用古典和半经验量子力学潜能对盖莱丁配体的终点亲和度估计
Jan Choutka1, Jakub Kaminský1, Ercheng Wang2
1Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Gilead Sciences & IOCB Research Centre, Flemingovo nám. 2, 166 10 Prague, Czech Republic.
Journal of chemical information and modeling
|January 4, 2025
概括
量子力学潜能显示出对蛋白质 - 配体亲和力预测的前景. 验证显示,各种方法的性能各不相同,而素结合联体构成了挑战.
科学领域:
- 计算化学的计算化学
- 结构生物学 结构生物学
- 药物发现 药物发现 药物发现
背景情况:
- 预测蛋白质-配体亲和力对药物发现至关重要.
- 量子力学潜能提供了更准确的方法,但需要验证.
- 加勒-3 是各种疾病的相关点.
研究的目的:
- 系统地评估现代力场和半经验量子力学 (SQM) 方法来预测蛋白质 - 连接体亲和力.
- 为了验证这些计算方法,使用了一大批针对galectin-3的galectin抑制剂的数据集来验证这些计算方法.
- 分析溶解模型和值术语对预测准确性的影响.
主要方法:
- 排列了超过一千种具有已知的亲属关系的加勒丁抑制剂.
- 将抑制剂接到加勒-3中,并评估了多重力场和SQM方法.
- 采用隐式溶解模型,并分析了能量术语和贡献.
主要成果:
- 最好的方法在计算和实验亲和力之间实现了0.7-0.8的皮尔森相关性.
- 各种方法的性能不同,特别是结构相似的配体和形成素键的配体.
- 溶解模型显著影响了预测的成功;热学术语显示了混合的结果.
结论:
- 现代计算方法显示了蛋白质 - 配体亲和力预测的潜力,但需要仔细选择和验证.
- 素结合对当前的计算方法来说是一个重大挑战.
- 为了提高准确性,需要进一步完善计算工作流程,包括溶解和效应.
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