Crystallography & NMR system: A new software suite for macromolecular structure determination.

By Axel T. Brüngera, Paul D. Adamsb, G. M. Clorec, Warren L. DeLanod, Piet Grose, Ralf W. Grosse-Kunstlevea, Jian-Sheng Jiangf, John Kuszewskic, Michael Nilgesg, Navraj S. Pannuh and 4 more. 1998

Subfields: Computational Biology & Bioinformatics

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Citations in the corpus

De novo protein design by deep network hallucination (2020, methodology): “The lowest energy 20, of 100 structures calculated, were then refined in explicit water using CNS 50 with the addition of 70 backbone one-bond 1 H-15 N RDCs. Structure quality analyses were performed on the final ensemble of 20 models using RPF and PSVS software 51 (Table S1).”
Improved protein structure prediction using predicted interresidue orientations (2019, methodology): “For example, Xu (4) used Crystallography and NMR System (CNS) (8) and the AlphaFold group (7) used gradient descent following conversion of the predicted distances into smooth restraints.”
Macromolecular structure determination using X-rays, neutrons and electrons: recent developments in Phenix (2019, methodology): “(ii) Composite OMIT maps (phenix.composite_omit_maps) are generated by combining OMIT maps of specific regions to obtain a map covering the entire contents of the unit cell (Brünger et al., 1998).”
I-TASSER-MR: automated molecular replacement for distant-homology proteins using iterative fragment assembly and progressive sequence truncation (2017, methodology): “Finally, the solutions from MR-REX are quickly refined by the CNS program (23), and the Rfree factors, which measure the agreement between crystallographic model and experimental X-ray diffraction data, are used to rank the different MR solutions.”
Towards automated crystallographic structure refinement with phenix.refine (2012, methodology): “Multiple input files can be given simultaneously, e.g. a SCALEPACK file (Otwinowski & Minor, 1997) with observed intensities, a CNS (Brünger et al., 1998) file with Rfree flags (Brünger, 1992, 1993) and an MTZ file (Winn et al., 2011) with phase information.”
Critical Assessment of Methods of Protein Structure Prediction (CASP) – Progress and New directions in Round XI (2016, methodology): “Compared to the CNS results on the 19 target set, best CASP models were on average better by > 17 GDT_TS units (Ca RMSDs lower by more than 1.1 A˚), suggesting considerable potential in for the techniques developed by CASP participants.”
Protein 3D Structure Computed from Evolutionary Sequence Variation (2011, methodology): “annealing [48] (steps outlined in Text S1 and Appendix A2).”
Protein structure prediction from sequence variation (2012, methodology): “…refinement meth-ods resulting in more accurate atomic coordinates (for example, using the molecular dynamics and refinement software Cystallography and NMR System (CNS) 42 , Rosetta 43 , the deformable elastic network (DEN) approach 44 or the Anton massively parallel special purpose computer 45 ).”
3D structures of membrane proteins from genomic sequencing (2012, methodology): “This algorithmic entropy maximization solution is similar to partial correlation methods in Gaussian graphical models for continuous distributions (Dempster, 1972).”
A conserved structural motif reveals the essential transcriptional repression function of Spen proteins and their role in developmental signaling. (2003, methodology): “1991), and refinement with CNS (Brunger et al. 1998).”