title: Structural-functional bioinformatics: knowledge-based NMR interpretation.
abstract: This paper describes a knowledge-based approach to a problem of structural-functional bioinformatics, specifically the determination of protein structure through the automated analysis of NMR data. Highly successful results in carrying out sequence-specific assignments of residues from multidimensional NMR datasets has led us to automation of NOE dataset interpretation and a design for integrating these results with other protein structure and function analysis programs.
authors.affiliation: Department of Computer Science, Rutgers University, New Brunswick, NJ, USA. kulikowski@cs.rutgers.edu
authors.forename: C A
authors.initials: CA
authors.lastname: Kulikowski
authors.forename: D
authors.initials: D
authors.lastname: Zimmerman
authors.forename: G
authors.initials: G
authors.lastname: Montelione
authors.forename: S
authors.initials: S
authors.lastname: Anderson
mesh_terms: D019295:Computational Biology; D005103:Expert Systems; D009682:Magnetic Resonance Spectroscopy; D011487:Protein Conformation