LAUSR

Ultraviolet resonance Raman (UVRR) spectroscopy is a powerful vibrational spectroscopic technique for the label-free monitoring of molecular recognition of peptides or proteins with supramolecular ligands such as guanidiniocarbonyl pyrroles (GCPs). The use of UV laser excitation enables Raman binding studies of this class of supramolecular ligands at submillimolar concentrations in aqueous solution and provides a selective signal enhancement of the carboxylate binding site (CBS). A current limitation for the extension of this promising UVRR approach from peptides to proteins as binding partners for GCPs is the UV-excited autofluorescence from aromatic amino acids observed for laser excitation wavelengths >260 nm. These excitation wavelengths are in the electronic resonance with the GCP for achieving both a signal enhancement and the selectivity for monitoring the CBS, but the resulting UVRR spectrum overlaps with the UV-excited autofluorescence from the aromatic binding partners. This necessitates the use of a laser excitation <260 nm for spectrally separating the UVRR spectrum of the supramolecular ligand from the UV-excited autofluorescence of the peptide or protein. Here, we demonstrate the use of UVRR spectroscopy with 244 nm laser excitation for the characterization of GCP as well as guanidiniocarbonyl indole (GCI), a next generation supramolecular ligand for the recognition of carboxylates. For demonstrating the feasibility of the UVRR binding studies without an interference from the disturbing UV-excited autofluorescence, benzoic acid (BA) was chosen as an aromatic binding partner for GCI. We also present the UVRR results from the binding of GCI to the ubiquitous RGD sequence (arginylglycylaspartic acid) as a biologically relevant peptide. In the case of RGD, the more pronounced differences between the UVRR spectra of the free and complexed GCI (1:1 mixture) clearly indicate a stronger binding of GCI to RGD compared with BA. A tentative assignment of the experimentally observed changes upon molecular recognition is based on the results from density functional theory (DFT) calculations.