Nearest-neighbor effects on backbone alpha and beta carbon chemical shifts in proteins

Abstract

We present a method for analyzing the chemical shift database to yield information on nearest-neighbor effects on carbon-13 chemical shift values for alpha and beta carbons of amino acids in proteins. For each amino acid sequence XYZ, we define two correction factors, Δ(^XY) _s and Δ(Y^Z) _s , representing the effects on (δ¹³ C ^α − δ¹³ C ^β) for residue Y from the preceding residue (X) and the following residue (Z), where X, Y, and Z represent one of the 20 naturally occurring amino acids, Δ designates the change in value or the correction factor (in ppm), and s is an index standing for one of three “pseudo secondary structure states” derived from chemical shift dispersions, which we show represent residues in primarily α-helix, β-strand, and non-αβ (coil). The correction factors were obtained from maximum likelihood fitting of (δ¹³ C ^α − δ¹³ C ^β) values from the chemical shifts of 651 proteins to a mixture of three Gaussians. These correction factors were derived strictly from the analysis of assigned chemical shifts, without regard to the three-dimensional structures of these proteins. The corrections factors were found to differ according to the secondary structural environment of the central residue (deduced from the chemical shift distribution) as well as by different identities of the nearest neighboring residues in the sequence. The areas subsumed by the sequence-dependent chemical shift distributions report on the relative energies of the sequences in different pseudo secondary structural environments, and the positions of the peaks indicate the chemical shifts of lowest energy conformations. As such, these results have potential applications to the determination of dihedral angle restraints from chemical shifts for structure determination and to more accurate predictions of chemical shifts in proteins of known structure. From a database of chemical shifts associated well-defined three-dimensional structures, comparisons were made between DSSP designations derived from three-dimensional structure and pseudo secondary structure designations derived from nearest-neighbor corrected chemical shift analysis. The high level of agreement between the two approaches to classifying secondary structure provides a measure of confidence in this chemical shift-based approach to the analysis of protein structure.