Rapid Classification of Protein Structure Models Using Unassigned Backbone RDCS and Probability Density Profile Analysis (PDPA)
A method of identifying the best structural model for a protein of unknown structure from a list of structural candidates using unassigned 15N1H residual dipolar coupling (RDC) data and probability density profile analysis (PDPA) is described. Ten candidate structures have been obtained for the structural genomics target protein PF2048.1 using ROBETTA. 15N1H residual dipolar couplings have been measured from NMR spectra of the protein in two alignment media and these data have been analyzed using PDPA to rank the models in terms of their ability to represent the actual structure.
A number of advantages in using this method to characterize a protein structure become apparent. RDCs can easily and rapidly be acquired, and without the need for assignment, the cost and duration of data acquisition is greatly reduced. The approach is quite robust with respect to imprecise and missing data. In the case of PF2048.1, a 79 residue protein, only 58 and 55 of the total RDC data were observed. The method can accelerate structure determination at higher resolution using traditional NMR spectroscopy by providing a starting point for the addition of NOEs and other NMR structural data.
Published in Journal of Magnetic Resonance, Volume 192, Issue 1, Summer 2008, pages 60-68.
©Journal of Magnetic Resonance 2008, Elsevier
Bansal, S., Miao, X., Adams, M., Prestegard, J., & Valafar, H. (2008). Rapid classification of protein structure models using unassigned backbone RDCs and probability density profile analysis (PDPA). Journal Of Magnetic Resonance, 192(1), 60-68. doi: 10.1016/j.jmr.2008.01.014