Automated sequencing of amino acid spin systems in proteins using multidimensional HCC(CO)NH-TOCSY spectroscopy and constraint propagation methods from artificial intelligence.
We have developed an automated approach for determining the sequential order of amino acid spin systems in small proteins. A key step in this procedure is the analysis of multidimensional HCC(CO)NH-TOCSY spectra that provide connections from the aliphatic resonances of residue i to the amide resonances of residue i + 1. These data, combined with information about the amino acid spin systems, provide sufficient constraints to assign most proton and nitrogen resonances of small proteins. Constraint propagation methods progressively narrow the set of possible assignments of amino acid spin systems to sequence-specific positions in the process of NMR data analysis. The constraint satisfaction paradigm provides a framework in which the necessary constraint-based reasoning can be expressed, while an object-oriented representation structures and facilitates the extensive list processing and indexing involved in matching. A prototype expert system, AUTOASSIGN, provides correct and nearly complete resonance assignments with one real and 31 simulated 3D NMR data sets for a 72-amino acid domain, derived from the Protein A of Staphylococcus aureus, and with 31 simulated NMR data sets for the 50-amino acid human type-alpha transforming growth factor.
Department of Computer Science, Rutgers University, Piscataway, NJ 08854.
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Zimmerman
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Kulikowski
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Wang
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Lyons
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Montelione
D000595:Amino Acid Sequence; D001322:Autoanalysis; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D008969:Molecular Sequence Data; D011506:Proteins / Q000737:chemistry*
D011506:Proteins