Abstract
The performance of quantum mechanical methods on the calculation of protein NMR chemical shifts is reviewed based on the recently developed automatic fragmentation quantum mechanics/molecular mechanics (AF-QM/MM) approach. By using the Poisson-Boltzmann (PB) model and first solvation water molecules, the influence of solvent effect is also discussed. Benefiting from the fragmentation algorithm, the AF-QM/MM approach is computationally efficient, linear-scaling with a low pre-factor, and thus can be applied to routinely calculate the ab initio NMR chemical shifts for proteins of any size. The results calculated using Density Functional Theory (DFT) show that when the solvent effect is included, this method can accurately reproduce the experimental 1H NMR chemical shifts, while the 13C NMR chemical shifts are less affected by the solvent. However, although the inclusion of solvent effect shows significant improvement for 15N chemical shifts, the calculated values still have large deviations from the experimental observations. Our study further demonstrates that AF-QM/MM calculated results accurately reflect the dependence of 13Cα NMR chemical shifts on the secondary structure of proteins, and the calculated 1H chemical shift can be utilized to discriminate the native structure of proteins from decoys.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grants No. 10974054, 20933002 and 21303057) and Shanghai PuJiang program (09PJ1404000). X.H. is also supported by the Specialized Research Fund for Doctoral Program of Higher Education (Grant No. 20130076120019) and the Fundamental Research Funds for the Central Universities. We thank the Supercomputer Center of East China Normal University for providing us computational time. X.H. also gratefully acknowledges many helpful discussions with Kenneth Merz, Bing Wang, Ning Liao, David Case and Sishi Tang.
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Zhu, T., Zhang, J.Z.H., He, X. (2015). Quantum Calculation of Protein NMR Chemical Shifts Based on the Automated Fragmentation Method. In: Wei, D., Xu, Q., Zhao, T., Dai, H. (eds) Advance in Structural Bioinformatics. Advances in Experimental Medicine and Biology, vol 827. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9245-5_5
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