Ligand Rebinding: Self-consistent Mean-field Theory and Numerical Simulations Applied to SPR Studies

Details Ligand Rebinding: Self-consistent Mean-field Theory and Numerical Simulations Applied to SPR Studies Ligand Rebinding: Self-consistent Mean-field Theory and Numerical Simulations Applied to SPR Studies

2004-06-02

arxiv.org/abs/q-bio/0406004v3

Eur. Biophys. J. 34 (2005) 943

Biology

Quantitative Biology

Quantitative Methods

minor errors in notation corrected, added figure, appendix and glossary, 37 pages, to appear in Eur. Biophys. J

Scientific paper

Rebinding of dissociated ligands from cell surface proteins can confound quantitative measurements of dissociation rates important for characterizing the affinity of binding interactions. This can be true also for in vitro techniques such as surface plasmon resonance (SPR). We present experimental results using SPR for the interaction of insulin-like growth factor-I (IGF-I) with one of its binding proteins, IGF binding protein-3 (IGFBP-3), and show that rebinding, even with the addition of soluble heparin in the dissociation phase, does not exhibit the expected exponential decay characteristic of a 1:1 binding reaction. We thus consider the effect of (multiple) rebinding events and, within a self-consistent mean-field approximation, we derive the complete mathematical form for the fraction of bound ligand as a function of time. We show that, except for very low surface coverage/association rate, this function is non-exponential at all times, indicating that multiple rebinding events strongly influence dissociation even at early times. We compare the mean-field results with numerical simulations and find good agreement, although deviations are measurable in certain cases. Our analysis of the IGF-I-IGFBP-3 data indicates that rebinding is prominent for this system and that the theoretical predictions fit the experimental data well. Our results provide a means for analyzing SPR biosensor data where rebinding is problematic and a methodology to do so is presented.

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