13C NMR chemical shift assignment based on 13C INADEQUATE and heteronuclear 13C, 1H-COSY experiments and absolute configuration of a new chiral pentacyclic pinocarvone dimer

1993 ◽  
pp. 437 ◽  
Author(s):  
Erkki Kolehmainen ◽  
Kari Rissanen ◽  
Katri Laihia ◽  
Petteri Malkavaara ◽  
Jorma Korvola ◽  
...  
Keyword(s):  
Chemical Shift ◽  
Absolute Configuration ◽  
13C Nmr ◽  
Chemical Shift Assignment ◽  
Nmr Chemical Shift ◽  
Shift Assignment

NMR chemical shift assignment of the C-terminal region of the Menangle virus phosphoprotein

2019 ◽  
Vol 13 (1) ◽  
pp. 195-199
Author(s):  
N. Herr ◽  
M. N. Webby ◽  
E. M. M. Bulloch ◽  
M. Schmitz ◽  
R. L. Kingston
Keyword(s):  
Chemical Shift ◽  
Chemical Shift Assignment ◽  
Terminal Region ◽  
Nmr Chemical Shift ◽  
Shift Assignment

Complete1H and13C NMR chemical shift assignment ofN1- andN3-alkylnitrohistidines and of 1,4,6,7-tetrahydroimidazo[4,5-b]pyridines

2003 ◽  
Vol 41 (3) ◽  
pp. 219-222
Author(s):  
Carmen Escolano ◽  
Marcelo J. Kogan ◽  
Mario Rubiralta ◽  
Ernest Giralt ◽  
Anna Diez
Keyword(s):  
Chemical Shift ◽  
Chemical Shift Assignment ◽  
Nmr Chemical Shift ◽  
Shift Assignment

scholarly journals Evaluation of Multi-Objective Optimization Algorithms for NMR Chemical Shift Assignment

Molecules ◽  
2021 ◽  
Vol 26 (12) ◽  
pp. 3699
Author(s):  
Emel Maden Yılmaz ◽  
Peter Güntert ◽  
Şima Etaner-Uyar
Keyword(s):  
Genetic Algorithm ◽  
Chemical Shift ◽  
Chemical Shift Assignment ◽  
Optimization Techniques ◽  
Hill Climbing ◽  
Multi Objective Optimization ◽  
Nmr Chemical Shift ◽  
Problem Model ◽  
Multi Objective ◽  
Shift Assignment

An automated NMR chemical shift assignment algorithm was developed using multi-objective optimization techniques. The problem is modeled as a combinatorial optimization problem and its objective parameters are defined separately in different score functions. Some of the heuristic approaches of evolutionary optimization are employed in this problem model. Both, a conventional genetic algorithm and multi-objective methods, i.e., the non-dominated sorting genetic algorithms II and III (NSGA2 and NSGA3), are applied to the problem. The multi-objective approaches consider each objective parameter separately, whereas the genetic algorithm followed a conventional way, where all objectives are combined in one score function. Several improvement steps and repetitions on these algorithms are performed and their combinations are also created as a hyper-heuristic approach to the problem. Additionally, a hill-climbing algorithm is also applied after the evolutionary algorithm steps. The algorithms are tested on several different datasets with a set of 11 commonly used spectra. The test results showed that our algorithm could assign both sidechain and backbone atoms fully automatically without any manual interactions. Our approaches could provide around a 65% success rate and could assign some of the atoms that could not be assigned by other methods.

Sign in / Sign up

Export Citation Format

Share Document