A random graph approach to NMR sequential assignment

Nuclear magnetic resonance (NMR) spectroscopy allows scientists to study protein structure, dynamics and interactions in solution. A necessary first step for such applications is determining the resonance assignment, mapping spectral data to atoms and residues in the primary sequence. Automated resonance assignment algorithms rely on information regarding connectivity (e.g., through-bond atomic interactions) and amino acid type, typically using the former to determine strings of connected residues and the latter to map those strings to positions in the primary sequence. Significant ambiguity exists in both connectivity and amino acid type information. This paper focuses on the information content available in connectivity alone and develops a novel random-graph theoretic framework and algorithm for connectivity-driven NMR sequential assignment. Our random graph model captures the structure of chemical shift degeneracy, a key source of connectivity ambiguity. We then give a simple and ...

• Type: Conference
• Year: 2004
• Where: RECOMB
• Authors: Chris Bailey-Kellogg, Sheetal Chainraj, Gopal Pandurangan
• Keywords: Amino Acid Type, Computational Biology, Nuclear Magnetic Resonance, Random Graph Model, RECOMB 2004