Literature DB >> 26579896

Sensitivity of ab Initio vs Empirical Methods in Computing Structural Effects on NMR Chemical Shifts for the Example of Peptides.

Chris Vanessa Sumowski1, Matti Hanni1, Sabine Schweizer1, Christian Ochsenfeld1.   

Abstract

The structural sensitivity of NMR chemical shifts as computed by quantum chemical methods is compared to a variety of empirical approaches for the example of a prototypical peptide, the 38-residue kaliotoxin KTX comprising 573 atoms. Despite the simplicity of empirical chemical shift prediction programs, the agreement with experimental results is rather good, underlining their usefulness. However, we show in our present work that they are highly insensitive to structural changes, which renders their use for validating predicted structures questionable. In contrast, quantum chemical methods show the expected high sensitivity to structural and electronic changes. This appears to be independent of the quantum chemical approach or the inclusion of solvent effects. For the latter, explicit solvent simulations with increasing number of snapshots were performed for two conformers of an eight amino acid sequence. In conclusion, the empirical approaches neither provide the expected magnitude nor the patterns of NMR chemical shifts determined by the clearly more costly ab initio methods upon structural changes. This restricts the use of empirical prediction programs in studies where peptide and protein structures are utilized for the NMR chemical shift evaluation such as in NMR refinement processes, structural model verifications, or calculations of NMR nuclear spin relaxation rates.

Year:  2014        PMID: 26579896     DOI: 10.1021/ct400713t

Source DB:  PubMed          Journal:  J Chem Theory Comput        ISSN: 1549-9618            Impact factor:   6.006


  7 in total

1.  Evaluating amber force fields using computed NMR chemical shifts.

Authors:  David R Koes; John K Vries
Journal:  Proteins       Date:  2017-07-21

2.  Modeling pH-Dependent NMR Chemical Shift Perturbations in Peptides.

Authors:  Efrosini Artikis; Charles L Brooks
Journal:  Biophys J       Date:  2019-06-12       Impact factor: 4.033

3.  AFNMR: automated fragmentation quantum mechanical calculation of NMR chemical shifts for biomolecules.

Authors:  Jason Swails; Tong Zhu; Xiao He; David A Case
Journal:  J Biomol NMR       Date:  2015-08-02       Impact factor: 2.835

4.  Error assessment in molecular dynamics trajectories using computed NMR chemical shifts.

Authors:  David R Koes; John K Vries
Journal:  Comput Theor Chem       Date:  2016-11-22       Impact factor: 1.926

5.  ProCS15: a DFT-based chemical shift predictor for backbone and Cβ atoms in proteins.

Authors:  Anders S Larsen; Lars A Bratholm; Anders S Christensen; Maher Channir; Jan H Jensen
Journal:  PeerJ       Date:  2015-10-20       Impact factor: 2.984

6.  Protein structure refinement using a quantum mechanics-based chemical shielding predictor.

Authors:  Lars A Bratholm; Jan H Jensen
Journal:  Chem Sci       Date:  2016-12-01       Impact factor: 9.825

7.  Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications.

Authors:  Tobias Morawietz; Nongnuch Artrith
Journal:  J Comput Aided Mol Des       Date:  2020-10-09       Impact factor: 3.686

  7 in total

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