scholarly journals Application of Computational Chemical Shift Prediction Techniques to the Cereoanhydride Structure Problem—Carboxylate Complications

Marine Drugs ◽  
2017 ◽  
Vol 15 (6) ◽  
pp. 171 ◽  
Author(s):  
◽  
Keyword(s):  
Chemical Shift ◽  
Chemical Shift Prediction ◽  
Structure Problem ◽  
Prediction Techniques

Comprehensive Strategy for Proton Chemical Shift Prediction: Linear Prediction with Nonlinear Corrections

2014 ◽  
Vol 54 (2) ◽  
pp. 419-430 ◽  
Author(s):  
Reino Laatikainen ◽  
Tommi Hassinen ◽  
Juuso Lehtivarjo ◽  
Mika Tiainen ◽  
Juha Jungman ◽  
...  
Keyword(s):  
Chemical Shift ◽  
Linear Prediction ◽  
Proton Chemical Shift ◽  
Comprehensive Strategy ◽  
Chemical Shift Prediction

scholarly journals Protein structure refinement using a quantum mechanics-based chemical shielding predictor

2016 ◽  
Author(s):  
Lars A. Bratholm ◽  
Jan H. Jensen
Keyword(s):  
Quantum Mechanics ◽  
Simulated Annealing ◽  
Chemical Shift ◽  
Force Field ◽  
Chemical Shifts ◽  
Protein Structures ◽  
Empirical Methods ◽  
Structural Refinement ◽  
Chemical Shielding ◽  
Chemical Shift Prediction

The accurate prediction of protein chemical shifts using quantum mechanics (QM)-based method has been the subject of intense research for more than 20 years but so far empirical methods for chemical shift prediction have proven more accurate. In this paper we show that a QM-based predictor of protein backbone and CB chemical shifts (ProCS15, PeerJ 2016, 3:e1344) is of comparable accuracy to empirical chemical shift predictors after chemical shift-based structural refinement that removes small structural errors. We present a method by which quantum chemistry based predictions of isotropic chemical shielding values (ProCS15) can be used to refine protein structures using Markov Chain Monte Carlo (MCMC) simulations, relating the chemical shielding values to the experimental chemical shifts probabilistically. Two kinds of MCMC structural refinement simulations were performed using force field geometry optimized X-ray structures as starting points: Simulated annealing of the starting structure and constant temperature MCMC simulation followed by simulated annealing of a representative ensemble structure. Annealing of the CHARMM structure changes the CA-RMSD by an average of 0.4 Å but lowers the chemical shift RMSD by 1.0 and 0.7 ppm for CA and N. Conformational averaging has a relatively small effect (0.1 - 0.2 ppm) on the overall agreement with carbon chemical shifts but lowers the error for nitrogen chemical shifts by 0.4 ppm. If a residue-specific offset is included the ProCS15 predicted chemical shifts have RMSD values relative to experiment that are comparable to popular empirical chemical shift predictors. The annealed representative ensemble structures differs in CA-RMSD relative to the initial structures by an average of 2.0 Å, with >2.0 Å difference for six proteins. In four of the cases, the largest structural differences arise in structurally flexible regions of the protein as determined by NMR, and in the remaining two cases, the large structural change may be due to force field deficiencies. The overall accuracy of the empirical methods are slightly improved by annealing the CHARMM structure with ProCS15, which may suggest that the minor structural changes introduced by ProCS15-based annealing improves the accuracy of the protein structures. Having established that QM-based chemical shift prediction can deliver the same accuracy as empirical shift predictors we hope this can help increase the accuracy of related approaches such as QM/MM or linear scaling approaches or interpreting protein structural dynamics from QM-derived chemical shift.

Ab initio study of halogenated diphenyl ethers. NMR chemical shift prediction

2000 ◽  
Vol 38 (12) ◽  
pp. 987-993 ◽  
Author(s):  
Jussi Eloranta ◽  
Jiwei Hu ◽  
Reijo Suontamo ◽  
Erkki Kolehmainen ◽  
Juha Knuutinen
Keyword(s):  
Chemical Shift ◽  
Ab Initio ◽  
Ab Initio Study ◽  
Nmr Chemical Shift ◽  
Chemical Shift Prediction ◽  
Diphenyl Ethers

scholarly journals NightShift: NMR shift inference by general hybrid model training - a framework for NMR chemical shift prediction

2013 ◽  
Vol 14 (1) ◽  
pp. 98 ◽  
Author(s):  
Anna Dehof ◽  
Simon Loew ◽  
Hans-Peter Lenhof ◽  
Andreas Hildebrandt
Keyword(s):  
Chemical Shift ◽  
Hybrid Model ◽  
Nmr Chemical Shift ◽  
Chemical Shift Prediction ◽  
Model Training

Validation of Structural Proposals by Substructure Analysis and13C NMR Chemical Shift Prediction

2002 ◽  
Vol 42 (2) ◽  
pp. 241-248 ◽  
Author(s):  
Jens Meiler ◽  
Erdogan Sanli ◽  
Jochen Junker ◽  
Reinhard Meusinger ◽  
Thomas Lindel ◽  
...  
Keyword(s):  
Chemical Shift ◽  
Nmr Chemical Shift ◽  
Chemical Shift Prediction ◽  
Substructure Analysis

Chemical Shift Prediction in the29Si MAS NMR of Titanosilicates

1998 ◽  
Vol 102 (16) ◽  
pp. 2897-2904 ◽  
Author(s):  
Andrea Labouriau ◽  
T. J. Higley ◽  
William L. Earl
Keyword(s):  
Chemical Shift ◽  
Mas Nmr ◽  
Chemical Shift Prediction

CAST/CNMR: highly accurate 13C NMR chemical shift prediction system considering stereochemistry

Tetrahedron ◽  
2003 ◽  
Vol 59 (25) ◽  
pp. 4539-4547 ◽  
Author(s):  
Hiroko Satoh ◽  
Hiroyuki Koshino ◽  
Jun Uzawa ◽  
Tadashi Nakata
Keyword(s):  
Chemical Shift ◽  
13C Nmr ◽  
Prediction System ◽  
Nmr Chemical Shift ◽  
Chemical Shift Prediction

scholarly journals Structural Analysis of Glycans by NMR Chemical Shift Prediction

2011 ◽  
Vol 83 (5) ◽  
pp. 1514-1517 ◽  
Author(s):  
Magnus Lundborg ◽  
Göran Widmalm
Keyword(s):  
Structural Analysis ◽  
Chemical Shift ◽  
Nmr Chemical Shift ◽  
Chemical Shift Prediction
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