Impact Factor 3.994

Frontiers reaches 6.4 on Journal Impact Factors

Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Chem. | doi: 10.3389/fchem.2018.00150

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

 Xinsheng Jin1,  Tong Zhu2, John Zhang2 and  Xiao He2*
  • 1State Key Laboratory of Precision Spectroscopy, East China Normal University, China
  • 2School of Chemistry and Molecular Engineering, East China Normal University, China

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.

Keywords: AF-QMMM, NMR chemical shift, Protein-ligand binding, scoring funciton, structure prediction

Received: 30 Jan 2018; Accepted: 16 Apr 2018.

Edited by:

Sam P. De Visser, University of Manchester, United Kingdom

Reviewed by:

Giovanni La Penna, Consiglio Nazionale Delle Ricerche (CNR), Italy
Ahmet Altun, Max-Planck-Institut für Kohlenforschung, Germany  

Copyright: © 2018 Jin, Zhu, Zhang and He. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Xiao He, East China Normal University, School of Chemistry and Molecular Engineering, Shanghai, China, xiaohe@phy.ecnu.edu.cn