Enhanced conformational space sampling improves the prediction of chemical shifts in proteins.

A biased-potential molecular dynamics simulation method, accelerated molecular dynamics (AMD), was combined with the chemical shift prediction algorithm SHIFTX to calculate (1)H(N), (15)N, (13)Calpha, (13)Cbeta, and (13)C' chemical shifts of the ankyrin repeat protein IkappaBalpha (residues 67-206), the primary inhibitor of nuclear factor kappa-B (NF-kappaB). Free-energy-weighted molecular ensembles were generated over a range of acceleration levels, affording systematic enhancement of the conformational space sampling of the protein. We have found that the predicted chemical shifts, particularly for the (15)N, (13)Calpha, and (13)Cbeta nuclei, improve substantially with enhanced conformational space sampling up to an optimal acceleration level. Significant improvement in the predicted chemical shift data coincides with those regions of the protein that exhibit backbone dynamics on longer time scales. Interestingly, the optimal acceleration level for reproduction of the chemical shift data has previously been shown to best reproduce the experimental residual dipolar coupling (RDC) data for this system, as both chemical shift data and RDCs report on an ensemble and time average in the millisecond range.