Crowl-Powers M L, Zimmerman J K
Department of Biological Sciences, Clemson University, SC 29634-1903.
Biophys Chem. 1988 Oct;32(1):131-46. doi: 10.1016/0301-4622(88)85041-5.
In developing a method for analyzing the heterogeneous association nA + mB in equilibrium AnBm, we have specifically investigated the case of n = 2, m = 1 for both the specific case of no appreciable intermediates and the more general case allowing intermediates. Computer-simulated three-dimensional surfaces of the 2:1 model generated from total concentrations of species A and B and the resulting weight-average molecular weights were analyzed with a Gauss-Newton nonlinear least-squares minimization routine. The surfaces generated included normalized random error of varying standard deviations imposed upon both the concentrations and weight-average molecular weights. For comparison purposes, these surfaces were analyzed not only by using the correct 2:1 model, but also by an incorrect (1:1) model and by the other (incorrect) 2:1 model. Except for those situations where the 'experimental' noise was consistently higher than the concentration of one of the species, correct K values were obtained and the correct model was easily distinguished from the incorrect model. The computer routine similarly distinguished between data correctly described as 1:1 and the same data incorrectly analyzed as either 2:1 model. For those cases in which a microscopic Ki value predicts an association such that all species involved for that particular Ki are in appreciable amounts, the Ki value is returned correctly. Correct overall equilibrium constants are also converged upon as long as adequate amounts of A2B, B and A are present.
在开发一种分析平衡态下AnBm中nA + mB非均相缔合的方法时,我们特别研究了n = 2、m = 1的情况,包括不存在明显中间体的特定情况以及更一般的允许有中间体的情况。由物种A和B的总浓度生成的2:1模型的计算机模拟三维表面以及由此产生的重均分子量,使用高斯-牛顿非线性最小二乘法最小化程序进行分析。生成的表面包括施加在浓度和重均分子量上的具有不同标准偏差的归一化随机误差。为了进行比较,不仅使用正确的2:1模型,还使用不正确的(1:1)模型和另一个(不正确的)2:1模型对这些表面进行分析。除了“实验”噪声始终高于其中一种物种浓度的情况外,都能获得正确的K值,并且能轻松将正确的模型与不正确的模型区分开来。该计算机程序同样能区分正确描述为1:1的数据和被错误分析为2:1模型的相同数据。对于微观Ki值预测出一种缔合情况,即对于该特定Ki所涉及的所有物种都有相当数量的情况,Ki值能正确返回。只要存在足够量的A2B、B和A,也能收敛得到正确的总平衡常数。