制剂或抑制剂敏感.
- 所有经过测试的基于结构的方法在分类中都表现出了可比的准确性.
- 基于物理的模拟对远离激酶活性位点的突变最有效.
- 起始配置和质子状态显著影响了特定突变 (T315A,L298F) 的预测准确性.
结论:
- 基于结构的方法为预测酶突变对药物结合的影响提供了有价值的工具.
- 这些方法可以帮助选择针对特定瘤突变的最佳疗法,并预测耐药性.
- 该研究为评估和改进精密瘤学计算模型建立了一个基准.
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