Protein Structure Refinement and Prediction via NMR Chemical Shifts and Quantum Chemistry

Hong Biao Le, John G. Pearson, Angel C. De Dios, Eric Oldfield

Research output: Contribution to journalArticlepeer-review

Abstract

An approach utilizing Bayesian probability and NMR chemical shifts to derive structural information about proteins is presented. The method is based on measurement of a spectroscopic parameter, P (such as a chemical shift or a coupling constant), which is then transformed via use of a corresponding parameter surface, P(α,β), into an unnormalized torsion angle probability or Z surface, Z(α,β). Using empirically determined parameter surfaces, the backbone ϕ,ψ error between prediction and experiment is about 17°, but for 10 Ala residues in Staphylococcal nuclease, this reduces to ~10° when quantum mechanically computed 13 C shielding surfaces are utilized. The Z-surface approach permits unique combination of a wide variety of spectroscopic observables for refinement and prediction of protein structure in both solution- or solid-state systems.

Original languageEnglish (US)
Pages (from-to)3800-3807
Number of pages8
JournalJournal of the American Chemical Society
Volume117
Issue number13
DOIs
StatePublished - Apr 1995

ASJC Scopus subject areas

  • Catalysis
  • Chemistry(all)
  • Biochemistry
  • Colloid and Surface Chemistry

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