scholarly journals CASD-NMR: critical assessment of automated structure determination by NMR

2009 ◽  
Vol 6 (9) ◽  
pp. 625-626 ◽  
Author(s):  
Antonio Rosato ◽  
Anurag Bagaria ◽  
David Baker ◽  
Benjamin Bardiaux ◽  
Andrea Cavalli ◽  
...  
Keyword(s):  
Structure Determination ◽  
Critical Assessment ◽  
Automated Structure Determination

scholarly journals The second round of Critical Assessment of Automated Structure Determination of Proteins by NMR: CASD-NMR-2013

2015 ◽  
Vol 62 (4) ◽  
pp. 413-424 ◽  
Author(s):  
Antonio Rosato ◽  
Wim Vranken ◽  
Rasmus H. Fogh ◽  
Timothy J. Ragan ◽  
Roberto Tejero ◽  
...  
Keyword(s):  
Structure Determination ◽  
Critical Assessment ◽  
Automated Structure Determination

Automated structure determination of proteins with the SAIL-FLYA NMR method

2007 ◽  
Vol 2 (11) ◽  
pp. 2896-2902 ◽  
Author(s):  
Mitsuhiro Takeda ◽  
Teppei Ikeya ◽  
Peter Güntert ◽  
Masatsune Kainosho
Keyword(s):  
Structure Determination ◽  
Nmr Method ◽  
Automated Structure Determination

scholarly journals Simultaneous Assignment and Structure Determination of Proteins From Sparsely Labeled NMR Datasets

2021 ◽  
Vol 8 ◽  
Author(s):  
Arup Mondal ◽  
Alberto Perez
Keyword(s):  
Bayesian Approach ◽  
Structure Determination ◽  
Structure Prediction ◽  
Critical Assessment ◽  
Limited Data ◽  
Computational Tools ◽  
Nmr Data

Sparsely labeled NMR samples provide opportunities to study larger biomolecular assemblies than is traditionally done by NMR. This requires new computational tools that can handle the sparsity and ambiguity in the NMR datasets. The MELD (modeling employing limited data) Bayesian approach was assessed to be the best performing in predicting structures from sparsely labeled NMR data in the 13th edition of the Critical Assessment of Structure Prediction (CASP) event—and limitations of the methodology were also noted. In this report, we evaluate the nature and difficulty in modeling unassigned sparsely labeled NMR datasets and report on an improved methodological pipeline leading to higher-accuracy predictions. We benchmark our methodology against the NMR datasets provided by CASP 13.

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