I'm studying a system with one binding site that accommodates two ligands simultaneously using isothermal titration calorimetry (ITC). I'm seeking advice on the appropriate modeling approach for understanding the binding behavior and determining the binding parameters.
Thank you for your insights and recommendations.
Are the two ligands chemically identical or not?
If the two ligands are identical, then, even if it looks like a single binding site, there are two binding sites because two ligands are bound (homotropy). And the analysis can be done with a model with two binding sites (identical or non-identical, independent or cooperative binding sites).
If the ligands are non-identical, this is also a two binding site model (heterotropy). In this case you can pre-mix one ligand with the protein and inject the other ligand, or inject the two ligands at once.
Thank You for your answer Adrian Velazquez-Campoy
Yes, they are identical.
I also successfully crystallized the complexes, and I observed that two ligands fit perfectly in the binding pocket. However, crystallization is not always successful.
Regarding the determination of the binding model, observing an N value of 2 for one binding site is sufficient to support a two-binding model? However, when the Kd is high (indicating weak binding), determining the N value correctly is almost impossible. Is it the correct approach to confirm the binding as 2? In cases of stronger binding, can I always reverse the titration to validate the result.
In cases where you are unsure about the binding model, it is not advisable to report ITC results at all or present one set of sites model or all possible models?
The value of N must be considered carefully. Uncertainties in the concentrations may result in large deviations for N values, even for high affinity interactions. And, as you say, low affinity complicates even more that evaluation.
You can always perform different analyses with different models and, then, apply parametric and non-parametric statistical test to select the (statistically) more appropriate model.
Additional experiments may provide valuable information on the stoichiometry.
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