A peptide that inhibits hydroxyapatite growth is in an extended conformation on the crystal surface

Proteins play an important role in the biological mechanisms controlling hard tissue development, but the details of molecular recognition at inorganic crystal interfaces remain poorly characterized. We have applied a recently developed homonuclear dipolar recoupling solid-state NMR technique, dipolar recoupling with a windowless sequence (DRAWS), to directly probe the conformation of an acidic peptide adsorbed to hydroxyapatite (HAP) crystals. The phosphorylated hexapeptide, DpSpSEEK (N6, where pS denotes phosphorylated serine), was derived from the N terminus of the salivary protein statherin. Constant-composition kinetic characterization demonstrated that, like the native statherin, this peptide inhibits the growth of HAP seed crystals when preadsorbed to the crystal surface. The DRAWS technique was used to measure the internuclear distance between two 13C labels at the carbonyl positions of the adjacent phosphoserine residues. Dipolar dephasing measured at short mixing times yielded a mean separation distance of 3.2 ± 0.1 Å. Data obtained by using longer mixing times suggest a broad distribution of conformations about this average distance. Using a more complex model with discrete α-helical and extended conformations did not yield a better fit to the data and was not consistent with chemical shift analysis. These results suggest that the peptide is predominantly in an extended conformation rather than an α-helical state on the HAP surface. Solid-state NMR approaches can thus be used to determine directly the conformation of biologically relevant peptides on HAP surfaces. A better understanding of peptide and protein conformation on biomineral surfaces may provide design principles useful for the modification of orthopedic and dental implants with coatings and biological growth factors that are designed to enhance biocompatibility with surrounding tissue.

The asymptotic distribution of canonical correlations and vectors in higher-order cointegrated models

The study of the large-sample distribution of the canonical correlations and variates in cointegrated models is extended from the first-order autoregression model to autoregression of any (finite) order. The cointegrated process considered here is nonstationary in some dimensions and stationary in some other directions, but the first difference (the “error-correction form”) is stationary. The asymptotic distribution of the canonical correlations between the first differences and the predictor variables as well as the corresponding canonical variables is obtained under the assumption that the process is Gaussian. The method of analysis is similar to that used for the first-order process.