在公用事业级量子处理器上预测蛋白质结合点结构的量子框架
Yuqi Zhang1,2, Yuxin Yang2, William Martin2
1Department of Computer Science, Kent State University, Kent, OH, 44240, USA.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|November 29, 2025
概括
一个新的量子计算框架准确地预测了蛋白质活性位结构,超过了当前的AlphaFold3.3等方法. 这种方法使用变量量子Eigensolver (VQE) 进行实际的近期量子硬件应用.
科学领域:
- 量子计算是一种量子计算.
- 结构生物学 结构生物学
- 计算化学计算化学
背景情况:
- 准确的蛋白质活性部位结构预测至关重要,但对于短,灵活的片段来说具有挑战性.
- 传统和基于模拟的方法经常与这些复杂的结构作斗争.
研究的目的:
- 开发一个量子计算框架,用于预测在实用级量子处理器上的蛋白质活性位结构.
- 为了解决小,灵活的片的现有方法的局限性.
主要方法:
- 利用变量量子Eigensolver (VQE) 将结构预测框架作为基本状态能量最小化问题.
- 用稀疏的保利运算符将编码的氨基酸连接性和约束 (固态,几何,奇拉性) 转化为特定问题的哈密尔顿式.
- 在量子处理器上实现了用于能源估计和减噪测量解码的两级架构.
主要成果:
- 使用IBM-克利夫兰诊所量子处理器,对30个蛋白质碎片进行了量子框架的评估,包括治疗目标.
- 与AlphaFold3 (AF3) 和经典模拟进行基准测试,量子方法在根-平均-平方偏差 (RMSD) 中表现出卓越的性能.
- 与AF3和经典基线相比,量子方法还显示了更好的对接效率.
结论:
- 开发的量子计算框架提供了一个实用的端到端管道,用于在真实量子硬件上预测生物相关的蛋白质结构.
- 该研究强调了近期量子设备的工程可行性和潜力,以应对复杂的结构生物学挑战.
- 这种量子方法在特定蛋白质片段预测任务的准确性和有效性方面超过了当前最先进的方法.
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