Automated analysis of protein NMR assignments using methods from artificial intelligence

Diane E. Zimmerman, Casimir A. Kulikowski, Yuanpeng Huang, Wenqing Feng, Mitsuru Tashiro, Sakurako Shimotakahara, Chen ya Chien, Robert Powers, Gaetano T. Montelione

Research output: Contribution to journalArticlepeer-review

254 Scopus citations


An expert system for determining resonance assignments from NMR spectra of proteins is described. Given the amino acid sequence, a two-dimensional 15N-1H heteronuclear correlation spectrum and seven to eight three-dimensional triple-resonance NMR spectra for seven proteins, AUTOASSIGN obtained an average of 98% of sequence-specific spin-system assignments with an error rate of less than 0.5%. Execution times on a Sparc 10 workstation varied from 16 seconds for smaller proteins with simple spectra to one to nine minutes for medium size proteins exhibiting numerous extra spin systems attributed to conformational isomerization. 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 various NMR experiments, and the expected chemical shift frequencies of different amino acids. The current implementation specializes in the analysis 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.

Original languageEnglish (US)
Pages (from-to)592-610
Number of pages19
JournalJournal of Molecular Biology
Issue number4
StatePublished - Jun 20 1997
Externally publishedYes


  • Constraint satisfaction
  • Expert system
  • Heteronuclear triple-resonance experiments
  • Isotope enrichment
  • Knowledge-based data structure

ASJC Scopus subject areas

  • Structural Biology
  • Molecular Biology

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