Morris Aimee M, Finke Richard G
Department of Chemistry, Colorado State University, Fort Collins, CO 80523, USA.
Biophys Chem. 2009 Mar;140(1-3):9-15. doi: 10.1016/j.bpc.2008.11.003. Epub 2008 Nov 18.
The aggregation of proteins is believed to be intimately connected to many neurodegenerative disorders. We recently reported an "Ockham's razor"/minimalistic approach to analyze the kinetic data of protein aggregation using the Finke-Watzky (F-W) 2-step model of nucleation (A-->B, rate constant k(1)) and autocatalytic growth (A+B-->2B, rate constant k(2)). With that kinetic model we have analyzed 41 representative protein aggregation data sets in two recent publications, including amyloid beta, alpha-synuclein, polyglutamine, and prion proteins (Morris, A. M., et al. (2008) Biochemistry 47, 2413-2427; Watzky, M. A., et al. (2008) Biochemistry 47, 10790-10800). Herein we use the F-W model to reanalyze protein aggregation kinetic data obtained under the experimental conditions of variable temperature or pH 2.0 to 8.5. We provide the average nucleation (k(1)) and growth (k(2)) rate constants and correlations with variable temperature or varying pH for the protein alpha-synuclein. From the variable temperature data, activation parameters DeltaG(double dagger), DeltaH(double dagger), and DeltaS(double dagger) are provided for nucleation and growth, and those values are compared to the available parameters reported in the previous literature determined using an empirical method. Our activation parameters suggest that nucleation and growth are energetically similar for alpha-synuclein aggregation (DeltaG(double dagger)(nucleation)=23(3) kcal/mol; DeltaG(double dagger)(growth)=22(1) kcal/mol at 37 degrees C). From the variable pH data, the F-W analyses show a maximal k(1) value at pH approximately 3, as well as minimal k(1) near the isoelectric point (pI) of alpha-synuclein. Since solubility and net charge are minimized at the pI, either or both of these factors may be important in determining the kinetics of the nucleation step. On the other hand, the k(2) values increase with decreasing pH (i.e., do not appear to have a minimum or maximum near the pI) which, when combined with the k(1) vs. pH (and pI) data, suggest that solubility and charge are less important factors for growth, and that charge is important in the k(1), nucleation step of alpha-synuclein. The chemically well-defined nucleation (k(1)) rate constants obtained from the F-W analysis are, as expected, different than the 1/lag-time empirical constants previously obtained. However, k(2)xA (where k(2) is the rate constant for autocatalytic growth and A is the initial protein concentration) is related to the empirical constant, k(app) obtained previously. Overall, the average nucleation and average growth rate constants for alpha-synuclein aggregation as a function of pH and variable temperature have been quantitated. Those values support the previously suggested formation of a partially folded intermediate that promotes aggregation under high temperature or acidic conditions.
蛋白质聚集被认为与许多神经退行性疾病密切相关。我们最近报道了一种“奥卡姆剃刀”/简约方法,用于使用成核的Finke-Watzky(F-W)两步模型(A→B,速率常数k(1))和自催化生长(A + B→2B,速率常数k(2))分析蛋白质聚集的动力学数据。利用该动力学模型,我们在最近的两篇出版物中分析了41个代表性的蛋白质聚集数据集,包括淀粉样β蛋白、α-突触核蛋白、多聚谷氨酰胺和朊病毒蛋白(Morris, A. M., 等人 (2008) Biochemistry 47, 2413 - 2427; Watzky, M. A., 等人 (2008) Biochemistry 47, 10790 - 10800)。在此,我们使用F-W模型重新分析在可变温度或pH 2.0至8.5的实验条件下获得的蛋白质聚集动力学数据。我们提供了α-突触核蛋白的平均成核(k(1))和生长(k(2))速率常数以及与可变温度或变化pH的相关性。从可变温度数据中,提供了成核和生长的活化参数ΔG‡、ΔH‡和ΔS‡,并将这些值与先前文献中使用经验方法确定的可用参数进行比较。我们的活化参数表明,α-突触核蛋白聚集的成核和生长在能量上相似(在37℃时,ΔG‡(成核)=23(3) kcal/mol;ΔG‡(生长)=22(1) kcal/mol)。从可变pH数据来看,F-W分析显示在pH约为3时k(1)值最大,以及在α-突触核蛋白的等电点(pI)附近k(1)最小。由于在pI时溶解度和净电荷最小化,这些因素中的一个或两个可能在决定成核步骤的动力学中很重要。另一方面,k(2)值随着pH降低而增加(即,在pI附近似乎没有最小值或最大值),这与k(1)对pH(和pI)的数据相结合,表明溶解度和电荷对于生长来说是不太重要的因素,而电荷在α-突触核蛋白的k(1)成核步骤中很重要。从F-W分析中获得的化学定义明确的成核(k(1))速率常数,正如预期的那样,与先前获得的1/滞后时间经验常数不同。然而,k(2)×A(其中k(2)是自催化生长的速率常数,A是初始蛋白质浓度)与先前获得的经验常数k(app)相关。总体而言,已经定量了α-突触核蛋白聚集的平均成核和平均生长速率常数作为pH和可变温度的函数。这些值支持了先前提出的形成部分折叠中间体的观点,该中间体在高温或酸性条件下促进聚集。