Combining biophysical methods for the analysis of protein complex stoichiometry and affinity inSEDPHAT

Abstract

Reversible macromolecular interactions are ubiquitous in signal transduction pathways, often forming dynamic multiprotein 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.

SEDPHAT – A platform for global ITC analysis and global multi-method analysis of molecular interactions

“…To facilitate this process, mass action law calculator functions and species population plots were already previously implemented in SEDPHAT. To enhance experimental planning, we have now introduced functions to simulate experimental data from different biophysical methods and to graphically depict their information content [30]. We have adapted these tools to account for the particular experimental setup of an ITC titration, to allow a comprehensive survey of experimental conditions, binding isotherms, and calorimetric signal contributions: Cell and syringe concentrations can be selected from within a graphical representation of the binding isotherm as a function of total concentrations of all components {c, tot }.…”

“…To allow more convenient statistical characterization of the analysis of ITC isotherms, automated methods to explore the error surface of the fit using F-statistics [63] were added. They include automated determination of confidence intervals of binding parameters (propagated from the error bars of the individual injection heats), the computation of error surface projections, as well as the display of two-dimensional error projections highlighting parameter correlations [30]. Finally, with the development of GMMA and the associated statistical tools in SEDPHAT, significant enhancement of ITC analyses became possible by incorporation of complementary data from orthogonal techniques [29–32].…”

On the acquisition and analysis of microscale thermophoresis data

“…2A) encountered in MST data analysis. Irregularly shaped error surfaces, such as those that might be encountered in the evaluation of interacting proteins by sedimentation equilibrium AUC [19], are best treated with interactive, user-guided explorations like those implemented in SEDPHAT [3,20]. The adaptive nature of the algorithm described here ensured that the sampling of parameter space is not too sparse.…”

Vascular Endothelial Growth Factor Peptide Ligands Explored by Competition Assay and Isothermal Titration Calorimetry

“…Graphical representations of results were from GUSSI. 20,21 The binding thermograms were analyzed with the two identical sites binding model of SEDPHAT. 22 In this software, the binding of an inhibitor I to a VEGF macromolecule V at two identical sites and in the absence of cooperativity effect is characterized by the unique microscopic constant k for a single site:…”

“…Scale correction factors to concentrations of titrated and titrating molecules were refined in each experiment, and the discrepancy with apparent stoichiometry was corrected as an underestimation of peptide concentration, when used as titrant, by SEDPHAT software. 20 The direct and reverse titrations for each peptide were combined, and the thermodynamic parameters were calculated by the global modeling of titration isotherms. This method is a particularly efficient way to evaluate potential cooperativity effects.…”