Kastantin Mark, Langdon Blake B, Schwartz Daniel K
Department of Chemical and Biological Engineering, University of Colorado, Boulder, CO 80309, United States.
Department of Chemical and Biological Engineering, University of Colorado, Boulder, CO 80309, United States.
Adv Colloid Interface Sci. 2014 May;207:240-52. doi: 10.1016/j.cis.2013.12.006. Epub 2013 Dec 28.
A common goal across different fields (e.g. separations, biosensors, biomaterials, pharmaceuticals) is to understand how protein behavior at solid-liquid interfaces is affected by environmental conditions. Temperature, pH, ionic strength, and the chemical and physical properties of the solid surface, among many factors, can control microscopic protein dynamics (e.g. adsorption, desorption, diffusion, aggregation) that contribute to macroscopic properties like time-dependent total protein surface coverage and protein structure. These relationships are typically studied through a top-down approach in which macroscopic observations are explained using analytical models that are based upon reasonable, but not universally true, simplifying assumptions about microscopic protein dynamics. Conclusions connecting microscopic dynamics to environmental factors can be heavily biased by potentially incorrect assumptions. In contrast, more complicated models avoid several of the common assumptions but require many parameters that have overlapping effects on predictions of macroscopic, average protein properties. Consequently, these models are poorly suited for the top-down approach. Because the sophistication incorporated into these models may ultimately prove essential to understanding interfacial protein behavior, this article proposes a bottom-up approach in which direct observations of microscopic protein dynamics specify parameters in complicated models, which then generate macroscopic predictions to compare with experiment. In this framework, single-molecule tracking has proven capable of making direct measurements of microscopic protein dynamics, but must be complemented by modeling to combine and extrapolate many independent microscopic observations to the macro-scale. The bottom-up approach is expected to better connect environmental factors to macroscopic protein behavior, thereby guiding rational choices that promote desirable protein behaviors.
不同领域（如分离、生物传感器、生物材料、制药）的一个共同目标是了解固液界面处蛋白质行为如何受到环境条件的影响。在众多因素中，温度、pH值、离子强度以及固体表面的化学和物理性质，可以控制微观蛋白质动力学（如吸附、解吸、扩散、聚集），这些动力学过程会影响诸如随时间变化的蛋白质总表面覆盖率和蛋白质结构等宏观性质。这些关系通常通过自上而下的方法进行研究，即在这种方法中，利用基于合理但并非普遍正确的关于微观蛋白质动力学的简化假设的分析模型来解释宏观观测结果。将微观动力学与环境因素联系起来的结论可能会因潜在的错误假设而产生严重偏差。相比之下，更复杂的模型避免了一些常见假设，但需要许多对宏观平均蛋白质性质预测有重叠影响的参数。因此，这些模型不太适合自上而下的方法。由于这些模型中纳入的复杂性最终可能被证明对于理解界面蛋白质行为至关重要，本文提出一种自下而上的方法，即对微观蛋白质动力学的直接观测确定复杂模型中的参数，然后这些模型生成宏观预测结果以与实验进行比较。在这个框架中，单分子追踪已被证明能够直接测量微观蛋白质动力学，但必须辅以建模，以便将许多独立的微观观测结果进行整合并外推到宏观尺度。预计自下而上的方法能更好地将环境因素与宏观蛋白质行为联系起来，从而指导做出促进理想蛋白质行为的合理选择。
