Journal
JOURNAL OF PHYSICAL CHEMISTRY B
Volume 124, Issue 15, Pages 3027-3035Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jpcb.0c01370
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Funding
- National Natural Science Foundation of China [21901157, 51672176, 21922301, 21673074, 21761132022]
- National Key R&D Program of China [2016YFA0501700, 2019YFA0905201]
- Intergovernmental International Scientific and Technological Cooperation of Shanghai [17520710200]
- Shanghai Municipal Natural Science Foundation [18ZR1412600]
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Accurate and efficient all-atom quantum mechanical (QM) calculations for biomolecules still present a challenge to computational physicists and chemists. In this study, an extensible generalized molecular fractionation with a conjugate caps method combined with neural networks (NN-GMFCC) is developed for efficient QM calculation of protein energy. In the NN-GMFCC scheme, the total energy of a given protein is calculated by taking a proper combination of the high-precision neural network potential energies of all capped residues and overlapping conjugate caps. In addition, the two-body interaction energies of residue pairs are calculated by molecular mechanics (MM). With reference to the GMFCC/MM calculation at the.B97XD/6-31G* level, the overall mean unsigned errors of the energy deviations and atomic force root-mean-squared errors calculated by NN-GMFCC are only 2.01 kcal/mol and 0.68 kcal/mol/A, respectively, for 14 proteins (containing up to 13,728 atoms). Meanwhile, the NN-GMFCC approach is about 4 orders of magnitude faster than the GMFCC/MM method. The NN-GMFCC method could be systematically improved by inclusion of two-body QM interaction and multibody electronic polarization effect. Moreover, the NN-GMFCC approach can also be applied to other macromolecular systems such as DNA/RNA, and it is capable of providing a powerful and efficient approach for exploration of structures and functions of proteins with QM accuracy.
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