ArticleJournal of biomolecular NMR2003
A use of Ramachandran potentials in protein solution structure determinations.
Article in Journal of biomolecular NMR, 2003. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Analysis of theFrontiers in cellular and infection microbiology · 2020Article
- Structural insights into betaine aldehyde dehydrogenase (BADH2) from Oryza sativa explored by modeling and simulations.Scientific reports · 2018Article
- Improving NMR Structures of RNA.Structure (London, England : 1993) · 2016Article
- Grid-based backbone correction to the ff12SB protein force field for implicit-solvent simulations.Journal of chemical theory and computation · 2015Article
- Deriving high-resolution protein backbone structure propensities from all crystal data using the information maximization device.PloS one · 2014Article
- Smooth statistical torsion angle potential derived from a large conformational database via adaptive kernel density estimation improves the quality of NMR protein structures.Protein science : a publication of the Protein Society · 2012Article
- 3D maps of RNA interhelical junctions.Nature protocols · 2011Article
- The war of tools: how can NMR spectroscopists detect errors in their structures?Journal of biomolecular NMR · 2008Article
- Traditional biomolecular structure determination by NMR spectroscopy allows for major errors.PLoS computational biology · 2006Article
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4 authors.
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Abstract
A strategy is developed to use database-derived phi-psi constraints during simulated annealing procedures for protein solution structure determination in order to improve the Ramachandran plot statistics, while maintaining the agreement with the experimental constraints as the sole criterion for the selection of the family. The procedure, fully automated, consists of two consecutive simulated annealing runs. In the first run, the database-derived phi-psi constraints are enforced for all amino acids (but prolines and glycines). A family of structures is then selected on the ground of the lowest violations of the experimental constraints only, and the phi-psi values for each residue are examined. In the second and final run, the database-derived phi-psi constraints are enforced only for those residues which in the first run have ended in one and the same favored phi-psi region. For residues which are either spread over different favored regions or concentrated in disallowed regions, the constraints are not enforced. The final family is then selected, after the second run, again only based on the agreement with the experimental constraints. This automated approach was implemented in DYANA and was tested on as many as 12 proteins, including some containing paramagnetic metals, whose structures had been previously solved in our laboratory. The quality of the structures, and of Ramachandran plot statistics in particular, was notably improved while preserving the agreement with the experimental constraints.
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