Sprague Brian L, Müller Florian, Pego Robert L, Bungay Peter M, Stavreva Diana A, McNally James G
Laboratory of Receptor Biology and Gene Expression, NCI, National Institutes of Health, Bethesda, Maryland, USA.
Biophys J. 2006 Aug 15;91(4):1169-91. doi: 10.1529/biophysj.105.073676. Epub 2006 May 5.
Cells contain many subcellular structures in which specialized proteins locally cluster. Binding interactions within such clusters may be analyzed in live cells using models for fluorescence recovery after photobleaching (FRAP). Here we analyze a three-dimensional FRAP model that accounts for a single spatially localized cluster of binding sites in the presence of both diffusion and impermeable boundaries. We demonstrate that models completely ignoring the spatial localization of binding yield poor estimates for the binding parameters within the binding site cluster. In contrast, we find that ignoring only the restricted axial height of the binding-site cluster is far less detrimental, thereby enabling the use of computationally less expensive models. We also identify simplified solutions to the FRAP model for limiting behaviors where either diffusion or binding dominate. We show how ignoring a role for diffusion can sometimes produce serious errors in binding parameter estimation. We illustrate application of the method by analyzing binding of a transcription factor, the glucocorticoid receptor, to a tandem array of mouse mammary tumor virus promoter sites in live cells, obtaining an estimate for an in vivo binding constant (10(-7) M), and a first approximation of an upper bound on the transcription-factor residence time at the promoter (approximately 170 ms). These FRAP analysis tools will be important for measuring key cellular binding parameters necessary for a complete and accurate description of the networks that regulate cellular behavior.
细胞包含许多亚细胞结构,其中特定蛋白质会在局部聚集。可以使用光漂白后荧光恢复(FRAP)模型在活细胞中分析此类聚集体内的结合相互作用。在此,我们分析了一个三维FRAP模型,该模型考虑了在存在扩散和不可渗透边界的情况下单个空间定位的结合位点聚集体。我们证明,完全忽略结合的空间定位的模型会对结合位点聚集体内的结合参数产生较差的估计。相比之下,我们发现仅忽略结合位点聚集体受限的轴向高度的危害要小得多,从而能够使用计算成本较低的模型。我们还确定了FRAP模型在扩散或结合占主导的极限行为下的简化解决方案。我们展示了忽略扩散作用有时会在结合参数估计中产生严重错误。我们通过分析转录因子糖皮质激素受体与活细胞中小鼠乳腺肿瘤病毒启动子位点串联阵列的结合来说明该方法的应用,获得了体内结合常数(10^(-7) M)的估计值,以及转录因子在启动子处停留时间上限的初步近似值(约170毫秒)。这些FRAP分析工具对于测量完整准确描述调节细胞行为的网络所需的关键细胞结合参数将非常重要。