1995
DOI: 10.1021/ja00118a016
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Protein Structure Refinement and Prediction via NMR Chemical Shifts and Quantum Chemistry

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Cited by 87 publications
(103 citation statements)
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“…One can either use chemical-shift tensors to provide torsional restraints in a simulated annealing program such as X-PLOR [102,114], or one can use probabilistic methods to actually predict dihedral angles based on measured chemicalshift tensors [103,105]. In either case, the complex dependence of chemical-shift tensors on their local environment must first be understood for such techniques to be possible.…”
Section: Resultsmentioning
confidence: 99%
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“…One can either use chemical-shift tensors to provide torsional restraints in a simulated annealing program such as X-PLOR [102,114], or one can use probabilistic methods to actually predict dihedral angles based on measured chemicalshift tensors [103,105]. In either case, the complex dependence of chemical-shift tensors on their local environment must first be understood for such techniques to be possible.…”
Section: Resultsmentioning
confidence: 99%
“…In either case, the complex dependence of chemical-shift tensors on their local environment must first be understood for such techniques to be possible. [lOO,103,114,115]. The main advantage to using theoretical chemical-shift surfaces for structure prediction and refinement is that they can cover the complete Ramachandran ljJ/lfI space, whereas empirical surfaces are confined to conformations that exist in the subset of proteins that are used to create the surface.…”
Section: Resultsmentioning
confidence: 99%
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“…Ab initio calculations (de Dios et al, 1993;de Dios & Oldfield, 1994Le et al, 1995;de Dios, 1996) already outlined the sensitive dependence of "C, chemical shift upon both cp, $ torsion angle variations. In addition, several previous studies of helical peptides showed that, in general, A6"C, chemical shift indexes were less affected than A6H, by periodic variations along the sequence.…”
Section: Investigation Of Conformer Populations In Partially Folded Pmentioning
confidence: 99%