Zhao Huaying, Schuck Peter
Dynamics of Macromolecular Assembly Section, Laboratory of Cellular Imaging and Macromolecular Biophysics, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD 20892, USA.
Acta Crystallogr D Biol Crystallogr. 2015 Jan 1;71(Pt 1):3-14. doi: 10.1107/S1399004714010372.
Reversible macromolecular interactions are ubiquitous in signal transduction pathways, often forming dynamic multi-protein complexes with three or more components. Multivalent binding and cooperativity in these complexes are often key motifs of their biological mechanisms. Traditional solution biophysical techniques for characterizing the binding and cooperativity are very limited in the number of states that can be resolved. A global multi-method analysis (GMMA) approach has recently been introduced that can leverage the strengths and the different observables of different techniques to improve the accuracy of the resulting binding parameters and to facilitate the study of multi-component systems and multi-site interactions. Here, GMMA is described in the software SEDPHAT for the analysis of data from isothermal titration calorimetry, surface plasmon resonance or other biosensing, analytical ultracentrifugation, fluorescence anisotropy and various other spectroscopic and thermodynamic techniques. The basic principles of these techniques are reviewed and recent advances in view of their particular strengths in the context of GMMA are described. Furthermore, a new feature in SEDPHAT is introduced for the simulation of multi-method data. In combination with specific statistical tools for GMMA in SEDPHAT, simulations can be a valuable step in the experimental design.
可逆大分子相互作用在信号转导通路中普遍存在，常形成由三个或更多组分组成的动态多蛋白复合物。这些复合物中的多价结合和协同作用通常是其生物学机制的关键基序。用于表征结合和协同作用的传统溶液生物物理技术在可分辨的状态数量方面非常有限。最近引入了一种全局多方法分析（GMMA）方法，该方法可以利用不同技术的优势和不同的可观测值，以提高所得结合参数的准确性，并促进对多组分系统和多位点相互作用的研究。本文在SEDPHAT软件中描述了GMMA，用于分析等温滴定量热法、表面等离子体共振或其他生物传感、分析超速离心、荧光各向异性以及各种其他光谱和热力学技术的数据。本文回顾了这些技术的基本原理，并描述了鉴于它们在GMMA背景下的特定优势而取得的最新进展。此外，还介绍了SEDPHAT中的一个新功能，用于模拟多方法数据。结合SEDPHAT中用于GMMA的特定统计工具，模拟可以成为实验设计中有价值的一步。
