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Empirical Inference Article 2008

ISD: A Software Package for Bayesian NMR Structure Calculation

SUMMARY: The conventional approach to calculating biomolecular structures from nuclear magnetic resonance (NMR) data is often viewed as subjective due to its dependence on rules of thumb for deriving geometric constraints and suitable values for theory parameters from noisy experimental data. As a result, it can be difficult to judge the precision of an NMR structure in an objective manner. The Inferential Structure Determination (ISD) framework, which has been introduced recently, addresses this problem by using Bayesian inference to derive a probability distribution that represents both the unknown structure and its uncertainty. It also determines additional unknowns, such as theory parameters, that normally need be chosen empirically. Here we give an overview of the ISD software package, which implements this methodology. AVAILABILITY: The program is available at http://www.bioc.cam.ac.uk/isd

Author(s): Rieping, W. and Nilges, M. and Habeck, M.
Journal: Bioinformatics
Volume: 24
Number (issue): 8
Pages: 1104-1105
Year: 2008
Month: February
Day: 0
Bibtex Type: Article (article)
DOI: 10.1093/bioinformatics/btn062
Digital: 0
Electronic Archiving: grant_archive
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik
Links:

BibTex 

@article{5063,
  title = {ISD: A Software Package for Bayesian NMR Structure Calculation},
  journal = {Bioinformatics},
  abstract = {SUMMARY: The conventional approach to calculating biomolecular structures from nuclear magnetic resonance (NMR) data is often viewed as subjective due to its dependence on rules of thumb for deriving geometric constraints and suitable values for theory parameters from noisy experimental data. As a result, it can be difficult to judge the precision of an NMR structure in an objective manner. The Inferential Structure Determination (ISD) framework, which has been introduced recently, addresses this problem by using Bayesian inference to derive a probability distribution that represents both the unknown structure and its uncertainty. It also determines additional unknowns, such as theory parameters, that normally need be chosen empirically. Here we give an overview of the ISD software package, which implements this methodology. AVAILABILITY: The program is available at http://www.bioc.cam.ac.uk/isd},
  volume = {24},
  number = {8},
  pages = {1104-1105},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = feb,
  year = {2008},
  slug = {5063},
  author = {Rieping, W. and Nilges, M. and Habeck, M.},
  month_numeric = {2}
}