Version 1.2 of the Crystallography and NMR system

By A. Brunger. 2007

1,378 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology, Spectroscopy

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

In Molecular Biology

In Spectroscopy

Citations in the corpus

Towards automated crystallographic structure refinement with phenix.refine (2012, methodology): “A bulk-solvent mask optimization was also performed (Brunger, 2007; DeLaBarre & Brunger, 2006).”
Distance-based protein folding powered by deep learning (2018, methodology): “Although our ResNet model does not directly yield 3D coordinates, it is not difficult to construct protein 3D models from predicted distance matrix using CNS.”
Critical Assessment of Methods of Protein Structure Prediction (CASP) – Progress and New directions in Round XI (2016, methodology): “…was a wide range of performance by CASP groups, with the best consistently generating more accurate models than obtained using conventional NMR structure generation methods on the same data (benchmarked with CNS 40,41 and partially with ASDP, by the Montelione group and the CASP assessor) (Fig.”
3D structures of membrane proteins from genomic sequencing (2012, positive): “The folding protocol uses default modules from the CNS software suite (Brunger, 2007), which consists of distance geometry, simulated annealing, and energy minimization stages.”
Folding membrane proteins by deep transfer learning (2017, methodology): “For each test protein, we feed top predicted contacts and predicted secondary structure to CNS, a software package that builds 3D models from distance and angle restraints, to predict the 3D models.”
CONFOLD: Residue-Residue Contact-guided ab initio Protein Folding (2015, methodology): “Most recent research9,36 used the Crystallography & NMR System (CNS),37,38 a method designed for building models from Nuclear magnetic resonance (NMR) experimental data, to reconstruct protein models from predicted contacts.”
Information-Driven, Ensemble Flexible Peptide Docking Using HADDOCK. (2017, methodology): “3: CNS [19, 20] is the engine used for energy minimization and molecular dynamics simulations in HADDOCK.”
Information-driven structural modelling of protein-protein interactions. (2015, background): “It uses CNS (Crystallography and NMR System) [9, 10] as computation engine and, as such, has access to a variety of energy functions that allow inclusion of various NMR-derived parameters such as chemical shift perturbations, NOE distances, residual dipolar couplings, and pseudo-contact shifts to dr”
SE(3)-Equivariant Energy-based Models for End-to-End Protein Folding (2021, methodology): “On the other hand, most structure prediction methods rely on traditional optimization toolkits, including Rosetta [33], I-TASSER [26], and CNS [5].”
Analysis of deep learning methods for blind protein contact prediction in CASP12 (2017, methodology): “The best of top 5 models built by CNS from CCMpred- and MetaPSICOV-predicted contacts have TMscore 0.221 and 0.385, respectively.”
pyconsFold: a fast and easy tool for modeling and docking using distance predictions (2021, background): “One of the most popular approaches is CONFOLD (Adhikari et al. , 2015), which is a wrapper around CNS (Brunger, 2007), that uses predicted binary contacts together with predicted secondary structure to model proteins.”
Combining Evolutionary Information and an Iterative Sampling Strategy for Accurate Protein Structure Prediction (2015, methodology): “The CONFOLD webserver uses the CNS suite [27,28] for a two-stage modeling approach.”