title: Improved chemical shift prediction by Rosetta conformational sampling.
abstract: Chemical shift frequencies represent a time-average of all the conformational states populated by a protein. Thus, chemical shift prediction programs based on sequence and database analysis yield higher accuracy for rigid rather than flexible protein segments. Here we show that the prediction accuracy can be significantly improved by averaging over an ensemble of structures, predicted solely from amino acid sequence with the Rosetta program. This approach to chemical shift and structure prediction has the potential to be useful for guiding resonance assignments, especially in solid-state NMR structural studies of membrane proteins in proteoliposomes.
authors.affiliation: Sanford Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA.
authors.forename: Ye
authors.initials: Y
authors.lastname: Tian
authors.forename: Stanley J
authors.initials: SJ
authors.lastname: Opella
authors.forename: Francesca M
authors.initials: FM
authors.lastname: Marassi
mesh_terms: D000595:Amino Acid Sequence; D008565:Membrane Proteins / Q000737:chemistry; D019906:Nuclear Magnetic Resonance, Biomolecular / Q000379:methods*; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D012984:Software
chemical_list: D008565:Membrane Proteins; C086060:MerP protein, Bacteria; D011506:Proteins