Combining the Fragmentation Approach and Neural Network Potential Energy Surfaces of Fragments for Accurate Calculation of Protein Energy

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 ωB97XD/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/Å,
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.