A bayesian framework for chemical shift assignment

Abstract

Nuclear magnetic resonance (NMR) spectroscopy is one of the techniques used in structural biology and drug discovery. A critical step in analysis of NMR images lies in automation of assigning NMR signals to nuclei in studied macromolecules. This procedure is known as sequence-specific resonance assignment and is carried out manually. Manual analysis of NMR data results in high costs, lengthy analysis and proneness to user-specific errors. To address this problem, we propose a new Bayesian approach, where resonance assignment is formulated as maximum a posteriori inference over continuous variables.