有效的蛋白质 - 连接物结合自由能量估计与粗粒道元动力学.
Andrea Grazzi1, Chelsea M Brown2, Maurizio Sironi1
1Department of Chemistry, University of Milan, Via C. Golgi 19, 20133 Milan, Italy.
Journal of chemical theory and computation
|January 10, 2026
概括
粗粒状漏斗元动力学 (CG-FMD) 提供了与全原子分子动力学 (AA-MD) 相比的精确的蛋白质-联体结合自由能量预测. 这种方法显著降低了计算成本,使药物发现的高通量选成为可能.
科学领域:
- 计算化学是一种计算化学.
- 生物物理学的生物物理.
- 药物发现 药物发现
背景情况:
- 准确预测蛋白质 - 配体结合的自由能量仍然是一个挑战.
- 全原子分子动力学 (AA-MD) 是准确的,但在计算上昂贵.
- 接方法很快,但缺乏准确性.
研究的目的:
- 开发一种计算效率高的方法,用于准确的结合自由能量预测.
- 为了弥合AA-MD准确度和对接吞吐量之间的差距.
- 为了验证粗粒度漏斗元动力学 (CG-FMD) 具有约束力的自由能量计算.
主要方法:
- 使用马提尼3力场的粗粒状道元动力学 (CG-FMD).
- 在全原子 (AA) 和粗粒度 (CG) 两种分辨率下,模拟素与两个蛋白质标的结合.
- 总计超过7毫秒的广泛模拟以评估预测的稳定性.
主要成果:
- 对于 ΔGbind 的 CG-FMD 预测与实验值相当.
- 该方法以AA-MD的计算成本的一小部分实现了这种准确性.
- 广泛的抽样减少了统计不确定性,弥补了简化的CG表示.
结论:
- CG-FMD提供了一种强大且计算效率高的方法,用于预测蛋白质-连接体结合的自由能量.
- 这种方法有望通过实现高通量查来加速药物发现.
- 进一步的研究应该扩大被调查的联结体和标的范围.
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