Exploiting the Propagation of Constrained Variables for Enhanced HDX-MS Data Optimization.
Exploiting the Propagation of Constrained Variables for Enhanced HDX-MS Data Optimization.
复制标题
利用约束变量的传播来增强 HDX-MS 数据优化。
DOI：
10.1021/acs.analchem.1c03082
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发表时间：
2021
影响因子：
7.4
通讯作者：
Salmas RE
中科院分区：
文献类型：
--
作者：
Salmas RE
非线性规划已经在蛋白质生物物理学中找到了有用的应用，以帮助理解氢-氘交换质谱（HDX-MS）获得的数据的微观交换动力学。寻找HDX-MS数据的微观动力学解决方案为局部蛋白质稳定性和能量学提供了一个窗口，使它们能够被量化和理解。然而，HDX-MS数据的优化是一项重大挑战，因为需要同时求解大量变量，且变量边界非常大。模型的速率经常是不确定的，与初始猜测值有明确的依赖关系。为了提高HDX-MS优化中对最小解的搜索能力，考虑了所选约束变量在整个数据中传播的能力。我们揭示了局部约束优化对所有变量产生全局效应。对局部约束的全局响应很大，而且出乎意料地长，但结果是不可预测的，这会意外地降低某些数据集的总体准确性，这取决于约束的严格程度。利用先前描述的基于协方差矩阵的内部验证标准，描述了一种能够准确确定约束是否有利于或损害HDX-MS数据优化的方法。由此，我们为在线优化器hdxmodeler建立了一种新的两阶段方法，该方法可以有效地利用局部绑定变量来增强HDX-MS数据建模。
Nonlinear programming has found useful applications in protein biophysics to help understand the microscopic exchange kinetics of data obtained using hydrogen–deuterium exchange mass spectrometry (HDX-MS). Finding a microscopic kinetic solution for HDX-MS data provides a window into local protein stability and energetics allowing them to be quantified and understood. Optimization of HDX-MS data is a significant challenge, however, due to the requirement to solve a large number of variables simultaneously with exceptionally large variable bounds. Modeled rates are frequently uncertain with an explicate dependency on the initial guess values. In order to enhance the search for a minimum solution in HDX-MS optimization, the ability of selected constrained variables to propagate throughout the data is considered. We reveal that locally bound constrained optimization induces a global effect on all variables. The global response to local constraints is large and surprisingly long-range, but the outcome is unpredictable, unexpectedly decreasing the overall accuracy of certain data sets depending on the stringency of the constraints. Utilizing previously described in-house validation criteria based on covariance matrices, a method is described that is able to accurately determine whether constraints benefit or impair the optimization of HDX-MS data. From this, we establish a new two-stage method for our online optimizer HDXmodeller that can effectively leverage locally bound variables to enhance HDX-MS data modeling.
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影响因子：
4.8
作者：
Oganesyan, Irina;Lento, Cristina;Wilson, Derek J.
通讯作者：
Wilson, Derek J.
在高分辨率 Q-TOF 质谱仪上进行 HDX-MS/MS-ETD 分析时，以最小的离子传输损失避免 H/D 扰乱。
影响因子：
7.4
作者：
Daniel T Weltz Wollenberg;S. Pengelley;J. Mouritsen;D. Suckau;C. I. Jørgensen;T. Jørgensen
通讯作者：
T. Jørgensen
使用胃蛋白酶/真菌蛋白酶 XIII 型混合床柱对细胞色素 c 进行高分辨率 HDX-MS
影响因子：
3.2
作者：
Y. Hamuro;Terry Zhang
通讯作者：
Terry Zhang
DOI：
10.1021/acs.jpcb.6b09358
发表时间：
2017-04-20
期刊：
The journal of physical chemistry. B
影响因子：
--
作者：
Saltzberg DJ;Broughton HB;Pellarin R;Chalmers MJ;Espada A;Dodge JA;Pascal BD;Griffin PR;Humblet C;Sali A
通讯作者：
Sali A
DOI：
10.1007/s13361-013-0639-4
发表时间：
2013-07-01
影响因子：
3.2
作者：
Sheff, Joey G.;Rey, Martial;Schriemer, David C.
通讯作者：
Schriemer, David C.