A quantitative, real-time assessment of binding of peptides and proteins to gold surfaces.

Interactions of peptides and proteins with inorganic surfaces are important to both natural and artificial systems; however, a detailed understanding of such interactions is lacking. In this study, we applied new approaches to quantitatively measure the binding of amino acids and proteins to gold surfaces. Real-time surface plasmon resonance (SPR) measurements showed that TEM1-ß-lactamase inhibitor protein (BLIP) interacts only weakly with Au nanoparticles (NPs). However, fusion of three histidine residues to BLIP (3H-BLIP) resulted in a significant increase in the binding to the Au NPs, which further increased when the histidine tail was extended to six histidines (6H-BLIP). Further increasing the number of His residues had no effect on the binding. A parallel study using continuous (111)-textured Au surfaces and single-crystalline, (111)-oriented, Au islands by ellipsometry, FTIR, and localized surface plasmon resonance (LSPR) spectroscopy further confirmed the results, validating the broad applicability of Au NPs as model surfaces. Evaluating the binding of all other natural amino acid homotripeptides fused to BLIP (except Cys and Pro) showed that aromatic and positively-charged residues bind preferentially to Au with respect to small aliphatic and negatively charged residues, and that the rate of association is related to the potency of binding. The binding of all fusions was irreversible. These findings were substantiated by SPR measurements of synthesized, free, soluble tripeptides using Au-NP-modified SPR chips. Here, however, the binding was reversible allowing for determination of binding affinities that correlate with the binding potencies of the related BLIP fusions. Competition assays performed between 3H-BLIP and the histidine tripeptide (3 His) suggest that Au binding residues promote the adsorption of proteins on the surface, and by this facilitate the irreversible interaction of the polypeptide chain with Au. The binding of amino acids to Au was simulated by using a continuum solvent model, showing agreement with the experimental values. These results, together with the observed binding potencies and kinetics of the BLIP fusions and free peptides, suggest a binding mechanism that is markedly different from biological protein-protein interactions.

Protein-surface interactions: challenging experiments and computations.

Protein-surface interactions are fundamental in natural processes, and have great potential for applications ranging from nanotechnology to medicine. A recent workshop highlighted the current achievements and the main challenges in the field.

Docking of antizyme to ornithine decarboxylase and antizyme inhibitor using experimental mutant and double-mutant cycle data.

Antizyme (Az) is a highly conserved key regulatory protein bearing a major role in regulating polyamine levels in the cell. It has the ability to bind and inhibit ornithine decarboxylase (ODC), targeting it for degradation. Az inhibitor (AzI) impairs the activity of Az. In this study, we mapped the binding sites of ODC and AzI on Az using Ala scan mutagenesis and generated models of the two complexes by constrained computational docking. In order to scan a large number of mutants in a short time, we developed a workflow combining high-throughput mutagenesis, small-scale parallel partial purification of His-tagged proteins and their immobilization on a tris-nitrilotriacetic-acid-coated surface plasmon resonance chip. This combination of techniques resulted in a significant reduction in time for production and measurement of large numbers of mutant proteins. The data-driven docking results suggest that both proteins occupy the same binding site on Az, with Az binding within a large groove in AzI and ODC. However, single-mutant data provide information concerning the location of the binding sites only, not on their relative orientations. Therefore, we generated a large number of double-mutant cycles between residues on Az and ODC and used the resulting interaction energies to restrict docking. The model of the complex is well defined and accounts for the mutant data generated here, and previously determined biochemical data for this system. Insights on the structure and function of the complexes, as well as general aspects of the method, are discussed.