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Automated Fragmentation QM/MM Calculation of NMR Chemical Shifts for Protein-Ligand Complexes

Overview of attention for article published in Frontiers in Chemistry, May 2018
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Title
Automated Fragmentation QM/MM Calculation of NMR Chemical Shifts for Protein-Ligand Complexes
Published in
Frontiers in Chemistry, May 2018
DOI 10.3389/fchem.2018.00150
Pubmed ID
Authors

Xinsheng Jin, Tong Zhu, John Z. H. Zhang, Xiao He

Abstract

In this study, the automated fragmentation quantum mechanics/molecular mechanics (AF-QM/MM) method was applied for NMR chemical shift calculations of protein-ligand complexes. In the AF-QM/MM approach, the protein binding pocket is automatically divided into capped fragments (within ~200 atoms) for density functional theory (DFT) calculations of NMR chemical shifts. Meanwhile, the solvent effect was also included using the Poission-Boltzmann (PB) model, which properly accounts for the electrostatic polarization effect from the solvent for protein-ligand complexes. The NMR chemical shifts of neocarzinostatin (NCS)-chromophore binding complex calculated by AF-QM/MM accurately reproduce the large-sized system results. The 1H chemical shift perturbations (CSP) between apo-NCS and holo-NCS predicted by AF-QM/MM are also in excellent agreement with experimental results. Furthermore, the DFT calculated chemical shifts of the chromophore and residues in the NCS binding pocket can be utilized as molecular probes to identify the correct ligand binding conformation. By combining the CSP of the atoms in the binding pocket with the Glide scoring function, the new scoring function can accurately distinguish the native ligand pose from decoy structures. Therefore, the AF-QM/MM approach provides an accurate and efficient platform for protein-ligand binding structure prediction based on NMR derived information.

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The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 29 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 21%
Researcher 6 21%
Student > Bachelor 4 14%
Professor 3 10%
Lecturer 1 3%
Other 2 7%
Unknown 7 24%
Readers by discipline Count As %
Chemistry 11 38%
Biochemistry, Genetics and Molecular Biology 5 17%
Arts and Humanities 2 7%
Pharmacology, Toxicology and Pharmaceutical Science 1 3%
Physics and Astronomy 1 3%
Other 1 3%
Unknown 8 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 May 2018.
All research outputs
#20,485,225
of 23,047,237 outputs
Outputs from Frontiers in Chemistry
#2,937
of 6,022 outputs
Outputs of similar age
#288,401
of 327,709 outputs
Outputs of similar age from Frontiers in Chemistry
#70
of 155 outputs
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