Predicting 15N chemical shifts in proteins using the preceding residue-specific individual shielding surfaces from phi, psi i-1, and chi 1 torsion angles.

Empirical shielding surfaces are most commonly used to predict chemical shifts in proteins from known backbone torsion angles, phi and psi. However, the prediction of (15)N chemical shifts using this technique is significantly poorer, compared to that for the other nuclei such as (1)H(alpha), (13)C(alpha), and (13)C(beta). In this study, we investigated the effects from the preceding residue and the side-chain geometry, chi(1), on (15)N chemical shifts by statistical methods. For an amino acid sequence XY, the (15)N chemical shift of Y is expressed as a function of the amino acid types of X and Y, as well as the backbone torsion angles, phi and psi(i-1). Accordingly, 380 empirical 'Preceding Residue Specific Individual (PRSI)' (15)N chemical shift shielding surfaces, representing all the combinations of X and Y (except for Y=Pro), were built and used to predict (15)N chemical shift from phi and psi(i-1). We further investigated the chi(1) effects, which were found to account for differences in (15)N chemical shifts by approximately 5 ppm for amino acids Val, Ile, Thr, Phe, His, Tyr, and Trp. Taking the chi(1) effects into account, the chi(1)-calibrated PRSI shielding surfaces (XPRSI) were built and used to predict (15)N chemical shifts for these amino acids. We demonstrated that (15)N chemical shift predictions are significantly improved by incorporating the preceding residue and chi(1) effects. The present PRSI and XPRSI shielding surfaces were extensively compared with three recently published programs, SHIFTX (Neal et al., 2003), SHIFTS (Xu and Case, 2001 and 2002), and PROSHIFT (Meiler, 2003) on a set of ten randomly selected proteins. A set of Java programs using XPRSI shielding surfaces to predict (15)N chemical shifts in proteins were developed and are freely available for academic users at http://www.pronmr.com or by sending email to one of the authors Yunjun Wang (yunjunwang@yahoo.com).