Literature DB >> 27992211

Automated Fragmentation Polarizable Embedding Density Functional Theory (PE-DFT) Calculations of Nuclear Magnetic Resonance (NMR) Shielding Constants of Proteins with Application to Chemical Shift Predictions.

Casper Steinmann1,2, Lars Andersen Bratholm3, Jógvan Magnus Haugaard Olsen2, Jacob Kongsted2.   

Abstract

Full-protein nuclear magnetic resonance (NMR) shielding constants based on ab initio calculations are desirable, because they can assist in elucidating protein structures from NMR experiments. In this work, we present NMR shielding constants computed using a new automated fragmentation (J. Phys. Chem. B 2009, 113, 10380-10388) approach in the framework of polarizable embedding density functional theory. We extend our previous work to give both basis set recommendations and comment on how large the quantum mechanical region should be to successfully compute 13C NMR shielding constants that are comparable with experiment. The introduction of a probabilistic linear regression model allows us to substantially reduce the number of snapshots that are needed to make comparisons with experiment. This approach is further improved by augmenting snapshot selection with chemical shift predictions by which we can obtain a representative subset of snapshots that gives the smallest predicted error, compared to experiment. Finally, we use this subset of snapshots to calculate the NMR shielding constants at the PE-KT3/pcSseg-2 level of theory for all atoms in the protein GB3.

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Year:  2017        PMID: 27992211     DOI: 10.1021/acs.jctc.6b00965

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


  3 in total

1.  Quantum mechanical force fields for condensed phase molecular simulations.

Authors:  Timothy J Giese; Darrin M York
Journal:  J Phys Condens Matter       Date:  2017-08-17       Impact factor: 2.333

2.  Predicting 19 F NMR Chemical Shifts: A Combined Computational and Experimental Study of a Trypanosomal Oxidoreductase-Inhibitor Complex.

Authors:  Johannes C B Dietschreit; Annika Wagner; T Anh Le; Philipp Klein; Hermann Schindelin; Till Opatz; Bernd Engels; Ute A Hellmich; Christian Ochsenfeld
Journal:  Angew Chem Int Ed Engl       Date:  2020-05-25       Impact factor: 15.336

3.  IMPRESSION - prediction of NMR parameters for 3-dimensional chemical structures using machine learning with near quantum chemical accuracy.

Authors:  Will Gerrard; Lars A Bratholm; Martin J Packer; Adrian J Mulholland; David R Glowacki; Craig P Butts
Journal:  Chem Sci       Date:  2019-11-20       Impact factor: 9.825

  3 in total

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