完全自动化,高度耐错的宏分子结构测定从多维核重整器增强光谱和化学转移分配
John Kuszewski1, Charles D Schwieters, Daniel S Garrett
1Contribution from the Division of Computational Bioscience, Building 12A, Center for Information Technology, National Institutes of Health, Bethesda, Maryland 20892-5624, USA.
Journal of the American Chemical Society
|May 20, 2004
概括
本研究引入了一种使用核Overhauser增强 (NOE) 数据来确定蛋白质结构的新自动化方法,即使存在重大错误. 强大的算法提高了准确性,并避免了计算蛋白质折叠的常见陷.
科学领域:
- 结构生物学 结构生物学
- 计算生物学 计算生物学
- 生物物理学的生物物理.
背景情况:
- 高通量核磁共振 (NMR) 蛋白质结构的确定通常受到获得准确的初始折叠和解决核Overhauser增强 (NOE) 赋值模两可的挑战的限制.
- 错误的NOE赋值,特别是对于远程相互作用,可以显著阻碍结构计算过程.
研究的目的:
- 开发一种强大的,自动化的计算方法,以高脊柱坐标准确度从NMR数据中确定蛋白质结构.
- 通过设计一个对高百分比不正确的NOE分配有耐心度的算法来克服现有方法的局限性,并避免从以前的计算周期中产生偏差.
主要方法:
- 该算法使用线性能量函数对NOE约束来最大限度地满足模拟回火过程中的同时满足.
- 它通过将每个作为独立的约束来处理多个NOE分配可能性,防止被局部最小值困住.
- 一种概率方法允许在模拟化过程中动态无线电阻塞的禁用和重新激活,防止永久移除并降低虚假最小值的风险.
主要成果:
- 该方法成功地计算了蛋白质结构,脊柱坐标准确度为1.0-1.5 Å,来自含有高达80%不正确的远程NOE信息的数据集.
- 在自动采集的峰值NOE实验数据上证明有效性,来自interleukin-4 (136残留物) 和cyanovirin-N (101残留物).
- 探索方法的局限性,使用模拟数据对Streptococcal蛋白G的56残留B1域的模拟数据.
结论:
- 开发的算法为自动化从NMR数据计算蛋白质结构提供了强大的,耐错误的解决方案.
- 这种方法显著提高了高通量NMR结构确定效率和可靠性,即使在杂或不完整的NOE分配.
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