Time averaging of NMR chemical shifts in the MLF peptide in the solid state.
|Time averaging of NMR chemical shifts in the MLF peptide in the solid state.
|Year of Publication
|De Gortari, Itzam, Portella Guillem, Salvatella Xavier, Bajaj Vikram S., van der Wel Patrick C. A., Yates Jonathan R., Segall Matthew D., Pickard Chris J., Payne Mike C., and Vendruscolo Michele
|J Am Chem Soc
|2010 May 5
|Biomolecular, Crystallization, N-Formylmethionine Leucyl-Phenylalanine, Nuclear Magnetic Resonance, Protein Conformation, Time Factors
Since experimental measurements of NMR chemical shifts provide time and ensemble averaged values, we investigated how these effects should be included when chemical shifts are computed using density functional theory (DFT). We measured the chemical shifts of the N-formyl-L-methionyl-L-leucyl-L-phenylalanine-OMe (MLF) peptide in the solid state, and then used the X-ray structure to calculate the (13)C chemical shifts using the gauge including projector augmented wave (GIPAW) method, which accounts for the periodic nature of the crystal structure, obtaining an overall accuracy of 4.2 ppm. In order to understand the origin of the difference between experimental and calculated chemical shifts, we carried out first-principles molecular dynamics simulations to characterize the molecular motion of the MLF peptide on the picosecond time scale. We found that (13)C chemical shifts experience very rapid fluctuations of more than 20 ppm that are averaged out over less than 200 fs. Taking account of these fluctuations in the calculation of the chemical shifts resulted in an accuracy of 3.3 ppm. To investigate the effects of averaging over longer time scales we sampled the rotameric states populated by the MLF peptides in the solid state by performing a total of 5 micros classical molecular dynamics simulations. By averaging the chemical shifts over these rotameric states, we increased the accuracy of the chemical shift calculations to 3.0 ppm, with less than 1 ppm error in 10 out of 22 cases. These results suggests that better DFT-based predictions of chemical shifts of peptides and proteins will be achieved by developing improved computational strategies capable of taking into account the averaging process up to the millisecond time scale on which the chemical shift measurements report.