1996
DOI: 10.1093/protein/9.11.1063
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An automated approach for clustering an ensemble of NMR-derived protein structures into conformationally related subfamilies

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Cited by 455 publications
(371 citation statements)
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“…After the MC simulations, the dimer structures were filtered to remove structures incompatible with the dimer symmetry. Then, we clustered the remaining structures by C␣ root-meansquare (RMS) distances using NMRCLUSTER (26). Finally, the median model from the largest cluster was selected as our final predicted structure.…”
Section: Methodsmentioning
confidence: 99%
“…After the MC simulations, the dimer structures were filtered to remove structures incompatible with the dimer symmetry. Then, we clustered the remaining structures by C␣ root-meansquare (RMS) distances using NMRCLUSTER (26). Finally, the median model from the largest cluster was selected as our final predicted structure.…”
Section: Methodsmentioning
confidence: 99%
“…As for loop 256-269 in Rrp43, 10 typical generated configurations were further sampled and refined using Protein Local Optimization Program (PLOP) [45]. Top 1000 ranked configurations were clustered by NMRCLUST [46] program according to the RMSD values of C-α atoms in the loop to select the representative models.…”
Section: Molecular Modeling and Structure Refinement Of Rna-free Exosmentioning
confidence: 99%
“…The ␣IIb and ␤3 TMD dimeric structures then were filtered to remove the structures incompatible with the bilayer constraints. We then clustered the remaining structures by C␣ root mean square distances using NMR CLUSTER (25), which resulted in two equally populated clusters: one with a crossing point near the N terminus and the other with a crossing point close to C terminus. Both models were evaluated for consistency with the experimental results (see below).…”
Section: Fig 3 Isolation and Characterization Of Clonal Cell Lines mentioning
confidence: 99%