Isothermal titration calorimetry and surface plasmon resonance analysis using the dynamic approach

Abstract

Biophysical techniques such as isothermal titration calorimetry (ITC) and surface plasmon resonance (SPR) are routinely used to ascertain the global binding mechanisms of protein-protein or protein-ligand interaction. Recently, Dumas etal, have explicitly modelled the instrument response of the ligand dilution and analysed the ITC thermogram to obtain kinetic rate constants. Adopting a similar approach, we have integrated the dynamic instrument response with the binding mechanism to simulate the ITC profiles of equivalent and independent binding sites, equivalent and sequential binding sites and aggregating systems. The results were benchmarked against the standard commercial software Origin-ITC. Further, the experimental ITC chromatograms of 2'-CMP + RNASE and BH3I-1 + hBCL_XL interactions were analysed and shown to be comparable with that of the conventional analysis. Dynamic approach was applied to simulate the SPR profiles of a two-state model, and could reproduce the experimental profile accurately.

Keywords: Aggregation model; BH3I-1; Dynamic approach; Equivalent binding; ITC; Instrument response; RNASE; SPR; Sequential binding; hBCLXL.

Graphical abstract

Fig. 1

Comparison of the thermogram and the NDH data obtained for a single binding site mechanism using four different approaches. (A,B) without instrument response; (C,D) with instrument response based on lumped modelling; (E,F) with instrument response based on kinetic modelling in a sequential manner; (G,H) with instrument response based on kinetic modelling in a parallel manner.

Fig. 2

Simulation of the ITC thermogram and its corresponding NDH data for different binding mechanisms (A,B) M equivalent single site binding; (C,D) M, N, two equivalent independent/parallel binding sites (FEOTF54); (E,F) M, N, two equivalent sequential binding sites (PROTDB). (G,H) M, N, O, R, four equivalent sequential binding sites (PERSSON). In the NDH plots of B, D, F, H, the open circle represents the NDH data points obtained independently through simulation based on algebraic model and the smooth line represents the NDH data obtained from integrating the simulated thermogram shown in A, C, E, G, respectively. The parameters used to simulate both algebraic and dynamic profiles (Table 1) were obtained through origin-ITC software by fitting the experimental data to appropriate models provided therein.

Fig. 3

The experimental data and its model fit for (A) 2′-CMP + RNASE system using M equivalent single site binding, (B) BH3I-1 + hBCL_XL using M, N, two sequential binding sites.

Fig. 4

Simulation of the SPR sensogram using dynamic approach for a single binding site mechanism. (A) Without any leakage of ligand during the dissociation phase (B) with leakage of ligand during the dissociation phase. The concentrations of the ligand used for each instance of the simulation is labelled above its respective traces.

Fig. 5

A comparison of the ligand dilution effect as addressed by ODE and PDE based dynamic modelling. The left and the right most figures represent the initial and final condition of the ligand concentrations immediately after injection and final equilibrium states, respectively. The darker shades represent higher concentration. In the upper scheme (ODE model), we assume an instantaneous mixing of sample being injected over discretized period of injection. Whereas, in the lower scheme (PDE model) we assume that the homogenization of the injected ligand is both time and spatial dependent.