Ramakrishnan A, Sadana A
Chemical Engineering Department, University of Mississippi, University, Mississippi, 38677-1848
J Colloid Interface Sci. 2000 Sep 15;229(2):628-640. doi: 10.1006/jcis.2000.7065.
A predictive approach using fractal analysis is presented for analyte-receptor binding and dissociation kinetics for biosensor applications. Data taken from the literature may be modeled, in the case of binding using a single-fractal analysis or a dual-fractal analysis. The dual-fractal analysis represents a change in the binding mechanism as the reaction progresses on the surface. A single-fractal analysis is adequate to model the dissociation kinetics in the examples presented. Predictive relationships developed for the binding and the affinity (k(diss)/k(bind)) as a function of the analyte concentration are of particular value since they provide a means by which the binding and the affinity rate coefficients may be manipulated. Relationships are also presented for the binding and the dissociation rate coefficients and for the affinity as a function of their corresponding fractal dimension, D(f), or the degree of heterogeneity that exists on the surface. When analyte-receptor binding or dissociation is involved, an increase in the heterogeneity on the surface (increase in D(f)) leads to an increase in the binding and in the dissociation rate coefficient. It is suggested that an increase in the degree of heterogeneity on the surface leads to an increase in the turbulence on the surface owing to the irregularities on the surface. This turbulence promotes mixing, minimizes diffusional limitations, and leads subsequently to an increase in the binding and in the dissociation rate coefficient. The binding and the dissociation rate coefficients are rather sensitive to the degree of heterogeneity, D(f,bind) (or D(f1)) and D(f,diss), respectively, that exists on the biosensor surface. For example, the order of dependence on D(f,bind) (or D(f1)) and D(f2) is 6.69 and 6.96 for k(bind,1) (or k(1)) and k(2), respectively, for the binding of 0.085 to 0.339 µM Fab fragment 48G7(L)48G7(H) in solution to p-nitrophenyl phosphonate (PNP) transition state analogue immobilized on a surface plasmon resonance (SPR) biosensor. The order of dependence on D(f,diss) (or D(f,d)) is 3.26 for the dissociation rate coefficient, k(diss), for the dissociation of the 48G7(L)48G7(H)-PNP complex from the SPR surface to the solution. The predictive relationships presented for the binding and the affinity as a function of the analyte concentration in solution provide further physical insights into the reactions on the surface and should assist in enhancing SPR biosensor performance. In general, the technique is applicable to other reactions occurring on different types of biosensor surfaces and other surfaces such as cell-surface reactions. Copyright 2000 Academic Press.
本文提出了一种利用分形分析的预测方法,用于生物传感器应用中的分析物-受体结合和解离动力学。对于从文献中获取的数据,在结合情况中可以使用单分形分析或双分形分析进行建模。双分形分析表示随着反应在表面上进行,结合机制发生了变化。在所给出的示例中,单分形分析足以对解离动力学进行建模。针对结合和亲和力(k(diss)/k(bind))作为分析物浓度的函数所建立的预测关系具有特别的价值,因为它们提供了一种可以操纵结合和亲和力速率系数的方法。还给出了结合和解离速率系数以及亲和力作为其相应分形维数D(f)或表面上存在的异质性程度的函数的关系。当涉及分析物-受体结合或解离时,表面上异质性的增加(D(f)增加)会导致结合和解离速率系数增加。有人认为,表面上异质性程度的增加会由于表面的不规则性导致表面上的湍流增加。这种湍流促进混合,最小化扩散限制,并随后导致结合和解离速率系数增加。结合和解离速率系数分别对生物传感器表面上存在的异质性程度D(f,bind)(或D(f1))和D(f,diss)相当敏感。例如,对于溶液中0.085至0.339μM Fab片段48G7(L)48G7(H)与固定在表面等离子体共振(SPR)生物传感器上的对硝基苯基膦酸酯(PNP)过渡态类似物的结合,k(bind,1)(或k(1))和k(2)对D(f,bind)(或D(f1))和D(f2)的依赖顺序分别为6.69和6.96。对于48G7(L)48G7(H)-PNP复合物从SPR表面解离到溶液中的解离速率系数k(diss),对D(f,diss)(或D(f,d))的依赖顺序为3.26。针对结合和亲和力作为溶液中分析物浓度的函数所呈现的预测关系为表面上的反应提供了进一步的物理见解,并应有助于提高SPR生物传感器的性能。一般来说,该技术适用于发生在不同类型生物传感器表面和其他表面(如细胞表面反应)上的其他反应。版权所有2000年学术出版社。