High-precision isothermal titration calorimetry with automated peak-shape analysis.
High-precision isothermal titration calorimetry with automated peak-shape analysis.
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DOI：
10.1021/ac3007522
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发表时间：
2012-06-05
影响因子：
7.4
通讯作者：
Schuck, Peter
中科院分区：
文献类型：
--
作者：
Keller, Sandro;Vargas, Carolyn;Zhao, Huaying;Piszczek, Grzegorz;Brautigam, Chad A.;Schuck, Peter
等温滴定量热法 (ITC) 是一种强大的经典方法，使许多领域的研究人员能够研究分子相互作用的热力学。主要 ITC 数据包括差值功率的时间演变，报告了将反应物等分试样一系列注射到样品池中期间的反应热。通过对每个注射峰进行积分，可以构建作为溶液成分变化函数的总焓变化的等温线，该等温线富含反应的热力学信息。然而，来自注射峰的信号与随机变化的仪器基线功率时间进程叠加，限制了 ITC 等温线的精度。在这里，我们描述了一种基于峰形分析的自动峰分配方法，通过奇异值分解结合局部注射前和注射后基线的详细最小二乘建模。这种方法可以有效地滤除功率轨迹中短期噪声和偶然事件的贡献。该方法还首次提供了各个等温线数据点的统计误差估计。反过来，这会提高高亲和力或低焓结合反应的检测限，并显着提高所得热力学参数的精度。
Isothermal titration calorimetry (ITC) is a powerful classical method that enables researchers in many fields to study the thermodynamics of molecular interactions. Primary ITC data comprise the temporal evolution of differential power reporting the heat of reaction during a series of injections of aliquots of a reactant into a sample cell. By integration of each injection peak, an isotherm can be constructed of total changes in enthalpy as a function of changes in solution composition, which is rich in thermodynamic information on the reaction. However, the signals from the injection peaks are superimposed by the stochastically varying time-course of the instrumental baseline power, limiting the precision of ITC isotherms. Here, we describe a method for automated peak assignment based on peak-shape analysis via singular value decomposition in combination with detailed least-squares modeling of local pre- and post-injection baselines. This approach can effectively filter out contributions of short-term noise and adventitious events in the power trace. This method also provides, for the first time, statistical error estimates for the individual isotherm data points. In turn, this results in improved detection limits for high-affinity or low-enthalpy binding reactions and significantly higher precision of the derived thermodynamic parameters.
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影响因子：
1.7
作者：
Komsta, L.
通讯作者：
Komsta, L.
影响因子：
120.1
作者：
Ladbury, John E.;Klebe, Gerhard;Freire, Ernesto
通讯作者：
Freire, Ernesto
影响因子：
2.7
作者：
Krishnamoorthy, Janarthanan;Mohanty, Smita
通讯作者：
Mohanty, Smita
DOI：
10.1006/meth.1999.0852
发表时间：
1999-10-01
期刊：
METHODS-A COMPANION TO METHODS IN ENZYMOLOGY
影响因子：
--
作者：
Pierce, MM;Raman, CS;Nall, BT
通讯作者：
Nall, BT
影响因子：
14.8
作者：
Heerklotz, Heiko;Tsamaloukas, Alekos D.;Keller, Sandro
通讯作者：
Keller, Sandro