A constraint reasoning system for automating sequence-specific resonance assignments from multidimensional protein NMR spectra.

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Abstract

AUTOASSIGN is a prototype expert system designed to aid in the determination of protein structure from nuclear magnetic resonance (NMR) measurements. In this paper we focus on one of the key steps of this process, the assignment of the observed NMR signals to specific atomic nuclei in the protein; i.e. the determination of sequence-specific resonance assignments. Recently developed triple-resonance (1H, 15N, and 13C) NMR experiments [Montelione et al., 1992] have provided an important breakthrough in this field, as the resulting data are more amenable to automated analysis than data sets generated using conventional strategies [Wuethrich, 1986]. The "assignment problem" can be stated as a constraint satisfaction problem (CSP) with some added complexities. There is very little internal structure to the problem, making it difficult to apply subgoaling and problem decomposition. Moreover, the data used to generate the constraints are incomplete, non-unique, and noisy, and constraints emerge dynamically as analysis progresses. The traditional inference engine is replaced by a set of very tightly-coupled modules which enforce extensive constraint propagation, with state information distributed over the objects whose relationships are being constrained. AUTOASSIGN provides correct and nearly complete resonance assignments with both simulated and real 3D triple-resonance data for a 72 amino acid protein.

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Magnetic Resonance Spectroscopy
Expert Systems
Proteins
Sequence Analysis
Amino Acids
Object Attachment
Datasets

All Science Journal Classification (ASJC) codes

  • Medicine(all)

Cite this

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title = "A constraint reasoning system for automating sequence-specific resonance assignments from multidimensional protein NMR spectra.",
abstract = "AUTOASSIGN is a prototype expert system designed to aid in the determination of protein structure from nuclear magnetic resonance (NMR) measurements. In this paper we focus on one of the key steps of this process, the assignment of the observed NMR signals to specific atomic nuclei in the protein; i.e. the determination of sequence-specific resonance assignments. Recently developed triple-resonance (1H, 15N, and 13C) NMR experiments [Montelione et al., 1992] have provided an important breakthrough in this field, as the resulting data are more amenable to automated analysis than data sets generated using conventional strategies [Wuethrich, 1986]. The {"}assignment problem{"} can be stated as a constraint satisfaction problem (CSP) with some added complexities. There is very little internal structure to the problem, making it difficult to apply subgoaling and problem decomposition. Moreover, the data used to generate the constraints are incomplete, non-unique, and noisy, and constraints emerge dynamically as analysis progresses. The traditional inference engine is replaced by a set of very tightly-coupled modules which enforce extensive constraint propagation, with state information distributed over the objects whose relationships are being constrained. AUTOASSIGN provides correct and nearly complete resonance assignments with both simulated and real 3D triple-resonance data for a 72 amino acid protein.",
author = "Zimmerman, {D. E.} and Casimir Kulikowski and Gaetano Montelione",
year = "1993",
month = "1",
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language = "English (US)",
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pages = "447--455",
journal = "Proceedings / . International Conference on Intelligent Systems for Molecular Biology ; ISMB. International Conference on Intelligent Systems for Molecular Biology",
issn = "1553-0833",
publisher = "American Association for Artificial Intelligence (AAAI) Press",

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T1 - A constraint reasoning system for automating sequence-specific resonance assignments from multidimensional protein NMR spectra.

AU - Zimmerman, D. E.

AU - Kulikowski, Casimir

AU - Montelione, Gaetano

PY - 1993/1/1

Y1 - 1993/1/1

N2 - AUTOASSIGN is a prototype expert system designed to aid in the determination of protein structure from nuclear magnetic resonance (NMR) measurements. In this paper we focus on one of the key steps of this process, the assignment of the observed NMR signals to specific atomic nuclei in the protein; i.e. the determination of sequence-specific resonance assignments. Recently developed triple-resonance (1H, 15N, and 13C) NMR experiments [Montelione et al., 1992] have provided an important breakthrough in this field, as the resulting data are more amenable to automated analysis than data sets generated using conventional strategies [Wuethrich, 1986]. The "assignment problem" can be stated as a constraint satisfaction problem (CSP) with some added complexities. There is very little internal structure to the problem, making it difficult to apply subgoaling and problem decomposition. Moreover, the data used to generate the constraints are incomplete, non-unique, and noisy, and constraints emerge dynamically as analysis progresses. The traditional inference engine is replaced by a set of very tightly-coupled modules which enforce extensive constraint propagation, with state information distributed over the objects whose relationships are being constrained. AUTOASSIGN provides correct and nearly complete resonance assignments with both simulated and real 3D triple-resonance data for a 72 amino acid protein.

AB - AUTOASSIGN is a prototype expert system designed to aid in the determination of protein structure from nuclear magnetic resonance (NMR) measurements. In this paper we focus on one of the key steps of this process, the assignment of the observed NMR signals to specific atomic nuclei in the protein; i.e. the determination of sequence-specific resonance assignments. Recently developed triple-resonance (1H, 15N, and 13C) NMR experiments [Montelione et al., 1992] have provided an important breakthrough in this field, as the resulting data are more amenable to automated analysis than data sets generated using conventional strategies [Wuethrich, 1986]. The "assignment problem" can be stated as a constraint satisfaction problem (CSP) with some added complexities. There is very little internal structure to the problem, making it difficult to apply subgoaling and problem decomposition. Moreover, the data used to generate the constraints are incomplete, non-unique, and noisy, and constraints emerge dynamically as analysis progresses. The traditional inference engine is replaced by a set of very tightly-coupled modules which enforce extensive constraint propagation, with state information distributed over the objects whose relationships are being constrained. AUTOASSIGN provides correct and nearly complete resonance assignments with both simulated and real 3D triple-resonance data for a 72 amino acid protein.

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