Quantitative Characterization of Bivalent Probes for a Dual Bromodomain Protein, Transcription Initiation Factor TFIID Subunit 1.
Suh JL., Watts B., Stuckey JI., Norris-Drouin JL., Cholensky SH., Dickson BM., An Y., Mathea S., Salah E., Knapp S., Khan A., Adams AT., Strahl BD., Sagum CA., Bedford MT., James LI., Kireev DB., Frye SV.
Multivalent binding is an efficient means to enhance the affinity and specificity of chemical probes targeting multidomain proteins in order to study their function and role in disease. While the theory of multivalent binding is straightforward, physical and structural characterization of bivalent binding encounters multiple technical difficulties. We present a case study where a combination of experimental techniques and computational simulations was used to comprehensively characterize the binding and structure-affinity relationships for a series of Bromosporine-based bivalent bromodomain ligands with a bivalent protein, Transcription Initiation Factor TFIID subunit 1 (TAF1). Experimental techniques-Isothermal Titration Calorimetry, X-ray Crystallography, Circular Dichroism, Size Exclusion Chromatography-Multi-Angle Light Scattering, and Surface Plasmon Resonance-were used to determine structures, binding affinities, and kinetics of monovalent ligands and bivalent ligands with varying linker lengths. The experimental data for monomeric ligands were fed into explicit computational simulations, in which both ligand and protein species were present in a broad range of concentrations, and in up to a 100 s time regime, to match experimental conditions. These simulations provided accurate estimates for apparent affinities (in good agreement with experimental data), individual dissociation microconstants and other microscopic details for each type of protein-ligand complex. We conclude that the expected efficiency of bivalent ligands in a cellular context is difficult to estimate by a single technique in vitro, due to higher order associations favored at the concentrations used, and other complicating processes. Rather, a combination of structural, biophysical, and computational approaches should be utilized to estimate and characterize multivalent interactions.