来自表面诱导解离的能量解析质谱数据改善了对蛋白质复杂结构的预测
Robert M Bolz1, Justin T Seffernick2, Zachary C Drake1
1Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, United States.
Analytical chemistry
|January 24, 2025
概括
来自原生质谱 (nMS) 的能量解析质谱 (ERMS) 数据显著提高了多重蛋白质结构预测的准确性,而不是单纯的外观能量. 这种增强的计算建模方法完善了蛋白质复杂结构.
科学领域:
- 生物化学 生物化学
- 结构生物学 结构生物学
- 计算化学计算化学
背景情况:
- 原生质谱 (nMS) 是蛋白质结构阐明的关键.
- 表面诱导解离 (SID) 协同MS提供了对蛋白质复合体静态度和接口强度的洞察.
- 来自SID-nMS的能量分辨质谱 (ERMS) 数据提供了界面断裂的详细模式.
研究的目的:
- 为了测试是否将ERMS数据纳入多重蛋白质结构预测,仅使用外观能量.
- 开发和验证一种用于预测多重蛋白质结构的新计算方法.
主要方法:
- 使用RosettaDock与AlphaFold2衍生子单元生成了20个蛋白质复杂模型.
- 模拟每个模型的ERMS数据,并根据与实验ERMS数据的一致性重新进行测量.
- 基于ERMS的重定位与基于外观能量的重定位和标准罗塞塔评分进行了比较.
主要成果:
- 准确预测的模型与实验ERMS数据更好地一致.
- 在20个案例中16个案例中,ERMS恢复改善或匹配罗塞塔的最佳模型RMSD.
- 在4个案例中,ERMS重塑产生了高度精确的结构 (低于5 Å),超过了在6个案例中产生这种结构的外观能量重塑.
结论:
- ERMS数据提供了一个比单独的外观能量更全面的数据集来改进多重蛋白质结构.
- 开发的基于ERMS的rescoring方法显著提高了计算蛋白质结构预测的准确性.
- 该方法通过Rosetta Commons公开提供,使其在结构生物学中更广泛地应用.
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