Aspartic Acid Binding on Hydroxyapatite Nanoparticles with Varying Morphologies Investigated by Solid-State NMR Spectroscopy and Molecular Dynamics Simulation

Hydroxyapatite (HAP) exhibits a highly oriented hierarchical structure in biological hard tissues. The formation and selective crystalline orientation of HAP is a process that involves functional biomineralization proteins abundant in acidic residues. To obtain insights into the process of HAP mineralization and acidic residue binding, synthesized HAP with specific lattice planes including (001), (100), and (011) are structurally characterized following the adsorption of aspartic acid (Asp). The adsorption affinity of Asp on HAP surfaces is evaluated quantitatively and demonstrates a high dependency on the HAP morphological form. Among the synthesized HAP nanoparticles (NPs), Asp exhibits the strongest adsorption affinity to short HAP nanorods, which are composed of (100) and (011) lattice planes, followed by nanosheets with a preferential expression of the (001) facet, to which Asp displays a similar but slightly lower binding affinity. HAP nanowires, with the (100) lattice plane preferentially developed, show significantly lower affinity to Asp and evidence of multilayer adsorption compared to the previous two types of HAP NPs. A combination of solid-state NMR (SSNMR) techniques including ¹³C and ¹⁵N CP-MAS, relaxation measurements and ¹³C–³¹P Rotational Echo DOuble Resonance (REDOR) is utilized to characterize the molecular structure and dynamics of Asp-HAP bionano interfaces with ¹³C- and ¹⁵N-enriched Asp. REDOR is used to determine ¹³C–³¹P internuclear distances, providing insight into the Asp binding geometry where stronger ¹³C–³¹P dipolar couplings correlate with binding affinity determined from Langmuir isotherms. The carboxyl sites are identified as the primary binding groups, facilitated by their interaction with surface calcium sites. The Asp chelation conformations revealed by SSNMR are further refined with molecular dynamics (MD) simulation where specific models strongly agree between the SSNMR and MD models for the various surfaces.