AUTOMATED-ANALYSIS OF PROTEIN NMR ASSIGNMENTS USING METHODS FROM ARTIFICIAL-INTELLIGENCE

An expert system for determining resonance assignments from NMR spectr a of proteins is described. Given the amino acid sequence, a two-dimen sional N-15-H-1 heteronuclear correlation spectrum and seven to eight three-dimensional triple-resonance NMR spectra for seven proteins, AUT OASSIGN obtained an average of 98% of sequence-specific spin-system as signments with an error rate of less than 0.5%. Execution times on a S parc 10 workstation varied from 16 seconds for smaller proteins with s imple spectra to one to nine minutes for medium size proteins exhibiti ng numerous extra spin systems attributed to conformational isomerizat ion. AUTOASSIGN combines symbolic constraint satisfaction methods with a domain-specific knowledge base to exploit the logical structure of the sequential assignment problem, the specific features of the variou s NMR experiments, and the expected chemical shift frequencies of diff erent amino acids. The current implementation specializes in the analy sis of data derived from the most sensitive of the currently available triple-resonance experiments. Potential extensions of the system for analysis of additional types of protein NMR data are also discussed. ( C) 1997 Academic Press Limited.